Improving country-wide individual tree detection using local maxima methods based on statistically modeled forest structure information

Schaller, Christoph; Ginzler, Christian; van Loon, Emiel; Moos, Christine; Seijmonsbergen, Arie C.; Dorren, Luuk (2023). Improving country-wide individual tree detection using local maxima methods based on statistically modeled forest structure information International Journal of Applied Earth Observation and Geoinformation, 123, p. 103480. Elsevier 10.1016/j.jag.2023.103480

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Individual tree detection using airborne laser scanning (ALS) can provide relevant data to complement forest inventory data. Local Maxima-based (LM) methods for individual tree detection are suitable for applications over large extents, but their performance depends on the type of pre-processing of the input data, as well as forest structure and composition. We developed a model that improves LM through statistical modeling using prior knowledge about forest structure. The model selects the optimal canopy height model (CHM) pre-processing filters based on forest structure variables like the dominant canopy height and degree of cover derived from ALS data, the dominant leaf type derived from Sentinel data, and terrain metrics. The model performance was evaluated by assessing tree detection errors for the canopy stem count in National Forest Inventory (NFI) plots in Switzerland (n=5254). For plots with point densities of more than 15 points per square meter and, at most, 6 years between ALS acquisition and inventory (n=2676), the results showed a mean absolute error of 61 stems per ha compared to 174 stems per ha when detecting trees using an unprocessed CHM. The model showed a stable performance for different dominant leaf types (broadleaved-dominated, mixed, coniferous-dominated) and for different degrees of cover. We consider the developed model to be suitable for applications that require data on forest structure or individual tree positions and heights over large areas.

Item Type:	Journal Article (Original Article)
Division/Institute:	School of Agricultural, Forest and Food Sciences HAFL School of Agricultural, Forest and Food Sciences HAFL > Multifunctional Forest Management School of Agricultural, Forest and Food Sciences HAFL > Multifunctional Forest Management > Mountain Forests, Natural Hazards and GIS
Name:	Schaller, Christoph0000-0002-8013-2892; Ginzler, Christian; van Loon, Emiel; Moos, Christine; Seijmonsbergen, Arie C. and Dorren, Luuk0000-0001-9344-9461
Subjects:	G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography Q Science > QA Mathematics > QA75 Electronic computers. Computer science S Agriculture > SD Forestry
ISSN:	1569-8432
Publisher:	Elsevier
Projects:	[UNSPECIFIED] FINT-CH
Language:	English
Submitter:	Christoph Schaller
Date Deposited:	02 Nov 2023 13:47
Last Modified:	02 Nov 2023 13:47
Publisher DOI:	10.1016/j.jag.2023.103480
Uncontrolled Keywords:	Local maxima-based individual tree detection Hybrid model Airborne laser scanning Forest inventory
ARBOR DOI:	10.24451/arbor.20290
URI:	https://arbor.bfh.ch/id/eprint/20290