Multi source Earth observation and machine learning framework for mapping land degradation vulnerability in salinity- and hydrothermal-stressed regions of the southern main Ethiopian Rift
9 affiliations
- 1Department of Remote Sensing, Space Science and Geospatial Institute, Addis Ababa, Ethiopia
- 2Department of Geology, College of Natural and Computational Sciences, Arba Minch University, Arba Minch, Ethiopia
- 3Department of Physics, College of Natural and Computational Sciences, Wolaita Sodo University, Sodo, Ethiopia
- 4Geography and Environmental Studies, Kotebe University of Education, Addis Ababa, Ethiopia
- 5Department of Environmental Science, College of Natural and Computational Sciences, Wolaita Sodo University, Sodo, Ethiopia
- 6Department of Biology, College of Natural and Computational Sciences, Wolaita Sodo University, Sodo, Ethiopia
- 7Wolaita Sodo University, Sodo, Ethiopia
- 8Space Science and Geospatial Institute, Addis Ababa, Ethiopia
- 9Department of Plant Science, College of Agricultural Sciences, Arba Minch University, Arba Minch, Ethiopia
Abstract
Soil erosion and land degradation are the main environmental challenges disturbing ecosystem services, agricultural productivity, and community livelihoods in Ethiopia. The Southern Main Ethiopian Rift is one of the regions experiencing high levels of soil erosion and land degradation, driven by active geological processes and anthropogenic activities. However, the contribution of these geological processes to salinization and their subsequent impact on soil erosion and land degradation in the region remains poorly understood and insufficiently studied. This study was conducted to assess and map the spatial patterns of soil erosion and land degradation vulnerability in the salinized and hydrothermally stressed region of the Southern Main Ethiopian Rift using integrated multi-source earth observation data and machine learning frameworks. Landsat, Sentinel-2, SRTM DEM, CHIRPS rainfall data, soil data, and field observations were integrated with Google Earth Engine and GIS platforms to analyze existing biological, chemical, and physical indicators of land degradation. Land degradation indicators like soil erosion, land use/land cover (LULC), soil-adjusted vegetation index (SAVI), Normalized Multi-Band Drought Index (NMDI), land surface temperature (LST), salinity, and soil erosion were computed using respective approaches and integrated to produce a land degradation vulnerability map of the study area. The results show that large portions of the study area are moderately to highly vulnerable to land degradation. The major driver of land degradation in the study area is salinization, which increases surface runoff and sediment removal through surface crusting. The annual soil loss values for the area range from 25 to 100 t ha-1 yr-1, with about 10.69% of the area under moderate to very severe erosion risk. Accordingly, the land degradation vulnerability map shows that 1.68%, 50.15%, 38.14%, 6.31%, and 3.72% of the study area fall into the very low, low, moderate, high, and very high vulnerability categories, respectively. The land degradation vulnerability (LDV) model demonstrated strong validation performance, with an overall accuracy of 92.34%, a Kappa coefficient of 0.87, an F1-score of 0.948, and an ROC-AUC of 0.901. These validation results demonstrate the accuracy and reliability of the LDV model for mapping land degradation vulnerability. The findings indicate that soil salinity, surface soil loss, and elevated land surface temperature, together with anthropogenic pressures, are the primary drivers of high land degradation risk in the study area.
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BibTeX
@article{reda2026multi,
title = {{Multi source Earth observation and machine learning framework for mapping land degradation vulnerability in salinity- and hydrothermal-stressed regions of the southern main Ethiopian Rift}},
author = {Talema Moged Reda and Alemu Tadese and Wondimu Haimanote and Muralitharan Jothimani and Asmamaw Hangibayna and Solomon Gunta and Gizachew Kabite Wedajo and Berhan Gessesse and Admasu Adamu and Zewdneh Tomass and Guchie Gulie and Abdisa Yilma and Dereje Tsegaye},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0359349},
url = {https://doi.org/10.1371/journal.pone.0359349},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi source Earth observation and machine learning framework for mapping land degradation vulnerability in salinity- and hydrothermal-stressed regions of the southern main Ethiopian Rift
AU - Talema Moged Reda
AU - Alemu Tadese
AU - Wondimu Haimanote
AU - Muralitharan Jothimani
AU - Asmamaw Hangibayna
AU - Solomon Gunta
AU - Gizachew Kabite Wedajo
AU - Berhan Gessesse
AU - Admasu Adamu
AU - Zewdneh Tomass
AU - Guchie Gulie
AU - Abdisa Yilma
AU - Dereje Tsegaye
PY - 2026
DA - 2026/09/24
JO - PLOS One
DO - 10.1371/journal.pone.0359349
UR - https://doi.org/10.1371/journal.pone.0359349
AB - Soil erosion and land degradation are the main environmental challenges disturbing ecosystem services, agricultural productivity, and community livelihoods in Ethiopia. The Southern Main Ethiopian Rift is one of the regions experiencing high levels of soil erosion and land degradation, driven by active geological processes and anthropogenic activities. However, the contribution of these geological processes to salinization and their subsequent impact on soil erosion and land degradation in the region remains poorly understood and insufficiently studied. This study was conducted to assess and map the spatial patterns of soil erosion and land degradation vulnerability in the salinized and hydrothermally stressed region of the Southern Main Ethiopian Rift using integrated multi-source earth observation data and machine learning frameworks. Landsat, Sentinel-2, SRTM DEM, CHIRPS rainfall data, soil data, and field observations were integrated with Google Earth Engine and GIS platforms to analyze existing biological, chemical, and physical indicators of land degradation. Land degradation indicators like soil erosion, land use/land cover (LULC), soil-adjusted vegetation index (SAVI), Normalized Multi-Band Drought Index (NMDI), land surface temperature (LST), salinity, and soil erosion were computed using respective approaches and integrated to produce a land degradation vulnerability map of the study area. The results show that large portions of the study area are moderately to highly vulnerable to land degradation. The major driver of land degradation in the study area is salinization, which increases surface runoff and sediment removal through surface crusting. The annual soil loss values for the area range from 25 to 100 t ha-1 yr-1, with about 10.69% of the area under moderate to very severe erosion risk. Accordingly, the land degradation vulnerability map shows that 1.68%, 50.15%, 38.14%, 6.31%, and 3.72% of the study area fall into the very low, low, moderate, high, and very high vulnerability categories, respectively. The land degradation vulnerability (LDV) model demonstrated strong validation performance, with an overall accuracy of 92.34%, a Kappa coefficient of 0.87, an F1-score of 0.948, and an ROC-AUC of 0.901. These validation results demonstrate the accuracy and reliability of the LDV model for mapping land degradation vulnerability. The findings indicate that soil salinity, surface soil loss, and elevated land surface temperature, together with anthropogenic pressures, are the primary drivers of high land degradation risk in the study area.
SN - 1932-6203
ER -