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IMPACTS OF PATCH SIZE AND LAND COVER HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY
Citation:
Smith, J H., J D. Wickham, S. V. Stehman, AND L. Yang. IMPACTS OF PATCH SIZE AND LAND COVER HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 68(1):65-70, (2002).
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:
Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of miss-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support these hypotheses, This study utilizes data gathered as part of the accuracy assessment of the I M National Land Cover Data (NLCD) set to identify and quantify the impacts of landscape heterogeneity and patch size. Logistic regression was utilized to assess the impacts of these variables, as well as the impact of land cover information. Guided step-wise procedures were utilized to assess the explanatory variables individually and with one another. The results reveal that accuracy decreases as land cover heterogeneity increases and as patch size decreases. These impacts remain significant in the presence of the land cover variables, thus signifying that they transcend the classification system utilized.