In the Wadi Biskra arid and semi-Arid area, sustainable development is limited by land degradation, such as secondary salinization of soils. As an important high quality date production region of Algeria, it needs continuous monitoring of desertification indicators, since the bio-physical setting defines it as highly exposed to climate-related risks. For this particular study, for which little ground truth data was possible to acquire, we set up an assessment of appropriate methods for the identification and change detection of salt-Affected areas, involving image interpretation and processing techniques employing Landsat imagery. After a first phase consisting of a visual interpretation study of the land cover types, two automated classification approaches were proposed and applied for this specific study: decision tree classification and principal components analysis (PCA) of Knepper ratios. Five of the indices employed in the Decision Tree construction were set up within the current study, among which we propose a salinity index (SMI) for the extraction of highly saline areas. The results of the 1984 to 2014 diachronic analysis of salt-affected areas variation were supported by the interpreted land cover map for accuracy estimation. Connecting the outputs with auxiliary bio-physical and socio-economic data, comprehensive results are discussed, which were indispensable for the understanding of land degradation dynamics and vulnerability to desertification. One aspect that emerged was the fact that the expansion of agricultural land in the last three decades may have led and continue to contribute to a secondary salinization of soils. This study is part of the WADIS-MAR Demonstration Project, funded by the European Commission through the Sustainable Water Integrated Management (SWIM) Program (www.wadismar.eu).

Diachronic analysis of salt-affected areas using remote sensing techniques: the case study of Biskra area, Algeria

AFRASINEI, GABRIELA MIHAELA;MELIS, MARIA TERESA;BUTTAU, CRISTINA;ARRAS, CLAUDIO;GHIGLIERI, GIORGIO
2015

Abstract

In the Wadi Biskra arid and semi-Arid area, sustainable development is limited by land degradation, such as secondary salinization of soils. As an important high quality date production region of Algeria, it needs continuous monitoring of desertification indicators, since the bio-physical setting defines it as highly exposed to climate-related risks. For this particular study, for which little ground truth data was possible to acquire, we set up an assessment of appropriate methods for the identification and change detection of salt-Affected areas, involving image interpretation and processing techniques employing Landsat imagery. After a first phase consisting of a visual interpretation study of the land cover types, two automated classification approaches were proposed and applied for this specific study: decision tree classification and principal components analysis (PCA) of Knepper ratios. Five of the indices employed in the Decision Tree construction were set up within the current study, among which we propose a salinity index (SMI) for the extraction of highly saline areas. The results of the 1984 to 2014 diachronic analysis of salt-affected areas variation were supported by the interpreted land cover map for accuracy estimation. Connecting the outputs with auxiliary bio-physical and socio-economic data, comprehensive results are discussed, which were indispensable for the understanding of land degradation dynamics and vulnerability to desertification. One aspect that emerged was the fact that the expansion of agricultural land in the last three decades may have led and continue to contribute to a secondary salinization of soils. This study is part of the WADIS-MAR Demonstration Project, funded by the European Commission through the Sustainable Water Integrated Management (SWIM) Program (www.wadismar.eu).
9781628418545
Arid areas; Change detection; Decision tree; Desertification; Knepper ratios; Land cover; Land degradation; Landsat; Principal component analysis; Salinization; Applied mathematics; Computer science applications; Computer vision and pattern recognition; Electrical and electronic engineering; Electronic, optical and magnetic materials; Condensed matter physics
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/196137
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