Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data

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Authors	MELICHOVA Zlatica PEKÁR Stanislav SUROVY Peter
Year of publication	2023
Type	Article in Periodical
Magazine / Source	Forests
MU Faculty or unit	Faculty of Science
Citation
Web	https://doi.org/10.3390/f14122408
Doi	http://dx.doi.org/10.3390/f14122408
Keywords	multitemporal laser scanning data; harvest detection; simplification polygons; clear-cut areas
Description	Forest harvest detection techniques have recently gained increased attention due to the varied results they provide. Correctly determining the acreage of clear-cut areas is crucial for carbon sequestration. Detecting clear-cut areas using airborne laser scanning (ALS) could be an accurate method for determining the extent of clear-cut areas and their subsequent map display in forest management plans. The shapes of ALS-detected clear-cut areas have uneven edges with protrusions that might not be readable when displayed correctly. Therefore, it is necessary to simplify these shapes for better comprehension. To simplify the shapes of ALS-scanned clear-cut areas, we tested four simplification algorithms using ArcGIS Pro 3.0.0 software: the retain critical points (Douglas-Peucker), retain critical bends (Wang-Muller), retain weighted effective areas (Zhou-Jones), and retain effective areas (Visvalingam-Whyatt) algorithms. Ground-truth data were obtained from clear-cut areas plotted in the forest management plan. Results showed that the Wang-Muller algorithm was the best of the four ALS algorithms at simplifying the shapes of detected clear-cut areas. Using the simplification algorithm reduced the time required to edit polygons to less than 1% of the time required for manual delineation.