Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data...

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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 12; H. 9; S. 1414
Hauptverfasser:	Scholl, Victoria, Cattau, Megan, Joseph, Maxwell, Balch, Jennifer
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.05.2020
Schlagworte:	Accuracy Airborne observation airborne remote sensing Airborne sensing Algorithms artificial intelligence automation Biodiversity Classification Colorado Composition Coniferous trees Data collection Datasets Decision analysis Decision trees Detection Diameters Ecology forests hyperspectral Image detection labor Learning algorithms Lidar Machine learning Mapping Mountains multispectral National Ecological Observatory Network Neon Neural networks Observatories Plant species Polygons prediction raster data Remote observing Remote sensing Remote sensing systems Species classification Species composition Species diversity Support vector machines Training tree crown tree species classification Trees Vegetation vegetation index Workflow United States > US Colorado
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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