This project supports the use of WELD data, specifically in the generation and validation of global land cover datasets, including percent tree, percent barren, percent water, and percent other vegetation. This procedure requires the creation of WELD-derived multi-spectral and multi-temporal features and training data to calibrate the land cover mapping algorithms. We will have considerable training data (derived from 1-4m very high spatial resolution imagery) for producing WELD land cover products, but will add to our very high spatial resolution data base, in particular for validation purposes. This sub-award will be coordinated existing global land cover investigations, currently funded through Year 2 of the proposed WELD activity. Specifically, current work supported by USGS and the Moore Foundation will be used to prototype methods for global land cover characterization using Landsat data, to be ready for implementation when global WELD data sets come online.
Hansen M.C., Egorov A., Potapov P. V., Stehman S. V., Tyukavina A., Turubanova S. A., Roy D. P., Goetz S. J., Loveland T. R., Ju J., Kommareddy A., Forsythe C., Bents T. (2014) Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD), Remote Sensing of Environment, 140, 466-484.
Hansen, M.C., Egorov, A., Roy, D.P., Potapov, P., Ju, J., Turubanova, S., Kommareddy, I., and Loveland, T.R. (2011) Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project, Remote Sensing Letters, 2, 279-288.