Estimating area errors for fine‐scale feature‐based ecological mapping

High spatial resolution feature‐based approaches are especially useful for ecological mapping in densely populated landscapes. This paper evaluates errors in estimating ecological map class areas from fine‐scale current (∼2002) and historical (∼1945) feature‐based ecological mapping by a set of trained interpreters across densely populated rural sites in China based on field‐validated interpretation of high spatial resolution (⩽1 m) imagery. Median overall map accuracy, corrected for chance, was greater than 85% for mapping by trained interpreters, with greater accuracy for current versus historical mapping. An error model based on feature perimeter proved as reliable in predicting 90% confidence intervals for map class areas as did models derived from the conventional error matrix. A conservative error model combining these approaches was developed and tested for statistical reliability in predicting confidence intervals for ecological map class areas from fine‐scale feature‐based mapping by a set of trained interpreters across rural China, providing a practical basis for statistically reliable ecological change detection in densely populated landscapes.

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