ArticleEnvironmental earth sciences2022
Mapping potential desertification-prone areas in North-Eastern Algeria using logistic regression model, GIS, and remote sensing techniques.
Article in Environmental earth sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Desertification is an environmental threat that affects many countries in the world, and it poses specially an ecological issue to Algeria. This study aimed to assess areas sensitive to desertification in North-Eastern Algeria (Tebessa province) using a logistic regression model (LRM), and geomatics-based approaches. Topsoil Grain Size Index (TGSI), Normalized Difference Vegetation Index (NDVI), Aridity index (AI), and Anthropic pressure on the steppe environment (APSE) were selected as desertification indicators for representing land surface conditions from soil, vegetation, climate, and anthropic disruptors. Results indicate that both AI and TGSI are the most crucial indices conditioning desertification risk. Other indices; NDVI and ASPE were appeared as secondary important indices. Herein, although vegetation generally is a key factor for reading desertification, this result shows that vegetation changes in this study are less important than other desertification conditioning parameters. Area under curve value equal 0.94 indicates a satisfactory accuracy for the proposed model. In total, desertification risk changes increasingly along a North-to-South gradient of the whole research area. Besides, slight, moderate, high, and very high classes occupied 0.87%, 21.08%, 19.33% and 58.72% of the total land area, respectively. LRM is recommended as an accurate and easily applied tool to monitor desertification, especially in scarce data environment in developing countries. Additionally, the results obtained in this paper represent a basic scientific tool for implementing current and future policies to control desertification at areas with high risk.
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