Merge branch 'main' of https://github.com/wiesehahn/precision-forestry

.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@v3
         with:
           path: '_site'
       - name: Deploy to GitHub Pages

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.
+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](https://wiesehahn.github.io/lidar-forestry/) (updated irregularly from time to time).

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]
@@ -24,6 +26,12 @@ 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]
+
+> 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)

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]

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]
+> 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]

content/_applications_stand-inventory.qmd

@@ -24,6 +24,12 @@ 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]
+
+> 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)
 

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)
 
@@ -40,6 +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)
 

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]
@@ -39,3 +41,5 @@ 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]

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]

content/_lidar_software.qmd

@@ -21,11 +21,14 @@
   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
 
 - [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 +43,7 @@
   "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