Dorren, Luuk

Ginzler, Christian

Moos, Christine

van Loon, Emiel

English

Berner Fachhochschule: ARBOR

EGU22-11723https://doi.org/10.5194/egusphere-egu22-11723EGU General Assembly 2022© Author(s) 2022. This work is distributed underthe Creative Commons Attribution 4.0 License.Improving Local Maxima-based Individual Tree Detection usingstatistically modelled Forest Structure InformationChristoph Schaller1, Christian Ginzler2, Emiel van Loon3, Christine Moos4, and Luuk Dorren41Bern University of Applied Sciences, Zollikofen, Switzerland (christoph.schaller@bfh.ch)2Swiss Federal Research Institute WSL, Birmensdorf, Switzerland3University of Amsterdam UVA - IBED, Amsterdam, the Netherlands4Bern University of Applied Sciences, Zollikofen, SwitzerlandLaser scanning-based tree detection has been used for many years to complement sample data offorest inventories. Local Maxima (LM) detection methods are suitable for individual tree detectionin the forest canopy and allow for detection over large areas due to their computational efficiency.However, the performance of LM methods depends on factors such as the resolution of the inputdata (point density of aerial laser scanning (ALS) and spatial resolution of the derived rasters), thepre-processing of the input data as well as the structure and species of the detected forest. Themain objective of our study was to evaluate to what extent LM tree detection can be improved byconsidering prior knowledge about forest structure using statistical modelling. To achieve thisgoal, we developed a statistical model for selecting between 10 different crown height model(CHM) pre-processing methods based on forest structure variables derived from remote sensingdata. We fitted linear regression models predicting the error between the number of detectedtrees and the field inventoried number of the trees reaching the canopy in the sample plot. Themodel used dominant canopy height, the degree of coverage overall and for different forest layersderived from the CHM, the dominant leaf type derived from Sentinel-2 data, and terraincharacteristics as explanatory variables. The model performance was evaluated by assessing treedetection errors using all national forest inventory plots in Switzerland using 10-fold cross-validation. The results showed a reduction of the RMSE to 91 stems per ha (respectively 1.3 whennormalized by the inventoried stem number) using the model-based pre-processed CHM fordetection compared to 205 stems per ha (normalized = 4) when detecting trees using anunprocessed CHM (number of used inventory plots n=5254). Excluding inventory plots with an ALSpoint density of less than 15 points per square meter (n=3797) improved the RMSE to 89 stemsper ha (normalized = 1.25).The RMSE further improves to 85 stems per ha (normalized = 1.2) byadditionally excluding plots with more than 6 years between ALS acquisition and inventory(n=2676). Although the results show a clear reduction of the detection error by our model, theyalso indicate potential for further refinements. Especially the integration of high-quality ALS data(becoming available for the entire area of Switzerland until 2024), detailed tree species data, andadditional, more recent inventory data are recommended. In the future, a combination of ourmethod with point cloud-based approaches will probably be able to further reduce detectionerrors at national scale.source: https://doi.org/10.24451/arbor.17817 | downloaded: 9.11.2022Powered by TCPDF (www.tcpdf.org)

Improving Local Maxima-based Individual Tree Detection using statistically modelled Forest Structure Information

https://arbor.bfh.ch/17817/1/EGU22-11723-print.pdf

Improving Local Maxima-based Individual Tree Detection using statistically modelled Forest Structure Information

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