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1 October 2004 Identifying historical and recent land-cover changes in Kansas using post-classification change detection techniques
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Statewide land-cover change detection analysis provides a useful tool for conservation planning and environmental monitoring and addresses issues of habitat fragmentation and urban sprawl. Furthermore, land-cover data offer a historical and recent perspective on landscape dynamics. To this end, the first alliance level land-cover map of Kansas (Kansas Vegetation Map) recently completed by the KARS Program was compared to Küchler's Potential Natural Vegetation map and the 1993 Kansas Land Cover Patterns map. The post-classification change detection technique was used along with co-occurrence matrices to identify areas and directions of land-cover change.

Comparisons showed that the land cover of Kansas has changed drastically since European settlement. Over 48% of the land is now cultivated and native vegetation types such as tallgrass and shortgrass prairie have been reduced dramatically in area. There are, however, millions of ha of these vegetation types remaining in Kansas. Comparisons between the two recent land-cover maps reveal that over 80% of the land in Kansas has remained unchanged in the five years between map development. Recent land-cover changes include conversion of grassland to cropland, cropland to grassland, and grassland to woodland. Many areas changing from cropland to grassland have been identified as land being enrolled in the Conservation Reserve Program (CRP). Post-classification change detection analysis also shows that forest and woodland types have increased over the five-year period and over 1 million ha of grassland have been converted to cropland. The magnitude of increases in woodland and forest is questionable, however, and may be due to registration errors and classification methodologies used to generate the land-cover maps.

Dana L. Peterson, Stephen L. Egbert, Kevin P. Price, and Edward A. Martinko "Identifying historical and recent land-cover changes in Kansas using post-classification change detection techniques," Transactions of the Kansas Academy of Science 107(3), 105-118, (1 October 2004).[0105:IHARLC]2.0.CO;2
Published: 1 October 2004

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