A combined change detection procedure to study desertification using opensource tools

Abstract

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. Results and Conclusions The results of the research, applied to two case studies in the Middle East region, indicate that the chosen techniques complement each other, since MAD/MAF gives a detailed spatial extent while CVA gives the semantic interpretation of the output. The research brings further understanding to the use of both unsupervised procedures and the methodology can be used as a fast semi-automatic preliminary step for more accurate change detection studies. A further output is a new add-on implementing CVA for the GFOSS (Geospatial Free and Open Source Software) project Grass GIS.