SBR-9513889 The research funded by this award will test the utility of artificial neural networks (ANN) in detecting changes in remotely sensed images. The ability to detect and monitor changes in conditions at the Earth's surface is fundamental to an understanding of human impacts on the environment and to the assessment of the sustainability of development. Ground based measurements are severely limited by logistical constraints, particularly in the research and assessment of global change. Therefore, remote sensing provides the potential for frequent assessment of surface conditions and their change over large areas. Currently, the greatest success in change detection via remotely sensed data is for situations of dramatic change. The goal of the research funded by this award is to test the utility of ANN in assessing less dramatic change. The expectation that ANN will allow significant improvements in change detection is based on two factors: ANN have proven more effective than conventional statistics-based methods for classification of remote sensing imagery, and the investigators have had some success in a preliminary effort to use ANN in this way. The testing entails several components: exploring alternative ANN architectures, interpreting the internal structure of the ANN to analyze the changes signal in the remotely sensed data, evaluating the performance of the ANN relative to conventional methods for change detection, developing measures to interpret ANN output signals for quantifying change, and estimating the robustness of trained ANN outside of the training domain. The investigators will use existing data sets containing remotely sensed and ground measurements for all these tests, allowing rapid progress at minimal cost. The research has both theoretical and applied implications. It will lead to a greater conceptual understanding of the spectral and temporal signals contained in remotely sensed images resulting from land surface change. It should help determine what analytic methods are better suited to measuring change in different contexts. The research will lead to an improved understanding of the use if neural networks in a data-analytic framework, and thus enhance the appropriate is of ANN in geographic research.
