Date of Graduation


Document Type


Degree Name

Master of Arts in Geography (MA)

Degree Level





John C. Dixon

Committee Member

Jason A. Tullis

Second Committee Member

Gary R. Huxel


Earth sciences, Biological sciences, Classification, Gis, Land cover, Mapping


Maintaining representative sampling of biologically rich and rare ecosystems has become an important means to preventing biodiversity loss. A limitation in indentifying and quantifying ecosystems is the cost of obtaining high resolution imagery necessary for a high resolution land cover assessment. This research shows how free, different resolution imagery (orthoimages and LANDSAT ETM+) could be combined to produce a hybrid dataset with enhanced spectral, spectral and temporal properties, and processed to obtain a object-based classification of land cover of bottomland and pine hardwood forest in south eastern Arkansas. Three classification techniques were evaluated: 1) a human derived, rule based method, 2) A nearest neighbor classification using only the infrared orthoimage (SRGB), and 3) A nearest neighbor classification using the infrared orthoimage and LANDSAT ETM+ derived multitemporal NDVI values (SNDVI). Overall accuracy of the rule based method and SNDVI were comparable, and significantly higher (~10-20%) than the SRGB. Further, when compared to existing land cover maps, both the rule based method and SNDVI had far greater visual appeal and accuracy.