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Monday, Oct 12
Joe Grengs, Policy & Planning for Social Equity in Transportation

Identification of "ever-cropped" land (1984-2010) using Landsat annual maximum NDVI image composites: Southwestern Kansas case study

Publication Abstract

Maxwell, S., and Kenneth M. Sylvester. 2012. "Identification of "ever-cropped" land (1984-2010) using Landsat annual maximum NDVI image composites: Southwestern Kansas case study." Remote Sensing of Environment, 121: 186-195.

A time series of 230 intra- and inter-annual Landsat Thematic Mapper images was used to identify land that was ever cropped during the years 1984 through 2010 for a five county region in southwestern Kansas. Annual maximum Normalized Difference Vegetation Index (NDVI) image composites (NDVI(ann-max)) were used to evaluate the inter-annual dynamics of cropped and non-cropped land. Three feature images were derived from the 27-year NDVI(ann-max) image time series and used in the classification: 1) maximum NDVI value that occurred over the entire 27 year time span (NDVI(max)), 2) standard deviation of the annual maximum NDVI values for all years (NDVI(sd)), and 3) standard deviation of the annual maximum NDVI values for years 1984-1986 (NDVI(sd84-86)) to improve Conservation Reserve Program land discrimination.Results of the classification were compared to three reference data sets: County-level USDA Census records (1982-2007) and two digital land cover maps (Kansas 2005 and USGS Trends Program maps (1986-2000)). Area of ever-cropped land for the five counties was on average 11.8 % higher than the area estimated from Census records. Overall agreement between the ever-cropped land map and the 2005 Kansas map was 91.9% and 97.2% for the Trends maps. Converting the intra-annual Landsat data set to a single annual maximum NDVI image composite considerably reduced the data set size, eliminated clouds and cloud-shadow affects, yet maintained information important for discriminating cropped land. Our results suggest that Landsat annual maximum NDVI image composites will be useful for characterizing land use and land cover change for many applications.

DOI:10.1016/j.rse.2012.01.022 (Full Text)

PMCID: PMC3298121. (Pub Med Central)

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