Perennial biomass crop establishment and its environmental impacts in the Midwestern United States.
U.S. Department of Agriculture (USDA)
PI: Cuizhen (Susan) Wang; Co-Is: Felix Fritschi, Ranjith Udawatta, Claire Baffaut
Summary: Bioenergy is of increasing interest in agriculture as biomass becomes the largest source of renewable energy in the United States. This project documents the current and future land use patterns of perennial biomass crops in the Midwest using satellite image series, and assesses the environmental impacts, e.g. soil erosion and environmental sensitive lands in the Corn Belt. The deliverables of this project include:
- the km-scale perennial grass and biomass maps in the Midwest;
- fine-resolution documentation of perennial grass establishment in the BCAP Project Area 1;
- perennial crop inventory database in the Midwest in 2000-2015;
- Soil erosion and environmental sensitivity assessment under bioenergy land use changes at local (BCAP land) and regional (Midwest) levels.
Wang, C., Q. Fan, Q. Li, W. M. SooHoo, and L. Lu, 2017. Energy crop mapping with enhanced TM/MODIS time series in the BCAP agricultural lands. ISPRS Journal of Photogrammetry and Remote Sensing, 124: 133-143.
SooHoo, W.M., C. Wang*, and H. Li, 2017. Geospatial assessment of bioenergy land use and its impacts on soil erosion in the U.S. Midwest. Journal of Environment Management, 190:188-196.
Li, Q., C. Wang, B. Zhang and L. Lu, 2015. Object-based crop classification with Landsat-MODIS enhanced time-series data. Remote Sensing, 7(12): 16091-16107.
Zhong, C., C. Wang*, and C. Wu, 2015. MODIS-based fractional crop mapping in the U.S. Midwest with spatially constrained phenological mixture analysis. Remote Sensing, 7(1): 512-529.
Wang, C., Zhong, C., Yang, Z., 2014. Assessing bioenergy-driven agricultural land use change and biomass quantities in the U.S. Midwest with MODIS time series. J. Appl. Remote Sens. 8 (1), 085198. doi:10.1117/1.JRS.8.085198.
Yuan, F., C. Wang and M. Mitchell, 2014. Spatial patterns of land surface phenology relative to monthly climate variations: US Great plains. GIScience and Remote Sensing, 51(1):30-50.