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- W2020984563 abstract "The automatic extraction of single trees from remotely sensed data is approached in numerous studies, but results are still insufficient in areas of dense temperate forest. Common watershed-based algorithms using digital surface models tend to produce erroneous results in difficult constellations because the treetop determination lacks an exact criterion for smoothing. In this article, a new approach is introduced that classifies crown size in advance and uses this information as prior knowledge for single-tree extraction. Crown size is classified from texture with an improved grey-scale granulometry followed by a crown size adapted watershed segmentation of single trees. The method is applied on a large area of 10 km2 and verified on six reference plots reflecting diverse and difficult compositions. The accuracy varies between 64% and 88%, and shows an average improvement of about 30% for deciduous and mixed stands compared to a non-crown-size-dependent algorithm." @default.
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- W2020984563 date "2011-06-24" @default.
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- W2020984563 title "Prior-knowledge-based single-tree extraction" @default.
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- W2020984563 doi "https://doi.org/10.1080/01431161.2010.494633" @default.
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