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Library Resource
Mexico, United States of America
A novel approach to image radiometric normalization for change detection is presented. The approach referred to as stratified relative radiometric normalization (SRRN) uses a time-series of imagery to stratify the landscape for localized radiometric normalization. The goal is to improve the detection accuracy of abrupt land cover changes (human-induced, natural disaster, etc.) while decreasing false detection of natural vegetation changes that are not of interest. These vegetation changes may be associated with such phenomena as phenology, growth and stress (e.g.
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Library Resource
Analysis of land-cover change in the seasonal tropical forests of the Southern Yucatán, Mexico presents a number of significant challenges for the fine-scale land-cover information required of land-change science. Subtle variation in mature forest types across the regional ecocline is compounded by vegetation transitions following agricultural land uses. Such complex mapping environments require innovation in multispectral classification methodologies. This research presents an application of a step-wise maximum likelihood/In-Process Classification Assessment (IPCA) procedure.
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