Crop Identification Using Seasonal Profiles of the Normalized Difference Vegetation Index: An Unsupervised Approach

다국어 초록 (Multilingual Abstract)

Landsat Thematic Mapper-derived, seasonal NDVI profile data were analyzed to classify cropland types in Kansas. Unsupervised image classification approach showed that cropland acreage did not deviate more than 10 percent from the USDA survey data in sixty-eight counties across the state. Four main cropland types, including wheat, summer row crop, fallow, and alfalfa, were identified based on their phenological characteristics of NDVI over the growing season. Accuracy assessment was performed on the acreage of each cropland type and its spatial distribution. Regarding acreage, the classification underestimated wheat and alfalfa and overestimated summer row crops and fallow areas. Except for some confusion between wheat and alfalfa, the crop identification accuracy values ranged from 78.8 to 100 % for a producer?s accuracy and from 58.8 to 98.3 % for a user?s accuracy with an overall accuracy as high as 93.5 %.

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