Since the mid-2000s, agricultural lands in the United States have been undergoing rapid change to meet the increasing bioenergy demand. In 2009 the USDA Biomass Crop Assistance Program (BCAP) was established. In its Project Area 1, land owners are financially supported to grow perennial prairie grasses (switchgrass) in their row-crop lands. To promote the program, this study tested the feasibility of biomass crop mapping based on unique timings of crop development. With a previously published data fusion algorithm-the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), a 10-day normalized difference vegetation index (NDVI) time series in 2007 was established by fusing MODIS reflectance into TM image series. Two critical dates-peak growing (PG) and peak drying (PD)-were extracted and a unique "PG-0-PD" timing sequence was defined for each crop. With a knowledge-based decision tree approach, the classification of enhanced TM/MODIS time series reached an overall accuracy of 76% against the USDA Crop Data layer (CDL). Especially, our results showed that winter wheat single cropping and wheat-soybean double cropping were much better classified, which may provide supplementary data for the CDL product. More importantly, this study extracted the first spatial layer of warm-season prairie grasses that have not been published in any national land cover products, which could serve as a base map for decision making of bioenergy land use in BCAP land.

This effort was funded by the Louisiana Coastal Protection and Restoration Authority's (CPRA) Barrier Island Comprehensive Monitoring program. We would like to thank Syed Khalil (CPRA) for numerous helpful discussions about the habitat mapping methods and analysis for this project. We thank John Barras for his assistance with the development of coastal reach boundaries for this effort. We are grateful to Hana Thurman (Cherokee Nation Technologies) for her assistance with designing the cover graphic. Finally, we thank Syed Khalil, Glen Curole (CPRA), Brady Couvillion (U.S. Geological Survey; USGS), and Richard Day (USGS) for reviewing this report.

Agricultural land use change, especially corn expansion since 2000s, has been accelerating to meet the growing bioenergy demand of the United States. This study identifies the environmentally sensitive lands (ESLs) in the U.S. Midwest using the distance-weighted Revised Universal Soil Loss Equation (RUSLE) associated with bioenergy land uses extracted from USDA Cropland Data Layers. The impacts of soil erosion to downstream wetlands and waterbodies in the river basin are counted in the RUSLE with an inverse distance weighting approach. In a GIS-ranking model, the ESLs in 2008 and 2011 (two representative years of corn expansion) are ranked based on their soil erosion severity in crop fields. Under scenarios of bioenergy land use change (corn to grass and grass to corn) on two land types (ESLs and non-ESLs) at three magnitudes (5%, 10% and 15% change), this study assesses the potential environmental impacts of bioenergy land use at a basin level. The ESL distributions and projected trends vary geographically responding to different agricultural conversions. Results support the idea of re-planting native prairie grasses in the identified High and Severe rank ESLs for sustainable bioenergy management in this important agricultural region.