In remote sensing, linear transformation methods like the Tasselled Cap (TC) transform have the advantage of reducing the amount and redundancy of data, providing different information in derived components. The TC transform though, has never been specifically developed to perform the study of desert areas. This paper addresses this issue discussing the possible approaches and performing the calculation of a new set of TC transform parameters for SPOT4 and Landsat5 satellites for Top of Atmosphere Reflectance images of selected arid and semi-arid locations in the Middle East and the USA. Compared to previously calculated transforms, results show some differences explained by desert conditions and give the chance for a proper use of this technique in change detection for drylands.
Background and Methods: The paper presents a combination of two unsupervised techniques for change detection studies in arid and semi-arid areas. Among Remote Sensing change detection techniques, unsupervised approaches have the advantage of promptly producing a map of the change between two dates, but often the interpretation of the results is not straightforward, and requires further processing of the image. The aim of the research is to propose a new time effective and semi-automated reproducible technique in order to reduce the weakness of the unsupervised approach in change detection. Two techniques, Change Vector Analysis (CVA) and Maximum Autocorrelation Factor transform of Multivariate Alteration Detector components (MAD/MAF) are chosen to serve the purpose.
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