Mapping Tree Species Diversity

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Bibliographische Detailangaben
Format:	E-Book
Sprache:	Englisch
Veröffentlicht:	MDPI - Multidisciplinary Digital Publishing Institute 2023
Schlagworte:	accuracy aerial imagery ALS AVIRIS-NG biodiversity biosecurity boreal forest BPWW classification climatic gradient CNN convex hull volume convolutional networks convolutional neural network cross-validation curve matching data fusion dead wood deep learning Earth Sciences, Geography, Environment, Planning endangered tree species feature extraction forest forest cover and species forest inventory forest pathology forest species forest stands classification forest structure analysis forestry GEE Geography high-resolution remote sensing imagery hyperspectral multitemporal information illumination correction imbalanced data individual tree crown delineation individual tree species recognition ISRO–NASA campaign Landsat LiDAR machine learning machine learning algorithm mapping Mount Taishan multi-layer perception multi-temporal multisource remote sensing data multitemporal myrtle rust object-based optical data phenological metrics pixel-based classification probability random forest radiative transfer model random forest Reference, Information and Interdisciplinary subjects Research and information: general RGB SAR savanna scale effect segmentation selective logging semideciduous forest Sentinel-1 Sentinel-2 Serbia Siberia single trees spatial autocorrelation spatial divergence species distribution model species diversity spectral diversity time series tree species tree species classification tree species mapping trees species identification tropical forests UAV up-scaling urban forestry Wienerwald biosphere reserve woody vegetation WorldView-3
ISBN:	9783036585260, 9783036585277, 3036585273, 3036585265
Online-Zugang:	Volltext
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Beschreibung
ISBN:	9783036585260 9783036585277 3036585273 3036585265
DOI:	10.3390/books978-3-0365-8527-7