Diagnostic tools to evaluate a spatial land change

Authors: Hao Chen Robert Gilmore Pontius

Publish Date: 2010/08/11

Volume: 25, Issue: 9, Pages: 1319-1331

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Abstract

This paper proposes a method to quantify the goodnessoffit of a land change projection along a gradient of an explanatory variable by classifying pixels as one of four types null successes false alarms hits and misses The method shows 1 how the correctness and error of a land change projection are distributed along the gradient of an explanatory variable 2 how the gradient of the explanatory variable relates to the stationarity of the land transition processes and 3 how to use the insights from the previous two points to search for additional explanatory variables The paper illustrates the method through a case study that applies the model Geomod in Central Massachusetts USA Results reveal that the model predicts more than the observed amount of change on flat slopes and less than the observed amount of change on steep slopes One reason for these types of errors is that the land change process during the calibration interval is different than the process during the prediction interval with respect to slope The method allows modelers to use the validation step as a diagnostic tool to search for potentially influential missing variables and to gain insight into land transition processes The technique is designed to be applicable to a variety of types of land change modelsThe National Science Foundation supported this work via three of its programs 1 HumanEnvironment Regional Observatory program via grant 9978052 2 Long Term Ecological Research via grant OCE0423565 and 3 Center for Integrated Study of the Human Dimensions of Global Change through a cooperative agreement between Carnegie Mellon University and the National Science Foundation SBR9521914 Clark Labs facilitated this work by creating the GIS software Idrisi® We thank anonymous reviewers for constructive comments that improved the quality of this paper

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