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Analyze of the socio-economic drivers of land use land cover changes in Western Ethiopia
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Abstract
A variety of socioeconomic and environmental factors have contributed to changes in LULC around the world in recent years. This study examines the socioeconomic factors that accelerated LULC in Western, Ethiopia. Data was generated from landsat images and through both primary and secondary sources. Primary data include household survey, field observation, group discussion; key informants’ and interviews. Landsat images classified with supervised classification technique and maximum likelihood classifier through arc GIS 10.3 to develop LULC maps of the study area. Accuracy assessment and kappa coefficient were used to approve the accuracy of the classified LULC, and farm land, settlement, bare land, forest land, and water body were the major LULC classes in the District. Forest cover in three decades (1990–2020) decreased from 12.1% in 1990 to 2.6% in 2020 in the study area. Binary logistic regression model examined the relationship between the (dependent) and the main socioeconomic (independent) variables. A logistic regression was performed to ascertain how independent variables and the driving forces for LULC change (Natural forces or anthropogenic forces) and the model was statistically significant (x2 = 23.971, df = 5, P < 0.001).The model explained 13.9% (Nagelekerke R2) of the variance in the driving forces for LULC dynamics and correctly classified 66.1% of cases. The study identified Age, Gender, Educational status and landholding sizes significantly determine driving forces for LULC dynamics and have the greatest chance to choice the anthropogenic forces. Thus relevant stakeholders should take integrated actions to decrease the driving forces for LULC dynamics through restoration of landscape.
Title: Analyze of the socio-economic drivers of land use land cover changes in Western Ethiopia
Description:
Abstract
A variety of socioeconomic and environmental factors have contributed to changes in LULC around the world in recent years.
This study examines the socioeconomic factors that accelerated LULC in Western, Ethiopia.
Data was generated from landsat images and through both primary and secondary sources.
Primary data include household survey, field observation, group discussion; key informants’ and interviews.
Landsat images classified with supervised classification technique and maximum likelihood classifier through arc GIS 10.
3 to develop LULC maps of the study area.
Accuracy assessment and kappa coefficient were used to approve the accuracy of the classified LULC, and farm land, settlement, bare land, forest land, and water body were the major LULC classes in the District.
Forest cover in three decades (1990–2020) decreased from 12.
1% in 1990 to 2.
6% in 2020 in the study area.
Binary logistic regression model examined the relationship between the (dependent) and the main socioeconomic (independent) variables.
A logistic regression was performed to ascertain how independent variables and the driving forces for LULC change (Natural forces or anthropogenic forces) and the model was statistically significant (x2 = 23.
971, df = 5, P < 0.
001).
The model explained 13.
9% (Nagelekerke R2) of the variance in the driving forces for LULC dynamics and correctly classified 66.
1% of cases.
The study identified Age, Gender, Educational status and landholding sizes significantly determine driving forces for LULC dynamics and have the greatest chance to choice the anthropogenic forces.
Thus relevant stakeholders should take integrated actions to decrease the driving forces for LULC dynamics through restoration of landscape.
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