A Quantitative Assessment of a Combined Spectral and GIS Rule-Based Land-Cover Classification in the Neuse River Basin of North Carolina

Abstract

mentation processes. Of particular importance, is the applicaThe 14,582 km 2 Neuse River Basin in North Carolina was tion of LC data for the generation of landscape-based assesscharacterized based on a user-defined land-cover (LC) classi- ment metrics to evaluate relative ecosystem condition over a fication system developed specifically to support spatially wide range of analysis scales (i.e., watershed to national) to asexplicit, non-point source nitrogen allocation modeling studies. sess impacts attributable to human land-use activities (WickData processing incorporated both spectral and GIS rule-based ham and Norton, 1994; Jones et al., 1997; Riitters et al., 1997). analytical techniques using multiple date SPOT 4 (XS), Landsat Currently, high priority non-point-source (NPS) issues are 7( ETM + ), and ancillary data sources. Unique LC classification focused on nutrient and sediment transport from the landscape elements included the identification of urban classes based to receiving streams. These NPS loadings are used to support the on impervious surfaces and specific row crop type identifi- development of total maximum daily loads (TMDL) determinacations. Individual pixels were aggregated to produce variable tions of streams and rivers (USEPA, 1999). These dynamic, ecominimum mapping units or landscape “patches” correspond- system NPS processes function at multiple analytical scales ing to both riparian buffer zones (0.1 ha), and general watershed and require relatively high-resolution geospatial data to supareas (0.4 ha). An accuracy assessment was performed using port watershed-scale modeling efforts. Landscape parameters reference data derived from in situ field measurements and required to support these spatially explicit modeling approaches, imagery (camera) data. Multiple data interpretations were used include the identification and delineation of individual LC eleto develop a reference database with known data variability ments or “patches.” Landscape “patches” typically represent to support a quantitative accuracy assessment of LC classi- the primary modeling unit of a spatially explicit landscape fication results. Confusion matrices were constructed to incor- model. They are defined in this study as contiguous and relaporate the variability of the reference data directly in the tively homogeneous LC types that can be repetitively mapped accuracy assessment process. Accuracies were reported for using remote sensor data. hierarchal classification levels with overall Level 1 classiThe characterization of riparian buffer zones is required to fication accuracy of 82 percent (n 825) for general watershed evaluate their functional capacity and ecosystem value. Typiareas, and 73 percent (n 391) for riparian buffer zone cally, riparian buffer zones are defined as areas directly adjacent locations. A Kappa Test Z statistic of 3.3 indicated a significant to the top-of-the-stream bank and extending outward in a perdifference between the two results. Classes that performed pendicular direction for a distance of approximately 20 to 30 m. poorly were largely associated with the confusion of herba- Riparian buffer zones play important functional roles in nutriceous classes with both urban and agricultural areas.