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Cited by 35 publications
(29 citation statements)
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References 32 publications
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“…We tested the accuracy of the classification using 37 randomly placed validation points (Magdon, Fischer, Fuchs, & Kleinn, 2014;McRoberts & Walters, 2012). First, we determined the bias of the model based on the following equation:…”
Section: Tree Covermentioning
confidence: 99%
“…We tested the accuracy of the classification using 37 randomly placed validation points (Magdon, Fischer, Fuchs, & Kleinn, 2014;McRoberts & Walters, 2012). First, we determined the bias of the model based on the following equation:…”
Section: Tree Covermentioning
confidence: 99%
“…Space-borne remote sensing data were initially used over extended regions for qualitative and quantitative mapping of forest biomass using machine learning approaches [83][84][85][86][87][88].…”
Section: Forest Biomass Retrievalmentioning
confidence: 99%
“…However, first, it should be noted that a direct comparison is difficult less, because such studies mapped forest with different tree species compositions or involved a variety of forest structures and geographic locations, but more because of no NFI forest definition was used. Second, mapping areas in these studies were substantially smaller and different input data types, such as from ALS [25][26][27] or satellite data were used [20].…”
Section: General Aspects Of the Mapping Approachmentioning
confidence: 99%
“…In fact, the VHM [37] is a reliable dataset and enables the derivation of the three geometric parameters of minimum height, minimum crown coverage and minimum width that are required for the NFI forest definition. Since these parameters are entirely based on the VHM they can be easily adapted to the thresholds required by other forest definitions, for example, as presented by [20,27].…”
Section: General Aspects Of the Mapping Approachmentioning
confidence: 99%