Classification accuracy analysis of selected land use and land cover products in a portion of West-Central Lower Michigan

Remote sensing satellites have been utilized to characterize and map land cover and its changes since the 1970s. However, uncertainties exist in almost all land use and land cover maps classified from remotely sensed images. In particular, it has been recognized that the spatial mis-registration of land cover maps can affect the true estimates of land use/land cover (LULC) changes. This dissertation addressed the following questions: what are the spatial patterns, magnitudes, and cover-dependencies of classification uncertainty associated with West-Central Lower Michigan's LULC products and how can the adverse effects of spatial misregistration on accuracy assessment be reduced? Two Michigan LULC products were chosen for comparison: 1998 Muskegon River Watershed (MRW) Michigan Resource Information Systems LULC map and a 2001 Integrated Forest Monitoring and Assessment Prescription Project (IFMAP). The 1m resolution 1998 MRW LULC map was derived from U.S. Geological Survey Digital Orthophoto Quarter Quadrangle (USGS DOQQs) color infrared imagery and was used as the reference map, since it has a thematic accuracy of 95%. The IFMAP LULC map was co-registered to a series of selected 1998 USGS DOQQs. The total combined root mean square error (rmse) distance of the georectified 2001 IFMAP was +/-12.20m. A spatial uncertainty buffer of at least 1.5 times the rmse was set at 20m so that polygon core areas would be unaffected by spatial misregistration noise. A new spatial misregistration buffer protocol (SPATIALM_ BUFFER) was developed to limit the effect of spatial misregistration on classification accuracy assessment. Spatial uncertainty buffer zones of 20m were generated around LULC polygons of both datasets.; Eight-hundred seventeen (817) stratified random accuracy assessment points (AAPs) were generated across the 1998 MRW map. Classification accuracy and kappa statistics were generated for both the 817 AAPs and 604 AAPs comparisons. For the 817 AAPs comparison, the overall classification accuracy was 68.79% (kappa=0.627). When the 817 AAPs were overlaid onto the 2001 IFMAP, 213 AAPs within the 20m spatial uncertainty buffer zone were removed. The remaining 604 AAPs were used to assess the map accuracy and results showed that overall classification accuracy was 78.64% (kappa=0.742). The residual, spatial registration noise caused an overall thematic accuracy underestimation of nearly 10%. Therefore, this SPATIALM_ BUFFER method was effective in reducing the effects of spatial misregistration on the accuracy assessment of LULC maps.; However, even after removing misregistration noise from consideration, 102 out of 604 AAPs were still misclassified (16.9%). The following land cover classes had the largest number of misclassified AAPs: grassland (12), urban/built up (10), coniferous forest (10), non-forested wetlands (10), and agriculture (9). Therefore, thematic misclassifications were still land-cover dependent and can be influenced by classification system, radiometric, temporal and phenological issues.