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A QUANTITATIVE ASSESSMENT OF A COMBINED SPECTRAL AND GIS RULE-BASED LAND-COVER CLASSIFICATION IN THE NEUSE RIVER BASIN OF NORTH CAROLINA
Citation:
Lunetta, R S., J. Ediriwickrema, J. Iiames, D. M. Johnson, J G. Lyon, A. McKerrow, AND A Pilant. A QUANTITATIVE ASSESSMENT OF A COMBINED SPECTRAL AND GIS RULE-BASED LAND-COVER CLASSIFICATION IN THE NEUSE RIVER BASIN OF NORTH CAROLINA. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 69(3):299-310, (2003).
Impact/Purpose:
Our research objectives are to: (a) develop new methods using satellite remote sensor data for the rapid characterization of LC condition and change at regional to national scales; (b) evaluate the utility of the new NASA-EOS MODIS (Moderate Resolution Imaging Spectrometer) leaf area index (LAI) measurements for regional scale application with landscape process models (e.g., biogenic emissions and atmospheric deposition); (c) provide remote sensor derived measurement data to advance the development of the next generation of distributed landscape process-based models to provide a predictive modeling capability for important ecosystem processes (e.g., nutrients, sedimentation, pathogens, etc.); and (d) integrate in situ monitoring measurement networks with UAV and satellite based remote sensor data to provide a continuous environmental monitoring capability.
Description:
The 14,582 km2 Neuse River Basin in North Carolina was characterized based on a user defined land-cover (LC) classification system developed specifically to support spatially explicit, non-point source nitrogen allocation modeling studies. Data processing incorporated both spectral and GIS rule-based analytical techniques using multiple date SPOT 4 (XS), Landsat 7 (ETM+), and ancillary data sources. Unique LC classification elements included the identification of urban classes based on impervious surfaces and specific row crop type identifications. Individual pixels were aggregated to produce variable minimum mapping units or landscape "patches" corresponding to both riparian buffer zones (0.1 ha), and general watershed areas (0.4 ha). An accuracy assessment was performed using reference data derived from in situ field measurements and imagery (camera) data. Multiple data interpretations were used to develop a reference database with known data variability to support a quantitative accuracy assessment of LC classification results. Confusion matrices were constructed to incorporate the variability of the reference data directly in the accuracy assessment process. Accuracies were reported for hierarchal classification levels with overall Level 1 classification accuracy of 82 percent (n=825) for general watershed areas, and 73 percent (n=391) for riparian buffer zone locations. A Kappa Test Z statistic of 3.3 indicated a significant difference between the two results. Classes that performed poorly were largely associated with the confusion of herbaceous classes with both urban and agricultural areas.