Assessing Data Preparation and Machine Learning for Tree Species Classification Using Hyperspectral Imagery
Author:
Affiliation:
1. Department of Geography and Environmental Science, Hunter College of the City University of New York, New York, NY 10065, USA
2. Earth and Environmental Sciences, The City University of New York Graduate Center, New York, NY 10016, USA
Abstract
Funder
NASA
Publisher
MDPI AG
Link
https://www.mdpi.com/2072-4292/16/17/3313/pdf
Reference45 articles.
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3. Review of Studies on Tree Species Classification from Remotely Sensed Data;Fassnacht;Remote Sens. Environ.,2016
4. Ni-Meister, W., Lee, S., Strahler, A.H., Woodcock, C.E., Schaaf, C., Yao, T., Ranson, K.J., Sun, G., and Blair, J.B. (2010). Assessing General Relationships between Aboveground Biomass and Vegetation Structure Parameters for Improved Carbon Estimate from Lidar Remote Sensing: Aboveground Biomass Estimate from Lidar. J. Geophys. Res., 115.
5. Direct Use of Large-Footprint Lidar Waveforms to Estimate Aboveground Biomass;Rojas;Remote Sens. Environ.,2022
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