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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
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