From b8bb58184214d97cd8d49cc04d014ae65b815213 Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn Date: Tue, 21 Nov 2023 14:25:01 +0100 Subject: [PATCH 1/9] add literature --- content/_applications_biomass.qmd | 2 + content/_applications_shading.qmd | 3 + content/_applications_stand-inventory.qmd | 3 + content/_applications_structure.qmd | 4 + content/_applications_tree-detection.qmd | 2 + references.bib | 90 ++++++++++++++++++++++- 6 files changed, 101 insertions(+), 3 deletions(-) diff --git a/content/_applications_biomass.qmd b/content/_applications_biomass.qmd index b52083e..fbdc8bf 100644 --- a/content/_applications_biomass.qmd +++ b/content/_applications_biomass.qmd @@ -24,6 +24,8 @@ ALS data can be used indirectly through a chain of models to estimate soil carbo > Dual-wavelength ALS was used in a two-step methodology to classify species, and estimate species-specific stem volume at the level of individual tree crowns. Demonstrate the added benefit of the green channel for the estimation of both species composition and species-specific stem volumes. [@axelssonUseDualwavelengthAirborne2023] +> errors in species composition have less impact on individual tree volume estimates than errors in height measurement. The implications of these results are that, with very accurate estimates of height provided by ALS and knowledge of what dominant species is expected in a stand, accurate estimates of volume can be generated in the absence of more detailed species composition information. [@tompalskiSimulatingImpactsError2014] + ::: {#fig-cwd} ![](https://media.springernature.com/full/springer-static/image/art%3A10.1186%2Fs13021-016-0048-7/MediaObjects/13021_2016_48_Fig5_HTML.gif?as=webp) diff --git a/content/_applications_shading.qmd b/content/_applications_shading.qmd index e68f71b..74eedea 100644 --- a/content/_applications_shading.qmd +++ b/content/_applications_shading.qmd @@ -8,3 +8,6 @@ With precise forest structure and height information from lidar data it is possi > LiDAR can be a suitable tool for modeling solar radiation at various levels and producing continuous information across large forested areas with complicated structure and species composition. [@olpendaEstimationSubcanopySolar2019] > light detection and ranging has, so far, been preferably used for modeling light under tree canopies. Laser system’s capability of generating 3D canopy structure at high spatial resolution makes it a reasonable choice as a source of spatial information about light condition in various parts of forest ecosystem. [@olpendaModelingSolarRadiation2018] + +> structural metrics derived from Airborne Laser Scanning (ALS) data, serving as an empirical gold-standard in modelling subcanopy light regimes. +> a combination of Sentinel-1 and Sentinel-2 time series has the potential to map subcanopy light conditions spatially and temporally independent of the availability of high-resolution ALS data [@glasmannMappingSubcanopyLight2023] \ No newline at end of file diff --git a/content/_applications_stand-inventory.qmd b/content/_applications_stand-inventory.qmd index 32bb628..9a71d78 100644 --- a/content/_applications_stand-inventory.qmd +++ b/content/_applications_stand-inventory.qmd @@ -24,6 +24,9 @@ The findings are important steps towards future individual-tree-based airborne l > goal is to inform and enable readers interested in using Airborne Laser Scanning data to characterize, in an operational forest inventory context, large forest areas in a cost-effective manner. [@whiteModelDevelopmentApplication2017] +> Use of auxiliary ALS metrics substantially improved sampling efficiency. methods can aid practitioners in planning cost-effective and statistically rigorous forest inventory campaigns, particularly in determining where to re-sample within an existing plot network. [@goodbodyAirborneLaserScanning2023] + + ::: {#fig-enhanced-stand-inventory} ![](https://www.researchgate.net/profile/Joanne-White/publication/323166566/figure/fig2/AS:593803119448065@1518585104493/Overview-of-the-steps-involved-in-implementing-the-area-based-approach-ABA_W640.jpg) diff --git a/content/_applications_structure.qmd b/content/_applications_structure.qmd index e391f7f..0b70899 100644 --- a/content/_applications_structure.qmd +++ b/content/_applications_structure.qmd @@ -26,6 +26,10 @@ and monitor the relations between forest structure and functions. [@belandPromo > To identify forests with high conservation value, we used vertical and horizontal variables derived from airborne laser scanning (ALS) data, along with NFI plot measurements. [@jutras-perreaultDetectingPresenceNatural2023] +> Lidar’s capabilities to measure vegetation structure in detail across wide areas are shifting the paradigm of how forests are analyzed, and the technology is now being adopted as a foundational data collection method for forest management in the same way aerial photography was more than half a century ago. [@kaneLidarSEyeView2022] + +> review the measurement of forest structure and structural diversity [@atkinsIntegratingForestStructural2023] + ::: {#fig-forest-structure} ![](https://onlinelibrary.wiley.com/cms/asset/a1635e91-2b86-4c09-8fdd-e7a43ee1ca9a/ddi13644-fig-0001-m.jpg) diff --git a/content/_applications_tree-detection.qmd b/content/_applications_tree-detection.qmd index 893d344..d036042 100644 --- a/content/_applications_tree-detection.qmd +++ b/content/_applications_tree-detection.qmd @@ -39,3 +39,5 @@ achieve ITC segmentation for forests with various structural attributes. The res > compared the performance of four widely used tree segmentation algorithms. All algorithms performed reasonably well on the canopy trees. However, all algorithms failed to accurately segment the understory trees. [@caoBenchmarkingAirborneLaser2023] + +> present a novel ITC segmentation approach based on the YOLOv5 CNN. [@strakerInstanceSegmentationIndividual2023] \ No newline at end of file diff --git a/references.bib b/references.bib index 159e925..e8f4108 100644 --- a/references.bib +++ b/references.bib @@ -92,6 +92,22 @@ @article{assmannEcoDesDK15HighresolutionEcological2022 langid = {english} } +@article{atkinsIntegratingForestStructural2023, + title = {Integrating Forest Structural Diversity Measurement into Ecological Research}, + author = {Atkins, Jeff W. and Bhatt, Parth and Carrasco, Luis and Francis, Emily and Garabedian, James E. and Hakkenberg, Christopher R. and Hardiman, Brady S. and Jung, Jinha and Koirala, Anil and LaRue, Elizabeth A. and Oh, Sungchan and Shao, Gang and Shao, Guofan and Shugart, H. H. and Spiers, Anna and Stovall, Atticus E. L. and Surasinghe, Thilina D. and Tai, Xiaonan and Zhai, Lu and Zhang, Tao and Krause, Keith}, + year = {2023}, + month = sep, + journal = {Ecosphere}, + volume = {14}, + number = {9}, + pages = {e4633}, + issn = {2150-8925, 2150-8925}, + doi = {10.1002/ecs2.4633}, + urldate = {2023-11-21}, + abstract = {Abstract The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity\textemdash an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.}, + langid = {english} +} + @article{axelssonUseDualwavelengthAirborne2023, title = {The Use of Dual-Wavelength Airborne Laser Scanning for Estimating Tree Species Composition and Species-Specific Stem Volumes in a Boreal Forest}, author = {Axelsson, Christoffer R. and Lindberg, Eva and Persson, Henrik J. and Holmgren, Johan}, @@ -433,12 +449,10 @@ @article{diltsImprovedTopographicRuggedness2023 langid = {english} } -@book{europeancommission.jointresearchcentre.NoncommercialLightDetection2021, +@misc{europeancommission.jointresearchcentre.NoncommercialLightDetection2021, title = {Non-Commercial {{Light Detection}} and {{Ranging}} ({{LiDAR}}) Data in {{Europe}}.}, author = {{European Commission. Joint Research Centre.}}, year = {2021}, - publisher = {{Publications Office}}, - address = {{LU}}, urldate = {2023-02-27}, langid = {english} } @@ -535,6 +549,20 @@ @article{gavilan-acunaEstimatingPotentialTree2022 langid = {english} } +@article{glasmannMappingSubcanopyLight2023, + title = {Mapping Subcanopy Light Regimes in Temperate Mountain Forests from {{Airborne Laser Scanning}}, {{Sentinel-1}} and {{Sentinel-2}}}, + author = {Glasmann, Felix and Senf, Cornelius and Seidl, Rupert and Annigh{\"o}fer, Peter}, + year = {2023}, + month = dec, + journal = {Science of Remote Sensing}, + volume = {8}, + pages = {100107}, + issn = {26660172}, + doi = {10.1016/j.srs.2023.100107}, + urldate = {2023-11-21}, + langid = {english} +} + @article{goodbodyAirborneLaserScanning2021, title = {Airborne Laser Scanning for Quantifying Criteria and Indicators of Sustainable Forest Management in {{Canada}}}, author = {Goodbody, Tristan R.H. and Coops, Nicholas C. and Luther, Joan E. and Tompalski, Piotr and Mulverhill, Christopher and Frizzle, Catherine and Fournier, Richard and Furze, Shane and Herniman, Sam}, @@ -551,6 +579,20 @@ @article{goodbodyAirborneLaserScanning2021 langid = {english} } +@article{goodbodyAirborneLaserScanning2023, + title = {Airborne Laser Scanning to Optimize the Sampling Efficiency of a Forest Management Inventory in {{South-Eastern Germany}}}, + author = {Goodbody, Tristan R.H. and Coops, Nicholas C. and Senf, Cornelius and Seidl, Rupert}, + year = {2023}, + month = dec, + journal = {Ecological Indicators}, + volume = {157}, + pages = {111281}, + issn = {1470160X}, + doi = {10.1016/j.ecolind.2023.111281}, + urldate = {2023-11-21}, + langid = {english} +} + @article{goodbodyDigitalAerialPhotogrammetry2019, title = {Digital {{Aerial Photogrammetry}} for {{Updating Area-Based Forest Inventories}}: {{A Review}} of {{Opportunities}}, {{Challenges}}, and {{Future Directions}}}, shorttitle = {Digital {{Aerial Photogrammetry}} for {{Updating Area-Based Forest Inventories}}}, @@ -954,6 +996,19 @@ @article{kamoskeLeafAreaDensity2019 langid = {english} } +@article{kaneLidarSEyeView2022, + title = {A {{Lidar}}'s-{{Eye View}} of {{How Forests Are Faring}}}, + author = {Kane, Van and Van Wagtendonk, Liz and Brenner, Andrew}, + year = {2022}, + month = apr, + journal = {Eos}, + volume = {103}, + issn = {2324-9250}, + doi = {10.1029/2022EO220218}, + urldate = {2023-11-21}, + abstract = {Success in Yosemite is driving the wider use of lidar surveys to support forest health and wildfire resilience, study wildlife habitats, and monitor water resources.} +} + @article{kopeckyTopographicWetnessIndex2021, title = {Topographic {{Wetness Index}} Calculation Guidelines Based on Measured Soil Moisture and Plant Species Composition}, author = {Kopeck{\'y}, Martin and Macek, Martin and Wild, Jan}, @@ -1871,6 +1926,21 @@ @article{stovallDevelopingNondestructiveSpecies2023 langid = {english} } +@article{strakerInstanceSegmentationIndividual2023, + title = {Instance Segmentation of Individual Tree Crowns with {{YOLOv5}}: {{A}} Comparison of Approaches Using the {{ForInstance}} Benchmark {{LiDAR}} Dataset}, + shorttitle = {Instance Segmentation of Individual Tree Crowns with {{YOLOv5}}}, + author = {Straker, Adrian and Puliti, Stefano and Breidenbach, Johannes and Kleinn, Christoph and Pearse, Grant and Astrup, Rasmus and Magdon, Paul}, + year = {2023}, + month = aug, + journal = {ISPRS Open Journal of Photogrammetry and Remote Sensing}, + volume = {9}, + pages = {100045}, + issn = {26673932}, + doi = {10.1016/j.ophoto.2023.100045}, + urldate = {2023-11-21}, + langid = {english} +} + @article{strimbuEstimatingBiomassSoil2023, title = {Estimating Biomass and Soil Carbon Change at the Level of Forest Stands Using Repeated Forest Surveys Assisted by Airborne Laser Scanner Data}, author = {Str{\^i}mbu, Victor F. and N{\ae}sset, Erik and {\O}rka, Hans Ole and Liski, Jari and Petersson, Hans and Gobakken, Terje}, @@ -2008,6 +2078,20 @@ @article{tompalskiQuantifyingPrecisionForest2021 langid = {english} } +@article{tompalskiSimulatingImpactsError2014, + title = {Simulating the Impacts of Error in Species and Height upon Tree Volume Derived from Airborne Laser Scanning Data}, + author = {Tompalski, Piotr and Coops, Nicholas C. and White, Joanne C. and Wulder, Michael A.}, + year = {2014}, + month = sep, + journal = {Forest Ecology and Management}, + volume = {327}, + pages = {167--177}, + issn = {03781127}, + doi = {10.1016/j.foreco.2014.05.011}, + urldate = {2023-11-21}, + langid = {english} +} + @article{trierTreeSpeciesClassification2018, title = {Tree Species Classification in {{Norway}} from Airborne Hyperspectral and Airborne Laser Scanning Data}, author = {Trier, {\O}ivind Due and Salberg, Arnt-B{\o}rre and Kermit, Martin and Rudjord, {\O}ystein and Gobakken, Terje and N{\ae}sset, Erik and Aarsten, Dagrun}, From 4bb5a5441077fb2e57f1cd63fa03ccff97d9fd59 Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn Date: Wed, 6 Dec 2023 15:17:01 +0100 Subject: [PATCH 2/9] add literature --- content/_applications_biomass.qmd | 4 + content/_applications_stand-inventory.qmd | 3 + content/_applications_structure.qmd | 2 + content/_lidar_photogrammetry_comparison.qmd | 1 + references.bib | 153 +++++++++++++------ 5 files changed, 115 insertions(+), 48 deletions(-) diff --git a/content/_applications_biomass.qmd b/content/_applications_biomass.qmd index fbdc8bf..0fc9a85 100644 --- a/content/_applications_biomass.qmd +++ b/content/_applications_biomass.qmd @@ -26,6 +26,10 @@ ALS data can be used indirectly through a chain of models to estimate soil carbo > errors in species composition have less impact on individual tree volume estimates than errors in height measurement. The implications of these results are that, with very accurate estimates of height provided by ALS and knowledge of what dominant species is expected in a stand, accurate estimates of volume can be generated in the absence of more detailed species composition information. [@tompalskiSimulatingImpactsError2014] +> simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available. [@schaferAssessingPotentialSynthetic2023] + +> this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. Compared deep learning and random forest models in terms of biomass estimation. Deep neural networks provided small performance gain compared to random forest. [@seelyModellingTreeBiomass2023] + ::: {#fig-cwd} ![](https://media.springernature.com/full/springer-static/image/art%3A10.1186%2Fs13021-016-0048-7/MediaObjects/13021_2016_48_Fig5_HTML.gif?as=webp) diff --git a/content/_applications_stand-inventory.qmd b/content/_applications_stand-inventory.qmd index 9a71d78..7d7c3b4 100644 --- a/content/_applications_stand-inventory.qmd +++ b/content/_applications_stand-inventory.qmd @@ -26,6 +26,9 @@ The findings are important steps towards future individual-tree-based airborne l > Use of auxiliary ALS metrics substantially improved sampling efficiency. methods can aid practitioners in planning cost-effective and statistically rigorous forest inventory campaigns, particularly in determining where to re-sample within an existing plot network. [@goodbodyAirborneLaserScanning2023] +> compared four inventory approaches for imputing stem frequency distributions from airborne laser scanner (ALS) data. +> Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. [@noordermeerImputingStemFrequency2023] + ::: {#fig-enhanced-stand-inventory} ![](https://www.researchgate.net/profile/Joanne-White/publication/323166566/figure/fig2/AS:593803119448065@1518585104493/Overview-of-the-steps-involved-in-implementing-the-area-based-approach-ABA_W640.jpg) diff --git a/content/_applications_structure.qmd b/content/_applications_structure.qmd index 0b70899..8e161ad 100644 --- a/content/_applications_structure.qmd +++ b/content/_applications_structure.qmd @@ -44,6 +44,8 @@ Variables to describe the vertical structure of the vegetation (from @moudryVege > compared the performance of LiDAR and DAP data for characterizing canopy openings. recommend that operational use of DAP in forests be limited to mapping large canopy openings [@dietmaierComparisonLiDARDigital2019] +> DAP data do not provide analogous results to ALS data for canopy gap detection and mapping, and that ALS data enable markedly superior accuracy and detailed gap characterizations. [@whiteComparisonAirborneLaser2018] + ::: {#fig-canopy-gaps} ![](https://raw.githubusercontent.com/carlos-alberto-silva/ForestGapR/master/readme/fig_4.png) diff --git a/content/_lidar_photogrammetry_comparison.qmd b/content/_lidar_photogrammetry_comparison.qmd index 6945a4b..860289f 100644 --- a/content/_lidar_photogrammetry_comparison.qmd +++ b/content/_lidar_photogrammetry_comparison.qmd @@ -46,3 +46,4 @@ Comparison of ALS- and image-based canopy height model (from @whiteUtilityImageB > Recent advances in computer sciences "demonstrate the potential of large-scale mapping and monitoring of tree height" from aerial imagery using U-NET (without overlap and image matching) [@wagnerSubMeterTreeHeight2023] +> DAP data do not provide analogous results to ALS data for canopy gap detection and mapping in coastal temperate rainforests, and that ALS data enable markedly superior accuracy and detailed gap characterizations. [@whiteComparisonAirborneLaser2018] \ No newline at end of file diff --git a/references.bib b/references.bib index 6cf3571..e10c3e0 100644 --- a/references.bib +++ b/references.bib @@ -71,7 +71,7 @@ @article{arumaePlanningCommercialThinnings2022 issn = {1999-4907}, doi = {10.3390/f13020206}, urldate = {2023-03-09}, - abstract = {The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km2 and 12,630 km2) were used with a total of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia. The thinnings were predicted based on the ALS measurements in 2019 and 2017. The two most important ALS metrics for predicting the need for thinning were the 95th height percentile and the canopy cover. The prediction accuracy based on validation stands was 93.5\% for southwestern Estonia and 85.7\% for southeastern Estonia. For comparison, the general linear model prediction accuracy was less for both test sites\textemdash 92.1\% for southwest and 81.8\% for southeast. The selected important predictive ALS metrics differed from those used in the RF algorithm. The cross-validation of the thinning necessity models of southeastern and southwestern Estonia showed a dependence on geographic regions.}, + abstract = {The goal of this study was to predict the need for commercial thinning using airborne lidar data (ALS) with random forest (RF) machine learning algorithm. Two test sites (with areas of 14,750 km2 and 12,630 km2) were used with a total of 1053 forest stands from southwestern Estonia and 951 forest stands from southeastern Estonia. The thinnings were predicted based on the ALS measurements in 2019 and 2017. The two most important ALS metrics for predicting the need for thinning were the 95th height percentile and the canopy cover. The prediction accuracy based on validation stands was 93.5\% for southwestern Estonia and 85.7\% for southeastern Estonia. For comparison, the general linear model prediction accuracy was less for both test sites{\textemdash}92.1\% for southwest and 81.8\% for southeast. The selected important predictive ALS metrics differed from those used in the RF algorithm. The cross-validation of the thinning necessity models of southeastern and southwestern Estonia showed a dependence on geographic regions.}, langid = {english} } @@ -104,7 +104,7 @@ @article{atkinsIntegratingForestStructural2023 issn = {2150-8925, 2150-8925}, doi = {10.1002/ecs2.4633}, urldate = {2023-11-21}, - abstract = {Abstract The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity\textemdash an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.}, + abstract = {Abstract The measurement of forest structure has evolved steadily due to advances in technology, methodology, and theory. Such advances have greatly increased our capacity to describe key forest structural elements and resulted in a range of measurement approaches from traditional analog tools such as measurement tapes to highly derived and computationally intensive methods such as advanced remote sensing tools (e.g., lidar, radar). This assortment of measurement approaches results in structural metrics unique to each method, with the caveat that metrics may be biased or constrained by the measurement approach taken. While forest structural diversity (FSD) metrics foster novel research opportunities, understanding how they are measured or derived, limitations of the measurement approach taken, as well as their biological interpretation is crucial for proper application. We review the measurement of forest structure and structural diversity{\textemdash}an umbrella term that includes quantification of the distribution of functional and biotic components of forests. We consider how and where these approaches can be used, the role of technology in measuring structure, how measurement impacts extend beyond research, and current limitations and potential opportunities for future research.}, langid = {english} } @@ -123,7 +123,7 @@ @article{axelssonUseDualwavelengthAirborne2023 } @article{beeseUsingRepeatAirborne2022, - title = {Using Repeat Airborne {{LiDAR}} to Map the Growth of Individual Oil Palms in {{Malaysian Borneo}} during the 2015\textendash 16 {{El Ni\~no}}}, + title = {Using Repeat Airborne {{LiDAR}} to Map the Growth of Individual Oil Palms in {{Malaysian Borneo}} during the 2015{\textendash}16 {{El Ni{\~n}o}}}, author = {Beese, Lucy and Dalponte, Michele and Asner, Gregory P. and Coomes, David A. and Jucker, Tommaso}, year = {2022}, month = dec, @@ -151,7 +151,7 @@ @article{belandPromotingUseLidar2019 } @article{bienzBilderkennungssoftwareFurFeinerschliessungen2022, - title = {Bilderkennungssoftware F\"ur {{Feinerschliessungen}} Im {{Wald}}}, + title = {Bilderkennungssoftware F{\"u}r {{Feinerschliessungen}} Im {{Wald}}}, author = {Bienz, Raffael and Freuler, Andreas}, year = {2022}, month = jul, @@ -162,7 +162,7 @@ @article{bienzBilderkennungssoftwareFurFeinerschliessungen2022 issn = {2235-1469, 0036-7818}, doi = {10.3188/szf.2022.0196}, urldate = {2023-03-02}, - abstract = {Zum Schutz des Waldbodens und als Hilfe bei Planungsaufgaben wird im Kanton Aargau die Feinerschliessung digitalisiert. Mithilfe der bisher erfassten Feinerschliessung wurde ein Bilderkennungsmodell trainiert, um auf der restlichen Kantonsfl\"ache die Feinerschliessung automatisch zu kartieren. Das Modell hat rund 90 Prozent der sichtbaren Fahrspuren zuverl\"assig erkannt. Die Daten dienen f\"ur die Erstellung von Arbeitsauftr\"agen oder Holzschlagskizzen und werden Maschinisten im Bord-GPS zur Verf\"ugung gestellt.}, + abstract = {Zum Schutz des Waldbodens und als Hilfe bei Planungsaufgaben wird im Kanton Aargau die Feinerschliessung digitalisiert. Mithilfe der bisher erfassten Feinerschliessung wurde ein Bilderkennungsmodell trainiert, um auf der restlichen Kantonsfl{\"a}che die Feinerschliessung automatisch zu kartieren. Das Modell hat rund 90 Prozent der sichtbaren Fahrspuren zuverl{\"a}ssig erkannt. Die Daten dienen f{\"u}r die Erstellung von Arbeitsauftr{\"a}gen oder Holzschlagskizzen und werden Maschinisten im Bord-GPS zur Verf{\"u}gung gestellt.}, langid = {english} } @@ -210,7 +210,7 @@ @article{brodicRefinementIndividualTree2022 issn = {2072-4292}, doi = {10.3390/rs14215345}, urldate = {2023-02-15}, - abstract = {Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km \texttimes{} 4 km. The classification model's training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient ({$\kappa$}) obtained from the ten-fold cross validation for the training data were 90.4\% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0\% and a {$\kappa$} = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.}, + abstract = {Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km {\texttimes} 4 km. The classification model's training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient ({$\kappa$}) obtained from the ten-fold cross validation for the training data were 90.4\% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0\% and a {$\kappa$} = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.}, langid = {english} } @@ -342,7 +342,7 @@ @article{coopsFrameworkRealtimeForest2023 issn = {0015-752X, 1464-3626}, doi = {10.1093/forestry/cpac015}, urldate = {2023-02-15}, - abstract = {Abstract Forestry inventory update is a critical component of sustainable forest management, requiring both the spatially explicit identification of forest cover change and integration of sampled or modelled components like growth and regeneration. Contemporary inventory data demands are shifting, with an increased focus on accurate attribute estimation via the integration of advanced remote sensing data such as airborne laser scanning (ALS). Key challenges remain, however, on how to maintain and update these next-generation inventories as they age. Of particular interest is the identification of remotely sensed data that can be applied cost effectively, as well as establishing frameworks to integrate these data to update information on forest condition, predict future growth and yield, and integrate information that can guide forest management or silvicultural decisions such as thinning and harvesting prescriptions. The purpose of this article is to develop a conceptual framework for forestry inventory update, which is also known as the establishment of a `living inventory'. The proposed framework contains the critical components of an inventory update including inventory and growth monitoring, change detection and error propagation. In the framework, we build on existing applications of ALS-derived enhanced inventories and integrate them with data from satellite constellations of free and open, analysis-ready moderate spatial resolution imagery. Based on a review of the current literature, our approach fits trajectories to chronosequences of pixel-level spectral index values to detect change. When stand-replacing change is detected, corresponding values of cell-level inventory attributes are reset and re-established based on an assigned growth curve. In the case of non\textendash stand-replacing disturbances, cell estimates are modified based on predictive models developed between the degree of observed spectral change and relative changes in the inventory attributes. We propose that additional fine-scale data can be collected over the disturbed area, from sources such as CubeSats or remotely piloted airborne systems, and attributes updated based on these data sources. Cells not identified as undergoing change are assumed unchanged with cell-level growth curves used to increment inventory attributes. We conclude by discussing the impact of error propagation on the prediction of forest inventory attributes through the proposed near real-time framework, computing needs and integration of other available remote sensing data.}, + abstract = {Abstract Forestry inventory update is a critical component of sustainable forest management, requiring both the spatially explicit identification of forest cover change and integration of sampled or modelled components like growth and regeneration. Contemporary inventory data demands are shifting, with an increased focus on accurate attribute estimation via the integration of advanced remote sensing data such as airborne laser scanning (ALS). Key challenges remain, however, on how to maintain and update these next-generation inventories as they age. Of particular interest is the identification of remotely sensed data that can be applied cost effectively, as well as establishing frameworks to integrate these data to update information on forest condition, predict future growth and yield, and integrate information that can guide forest management or silvicultural decisions such as thinning and harvesting prescriptions. The purpose of this article is to develop a conceptual framework for forestry inventory update, which is also known as the establishment of a `living inventory'. The proposed framework contains the critical components of an inventory update including inventory and growth monitoring, change detection and error propagation. In the framework, we build on existing applications of ALS-derived enhanced inventories and integrate them with data from satellite constellations of free and open, analysis-ready moderate spatial resolution imagery. Based on a review of the current literature, our approach fits trajectories to chronosequences of pixel-level spectral index values to detect change. When stand-replacing change is detected, corresponding values of cell-level inventory attributes are reset and re-established based on an assigned growth curve. In the case of non{\textendash}stand-replacing disturbances, cell estimates are modified based on predictive models developed between the degree of observed spectral change and relative changes in the inventory attributes. We propose that additional fine-scale data can be collected over the disturbed area, from sources such as CubeSats or remotely piloted airborne systems, and attributes updated based on these data sources. Cells not identified as undergoing change are assumed unchanged with cell-level growth curves used to increment inventory attributes. We conclude by discussing the impact of error propagation on the prediction of forest inventory attributes through the proposed near real-time framework, computing needs and integration of other available remote sensing data.}, langid = {english} } @@ -373,7 +373,7 @@ @article{cushmanSmallFieldPlots2023 issn = {2072-4292}, doi = {10.3390/rs15143509}, urldate = {2023-10-20}, - abstract = {Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8\textendash 33.3 Mg ha-1 for site-specific models (one standard deviation), 11.1\textendash 28.2 Mg ha-1 for ecoregion-specific models, and 21.1\textendash 22.1 Mg ha-1 for the general model for pixels in the AGB range of 80\textendash 100 Mg ha-1. Only 3 of 11 site-specific models had a total uncertainty of {$<$}15 Mg ha-1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha-1 using 0.04 ha plots to 10.9 Mg ha-1 using 0.25 ha plots and 10.1 Mg ha-1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales ({$\geq$}0.25 ha).}, + abstract = {Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8{\textendash}33.3 Mg ha-1 for site-specific models (one standard deviation), 11.1{\textendash}28.2 Mg ha-1 for ecoregion-specific models, and 21.1{\textendash}22.1 Mg ha-1 for the general model for pixels in the AGB range of 80{\textendash}100 Mg ha-1. Only 3 of 11 site-specific models had a total uncertainty of {$<$}15 Mg ha-1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha-1 using 0.04 ha plots to 10.9 Mg ha-1 using 0.25 ha plots and 10.1 Mg ha-1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales ({$\geq$}0.25 ha).}, langid = {english} } @@ -389,7 +389,7 @@ @article{dalagnolLargescaleVariationsDynamics2021 issn = {2045-2322}, doi = {10.1038/s41598-020-80809-w}, urldate = {2023-02-15}, - abstract = {Abstract We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20\textendash 35\% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60\% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.}, + abstract = {Abstract We report large-scale estimates of Amazonian gap dynamics using a novel approach with large datasets of airborne light detection and ranging (lidar), including five multi-temporal and 610 single-date lidar datasets. Specifically, we (1) compared the fixed height and relative height methods for gap delineation and established a relationship between static and dynamic gaps (newly created gaps); (2) explored potential environmental/climate drivers explaining gap occurrence using generalized linear models; and (3) cross-related our findings to mortality estimates from 181 field plots. Our findings suggest that static gaps are significantly correlated to dynamic gaps and can inform about structural changes in the forest canopy. Moreover, the relative height outperformed the fixed height method for gap delineation. Well-defined and consistent spatial patterns of dynamic gaps were found over the Amazon, while also revealing the dynamics of areas never sampled in the field. The predominant pattern indicates 20{\textendash}35\% higher gap dynamics at the west and southeast than at the central-east and north. These estimates were notably consistent with field mortality patterns, but they showed 60\% lower magnitude likely due to the predominant detection of the broken/uprooted mode of death. While topographic predictors did not explain gap occurrence, the water deficit, soil fertility, forest flooding and degradation were key drivers of gap variability at the regional scale. These findings highlight the importance of lidar in providing opportunities for large-scale gap dynamics and tree mortality monitoring over the Amazon.}, langid = {english} } @@ -408,7 +408,7 @@ @article{davisonEffectLeafonLeafoff2020 } @article{derschCompleteTreeCrown2023, - title = {Towards Complete Tree Crown Delineation by Instance Segmentation with {{Mask R}}\textendash{{CNN}} and {{DETR}} Using {{UAV-based}} Multispectral Imagery and Lidar Data}, + title = {Towards Complete Tree Crown Delineation by Instance Segmentation with {{Mask R}}{\textendash}{{CNN}} and {{DETR}} Using {{UAV-based}} Multispectral Imagery and Lidar Data}, author = {Dersch, S. and Sch{\"o}ttl, A. and Krzystek, P. and Heurich, M.}, year = {2023}, month = apr, @@ -433,7 +433,7 @@ @article{dietmaierComparisonLiDARDigital2019 issn = {2072-4292}, doi = {10.3390/rs11161919}, urldate = {2023-03-20}, - abstract = {Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87\% overall accuracy), followed by the hybrid and DAP models (47\% and 46\%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9\textendash 53\%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP's sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology's capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.}, + abstract = {Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87\% overall accuracy), followed by the hybrid and DAP models (47\% and 46\%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9{\textendash}53\%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP's sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology's capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.}, langid = {english} } @@ -504,7 +504,7 @@ @article{fuhrDetectingOvermatureForests2022 } @article{ganzMeasuringTreeHeight2019, - title = {Measuring {{Tree Height}} with {{Remote Sensing}}\textemdash{{A Comparison}} of {{Photogrammetric}} and {{LiDAR Data}} with {{Different Field Measurements}}}, + title = {Measuring {{Tree Height}} with {{Remote Sensing}}{\textemdash}{{A Comparison}} of {{Photogrammetric}} and {{LiDAR Data}} with {{Different Field Measurements}}}, author = {Ganz, Selina and K{\"a}ber, Yannek and Adler, Petra}, year = {2019}, month = aug, @@ -545,7 +545,7 @@ @article{gavilan-acunaEstimatingPotentialTree2022 issn = {0045-5067, 1208-6037}, doi = {10.1139/cjfr-2022-0121}, urldate = {2023-03-01}, - abstract = {Representing the spatial distribution of trees and competition interactions in growth models improves growth prediction and provides insights into spatially explicit forecasts for precise silvicultural interventions. However, this information is rarely taken into account over large areas because obtaining the spatial distribution of individual trees and estimating their competition is both expensive and time consuming. Airborne laser scanning enables rapid estimation of tree height and other attributes over large areas. In this study, we implemented an individual tree detection approach to first extract tree attributes of Pinus radiata D.~Don plantations, and second to use this spatially explicit information on tree location and competition to forecast potential tree height, defined as a maximum projected tree height at rotation age. To do so, using a chronosequence of tree heights, we developed a tree height growth model using a Chapman\textendash Richards function, utilizing the effect of inter-tree competition and stand-level top height (TH) on the tree height growth. The results showed that using chronosequence of heights, competition, and TH resulted in accurate predictions of potential tree height (root mean square error~=~2.9~m; mean absolute percentage error~=~0.154\%). We concluded that individual tree height growth is significantly influenced by competition, with increased competition values associated with reductions in potential height growth by 22.2\% at 30 years.}, + abstract = {Representing the spatial distribution of trees and competition interactions in growth models improves growth prediction and provides insights into spatially explicit forecasts for precise silvicultural interventions. However, this information is rarely taken into account over large areas because obtaining the spatial distribution of individual trees and estimating their competition is both expensive and time consuming. Airborne laser scanning enables rapid estimation of tree height and other attributes over large areas. In this study, we implemented an individual tree detection approach to first extract tree attributes of Pinus radiata D.~Don plantations, and second to use this spatially explicit information on tree location and competition to forecast potential tree height, defined as a maximum projected tree height at rotation age. To do so, using a chronosequence of tree heights, we developed a tree height growth model using a Chapman{\textendash}Richards function, utilizing the effect of inter-tree competition and stand-level top height (TH) on the tree height growth. The results showed that using chronosequence of heights, competition, and TH resulted in accurate predictions of potential tree height (root mean square error~=~2.9~m; mean absolute percentage error~=~0.154\%). We concluded that individual tree height growth is significantly influenced by competition, with increased competition values associated with reductions in potential height growth by 22.2\% at 30 years.}, langid = {english} } @@ -670,7 +670,7 @@ @incollection{guoLiDARRemoteSensing2022 } @article{haesenForestTempSubCanopy2021, - title = {{{ForestTemp}} \textendash{} {{Sub}}-canopy Microclimate Temperatures of {{European}} Forests}, + title = {{{ForestTemp}} {\textendash} {{Sub}}-canopy Microclimate Temperatures of {{European}} Forests}, author = {Haesen, Stef and Lembrechts, Jonas J. and De Frenne, Pieter and Lenoir, Jonathan and Aalto, Juha and Ashcroft, Michael B. and Kopeck{\'y}, Martin and Luoto, Miska and Maclean, Ilya and Nijs, Ivan and Niittynen, Pekka and Hoogen, Johan and Arriga, Nicola and Br{\r{u}}na, Josef and Buchmann, Nina and {\v C}iliak, Marek and Collalti, Alessio and De Lombaerde, Emiel and Descombes, Patrice and Gharun, Mana and Goded, Ignacio and Govaert, Sanne and Greiser, Caroline and Grelle, Achim and Gruening, Carsten and Hederov{\'a}, Lucia and Hylander, Kristoffer and Kreyling, J{\"u}rgen and Kruijt, Bart and Macek, Martin and M{\'a}li{\v s}, Franti{\v s}ek and Man, Mat{\v e}j and Manca, Giovanni and Matula, Radim and Meeussen, Camille and Merinero, Sonia and Minerbi, Stefano and Montagnani, Leonardo and Muffler, Lena and Ogaya, Rom{\`a} and Penuelas, Josep and Plichta, Roman and Portillo-Estrada, Miguel and Schmeddes, Jonas and Shekhar, Ankit and Spicher, Fabien and Ujh{\'a}zyov{\'a}, Mariana and Vangansbeke, Pieter and Weigel, Robert and Wild, Jan and Zellweger, Florian and Van Meerbeek, Koenraad}, year = {2021}, month = dec, @@ -696,11 +696,11 @@ @article{hauglinLargeScaleMapping2021 issn = {2197-5620}, doi = {10.1186/s40663-021-00338-4}, urldate = {2023-02-24}, - abstract = {Abstract Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10\,years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35\%, 34\%, 31\%, and 12\% for volume, aboveground biomass, basal area, and Lorey's height, respectively. These RMSEs were 2\textendash 7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0\textendash 4\,pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8\,pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0\textendash 3.9\,pp., depending on the main tree species. RMSEs for timber volume ranged between 19\%\textendash 59\% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.}, + abstract = {Abstract Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10\,years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35\%, 34\%, 31\%, and 12\% for volume, aboveground biomass, basal area, and Lorey's height, respectively. These RMSEs were 2{\textendash}7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0{\textendash}4\,pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8\,pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0{\textendash}3.9\,pp., depending on the main tree species. RMSEs for timber volume ranged between 19\%{\textendash}59\% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.}, langid = {english} } -@article{heinaroAirborneLaserScanning2021, +@article{heinaroAirborneLaserScanning2021a, title = {Airborne Laser Scanning Reveals Large Tree Trunks on Forest Floor}, author = {Heinaro, Einari and Tanhuanp{\"a}{\"a}, Topi and Yrttimaa, Tuomas and Holopainen, Markus and Vastaranta, Mikko}, year = {2021}, @@ -798,7 +798,7 @@ @article{hopfstockAufWegDigitalen2021 title = {{Auf dem Weg zu einem Digitalen Zwilling von Deutschland}}, author = {Hopfstock, Anja}, year = {2021}, - journal = {zfv \textendash{} Zeitschrift f\"ur Geod\"asie, Geoinformation und Landmanagement}, + journal = {zfv {\textendash} Zeitschrift f{\"u}r Geod{\"a}sie, Geoinformation und Landmanagement}, number = {6/2021}, pages = {385--390}, issn = {1618-8950}, @@ -1052,7 +1052,7 @@ @article{kostensaloRecreatingStructurallyRealistic2023 } @article{krisanskiForestStructuralComplexity2021, - title = {Forest {{Structural Complexity Tool}}\textemdash{{An Open Source}}, {{Fully-Automated Tool}} for {{Measuring Forest Point Clouds}}}, + title = {Forest {{Structural Complexity Tool}}{\textemdash}{{An Open Source}}, {{Fully-Automated Tool}} for {{Measuring Forest Point Clouds}}}, author = {Krisanski, Sean and Taskhiri, Mohammad Sadegh and Gonzalez Aracil, Susana and Herries, David and Muneri, Allie and Gurung, Mohan Babu and Montgomery, James and Turner, Paul}, year = {2021}, month = nov, @@ -1068,7 +1068,7 @@ @article{krisanskiForestStructuralComplexity2021 } @article{krzystekLargeScaleMappingTree2020, - title = {Large-{{Scale Mapping}} of {{Tree Species}} and {{Dead Trees}} in {{\v{S}umava National Park}} and {{Bavarian Forest National Park Using Lidar}} and {{Multispectral Imagery}}}, + title = {Large-{{Scale Mapping}} of {{Tree Species}} and {{Dead Trees}} in {{{\v S}umava National Park}} and {{Bavarian Forest National Park Using Lidar}} and {{Multispectral Imagery}}}, author = {Krzystek, Peter and Serebryanyk, Alla and Schn{\"o}rr, Claudius and {\v C}ervenka, Jaroslav and Heurich, Marco}, year = {2020}, month = feb, @@ -1079,7 +1079,7 @@ @article{krzystekLargeScaleMappingTree2020 issn = {2072-4292}, doi = {10.3390/rs12040661}, urldate = {2023-02-15}, - abstract = {Knowledge of forest structures\textemdash and of dead wood in particular\textemdash is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species\textemdash subdivided into conifers and broadleaf trees\textemdash and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in \v{S}umava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for \v{S}umava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood\textemdash located in areas of pest infestation\textemdash is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13\%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90\%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.}, + abstract = {Knowledge of forest structures{\textemdash}and of dead wood in particular{\textemdash}is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species{\textemdash}subdivided into conifers and broadleaf trees{\textemdash}and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in {\v S}umava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for {\v S}umava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood{\textemdash}located in areas of pest infestation{\textemdash}is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13\%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90\%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.}, langid = {english} } @@ -1123,7 +1123,7 @@ @article{lampingComparisonLowCostCommercial2021 issn = {2072-4292}, doi = {10.3390/rs13214292}, urldate = {2023-10-20}, - abstract = {Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74\textendash 0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17\textendash 0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53\textendash 0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions.}, + abstract = {Science-based forest management requires quantitative estimation of forest attributes traditionally collected via sampled field plots in a forest inventory program. Three-dimensional (3D) remotely sensed data such as Light Detection and Ranging (lidar), are increasingly utilized to supplement and even replace field-based forest inventories. However, lidar remains cost prohibitive for smaller areas and repeat measurements, often limiting its use to single acquisitions of large contiguous areas. Recent advancements in unpiloted aerial systems (UAS), digital aerial photogrammetry (DAP) and high precision global positioning systems (HPGPS) have the potential to provide low-cost time and place flexible 3D data to support forest inventory and monitoring. The primary objective of this study was to assess the ability of low-cost commercial off the shelf UAS DAP and HPGPS to create accurate 3D data and predictions of key forest attributes, as compared to both lidar and field observations, in a wide range of forest conditions in California, USA. A secondary objective was to assess the accuracy of nadir vs. off-nadir UAS DAP, to determine if oblique imagery provides more accurate 3D data and forest attribute predictions. UAS DAP digital terrain models (DTMs) were comparable to lidar DTMS across most sites and nadir vs. off-nadir imagery collection (R2 = 0.74{\textendash}0.99), although model accuracy using off-nadir imagery was very low in mature Douglas-fir forest (R2 = 0.17) due to high canopy density occluding the ground from the image sensor. Surface and canopy height models were shown to have less agreement to lidar (R2 = 0.17{\textendash}0.69), with off-nadir imagery surface models at high canopy density sites having the lowest agreement with lidar. UAS DAP models predicted key forest metrics with varying accuracy compared to field data (R2 = 0.53{\textendash}0.85), and were comparable to predictions made using lidar. Although lidar provided more accurate estimates of forest attributes across a range of forest conditions, this study shows that UAS DAP models, when combined with low-cost HPGPS, can accurately predict key forest attributes across a range of forest types, canopies densities, and structural conditions.}, langid = {english} } @@ -1277,7 +1277,7 @@ @article{mandlburgerCOMPARISONSINGLEPHOTON2019 issn = {2194-9050}, doi = {10.5194/isprs-annals-IV-2-W5-397-2019}, urldate = {2023-03-13}, - abstract = {Abstract. Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2\textendash 3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10\,cm.}, + abstract = {Abstract. Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2{\textendash}3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10\,cm.}, langid = {english} } @@ -1306,7 +1306,7 @@ @article{moanDetectingExcludingDisturbed2023 issn = {0015-752X, 1464-3626}, doi = {10.1093/forestry/cpad025}, urldate = {2023-05-17}, - abstract = {Abstract Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). SI determination using bitemporal ALS data requires undisturbed height growth of dominant trees. Therefore, areas with disturbed top height development are unsuitable for SI determination, and should be identified and omitted before modelling, predicting and estimating SI using bitemporal ALS data. The aim of this study was to explore methods for classifying the suitability of forest areas for SI determination based on bitemporal ALS data. The modelling approaches k-nearest neighbour, logistic regression and random forest were compared for classifying disturbed (at least one dominant tree has disappeared) and undisturbed plots. A forest inventory with plot re-measurements and corresponding bitemporal ALS data from the Petawawa Research Forest in Ontario, Canada, was used as a case study. Based on the field data, two definitions of a disturbed plot were developed: (1) at least one dominant tree had died, was harvested or had fallen during the observation period, or (2) at least one dominant tree was harvested or had fallen during the observation period. The first definition included standing dead trees, which we hypothesized would be more difficult to accurately classify from bitemporal ALS data. Models of disturbance definition 1 and 2 yielded Matthews correlation coefficients of 0.46\textendash 0.59 and 0.62\textendash 0.80, respectively. Fit statistics of SI prediction models fitted to undisturbed plots were significantly better (P\,\<\,0.05) than fit statistics of SI prediction models fitted to all plots. Our results show that bitemporal ALS data can be used to separate disturbed from undisturbed forest areas with moderate to high accuracy in complex temperate mixedwood forests and that excluding disturbed forest areas significantly improves fit statistics of SI prediction models.}, + abstract = {Abstract Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). SI determination using bitemporal ALS data requires undisturbed height growth of dominant trees. Therefore, areas with disturbed top height development are unsuitable for SI determination, and should be identified and omitted before modelling, predicting and estimating SI using bitemporal ALS data. The aim of this study was to explore methods for classifying the suitability of forest areas for SI determination based on bitemporal ALS data. The modelling approaches k-nearest neighbour, logistic regression and random forest were compared for classifying disturbed (at least one dominant tree has disappeared) and undisturbed plots. A forest inventory with plot re-measurements and corresponding bitemporal ALS data from the Petawawa Research Forest in Ontario, Canada, was used as a case study. Based on the field data, two definitions of a disturbed plot were developed: (1) at least one dominant tree had died, was harvested or had fallen during the observation period, or (2) at least one dominant tree was harvested or had fallen during the observation period. The first definition included standing dead trees, which we hypothesized would be more difficult to accurately classify from bitemporal ALS data. Models of disturbance definition 1 and 2 yielded Matthews correlation coefficients of 0.46{\textendash}0.59 and 0.62{\textendash}0.80, respectively. Fit statistics of SI prediction models fitted to undisturbed plots were significantly better (P\,\<\,0.05) than fit statistics of SI prediction models fitted to all plots. Our results show that bitemporal ALS data can be used to separate disturbed from undisturbed forest areas with moderate to high accuracy in complex temperate mixedwood forests and that excluding disturbed forest areas significantly improves fit statistics of SI prediction models.}, langid = {english} } @@ -1337,7 +1337,7 @@ @article{morleyUpdatingForestRoad2023 issn = {0015-752X, 1464-3626}, doi = {10.1093/forestry/cpad021}, urldate = {2023-05-17}, - abstract = {Abstract Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario's Boreal and Great Lakes\textemdash St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of `Full access' roads and 29 per cent of `Partial access' roads being undriveable by standard vehicles and 45 per cent of `Status unknown' roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4~m, and the average road width error is 2~m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available.}, + abstract = {Abstract Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario's Boreal and Great Lakes{\textemdash}St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of `Full access' roads and 29 per cent of `Partial access' roads being undriveable by standard vehicles and 45 per cent of `Status unknown' roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4~m, and the average road width error is 2~m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available.}, langid = {english} } @@ -1358,7 +1358,7 @@ @incollection{morsdorfLaegerenSiteAugmented2020 langid = {english} } -@article{moudryVegetationStructureDerived2023, +@article{moudryVegetationStructureDerived2023a, title = {Vegetation Structure Derived from Airborne Laser Scanning to Assess Species Distribution and Habitat Suitability: {{The}} Way Forward}, shorttitle = {Vegetation Structure Derived from Airborne Laser Scanning to Assess Species Distribution and Habitat Suitability}, author = {Moudr{\'y}, V{\'i}t{\v e}zslav and Cord, Anna F. and G{\'a}bor, Luk{\'a}{\v s} and Laurin, Gaia Vaglio and Bart{\'a}k, Vojt{\v e}ch and Gdulov{\'a}, Kate{\v r}ina and Malavasi, Marco and Rocchini, Duccio and Stere{\'n}czak, Krzysztof and Pro{\v s}ek, Ji{\v r}{\'i} and Kl{\'a}p{\v s}t{\v e}, Petr and Wild, Jan}, @@ -1416,6 +1416,20 @@ @article{noordermeerComparingAccuraciesForest2019 langid = {english} } +@article{noordermeerImputingStemFrequency2023, + title = {Imputing Stem Frequency Distributions Using Harvester and Airborne Laser Scanner Data: A Comparison of Inventory Approaches}, + shorttitle = {Imputing Stem Frequency Distributions Using Harvester and Airborne Laser Scanner Data}, + author = {Noordermeer, Lennart and {\O}rka, Hans Ole and Gobakken, Terje}, + year = {2023}, + journal = {Silva Fennica}, + volume = {57}, + number = {3}, + issn = {00375330, 22424075}, + doi = {10.14214/sf.23023}, + urldate = {2023-12-06}, + abstract = {Stem frequency distributions provide useful information for pre-harvest planning. We compared four inventory approaches for imputing stem frequency distributions using harvester data as reference data and predictor variables computed from airborne laser scanner (ALS) data. We imputed distributions and stand mean values of stem diameter, tree height, volume, and sawn wood volume using the k-nearest neighbor technique. We compared the inventory approaches: (1) individual tree crown (ITC), semi-ITC, area-based (ABA) and enhanced ABA (EABA). We assessed the accuracies of imputed distributions using a variant of the Reynold's error index, obtaining the best mean accuracies of 0.13, 0.13, 0.10 and 0.10 for distributions of stem diameter, tree height, volume and sawn wood volume, respectively. Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. The forest attribute, inventory approach, stand size and the laser pulse density had significant effects on the accuracies of imputed frequency distributions, however the ALS delay and percentage of deciduous trees did not. This study highlights the utility of harvester and ALS data for imputing stem frequency distributions in pre-harvest inventories.} +} + @article{noordermeerPredictingMappingSite2020, title = {Predicting and Mapping Site Index in Operational Forest Inventories Using Bitemporal Airborne Laser Scanner Data}, author = {Noordermeer, Lennart and Gobakken, Terje and N{\ae}sset, Erik and Bollands{\aa}s, Ole Martin}, @@ -1460,7 +1474,7 @@ @article{nowakHiddenGapsCanopy2022 } @misc{ogfdroneteamLiDARALSIm2022, - title = {{LiDAR (ALS) im Wald Teil 1: R\"uckegassen sichtbar machen | Drohnenbefliegungen in Forst- und Landwirtschaft}}, + title = {{LiDAR (ALS) im Wald Teil 1: R{\"u}ckegassen sichtbar machen | Drohnenbefliegungen in Forst- und Landwirtschaft}}, shorttitle = {{LiDAR (ALS) im Wald Teil 1}}, author = {{OGF drone team}}, year = {2022}, @@ -1470,7 +1484,7 @@ @misc{ogfdroneteamLiDARALSIm2022 } @article{olpendaEstimationSubcanopySolar2019, - title = {Estimation of Sub-Canopy Solar Radiation from {{LiDAR}} Discrete Returns in Mixed Temporal Forest of {{Bia\l owie\.za}}, {{Poland}}}, + title = {Estimation of Sub-Canopy Solar Radiation from {{LiDAR}} Discrete Returns in Mixed Temporal Forest of {{Bia{\l}owie{\.z}a}}, {{Poland}}}, author = {Olpenda, Alex S. and Stere{\'n}czak, Krzysztof and B{\k{e}}dkowski, Krzysztof}, year = {2019}, month = jul, @@ -1500,7 +1514,7 @@ @article{olpendaModelingSolarRadiation2018 } @article{packalenCircularSquarePlots2023, - title = {Circular or Square Plots in {{ALS-based}} Forest Inventories\textemdash Does It Matter?}, + title = {Circular or Square Plots in {{ALS-based}} Forest Inventories{\textemdash}Does It Matter?}, author = {Packalen, Petteri and Strunk, Jacob and Maltamo, Matti and Myllym{\"a}ki, Mari}, year = {2023}, month = jan, @@ -1511,7 +1525,7 @@ @article{packalenCircularSquarePlots2023 issn = {0015-752X, 1464-3626}, doi = {10.1093/forestry/cpac032}, urldate = {2023-04-11}, - abstract = {Abstract In airborne laser scanning (ALS)-based forest inventories, there is commonly a discrepancy between the plot shape used for model fitting (typically circular) and the shape of population elements (typically square) where predictions are needed. Circular plots are easy to establish, locate and have the smallest number of edge trees on average. Therefore, a circle is the most common plot shape in both traditional and remote sensing-based forest inventories. In contrast, the shape of population elements used for remote sensing-based predictions is nearly always a square because it enables division of the target population into a grid of non-overlapping plots. In this study, we investigate shape effects for ALS-based forest inventories using circular and square plot shapes. This has not been examined earlier. Aboveground biomass was used as the response variable. The sampling design was created in a way that the probability of selection for any location inside a stem-mapped 30~m\,\texttimes\,30~m plot was the same for the circular (radius 7.95~m) and square (side length 14.09~m) plot. This configuration enabled us to compare circular and square plots with the same areas and identical sampling probabilities for every tree in the population. Our primary finding is that for equal area square and circular plots, there is no evidence of systematic prediction error when a model fitted to one shape is used to predict for the other shape. Our secondary finding is that root mean square error (RMSE) value is slightly underestimated (1.2 per cent) when a model fitted to circular plots is used to predict for square plots. A small underestimation of RMSE due to plot shape effect has hardly practical significance in stand-level forest management inventories, but the plot shape effect may be problematic in large area forest surveys.}, + abstract = {Abstract In airborne laser scanning (ALS)-based forest inventories, there is commonly a discrepancy between the plot shape used for model fitting (typically circular) and the shape of population elements (typically square) where predictions are needed. Circular plots are easy to establish, locate and have the smallest number of edge trees on average. Therefore, a circle is the most common plot shape in both traditional and remote sensing-based forest inventories. In contrast, the shape of population elements used for remote sensing-based predictions is nearly always a square because it enables division of the target population into a grid of non-overlapping plots. In this study, we investigate shape effects for ALS-based forest inventories using circular and square plot shapes. This has not been examined earlier. Aboveground biomass was used as the response variable. The sampling design was created in a way that the probability of selection for any location inside a stem-mapped 30~m\,{\texttimes}\,30~m plot was the same for the circular (radius 7.95~m) and square (side length 14.09~m) plot. This configuration enabled us to compare circular and square plots with the same areas and identical sampling probabilities for every tree in the population. Our primary finding is that for equal area square and circular plots, there is no evidence of systematic prediction error when a model fitted to one shape is used to predict for the other shape. Our secondary finding is that root mean square error (RMSE) value is slightly underestimated (1.2 per cent) when a model fitted to circular plots is used to predict for square plots. A small underestimation of RMSE due to plot shape effect has hardly practical significance in stand-level forest management inventories, but the plot shape effect may be problematic in large area forest surveys.}, langid = {english} } @@ -1526,7 +1540,7 @@ @article{pennerAutomatedCharacterizationForest2023 issn = {0015-752X, 1464-3626}, doi = {10.1093/forestry/cpad033}, urldate = {2023-07-13}, - abstract = {Abstract Forest canopy vertical layering influences stand development and yield and is critical information for forest management planning and wood supply analysis. It is also relevant for other applications including habitat modelling, forest fuels management and assessing forest resilience. Forest inventories that use coincident airborne Light Detection and Ranging (LiDAR) data and field plots (i.e. area-based approach) to predict forest attributes generally do not consider the multi-layer canopy structure that may be found in many natural and managed forest stands. With airborne LiDAR, it is possible to separate single-layer and multi-layer stands. This information can be used to allocate predictions of forest attributes such as timber volume (m3 ha-1), by canopy layer. In this study, we used single-photon LiDAR data to automate the mapping of vertical stand layering in a temperate mixedwood forest with a variety of forest types and vertical complexities. We first predicted whether each 25 \texttimes{} 25~m grid cell had one or two canopy layers, and then partitioned inventory attributes (e.g. basal area (BA), gross total stem volume (GTV)) by canopy layer. We compared two methods for estimating attributes by layer at the stand level using nine independent validation stands. Overall agreement between the reference and predicted structure for the calibration plots was 74\% (n~=\,266). At the grid-cell level, attributes were generally underestimated for the upper layer and overestimated for the lower layer. For the validation stands, the relative height of the lower layer was under-predicted compared to the reference data (46\textendash 52\% versus 57\%), while the proportion of BA and GTV in the lower layer were very similar to the reference values (17\textendash 19\% versus 18\% for BA and 12\textendash 15\% versus 12\% for GTV). Overall, the approach showed promise in distinguishing single- and two-layered stand conditions and partitioning estimates of inventory attributes such as BA and GTV by layer\textemdash both for grid cells and at the stand level. The inclusion of forest information by canopy layer enhances the utility of LiDAR-derived forest inventories for forest management in forest areas with complex, multi-layer stand conditions.}, + abstract = {Abstract Forest canopy vertical layering influences stand development and yield and is critical information for forest management planning and wood supply analysis. It is also relevant for other applications including habitat modelling, forest fuels management and assessing forest resilience. Forest inventories that use coincident airborne Light Detection and Ranging (LiDAR) data and field plots (i.e. area-based approach) to predict forest attributes generally do not consider the multi-layer canopy structure that may be found in many natural and managed forest stands. With airborne LiDAR, it is possible to separate single-layer and multi-layer stands. This information can be used to allocate predictions of forest attributes such as timber volume (m3 ha-1), by canopy layer. In this study, we used single-photon LiDAR data to automate the mapping of vertical stand layering in a temperate mixedwood forest with a variety of forest types and vertical complexities. We first predicted whether each 25 {\texttimes} 25~m grid cell had one or two canopy layers, and then partitioned inventory attributes (e.g. basal area (BA), gross total stem volume (GTV)) by canopy layer. We compared two methods for estimating attributes by layer at the stand level using nine independent validation stands. Overall agreement between the reference and predicted structure for the calibration plots was 74\% (n~=\,266). At the grid-cell level, attributes were generally underestimated for the upper layer and overestimated for the lower layer. For the validation stands, the relative height of the lower layer was under-predicted compared to the reference data (46{\textendash}52\% versus 57\%), while the proportion of BA and GTV in the lower layer were very similar to the reference values (17{\textendash}19\% versus 18\% for BA and 12{\textendash}15\% versus 12\% for GTV). Overall, the approach showed promise in distinguishing single- and two-layered stand conditions and partitioning estimates of inventory attributes such as BA and GTV by layer{\textemdash}both for grid cells and at the stand level. The inclusion of forest information by canopy layer enhances the utility of LiDAR-derived forest inventories for forest management in forest areas with complex, multi-layer stand conditions.}, langid = {english} } @@ -1545,7 +1559,7 @@ @article{perssonTwophaseForestInventory2022 } @article{plotheDigitalisierungImCluster2022, - title = {{Der Digitalisierung im Cluster Forst und Holz auf die Spr\"unge helfen}}, + title = {{Der Digitalisierung im Cluster Forst und Holz auf die Spr{\"u}nge helfen}}, author = {Plothe, Martina}, year = {2022}, month = oct, @@ -1553,7 +1567,7 @@ @article{plotheDigitalisierungImCluster2022 volume = {2022}, number = {19}, urldate = {2023-03-27}, - abstract = {Borkenk\"afer ,,riechende`` Drohnen, eine lernf\"ahige App zur Holzeinschlagsplanung oder digitale Zwillinge bei der Hol...}, + abstract = {Borkenk{\"a}fer ,,riechende`` Drohnen, eine lernf{\"a}hige App zur Holzeinschlagsplanung oder digitale Zwillinge bei der Hol...}, langid = {ngerman} } @@ -1569,7 +1583,7 @@ @article{prieurComparisonThreeAirborne2021 issn = {1424-8220}, doi = {10.3390/s22010035}, urldate = {2023-02-15}, - abstract = {Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83\textendash 90\%; 12 species, 46\textendash 54\%; 4 species, 68\textendash 79\%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91\%; 12 species, 58\%; 4 species, 84\%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.}, + abstract = {Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83{\textendash}90\%; 12 species, 46{\textendash}54\%; 4 species, 68{\textendash}79\%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91\%; 12 species, 58\%; 4 species, 84\%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.}, langid = {english} } @@ -1632,7 +1646,7 @@ @article{riofrioHarmonizingMultitemporalAirborne2022 issn = {0045-5067, 1208-6037}, doi = {10.1139/cjfr-2022-0055}, urldate = {2023-02-15}, - abstract = {When combining multi-temporal airborne laser scanning (ALS) data sets, forest height growth assessments can be compromised due to variations in ALS acquisitions. Herein, we demonstrate the importance of assessing and harmonizing the vertical alignment of multi-temporal ALS data sets used for height growth calculations. Using four ALS acquisitions (2005\textendash 2018) in a temperate mixedwood forest, we developed an ALS data harmonization approach and quantified the impact of the harmonization on derived height periodic annual increment (PAI), comparing the ALS-derived PAI to PAI derived from non-harmonized ALS data sets and field measurements. We found significant differences in PAI derived from harmonized and non-harmonized data, and these differences were greater for shorter growth intervals. Data harmonization resulted in a consistent PAI series that reduced uncertainties associated with the different ALS acquisitions. Although overall there was a strong relationship between field and ALS height measures ( R 2 ~{$\geq~$}0.88), we found a weak relationship between the field- and ALS-derived PAI ( R 2 ~=~0.12). We identified systematic errors in field-based tree height measures in plots with complex crowns, tall trees, and restricted visibility. We demonstrate the need for harmonizing multi-temporal ALS data sets for the generation of PAI and, likewise, highlight the need of carefully scrutinize field-measured heights and associated increments.}, + abstract = {When combining multi-temporal airborne laser scanning (ALS) data sets, forest height growth assessments can be compromised due to variations in ALS acquisitions. Herein, we demonstrate the importance of assessing and harmonizing the vertical alignment of multi-temporal ALS data sets used for height growth calculations. Using four ALS acquisitions (2005{\textendash}2018) in a temperate mixedwood forest, we developed an ALS data harmonization approach and quantified the impact of the harmonization on derived height periodic annual increment (PAI), comparing the ALS-derived PAI to PAI derived from non-harmonized ALS data sets and field measurements. We found significant differences in PAI derived from harmonized and non-harmonized data, and these differences were greater for shorter growth intervals. Data harmonization resulted in a consistent PAI series that reduced uncertainties associated with the different ALS acquisitions. Although overall there was a strong relationship between field and ALS height measures ( R 2 ~{$\geq~$}0.88), we found a weak relationship between the field- and ALS-derived PAI ( R 2 ~=~0.12). We identified systematic errors in field-based tree height measures in plots with complex crowns, tall trees, and restricted visibility. We demonstrate the need for harmonizing multi-temporal ALS data sets for the generation of PAI and, likewise, highlight the need of carefully scrutinize field-measured heights and associated increments.}, langid = {english} } @@ -1678,7 +1692,7 @@ @article{rodriguez-vivancosAnalysisStructureMotion2022 issn = {1612-4669, 1612-4677}, doi = {10.1007/s10342-022-01447-7}, urldate = {2023-02-15}, - abstract = {Abstract Airborne Laser Scanning (ALS) is widely extended in forest evaluation, although photogrammetry-based Structure from Motion (SfM) has recently emerged as a more affordable alternative. Return cloud metrics and their normalization using different typologies of Digital Terrain Models (DTM), either derived from SfM or from private or free access ALS, were evaluated. In addition, the influence of the return density (0.5\textendash 6.5 returns m -2 ) and the sampling intensity (0.3\textendash 3.4\%) on the estimation of the most common stand structure variables were also analysed. The objective of this research is to gather all these questions in the same document, so that they serve as support for the planning of forest management. This study analyses the variables collected from 60 regularly distributed circular plots (r\,=\,18~m) in a 150-ha of uneven-aged Scots pine stand. Results indicated that both ALS and SfM can be equally used to reduce the sampling error in the field inventories, but they showed differences when estimating the stand structure variables. ALS produced significantly better estimations than the SfM metrics for all the variables of interest, as well as the ALS-based normalization. However, the SfM point cloud produced better estimations when it was normalized with its own DTM, except for the dominant height. The return density did not have significant influence on the estimation of the stand structure variables in the range studied, while higher sampling intensities decreased the estimation errors. Nevertheless, these were stabilized at certain intensities depending on the variance of the stand structure variable.}, + abstract = {Abstract Airborne Laser Scanning (ALS) is widely extended in forest evaluation, although photogrammetry-based Structure from Motion (SfM) has recently emerged as a more affordable alternative. Return cloud metrics and their normalization using different typologies of Digital Terrain Models (DTM), either derived from SfM or from private or free access ALS, were evaluated. In addition, the influence of the return density (0.5{\textendash}6.5 returns m -2 ) and the sampling intensity (0.3{\textendash}3.4\%) on the estimation of the most common stand structure variables were also analysed. The objective of this research is to gather all these questions in the same document, so that they serve as support for the planning of forest management. This study analyses the variables collected from 60 regularly distributed circular plots (r\,=\,18~m) in a 150-ha of uneven-aged Scots pine stand. Results indicated that both ALS and SfM can be equally used to reduce the sampling error in the field inventories, but they showed differences when estimating the stand structure variables. ALS produced significantly better estimations than the SfM metrics for all the variables of interest, as well as the ALS-based normalization. However, the SfM point cloud produced better estimations when it was normalized with its own DTM, except for the dominant height. The return density did not have significant influence on the estimation of the stand structure variables in the range studied, while higher sampling intensities decreased the estimation errors. Nevertheless, these were stabilized at certain intensities depending on the variance of the stand structure variable.}, langid = {english} } @@ -1737,7 +1751,22 @@ @article{saarelaMappingAbovegroundBiomass2020 issn = {2197-5620}, doi = {10.1186/s40663-020-00245-0}, urldate = {2023-04-11}, - abstract = {Abstract Background The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km 2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m \texttimes 18 m map units was found to range between 9 and 447 Mg {$\cdot$} ha -1 . The corresponding root mean square errors ranged between 10 and 162 Mg {$\cdot$} ha -1 . For the entire study region, the mean aboveground biomass was 55 Mg {$\cdot$} ha -1 and the corresponding relative root mean square error 8\%. At this level 75\% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.}, + abstract = {Abstract Background The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km 2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m {\texttimes}18 m map units was found to range between 9 and 447 Mg {$\cdot$} ha -1 . The corresponding root mean square errors ranged between 10 and 162 Mg {$\cdot$} ha -1 . For the entire study region, the mean aboveground biomass was 55 Mg {$\cdot$} ha -1 and the corresponding relative root mean square error 8\%. At this level 75\% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.}, + langid = {english} +} + +@article{schaferAssessingPotentialSynthetic2023, + title = {Assessing the Potential of Synthetic and {\emph{Ex Situ}} Airborne Laser Scanning and Ground Plot Data to Train Forest Biomass Models}, + author = {Sch{\"a}fer, Jannika and Winiwarter, Lukas and Weiser, Hannah and Novotn{\'y}, Jan and H{\"o}fle, Bernhard and Schmidtlein, Sebastian and Henniger, Hans and Krok, Grzegorz and Stere{\'n}czak, Krzysztof and Fassnacht, Fabian Ewald}, + editor = {Lam, Tzeng Yih}, + year = {2023}, + month = dec, + journal = {Forestry: An International Journal of Forest Research}, + pages = {cpad061}, + issn = {0015-752X, 1464-3626}, + doi = {10.1093/forestry/cpad061}, + urldate = {2023-12-06}, + abstract = {Abstract Airborne laser scanning data are increasingly used to predict forest biomass over large areas. Biomass information cannot be derived directly from airborne laser scanning data; therefore, field measurements of forest plots are required to build regression models. We tested whether simulated laser scanning data of virtual forest plots could be used to train biomass models and thereby reduce the amount of field measurements required. We compared the performance of models that were trained with (i) simulated data only, (ii) a combination of simulated and real data, (iii) real data collected from different study sites, and (iv) real data collected from the same study site the model was applied to. We additionally investigated whether using a subset of the simulated data instead of using all simulated data improved model performance. The best matching subset of the simulated data was sampled by selecting the simulated forest plot with the highest correlation of the return height distribution profile for each real forest plot. For comparison, a randomly selected subset was evaluated. Models were tested on four forest sites located in Poland, the Czech Republic, and Canada. Model performance was assessed by root mean squared error (RMSE), squared Pearson correlation coefficient (r\$\^\{2\}\$), and mean error (ME) of observed and predicted biomass. We found that models trained solely with simulated data did not achieve the accuracy of models trained with real data (RMSE increase of 52{\textendash}122 \%, r\$\^\{2\}\$ decrease of 4{\textendash}18 \%). However, model performance improved when only a subset of the simulated data was used (RMSE increase of 21{\textendash}118 \%, r\$\^\{2\}\$ decrease of 5{\textendash}14 \% compared to the real data model), albeit differences in model performance when using the best matching subset compared to using a randomly selected subset were small. Using simulated data for model training always resulted in a strong underprediction of biomass. Extending sparse real training datasets with simulated data decreased RMSE and increased r\$\^\{2\}\$, as long as no more than 12{\textendash}346 real training samples were available, depending on the study site. For three of the four study sites, models trained with real data collected from other sites outperformed models trained with simulated data and RMSE and r\$\^\{2\}\$ were similar to models trained with data from the respective sites. Our results indicate that simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available.}, langid = {english} } @@ -1751,7 +1780,7 @@ @article{schaferGeneratingSyntheticLaser2023 issn = {0015-752X, 1464-3626}, doi = {10.1093/forestry/cpad006}, urldate = {2023-05-24}, - abstract = {Abstract Airborne laser scanning (ALS) data are routinely used to estimate and map structure-related forest inventory variables. The further development, refinement and evaluation of methods to derive forest inventory variables from ALS data require extensive datasets of forest stand information on an individual tree-level and corresponding ALS data. A cost-efficient method to obtain such datasets is the combination of virtual forest stands with a laser scanning simulator. We present an approach to simulate ALS data of forest stands by combining forest inventory information, a tree point cloud database and the laser scanning simulation framework HELIOS++. ALS data of six 1-ha plots were simulated and compared to real ALS data of these plots. The synthetic 3D representations of the forest stands were composed of real laser scanning point clouds of individual trees that were acquired by an uncrewed aerial vehicle (UAV), and, for comparison, simplified tree models with cylindrical stems and spheroidal crowns. The simulated ALS point clouds of the six plots were compared with the real point clouds based on canopy cover, height distribution of returns and several other point cloud metrics. In addition, the performance of biomass models trained using these synthetic data was evaluated. The comparison revealed that, in general, both the real tree models and the simplified tree models can be used to generate synthetic data. The results differed for the different study sites and depending on whether all returns or only first returns were considered. The measure of canopy cover was better represented by the data of the simplified tree models, whereas the height distribution of the returns was \textendash{} for most of the study sites \textendash{} better represented by the real tree model data. Training biomass models with metrics derived from the real tree model data led to an overestimation of biomass, while using metrics of the simplified tree model data resulted in an underestimation of biomass. Still, the accuracy of models trained with simulated data was only slightly lower compared to models trained with real ALS data. Our results suggest that the presented approach can be used to generate ALS data that are sufficiently realistic for many applications. The synthetic data may be used to develop new or refine existing ALS-based forest inventory methods, to systematically investigate the relationship between point cloud metrics and forest inventory variables and to analyse how this relationship is affected by laser scanning acquisition settings and field reference data characteristics.}, + abstract = {Abstract Airborne laser scanning (ALS) data are routinely used to estimate and map structure-related forest inventory variables. The further development, refinement and evaluation of methods to derive forest inventory variables from ALS data require extensive datasets of forest stand information on an individual tree-level and corresponding ALS data. A cost-efficient method to obtain such datasets is the combination of virtual forest stands with a laser scanning simulator. We present an approach to simulate ALS data of forest stands by combining forest inventory information, a tree point cloud database and the laser scanning simulation framework HELIOS++. ALS data of six 1-ha plots were simulated and compared to real ALS data of these plots. The synthetic 3D representations of the forest stands were composed of real laser scanning point clouds of individual trees that were acquired by an uncrewed aerial vehicle (UAV), and, for comparison, simplified tree models with cylindrical stems and spheroidal crowns. The simulated ALS point clouds of the six plots were compared with the real point clouds based on canopy cover, height distribution of returns and several other point cloud metrics. In addition, the performance of biomass models trained using these synthetic data was evaluated. The comparison revealed that, in general, both the real tree models and the simplified tree models can be used to generate synthetic data. The results differed for the different study sites and depending on whether all returns or only first returns were considered. The measure of canopy cover was better represented by the data of the simplified tree models, whereas the height distribution of the returns was {\textendash} for most of the study sites {\textendash} better represented by the real tree model data. Training biomass models with metrics derived from the real tree model data led to an overestimation of biomass, while using metrics of the simplified tree model data resulted in an underestimation of biomass. Still, the accuracy of models trained with simulated data was only slightly lower compared to models trained with real ALS data. Our results suggest that the presented approach can be used to generate ALS data that are sufficiently realistic for many applications. The synthetic data may be used to develop new or refine existing ALS-based forest inventory methods, to systematically investigate the relationship between point cloud metrics and forest inventory variables and to analyse how this relationship is affected by laser scanning acquisition settings and field reference data characteristics.}, langid = {english} } @@ -1767,7 +1796,21 @@ @article{schneiderMappingFunctionalDiversity2017 issn = {2041-1723}, doi = {10.1038/s41467-017-01530-3}, urldate = {2023-05-17}, - abstract = {Abstract Assessing functional diversity from space can help predict productivity and stability of forest ecosystems at global scale using biodiversity\textendash ecosystem functioning relationships. We present a new spatially continuous method to map regional patterns of tree functional diversity using combined laser scanning and imaging spectroscopy. The method does not require prior taxonomic information and integrates variation in plant functional traits between and within plant species. We compare our method with leaf-level field measurements and species-level plot inventory data and find reasonable agreement. Morphological and physiological diversity show consistent change with topography and soil, with low functional richness at a mountain ridge under specific environmental conditions. Overall, functional richness follows a logarithmic increase with area, whereas divergence and evenness are scale invariant. By mapping diversity at scales of individual trees to whole communities we demonstrate the potential of assessing functional diversity from space, providing a pathway only limited by technological advances and not by methodology.}, + abstract = {Abstract Assessing functional diversity from space can help predict productivity and stability of forest ecosystems at global scale using biodiversity{\textendash}ecosystem functioning relationships. We present a new spatially continuous method to map regional patterns of tree functional diversity using combined laser scanning and imaging spectroscopy. The method does not require prior taxonomic information and integrates variation in plant functional traits between and within plant species. We compare our method with leaf-level field measurements and species-level plot inventory data and find reasonable agreement. Morphological and physiological diversity show consistent change with topography and soil, with low functional richness at a mountain ridge under specific environmental conditions. Overall, functional richness follows a logarithmic increase with area, whereas divergence and evenness are scale invariant. By mapping diversity at scales of individual trees to whole communities we demonstrate the potential of assessing functional diversity from space, providing a pathway only limited by technological advances and not by methodology.}, + langid = {english} +} + +@article{seelyModellingTreeBiomass2023, + title = {Modelling Tree Biomass Using Direct and Additive Methods with Point Cloud Deep Learning in a Temperate Mixed Forest}, + author = {Seely, Harry and Coops, Nicholas C. and White, Joanne C. and Montw{\'e}, David and Winiwarter, Lukas and Ragab, Ahmed}, + year = {2023}, + month = dec, + journal = {Science of Remote Sensing}, + volume = {8}, + pages = {100110}, + issn = {26660172}, + doi = {10.1016/j.srs.2023.100110}, + urldate = {2023-12-06}, langid = {english} } @@ -1815,7 +1858,7 @@ @article{slavikSpatialAnalysisDense2023 issn = {1999-4907}, doi = {10.3390/f14081581}, urldate = {2023-08-04}, - abstract = {This study presents a method of tree species classification using individual tree metrics derived from a three-dimensional point cloud from unmanned aerial vehicle laser scanning (ULS). In this novel approach, we evaluated the metrics of 1045 trees using generalized linear model (GLM) and random forest (RF) techniques to automatically assign individual trees into either a coniferous or broadleaf group. We evaluated several statistical descriptors, including a novel approach using the Clark\textendash Evans spatial aggregation index (CE), which indicates the level of clustering in point clouds. A comparison of classifiers that included and excluded the CE indicator values demonstrated their importance for improved classification of the individual tree point clouds. The overall accuracy when including the CE index was 94.8\% using a GLM approach and 95.1\% using an RF approach. With the RF approach, the inclusion of CE yielded a significant improvement in overall classification accuracy, and for the GLM approach, the CE index was always selected as a significant variable for correct tree class prediction. Compared to other studies, the above-mentioned accuracies prove the benefits of CE for tree species classification, as do the worse results of excluding the CE, where the derived GLM achieved an accuracy of 92.6\% and RF an accuracy of 93.8\%.}, + abstract = {This study presents a method of tree species classification using individual tree metrics derived from a three-dimensional point cloud from unmanned aerial vehicle laser scanning (ULS). In this novel approach, we evaluated the metrics of 1045 trees using generalized linear model (GLM) and random forest (RF) techniques to automatically assign individual trees into either a coniferous or broadleaf group. We evaluated several statistical descriptors, including a novel approach using the Clark{\textendash}Evans spatial aggregation index (CE), which indicates the level of clustering in point clouds. A comparison of classifiers that included and excluded the CE indicator values demonstrated their importance for improved classification of the individual tree point clouds. The overall accuracy when including the CE index was 94.8\% using a GLM approach and 95.1\% using an RF approach. With the RF approach, the inclusion of CE yielded a significant improvement in overall classification accuracy, and for the GLM approach, the CE index was always selected as a significant variable for correct tree class prediction. Compared to other studies, the above-mentioned accuracies prove the benefits of CE for tree species classification, as do the worse results of excluding the CE, where the derived GLM achieved an accuracy of 92.6\% and RF an accuracy of 93.8\%.}, langid = {english} } @@ -1862,7 +1905,7 @@ @article{sparksAccuracyLiDARBasedIndividual2021 issn = {1999-4907}, doi = {10.3390/f13010003}, urldate = {2023-10-20}, - abstract = {Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView\textregistered{} algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. {$\pm$}387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer's/user's accuracies {$>$} \textasciitilde 60\%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.}, + abstract = {Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView{\textregistered} algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. {$\pm$}387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer's/user's accuracies {$>$} {\textasciitilde}60\%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.}, langid = {english} } @@ -1878,7 +1921,7 @@ @article{sparksCrossComparisonIndividualTree2022 issn = {2072-4292}, doi = {10.3390/rs14143480}, urldate = {2023-02-15}, - abstract = {Numerous individual tree detection (ITD) methods have been developed for use with airborne laser scanning (ALS) data to provide tree-scale forest inventories across large spatial extents. Despite the growing number of methods, relatively few have been comparatively assessed using a single benchmark forest inventory validation dataset, limiting their operational application. In this study, we assessed seven ITD methods, representing three common approaches (point-cloud-based, raster-based, hybrid), across coniferous forest stands with diverse structure and composition to understand how ITD and height measurement accuracy vary with method, input parameters and data, and stand density. There was little variability in accuracy between the ITD methods where the average F-score and standard deviation ({$\pm$}SD) were 0.47 {$\pm$} 0.03 using a lower pulse density ALS dataset with an average of 8 pulses per square meter (ppm2) and 0.50 {$\pm$} 0.02 using a higher pulse density ALS dataset with an average of 22 ppm2. Using higher ALS pulse density data produced higher ITD accuracies (F-score increase of 10\textendash 13\%) in some of the methods versus more modest gains in other methods (F-score increase of 1\textendash 3\%). Omission errors were strongly related with stand density and largely consisted of suppressed trees underneath the dominant canopy. Simple canopy height model (CHM)-based methods that utilized fixed-size local maximum filters had the lowest omission errors for trees across all canopy positions. ITD accuracy had large intra-method variation depending on input parameters; however, the highest accuracies were obtained when parameters such as search window size and spacing thresholds were equal to or less than the average crown diameter of trees in the study area. All ITD methods produced height measurements for the detected trees that had low RMSE ({$<$}1.1 m) and bias ({$<$}0.5 m). Overall, the results from this study may help guide end-users with ITD method application and highlight future ITD method improvements.}, + abstract = {Numerous individual tree detection (ITD) methods have been developed for use with airborne laser scanning (ALS) data to provide tree-scale forest inventories across large spatial extents. Despite the growing number of methods, relatively few have been comparatively assessed using a single benchmark forest inventory validation dataset, limiting their operational application. In this study, we assessed seven ITD methods, representing three common approaches (point-cloud-based, raster-based, hybrid), across coniferous forest stands with diverse structure and composition to understand how ITD and height measurement accuracy vary with method, input parameters and data, and stand density. There was little variability in accuracy between the ITD methods where the average F-score and standard deviation ({$\pm$}SD) were 0.47 {$\pm$} 0.03 using a lower pulse density ALS dataset with an average of 8 pulses per square meter (ppm2) and 0.50 {$\pm$} 0.02 using a higher pulse density ALS dataset with an average of 22 ppm2. Using higher ALS pulse density data produced higher ITD accuracies (F-score increase of 10{\textendash}13\%) in some of the methods versus more modest gains in other methods (F-score increase of 1{\textendash}3\%). Omission errors were strongly related with stand density and largely consisted of suppressed trees underneath the dominant canopy. Simple canopy height model (CHM)-based methods that utilized fixed-size local maximum filters had the lowest omission errors for trees across all canopy positions. ITD accuracy had large intra-method variation depending on input parameters; however, the highest accuracies were obtained when parameters such as search window size and spacing thresholds were equal to or less than the average crown diameter of trees in the study area. All ITD methods produced height measurements for the detected trees that had low RMSE ({$<$}1.1 m) and bias ({$<$}0.5 m). Overall, the results from this study may help guide end-users with ITD method application and highlight future ITD method improvements.}, langid = {english} } @@ -2042,7 +2085,7 @@ @article{tompalskiEstimatingChangesForest2021 issn = {2198-6436}, doi = {10.1007/s40725-021-00135-w}, urldate = {2023-02-15}, - abstract = {Abstract Purpose of Review The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure. Recent Findings Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories\textemdash{} \textendash{} approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies. Summary To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.}, + abstract = {Abstract Purpose of Review The increasing availability of three-dimensional point clouds, including both airborne laser scanning and digital aerial photogrammetry, allow for the derivation of forest inventory information with a high level of attribute accuracy and spatial detail. When available at two points in time, point cloud datasets offer a rich source of information for detailed analysis of change in forest structure. Recent Findings Existing research across a broad range of forest types has demonstrated that those analyses can be performed using different approaches, levels of detail, or source data. By reviewing the relevant findings, we highlight the potential that bi- and multi-temporal point clouds have for enhanced analysis of forest growth. We divide the existing approaches into two broad categories{\textemdash} {\textendash} approaches that focus on estimating change based on predictions of two or more forest inventory attributes over time, and approaches for forecasting forest inventory attributes. We describe how point clouds acquired at two or more points in time can be used for both categories of analysis by comparing input airborne datasets, before discussing the methods that were used, and resulting accuracies. Summary To conclude, we outline outstanding research gaps that require further investigation, including the need for an improved understanding of which three-dimensional datasets can be applied using certain methods. We also discuss the likely implications of these datasets on the expected outcomes, improvements in tree-to-tree matching and analysis, integration with growth simulators, and ultimately, the development of growth models driven entirely with point cloud data.}, langid = {english} } @@ -2196,7 +2239,7 @@ @article{vincentMultisensorAirborneLidar2023 } @article{virtanenNationwidePointCloud2017, - title = {Nationwide {{Point Cloud}}\textemdash{{The Future Topographic Core Data}}}, + title = {Nationwide {{Point Cloud}}{\textemdash}{{The Future Topographic Core Data}}}, author = {Virtanen, Juho-Pekka and Kukko, Antero and Kaartinen, Harri and Jaakkola, Anttoni and Turppa, Tuomas and Hyypp{\"a}, Hannu and Hyypp{\"a}, Juha}, year = {2017}, month = aug, @@ -2211,7 +2254,7 @@ @article{virtanenNationwidePointCloud2017 } @article{wangFieldmeasuredTreeHeight2019, - title = {Is Field-Measured Tree Height as Reliable as Believed \textendash{} {{A}} Comparison Study of Tree Height Estimates from Field Measurement, Airborne Laser Scanning and Terrestrial Laser Scanning in a Boreal Forest}, + title = {Is Field-Measured Tree Height as Reliable as Believed {\textendash} {{A}} Comparison Study of Tree Height Estimates from Field Measurement, Airborne Laser Scanning and Terrestrial Laser Scanning in a Boreal Forest}, author = {Wang, Yunsheng and Lehtom{\"a}ki, Matti and Liang, Xinlian and Py{\"o}r{\"a}l{\"a}, Jiri and Kukko, Antero and Jaakkola, Anttoni and Liu, Jingbin and Feng, Ziyi and Chen, Ruizhi and Hyypp{\"a}, Juha}, year = {2019}, month = jan, @@ -2268,7 +2311,7 @@ @misc{weiserhannahTerrestrialUAVborneAirborne2022 publisher = {{PANGAEA}}, doi = {10.1594/PANGAEA.942856}, urldate = {2023-02-15}, - abstract = {Laser scanning point clouds of forest stands were acquired in southwest Germany in 2019 and 2020 from different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The UAV-borne and airborne laser scanning campaigns cover twelve forest plots of approximately 1 ha. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-W\"urttemberg, Germany. Terrestrial laser scanning was performed in selected locations within the twelve forest plots. Airborne and terrestrial laser scanning point clouds were acquired under leaf-on conditions, UAV-borne laser scans were acquired both under leaf-on and later under leaf-off conditions. In addition to the laser scanning campaigns, forest inventory tree properties (species, height, diameter at breast height, crown base height, crown diameter) were measured in-situ during summer 2019 in six of the twelve 1-ha plots. Single tree point clouds were extracted from the different laser scanning datasets and matched to the field measurements. For each tree entry, point clouds, tree species, position, and field-measured and point cloud-derived tree metrics are provided. For 249 trees, point clouds from all three platforms are available. The tree models form the basis of a single tree database covering a range of species typical for central European forests which is currently being established in the framework of the SYSSIFOSS project.}, + abstract = {Laser scanning point clouds of forest stands were acquired in southwest Germany in 2019 and 2020 from different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The UAV-borne and airborne laser scanning campaigns cover twelve forest plots of approximately 1 ha. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-W{\"u}rttemberg, Germany. Terrestrial laser scanning was performed in selected locations within the twelve forest plots. Airborne and terrestrial laser scanning point clouds were acquired under leaf-on conditions, UAV-borne laser scans were acquired both under leaf-on and later under leaf-off conditions. In addition to the laser scanning campaigns, forest inventory tree properties (species, height, diameter at breast height, crown base height, crown diameter) were measured in-situ during summer 2019 in six of the twelve 1-ha plots. Single tree point clouds were extracted from the different laser scanning datasets and matched to the field measurements. For each tree entry, point clouds, tree species, position, and field-measured and point cloud-derived tree metrics are provided. For 249 trees, point clouds from all three platforms are available. The tree models form the basis of a single tree database covering a range of species typical for central European forests which is currently being established in the framework of the SYSSIFOSS project.}, copyright = {Creative Commons Attribution Share Alike 4.0 International}, langid = {english}, keywords = {3D point clouds,Binary Object,Binary Object (File Size),Central Europe,ecology,Event label,forest,forest inventory,Germany,Lidar,Multiple investigations,Synthetic structural remote sensing data for improved forest inventory models (SYSSIFOSS),tree,vegetation} @@ -2282,7 +2325,7 @@ @misc{weiserhannahTerrestrialUAVborneAirborne2022a publisher = {{PANGAEA}}, doi = {10.1594/PANGAEA.942856}, urldate = {2023-02-15}, - abstract = {Laser scanning point clouds of forest stands were acquired in southwest Germany in 2019 and 2020 from different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The UAV-borne and airborne laser scanning campaigns cover twelve forest plots of approximately 1 ha. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-W\"urttemberg, Germany. Terrestrial laser scanning was performed in selected locations within the twelve forest plots. Airborne and terrestrial laser scanning point clouds were acquired under leaf-on conditions, UAV-borne laser scans were acquired both under leaf-on and later under leaf-off conditions. In addition to the laser scanning campaigns, forest inventory tree properties (species, height, diameter at breast height, crown base height, crown diameter) were measured in-situ during summer 2019 in six of the twelve 1-ha plots. Single tree point clouds were extracted from the different laser scanning datasets and matched to the field measurements. For each tree entry, point clouds, tree species, position, and field-measured and point cloud-derived tree metrics are provided. For 249 trees, point clouds from all three platforms are available. The tree models form the basis of a single tree database covering a range of species typical for central European forests which is currently being established in the framework of the SYSSIFOSS project.}, + abstract = {Laser scanning point clouds of forest stands were acquired in southwest Germany in 2019 and 2020 from different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The UAV-borne and airborne laser scanning campaigns cover twelve forest plots of approximately 1 ha. The plots are located in mixed central European forests close to Bretten and Karlsruhe, in the federal state of Baden-W{\"u}rttemberg, Germany. Terrestrial laser scanning was performed in selected locations within the twelve forest plots. Airborne and terrestrial laser scanning point clouds were acquired under leaf-on conditions, UAV-borne laser scans were acquired both under leaf-on and later under leaf-off conditions. In addition to the laser scanning campaigns, forest inventory tree properties (species, height, diameter at breast height, crown base height, crown diameter) were measured in-situ during summer 2019 in six of the twelve 1-ha plots. Single tree point clouds were extracted from the different laser scanning datasets and matched to the field measurements. For each tree entry, point clouds, tree species, position, and field-measured and point cloud-derived tree metrics are provided. For 249 trees, point clouds from all three platforms are available. The tree models form the basis of a single tree database covering a range of species typical for central European forests which is currently being established in the framework of the SYSSIFOSS project.}, copyright = {Creative Commons Attribution Share Alike 4.0 International}, langid = {english}, keywords = {3D point clouds,Binary Object,Binary Object (File Size),Central Europe,ecology,Event label,forest,forest inventory,Germany,Lidar,Multiple investigations,Synthetic structural remote sensing data for improved forest inventory models (SYSSIFOSS),tree,vegetation} @@ -2319,6 +2362,20 @@ @article{whiteBestPracticesGuide2013 langid = {english} } +@article{whiteComparisonAirborneLaser2018, + title = {Comparison of Airborne Laser Scanning and Digital Stereo Imagery for Characterizing Forest Canopy Gaps in Coastal Temperate Rainforests}, + author = {White, Joanne C. and Tompalski, Piotr and Coops, Nicholas C. and Wulder, Michael A.}, + year = {2018}, + month = apr, + journal = {Remote Sensing of Environment}, + volume = {208}, + pages = {1--14}, + issn = {00344257}, + doi = {10.1016/j.rse.2018.02.002}, + urldate = {2023-12-06}, + langid = {english} +} + @article{whiteEvaluatingCapacitySingle2021, title = {Evaluating the Capacity of Single Photon Lidar for Terrain Characterization under a Range of Forest Conditions}, author = {White, J.C. and Woods, M. and Krahn, T. and Papasodoro, C. and B{\'e}langer, D. and Onafrychuk, C. and Sinclair, I.}, @@ -2409,7 +2466,7 @@ @phdthesis{zeinerEinsatzAirborneLaserscanning2012 year = {2012}, journal = {Der Einsatz von Airborne Laserscanning Daten im forstbetrieblichen Informationssystem}, urldate = {2023-02-28}, - abstract = {ger: Kenntnisse \"uber die Verh\"altnisse wichtiger Bestandesparameter, wie den Holzvorrat, stellen die Grundlage strategischer und operativer \"Uberlegungen eines Forstbetriebes dar. Die daf\"ur ben\"otigten Daten werden traditionell \"uber zeit- und kostenintensive Stichprobeninventuren und Taxationen erhoben. H\"aufig auftretende Wind- und darauf folgende K\"aferkalamit\"aten erfordern oftmals eine kurzfristige Aktualisierung der aufgenommenen Daten. Vor diesem Hintergrund sind Verfahren, welche eine rasche, kosteng\"unstige und gro\ss fl\"achige Datenerhebung erm\"oglichen, von besonderem Interesse. Die positiven Berichte \"uber die Airborne Laserscanning (ALS) Technologie aus dem skandinavischen Raum legen nahe, die Anwendungsm\"oglichkeiten von ALS in den vergleichsweise heterogenen W\"aldern Mitteleuropas zu untersuchen. Die Arbeit besch\"aftigt sich aus \"osterreichischer Sicht mit den Einsatzm\"oglichkeiten von ALS im forstbetrieblichen Informationssystem im Allgemeinen und in der Forsteinrichtung im Besonderen. Dazu wird anhand einer Literaturanalyse aufgezeigt, welche Prim\"ardaten aus ALS gewonnen werden k\"onnen und welche f\"ur den Forsteinrichtungsprozess relevanten Standorts- und Bestandesparameter direkt daraus abgeleitet oder durch \"Aquivalente angen\"ahert werden k\"onnen. In weiterer Folge werden Faktoren behandelt, die f\"ur eine etwaige Implementation dieser Technologie in den Betriebsalltag ausschlaggebend sind. Anhand des Analyserahmens kann insbesondere gezeigt werden, dass Parameter, die nahe den Prim\"ardaten sind, eine hohe Genauigkeit aufweisen, w\"ahrend abgeleitete Parameter wie der Holzvorrat deutlich st\"arkere Abweichungen von terrestrisch erhobenen Daten aufweisen. Insgesamt kann festgestellt werden, dass sich besonders ALS-gest\"utzte Gel\"andemodelle zur Verwendung in der betrieblichen Praxis eignen, w\"ahrend bei den meisten abgeleiteten Gr\"o\ss en noch Forschungsbedarf besteht. Vor allem das Bestandesalter und darauf aufbauend die Bonit\"at lassen sich mit Hilfe von ALS nur unzureichend erschlie\ss en. Der Gang in den Wald wird daher auch in Zukunft nicht entbehrlich., eng: Information about stand parameters such as the growing stock is of utmost importance for the strategic as well as the operational planning of a forest enterprise. Typically, such data are derived from forest inventories. Standard sampling techniques are quite expensive and time consuming, however. Frequent wind throws and subsequent beetle damage necessitate a timely update of respective data. Hence, technologies allowing for a quick collection of the data at acceptable cost are of high interest. Airborne Laserscanning (ALS) Technology has been introduced successfully in Scandinavian countries for capturing various forestry features. The thesis investigates the possibilities of applying ALS under Austrian conditions. The question is to what extent ALS could be integrated into the information system of a forest enterprise and forest management planning schemes respectively. A literature review reveals, what kind of primary data can be acquired by means of ALS and which parameters of the terrain and forest stands can be derived. In a further chapter, several factors which are crucial for the implementation of this technology into practical forest management are investigated. Especially the terrain models and several parameters such as tree height, achieve a high level of accuracy. Other parameters of interest which have to be derived by means of various algorithms, such as the growing stock, stand age or yield class, show a greater deviation from reference data. ALS may assist terrestrial inventory procedures but cannot fully replace them.}, + abstract = {ger: Kenntnisse {\"u}ber die Verh{\"a}ltnisse wichtiger Bestandesparameter, wie den Holzvorrat, stellen die Grundlage strategischer und operativer {\"U}berlegungen eines Forstbetriebes dar. Die daf{\"u}r ben{\"o}tigten Daten werden traditionell {\"u}ber zeit- und kostenintensive Stichprobeninventuren und Taxationen erhoben. H{\"a}ufig auftretende Wind- und darauf folgende K{\"a}ferkalamit{\"a}ten erfordern oftmals eine kurzfristige Aktualisierung der aufgenommenen Daten. Vor diesem Hintergrund sind Verfahren, welche eine rasche, kosteng{\"u}nstige und gro{\ss}fl{\"a}chige Datenerhebung erm{\"o}glichen, von besonderem Interesse. Die positiven Berichte {\"u}ber die Airborne Laserscanning (ALS) Technologie aus dem skandinavischen Raum legen nahe, die Anwendungsm{\"o}glichkeiten von ALS in den vergleichsweise heterogenen W{\"a}ldern Mitteleuropas zu untersuchen. Die Arbeit besch{\"a}ftigt sich aus {\"o}sterreichischer Sicht mit den Einsatzm{\"o}glichkeiten von ALS im forstbetrieblichen Informationssystem im Allgemeinen und in der Forsteinrichtung im Besonderen. Dazu wird anhand einer Literaturanalyse aufgezeigt, welche Prim{\"a}rdaten aus ALS gewonnen werden k{\"o}nnen und welche f{\"u}r den Forsteinrichtungsprozess relevanten Standorts- und Bestandesparameter direkt daraus abgeleitet oder durch {\"A}quivalente angen{\"a}hert werden k{\"o}nnen. In weiterer Folge werden Faktoren behandelt, die f{\"u}r eine etwaige Implementation dieser Technologie in den Betriebsalltag ausschlaggebend sind. Anhand des Analyserahmens kann insbesondere gezeigt werden, dass Parameter, die nahe den Prim{\"a}rdaten sind, eine hohe Genauigkeit aufweisen, w{\"a}hrend abgeleitete Parameter wie der Holzvorrat deutlich st{\"a}rkere Abweichungen von terrestrisch erhobenen Daten aufweisen. Insgesamt kann festgestellt werden, dass sich besonders ALS-gest{\"u}tzte Gel{\"a}ndemodelle zur Verwendung in der betrieblichen Praxis eignen, w{\"a}hrend bei den meisten abgeleiteten Gr{\"o}{\ss}en noch Forschungsbedarf besteht. Vor allem das Bestandesalter und darauf aufbauend die Bonit{\"a}t lassen sich mit Hilfe von ALS nur unzureichend erschlie{\ss}en. Der Gang in den Wald wird daher auch in Zukunft nicht entbehrlich., eng: Information about stand parameters such as the growing stock is of utmost importance for the strategic as well as the operational planning of a forest enterprise. Typically, such data are derived from forest inventories. Standard sampling techniques are quite expensive and time consuming, however. Frequent wind throws and subsequent beetle damage necessitate a timely update of respective data. Hence, technologies allowing for a quick collection of the data at acceptable cost are of high interest. Airborne Laserscanning (ALS) Technology has been introduced successfully in Scandinavian countries for capturing various forestry features. The thesis investigates the possibilities of applying ALS under Austrian conditions. The question is to what extent ALS could be integrated into the information system of a forest enterprise and forest management planning schemes respectively. A literature review reveals, what kind of primary data can be acquired by means of ALS and which parameters of the terrain and forest stands can be derived. In a further chapter, several factors which are crucial for the implementation of this technology into practical forest management are investigated. Especially the terrain models and several parameters such as tree height, achieve a high level of accuracy. Other parameters of interest which have to be derived by means of various algorithms, such as the growing stock, stand age or yield class, show a greater deviation from reference data. ALS may assist terrestrial inventory procedures but cannot fully replace them.}, langid = {german}, lccn = {D-15547}, keywords = {Forstwirtschaft,Laserscanner,Luftbildauswertung} @@ -2429,7 +2486,7 @@ @article{zhangImprovedAreabasedApproach2023 } @article{zweifelNetworkingForestInfrastructure2023, - title = {Networking the Forest Infrastructure towards near Real-Time Monitoring \textendash{} {{A}} White Paper}, + title = {Networking the Forest Infrastructure towards near Real-Time Monitoring {\textendash} {{A}} White Paper}, author = {Zweifel, Roman and Pappas, Christoforos and Peters, Richard L. and Babst, Flurin and Balanzategui, Daniel and Basler, David and Bastos, Ana and Beloiu, Mirela and Buchmann, Nina and Bose, Arun K. and Braun, Sabine and Damm, Alexander and D'Odorico, Petra and Eitel, Jan U.H. and Etzold, Sophia and Fonti, Patrick and Rouholahnejad Freund, Elham and Gessler, Arthur and Haeni, Matthias and Hoch, G{\"u}nter and Kahmen, Ansgar and K{\"o}rner, Christian and Krejza, Jan and Krumm, Frank and Leuchner, Michael and Leuschner, Christoph and Lukovic, Mirko and {Mart{\'i}nez-Vilalta}, Jordi and Matula, Radim and Meesenburg, Henning and Meir, Patrick and Plichta, Roman and Poyatos, Rafael and Rohner, Brigitte and Ruehr, Nadine and Salom{\'o}n, Roberto L. and Scharnweber, Tobias and Schaub, Marcus and Steger, David N. and Steppe, Kathy and Still, Christopher and Stojanovi{\'c}, Marko and Trotsiuk, Volodymyr and Vitasse, Yann and {von Arx}, Georg and Wilmking, Martin and Zahnd, Cedric and Sterck, Frank}, year = {2023}, month = may, From 6d3c9f00f32b16f5c8fca827355946af7308391b Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn Date: Wed, 6 Dec 2023 17:17:34 +0100 Subject: [PATCH 3/9] add software --- content/_lidar_software.qmd | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/_lidar_software.qmd b/content/_lidar_software.qmd index bec60ab..f8e7606 100644 --- a/content/_lidar_software.qmd +++ b/content/_lidar_software.qmd @@ -26,6 +26,7 @@ - [lidR package](https://github.com/r-lidar/lidR) An R package for analysis of Airborne Laser Scanning (ALS) data [@rousselLidRPackageAnalysis2020] +- [https://github.com/ptompalski/lidRmetrics](lidRmetrics) Additional point cloud metrics to use with *_metric functions in lidR. - [rLIDAR package](https://github.com/carlos-alberto-silva/rLiDAR) LiDAR Data Processing and Visualization - [canopyLazR package](https://github.com/akamoske/canopyLazR) @@ -40,6 +41,7 @@ "Road corrections and measurements from ALS data " - [Forest Structural Complexity Tool](https://github.com/SKrisanski/FSCT) plot scale measurements to be extracted automatically from most high-resolution forest point clouds from a variety of sensor sources +- [lasR](https://github.com/r-lidar/lasR)R Package for Fast Airborne LiDAR Data Processing #### Apps From 58e564345b029a3c68f777bf4c6d76966a1e694f Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn Date: Thu, 4 Jan 2024 10:39:07 +0100 Subject: [PATCH 4/9] update --- content/_applications_biomass.qmd | 2 ++ content/_applications_species.qmd | 4 +++- content/_applications_tree-detection.qmd | 2 ++ content/_lidar_software.qmd | 2 ++ references.bib | 28 ++++++++++++++++++++++++ 5 files changed, 37 insertions(+), 1 deletion(-) diff --git a/content/_applications_biomass.qmd b/content/_applications_biomass.qmd index 0fc9a85..f8f9393 100644 --- a/content/_applications_biomass.qmd +++ b/content/_applications_biomass.qmd @@ -3,6 +3,8 @@ > Laser scanning reveals potential underestimation of biomass carbon in temperate forest [@caldersLaserScanningReveals2022]. +> Current allometry has low sample size and excludes large trees. Terrestrial LiDAR precisely and non-destructively estimates tree biomass. We developed high sample size species-specific allometry with terrestrial LiDAR. [@stovallDevelopingNondestructiveSpecies2023] + > even with the highest average pulse density of 11 pulses/m², at least 25% of the forest canopy volume remains occluded in the ALS acquisition under leaf-on conditions [@kukenbrinkQuantificationHiddenCanopy2017] > practical solutions to challenges faced in using spatiotemporal patchworks of LiDAR to meet growing needs for AGB prediction and mapping in support of broad-scale applications in forest carbon accounting and ecosystem stewardship [@johnsonFineresolutionLandscapescaleBiomass2022] diff --git a/content/_applications_species.qmd b/content/_applications_species.qmd index d189de0..aaac8b6 100644 --- a/content/_applications_species.qmd +++ b/content/_applications_species.qmd @@ -16,4 +16,6 @@ Our results provide new insights for enhancing tree species identification by us > presents a method of tree species classification using individual tree metrics derived from a three-dimensional point cloud from unmanned aerial vehicle laser scanning (ULS) [@slavikSpatialAnalysisDense2023] -> The fusion of spectral information from optical images and the structural information provided by ALS was highly advantageous in studies where tree species were considered. [@toivonenAssessingBiodiversityUsing2023] \ No newline at end of file +> The fusion of spectral information from optical images and the structural information provided by ALS was highly advantageous in studies where tree species were considered. [@toivonenAssessingBiodiversityUsing2023] + +> estimate tree species compositions in a Canadian boreal forest environment using ALS data and point-based deep learning techniques. [@murrayEstimatingTreeSpecies2024] \ No newline at end of file diff --git a/content/_applications_tree-detection.qmd b/content/_applications_tree-detection.qmd index d036042..8937ab0 100644 --- a/content/_applications_tree-detection.qmd +++ b/content/_applications_tree-detection.qmd @@ -11,6 +11,8 @@ > The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. [@wangFieldmeasuredTreeHeight2019] +> A comparison of the six considered ALS-derived proxies of tree height showed that the individual tree detection approach was the most accurate. [@hawryloHowAdequatelyDetermine2024] + > The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions. [@eysnBenchmarkLidarBasedSingle2015] > proposed a novel ITC segmentation method based on computer vision theory which combines a dual Gaussian filter and a treetop screening strategy to achieve a flexible filtering process for varying tree sizes and the exclusion of false treetops generated by lateral branches. [@yunIndividualTreeCrown2021] diff --git a/content/_lidar_software.qmd b/content/_lidar_software.qmd index f8e7606..39c9f65 100644 --- a/content/_lidar_software.qmd +++ b/content/_lidar_software.qmd @@ -21,6 +21,8 @@ Python package for segmenting aerial LiDAR data using Segment-Anything Model (SAM) from Meta AI. - [LAPIS](https://github.com/jontkane/Lapis) "an open-source program optimized for processing aerial lidar for forestry applications" +- [Terrascan](https://terrasolid.com/products/terrascan/) + "TerraScan is the main application in the Terrasolid Software family for managing and processing all types of point clouds" #### RStats diff --git a/references.bib b/references.bib index e10c3e0..2b8cabe 100644 --- a/references.bib +++ b/references.bib @@ -700,6 +700,20 @@ @article{hauglinLargeScaleMapping2021 langid = {english} } +@article{hawryloHowAdequatelyDetermine2024, + title = {How to Adequately Determine the Top Height of Forest Stands Based on Airborne Laser Scanning Point Clouds?}, + author = {Hawry{\l}o, Pawe{\l} and Socha, Jaros{\l}aw and W{\k{e}}{\.z}yk, Piotr and Ocha{\l}, Wojciech and Krawczyk, Wojciech and Miszczyszyn, Jakub and {Tymi{\'n}ska-Czaba{\'n}ska}, Luiza}, + year = {2024}, + month = jan, + journal = {Forest Ecology and Management}, + volume = {551}, + pages = {121528}, + issn = {03781127}, + doi = {10.1016/j.foreco.2023.121528}, + urldate = {2024-01-04}, + langid = {english} +} + @article{heinaroAirborneLaserScanning2021a, title = {Airborne Laser Scanning Reveals Large Tree Trunks on Forest Floor}, author = {Heinaro, Einari and Tanhuanp{\"a}{\"a}, Topi and Yrttimaa, Tuomas and Holopainen, Markus and Vastaranta, Mikko}, @@ -1374,6 +1388,20 @@ @article{moudryVegetationStructureDerived2023a langid = {english} } +@article{murrayEstimatingTreeSpecies2024, + title = {Estimating Tree Species Composition from Airborne Laser Scanning Data Using Point-Based Deep Learning Models}, + author = {Murray, Brent A. and Coops, Nicholas C. and Winiwarter, Lukas and White, Joanne C. and Dick, Adam and Barbeito, Ignacio and Ragab, Ahmed}, + year = {2024}, + month = jan, + journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, + volume = {207}, + pages = {282--297}, + issn = {09242716}, + doi = {10.1016/j.isprsjprs.2023.12.008}, + urldate = {2024-01-04}, + langid = {english} +} + @article{neudamSimulationSilviculturalTreatments2023, title = {Simulation of Silvicultural Treatments Based on Real {{3D}} Forest Data from Mobile Laser Scanning Point Clouds}, author = {Neudam, Liane C. and Fuchs, Jasper M. and Mjema, Ezekiel and Johannmeier, Alina and Ammer, Christian and Annigh{\"o}fer, Peter and Paul, Carola and Seidel, Dominik}, From 87c54a20b84533bd10dbd651d3805bbe1beaac64 Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn Date: Thu, 4 Jan 2024 10:51:10 +0100 Subject: [PATCH 5/9] update github actions --- .github/workflows/publish.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 4f7227e..d6a2da4 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -24,15 +24,15 @@ jobs: url: ${{ steps.deployment.outputs.page_url }} steps: - name: Checkout repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Install Quarto uses: quarto-dev/quarto-actions/setup@v2 - name: Setup Pages - uses: actions/configure-pages@v1 + uses: actions/configure-pages@v4 - name: Render Website run: quarto render - name: Upload artifact - uses: actions/upload-pages-artifact@v1 + uses: actions/upload-pages-artifact@v4 with: path: '_site' - name: Deploy to GitHub Pages From 91a943fe1a75bc752277e27c6160a439dd0b96eb Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn Date: Thu, 4 Jan 2024 10:53:43 +0100 Subject: [PATCH 6/9] update github actions --- .github/workflows/publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index d6a2da4..3b367b3 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -32,7 +32,7 @@ jobs: - name: Render Website run: quarto render - name: Upload artifact - uses: actions/upload-pages-artifact@v4 + uses: actions/upload-pages-artifact@v3 with: path: '_site' - name: Deploy to GitHub Pages From 27444c00a35b7ef31dada0ed99ab625785132c22 Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn Date: Thu, 4 Jan 2024 11:04:24 +0100 Subject: [PATCH 7/9] fix references (add missing, change key) --- references.bib | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/references.bib b/references.bib index 2b8cabe..85f7f18 100644 --- a/references.bib +++ b/references.bib @@ -714,7 +714,7 @@ @article{hawryloHowAdequatelyDetermine2024 langid = {english} } -@article{heinaroAirborneLaserScanning2021a, +@article{heinaroAirborneLaserScanning2021, title = {Airborne Laser Scanning Reveals Large Tree Trunks on Forest Floor}, author = {Heinaro, Einari and Tanhuanp{\"a}{\"a}, Topi and Yrttimaa, Tuomas and Holopainen, Markus and Vastaranta, Mikko}, year = {2021}, @@ -1372,7 +1372,7 @@ @incollection{morsdorfLaegerenSiteAugmented2020 langid = {english} } -@article{moudryVegetationStructureDerived2023a, +@article{moudryVegetationStructureDerived2023, title = {Vegetation Structure Derived from Airborne Laser Scanning to Assess Species Distribution and Habitat Suitability: {{The}} Way Forward}, shorttitle = {Vegetation Structure Derived from Airborne Laser Scanning to Assess Species Distribution and Habitat Suitability}, author = {Moudr{\'y}, V{\'i}t{\v e}zslav and Cord, Anna F. and G{\'a}bor, Luk{\'a}{\v s} and Laurin, Gaia Vaglio and Bart{\'a}k, Vojt{\v e}ch and Gdulov{\'a}, Kate{\v r}ina and Malavasi, Marco and Rocchini, Duccio and Stere{\'n}czak, Krzysztof and Pro{\v s}ek, Ji{\v r}{\'i} and Kl{\'a}p{\v s}t{\v e}, Petr and Wild, Jan}, @@ -2281,6 +2281,18 @@ @article{virtanenNationwidePointCloud2017 langid = {english} } +@article{wagnerSubMeterTreeHeight2023, + title = {Sub-{{Meter Tree Height Mapping}} of {{California}} Using {{Aerial Images}} and {{LiDAR-Informed U-Net Model}}}, + author = {Wagner, Fabien H and Roberts, Sophia and Ritz, Alison L and Carter, Griffin and Dalagnol, Ricardo and Favrichon, Samuel and Hirye, Mayumi CM and Brandt, Martin and Ciais, Philipe and Saatchi, Sassan}, + year = {2023}, + publisher = {{arXiv}}, + doi = {10.48550/ARXIV.2306.01936}, + urldate = {2024-01-04}, + abstract = {Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery (60 cm) from the USDA-NAIP program. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP images collected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km\$\^2\$ sites across various forest types and landscape variations in California. Our predictions of tree heights exhibited a mean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present in California. In 2020, trees taller than 5 m covered {\textasciitilde} 19.3\% of California. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models. The approach we used allowed for the reconstruction of the three-dimensional structure of individual trees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation and mapping capability, even in the presence of image distortion. These findings demonstrate the potential of large-scale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery.}, + copyright = {Creative Commons Attribution 4.0 International}, + keywords = {92-08,Computer Vision and Pattern Recognition (cs.CV),FOS: Computer and information sciences,{FOS: Electrical engineering, electronic engineering, information engineering},I.4.9; I.5.4,Image and Video Processing (eess.IV)} +} + @article{wangFieldmeasuredTreeHeight2019, title = {Is Field-Measured Tree Height as Reliable as Believed {\textendash} {{A}} Comparison Study of Tree Height Estimates from Field Measurement, Airborne Laser Scanning and Terrestrial Laser Scanning in a Boreal Forest}, author = {Wang, Yunsheng and Lehtom{\"a}ki, Matti and Liang, Xinlian and Py{\"o}r{\"a}l{\"a}, Jiri and Kukko, Antero and Jaakkola, Anttoni and Liu, Jingbin and Feng, Ziyi and Chen, Ruizhi and Hyypp{\"a}, Juha}, From 6dfc494d12cbf58b711707c4b5425f85eb6c6a81 Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn <41429613+wiesehahn@users.noreply.github.com> Date: Tue, 12 Mar 2024 21:15:56 +0100 Subject: [PATCH 8/9] Update README.md --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index a307dc5..bd09739 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,7 @@ ## Lidar for Forestry -This repository is meant as a personal spcae to gather and organize information about research and applications of lidar data for forestry. \ No newline at end of file +This repository is meant as a personal space to gather and organize information about research and applications of lidar data for forestry. + +Information is collected via Github issues and rudimentally organized by Tags. For example its possible to filter recent [literature](https://github.com/wiesehahn/lidar-forestry/issues?q=is%3Aissue+sort%3Aupdated-desc+label%3Aliterature+) about lidar applications for forestry. + +This information is further summarized as a [website](wiesehahn.github.io/lidar-forestry/) (updated irregularly from time to time). From 474ec7896b77ff71f72ec4038e02dc122f8d1d33 Mon Sep 17 00:00:00 2001 From: Jens Wiesehahn <41429613+wiesehahn@users.noreply.github.com> Date: Tue, 2 Apr 2024 13:20:09 +0200 Subject: [PATCH 9/9] fix link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bd09739..385b2c4 100644 --- a/README.md +++ b/README.md @@ -4,4 +4,4 @@ This repository is meant as a personal space to gather and organize information Information is collected via Github issues and rudimentally organized by Tags. For example its possible to filter recent [literature](https://github.com/wiesehahn/lidar-forestry/issues?q=is%3Aissue+sort%3Aupdated-desc+label%3Aliterature+) about lidar applications for forestry. -This information is further summarized as a [website](wiesehahn.github.io/lidar-forestry/) (updated irregularly from time to time). +This information is further summarized as a [website](https://wiesehahn.github.io/lidar-forestry/) (updated irregularly from time to time).