Mapping Agricultural Landuse in the 
Mississippi Alluvial Valley of Arkansas

Report on the Mississippi Alluvial Valley of Arkansas 

Landuse/Landcover (MAVA-LULC) Project


Copyright 1999; Comments to
Bruce Gorham; bruce@cast.uark.edu



Section 1:
Abstract

Optimal use of soil and water resources is one of the principal challenges facing the agricultural resource community. Numerous water and soil conservation problems are directly related to agriculture including surface water pollution, ground water pollution, topsoil loss, increasing soil salinity levels, and ground water depletion. Specific crops have different impacts on soil and water resources (e.g. cotton generally requires more pesticides than other crops, rice requires more water, etc.). Therefore, it is important to know where specific crops are being grown. Accurate agricultural land-use maps can help soil and water scientists to identify potential problem areas, predict where problems are likely to occur in the future, and to model appropriate solutions.

Nearly all of Arkansas' agricultural crop production occurs in the eastern contiguous counties of the Mississippi Alluvial Valley (MAV) commonly known as the Delta. This area also displays many of the problems associated with large-scale agricultural production. In 1996 the Arkansas Soil and Water Conservation Commission (ASWCC) provided funding to the Center for Advanced Spatial Technologies at the University of Arkansas to develop digital land-use/land-cover maps focusing on agricultural land-use for the 27 Arkansas counties within MAV. Combined with existing spatial data, the information produced from this project will serve as a basis for the formulation of water, soil, and farm management policies and practices. This report outlines the development of the "Mississippi Alluvial Valley of Arkansas - Landuse Landcover Project" (MAVA-LULC). The report discusses the multi-temporal approach, based on crop phenologies and levelized feature extraction, employed in the project. Each significant step in the mapping process from training data collection to image classification and accuracy assessment is examined.