In the Wadi Biskra arid and semiarid areas, sustainable development is restricted by land degradation processes such as secondary salinization of soils. Being an important highquality date production region of Algeria, this area needs continuous monitoring of desertification indicators, hence highly exposed to climate-related risks. Given the limited access to field data, appropriate methods were assessed for the identification and change detection of salt-affected areas, involving image interpretation and automated classifications employing Landsat imagery, ancillary and multisource ground truth data. First, a visual photointerpretation study of the land cover and land use classes was undergone according to acknowledged methodologies. Second, two automated classification approaches were developed: a customized decision tree classification (DTC) and an unsupervised one applied to the principal components of Knepper ratios composite. Five indices were employed in the DTC construction, among which also is a salinity index. The diachronic analysis was undergone for the 1984 to 2015 images (including seasonal approach), being supported by the interpreted land cover/land use map for error estimation. Considering also biophysical and socioeconomic data, comprehensive results are discussed. One of the most important aspects that emerged was that the accelerated expansion of agricultural land in the last three decades has led and continues to contribute to a secondary salinization of soils.

Assessment of remote sensing-based classification methods for change detection of salt-affected areas (Biskra area, Algeria)

AFRASINEI, GABRIELA MIHAELA;MELIS, MARIA TERESA;BUTTAU, CRISTINA;ARRAS, CLAUDIO;GHIGLIERI, GIORGIO
2017-01-01

Abstract

In the Wadi Biskra arid and semiarid areas, sustainable development is restricted by land degradation processes such as secondary salinization of soils. Being an important highquality date production region of Algeria, this area needs continuous monitoring of desertification indicators, hence highly exposed to climate-related risks. Given the limited access to field data, appropriate methods were assessed for the identification and change detection of salt-affected areas, involving image interpretation and automated classifications employing Landsat imagery, ancillary and multisource ground truth data. First, a visual photointerpretation study of the land cover and land use classes was undergone according to acknowledged methodologies. Second, two automated classification approaches were developed: a customized decision tree classification (DTC) and an unsupervised one applied to the principal components of Knepper ratios composite. Five indices were employed in the DTC construction, among which also is a salinity index. The diachronic analysis was undergone for the 1984 to 2015 images (including seasonal approach), being supported by the interpreted land cover/land use map for error estimation. Considering also biophysical and socioeconomic data, comprehensive results are discussed. One of the most important aspects that emerged was that the accelerated expansion of agricultural land in the last three decades has led and continues to contribute to a secondary salinization of soils.
2017
Change detection; Decision tree; Land cover mapping; Land degradation; Landsat; Salinization; Earth and Planetary Sciences (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/209139
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