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AN EXPERIMENTAL ASSESSMENT OF MINIMUM MAPPING UNIT SIZE
Citation:
Knight, J F. AND R S. Lunetta. AN EXPERIMENTAL ASSESSMENT OF MINIMUM MAPPING UNIT SIZE. U.S. Environmental Protection Agency, Washington, DC, (9), EPA/600/N-03/006, 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:
Land-cover (LC) maps derived from remotely sensed data are often presented using a minimum mapping unit (MMU). The choice of a MMU that is appropriate for the projected use of a classification is important. The objective of this experiment was to determine the optimal MMU of a LC classification of the Neuse River Basin (NRB) in North Carolina. The results of these analyses indicated that there was no statistically significant difference between the accuracy estimates for the classification when using either the 0.36 ha or 1.4 ha hectare MMUs. Therefore, the MMU of 0.42 hectares that was selected during the creation of the NRB classification was also not significantly different from either of these. Since a classification would commonly use the smallest MMU that has sufficient accuracy, the 0.42 hectare MMU was a valid choice. An analyst might also choose to use other values in the 0.36 ha to 1.4 ha range, depending on the application, while maintaining statistically insignificant differences in overall accuracy estimates for this classification.