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wiesehahn · Jan 4, 2024 · 58e5643 · 58e5643
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diff --git 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
@@ -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]
diff --git 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
@@ -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
@@ -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},