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001-es BibID:	BIBFORM123733
Első szerző:	Diószegi Gergő
Cím:	Testing 'treecbh' in Central European forests: an R package for crown base height detection using high-resolution aerial laser-scanned data / Gyergő Diószegi, Vanda Éva Molnár, Loránd Attila Nagy, Péter Enyedi, Péter Török, Szilárd Szabó
Dátum:	2024
ISSN:	0015-752X
Megjegyzések:	Accurate information regarding tree canopy characteristics is crucial for forest management, but it is often difficult to assess. This study presents an innovative framework designed for crown base height (CBH) detection using high-resolution laser-scanned data, with a specific focus on individual trees within forests. The framework comprises three key steps: (i) segmenting the input tree point cloud to identify the tree trunk and its branches using the treesio software; (ii) applying vertical cross-sectional K-means clustering to cluster the identified tree and to define the elevation threshold for removing low-lying understory vegetation; (iii) employing a novel 2D kernel method for detecting CBH after eliminating low-lying understory vegetation. The 2D kernel method, developed for broadleaf forests using leaf-off airborne laser scanning (ALS) data, underpins the treecbh tool. This tool features a visual CBH adjustment component that shows a 2D profile plot of the tree point cloud, and suggests a CBH value for user approval or adjustment. To evaluate accuracy, in situ measured CBH data from five forest plots in Germany and Hungary with varied species compositions were used. ALS data were collected during leaf-off conditions for the two Hungarian plots and during leaf-on conditions for the three German plots. Leaf-off terrestrial laser-scanned data from individual trees were also used in the accuracy assessment. A sensitivity analysis using random point decimation was conducted on the terrestrial laser-scanned data to assess treecbh's sensitivity to point density. The initial results exhibited matching rates of 45% and 60% for leaf-off ALS plots, which significantly improved to 71% and 77%, respectively, when using the visual CBH adjustment feature of the tool. The leaf-on ALS results demonstrated matching rates between 24% and 33%, whereas the CBHs of individual terrestrial laser-scanned trees could be detected with 93% accuracy in visual mode. It was observed that treecbh operates effectively when the input ALS data have a minimum point density of 20 pts/ ?, with its optimal performance achieved at 110 pts/ ?. These findings indicated treecbh's sensitivity to ALS data quality, scanning season (leaf-on and leaf-off), and point density. This sensitivity can be effectively mitigated in the case of leaf-off ALS data by utilizing the visual CBH adjustment feature of the tool.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk
Megjelenés:	Forestry. - [Epub ahead of print] : - (2024), p. 1-15. -
További szerzők:	Molnár Vanda Éva (1994-) (környezetkutató) Nagy Loránd Attila (1993-) (geográfus) Enyedi Péter (1982-) (környezettudós) Török Péter (1979-) (biológus-ökológus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	K138079 Egyéb KKP 144068 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM122075
035-os BibID:	(Scopus)85195669359 (WoS)001244602100001
Első szerző:	Diószegi Gergő
Cím:	A new method for individual treetop detection with low-resolution aerial laser scanned data / Gergő Diószegi, Vanda Éva Molnár, Loránd Attila Nagy, Péter Enyedi, Péter Török, Szilárd Szabó
Dátum:	2024
ISSN:	2363-6203 2363-6211
Megjegyzések:	In the past decade, the use of three-dimensional forest information from airborne Light Detection and Ranging (LiDAR) has become widespread in forest inventories. Accurate Individual Treetop Detection (ITD) and crown boundary delineation using LiDAR data are critical for obtaining precise inventory metrics. To address this need, we introduced a novel growing tree region (GTR)-driven ITD method that utilizes canopy height models (CHM) derived from very low-resolution airborne LiDAR data. The GTR algorithm consists of three key stages: (i) preserving all height layers through incremental cutting and stacking of CHM; (ii) employing a three-layer concept to identify individual treetops; and (iii) refining the detected treetops using a distance-based filter. Our method was tested in five temperate forests across Central Europe and was compared against the widely-used local maxima (LM) search combined with an optimized variable window filtering (VWF) technique. Our results showed that the GTR method outperformed LM with VWF, particularly in forests with high canopy density. The achieved root mean square accuracies were 74% for the matching rate, 19% for commission errors, and 27% for omission errors. In comparison, the LM with the VWF method resulted in a matching rate of 71%, commission errors of 20%, and omission errors of 31%. To facilitate the application of our algorithm, we developed an R package called TREETOPS, which seamlessly integrates with the lidR package, ensuring compatibility with existing treetop-based segmentation methods. By introducing TREETOPS, we provide the most accurate open-source tool for detecting treetops using low-resolution LiDAR-derived CHM.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk CHM-based treetop detection Growing tree region Local maxima Variable window filtering Low-resolution LiDAR Central European forest R
Megjelenés:	Modeling Earth Systems and Environment. - 10 (2024), p. 5225-5240. -
További szerzők:	Molnár Vanda Éva (1994-) (környezetkutató) Nagy Loránd Attila (1993-) (geográfus) Enyedi Péter (1982-) (környezettudós) Török Péter (1979-) (biológus-ökológus) Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	K138079 NKFIH KKP 144068 NKFIH
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: