Mapping of forest alliances with simulated multi-seasonal hyperspectral satellite imagery

A consistent and hierarchical classification of vegetation, such as the U.S. National Vegetation Classification (NVC) system, supports comprehensive conservation and management of natural ecosystems. At a detailed level, the NVC alliance is defined by diagnostic species and composition. Maps at this...

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Veröffentlicht in:	Remote sensing of environment 2018-06, Vol.210, p.490-507
Hauptverfasser:	Clark, Matthew L., Buck-Diaz, Jennifer, Evens, Julie
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence AVIRIS Classification Conservation County Diagnostic systems Ecosystem management Ecosystems Forest alliance Forests Hyperspectral satellite HyspIRI Image acquisition Image classification Image resolution Imaging spectroscopy Infrared imaging Infrared spectrometers Land cover Land use Learning algorithms Machine learning Mapping Multi-seasonal Pixels Reflectance Regional Satellite imagery Satellites Spectrum analysis Strategic management Summer Support Vector Machine Support vector machines Temporal variations U.S. National Vegetation Classification (NVC) Vegetation Vegetation mapping
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Beschreibung
Zusammenfassung:	A consistent and hierarchical classification of vegetation, such as the U.S. National Vegetation Classification (NVC) system, supports comprehensive conservation and management of natural ecosystems. At a detailed level, the NVC alliance is defined by diagnostic species and composition. Maps at this level of classification are often produced at local to regional scales (areas
ISSN:	0034-4257 1879-0704
DOI:	10.1016/j.rse.2018.03.021