Bioenergy land use is expanding today as biofuel is consuming higher amount of agricultural production. In 2007, a widespread expansion of corn planting areas was recorded in the United States Department of Agriculture crop census. To better document the corn-related land use change, this study mapped the spatial distributions of four major annual crops (corn, soybean, winter wheat, and spring wheat) and three perennial crops (shortgrass, warm-season tallgrass, and cool-season tallgrass) in the Midwest. From 2006 to 2008, the 8-day, 500-m moderate resolution imaging spectroradiometer (MODIS) surface reflectance products were used to retrieve the normalized difference vegetation index (NDVI) composites. A support vector machine classifier was applied to identify these crops based on their unique growth cycles reflected from NDVI trajectories. The results showed a net increase of 15% of corn fields in 2007 accompanied by a net decrease of 16% in 2008. With the season-long integrated NDVI, this study also explored the geographic context and biomass proxy of native perennial grasses, an important feedstock of cellulosic biofuel. Mostly growing in North Dakota, South Dakota, Nebraska, and Kansas, their biomass quantities increased from west to east. This study indicates that frequent satellite observations may provide an efficient tool for monitoring biomass supplies and land use changes to assist national bioenergy decision-making.
Citrus grove change detection is of great importance to citrus production inventory monitoring. Using remotely sensed imagery to detect the land use and land coverage is one of the most widely-used, cost-effective approaches. However, there is little published research on citrus grove change detection using remotely sensed multi-spectral imagery, especially for those acquired by heterogeneous sensors. The purpose of this paper is to investigate the effectiveness of the citrus change detection based on the histogram matching normalization to the heterogeneously sensed imagery. In this paper, it is found that different reference image and band selection will result in different normalization performance. Based on this finding, a concept of finding optimal reference image and best spectral band for normalization in terms of the minimum Manhattan distance measure is presented. In this paper, the comparison of change detection results of unnormalized and histogram matching normalized images is presented. The experimental results show that histogram matching normalization significantly improves the image differencing based change detection results of the heterogeneously sensed citrus images, and the optimal reference image and band found with proposed optimization algorithm gives the best change detection results.
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