Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 122; pp. 206 - 221
• Main Authors: Zhu, Zhe, Gallant, Alisa L., Woodcock, Curtis E., Pengra, Bruce, Olofsson, Pontus, Loveland, Thomas R., Jin, Suming, Dahal, Devendra, Yang, Limin, Auch, Roger F.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2016
• Subjects: algorithms; Auxiliary data; Continuous Change Detection and Classification (CCDC); digital elevation models; land cover; Land cover classification; Landsat; monitoring; probability; resource management; screening; snow; Training strategy; United States; United States Geological Survey; wetlands; United States; Auxiliary data; Land cover classification; Training strategy; Continuous Change Detection and Classification (CCDC); Landsat

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environme...