Remote Sensing Techniques for Change Detection Analysis in Arid and Semi-arid areas

Zanchetta, Anna (2017) Remote Sensing Techniques for Change Detection Analysis in Arid and Semi-arid areas, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Ingegneria civile, ambientale e dei materiali, 28 Ciclo. DOI 10.6092/unibo/amsdottorato/8134.
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Abstract

Desertification constitutes a natural hazard for human livelihood, wildlife and vegetation worldwide. Arid and semi-arid areas are the most likely to undergo processes of desertification, and it is a concern of the international community to control, monitor and prevent such a phenomenon. As a mainly arid region, the Middle East is particularly vulnerable to climate-induced impacts on water resources, challenged by high growth population rates and a water-stressed situation. Aim of the reasearch is to investigate Remote Sensing (RS) techniques for desertication studies, with a special focus on the Middle East region. RS is an efficient tool for environmental studies on wide areas of the Earth surface, allowing fast and reproducible analysis on regional and continental scales . For this research two RS methods of change detection analysis have been investigated and further implemented: Change Vector Analysis (CVA), applied to the Tasselled Cap Transform (TCT) outputs, and the Maximum Autocorrelation Factor (MAF) transformation of the Multivariate Alteration Detector (MAD) components (MAD/MAF). The research introduces improvements in the use of both techniques adapting them to desertification studies and proposes a new RS methodology, which has been proven effective in detecting the surface change in arid and semi-arid areas. An added value of the research is the availability of the source code, implemented for this study, to other users, through GFOSS software.

Abstract
Tipologia del documento
Tesi di dottorato
Autore
Zanchetta, Anna
Supervisore
Dottorato di ricerca
Scuola di dottorato
Ingegneria civile ed architettura
Ciclo
28
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Desertification, Water, Remote Sensing, Change Detection Analysis, Middle East, Syria, GFOSS, FOSS, Change Vector Analysis, Tasselled Cap Transform, Multivariate Alteration Detector, Maximum Autocorrelation Factor
URN:NBN
DOI
10.6092/unibo/amsdottorato/8134
Data di discussione
17 Maggio 2017
URI

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