Socioeconomic Drivers of Land Use and Land Cover Change in Western Ethiopia

Abstract

A variety of socioeconomic and environmental drivers have contributed to changes in LULC around the world in recent years. This study examines the socioeconomic drivers that accelerated LULC in western Ethiopia. The data were generated from terrestrial satellite images primary and secondary sources. Primary data sources include household surveys, field observations, group discussions, interviews, key informants, and interpreting remote sensing data. Secondary data were reviewed mainly from relevant literature both published and unpublished materials. Landsat images were classified using the supervised classification technique and maximum likelihood classifier using arc GIS 10.3 to create LULC maps of the study area. Accuracy score and kappa coefficient were used to confirm the accuracy of the classified LULC, and agricultural land, settlement, bare land, forest land, and water body were the main LULC classes in the district. Forest cover in three decades (1990–2020) in the study area decreased from 12.1% in 1990 to 2.6% in 2020. The data were also analyzed using a descriptive model, Pearson correlation, and binary logistic regression. The independent variables (age and gender) show a Pearson’s positive correlation with the drivers of LULC dynamics; that is, as these independent variables increase, the drivers of LULC dynamics also increase, whereas educational status and land holding size show a negative correlation. This shows that the drivers of the anthropogenic forces of LULC dynamics decreased as the number of educated populations and the size of land holdings increased, and vice versa. Then, the binary logistic regression model examined the relationship between the dependent and the major socioeconomic (independent) variables. Logistic regression was performed to determine how independent variables and the drivers of LULC (natural forces or anthropogenic forces) change and the model was statistically significant (x2 = 23.971, df = 5, $P$ < 0.001). The model explained 13.9% (Nagelkerke R2) of the variance in the drivers of LULC dynamics and correctly classified 66.1% of the cases. The study found that age, gender, and educational status largely determine the drivers of LULC dynamics and have the greatest chance of determining the anthropogenic forces. Therefore, relevant stakeholders should take integrated measures to reduce the drivers of LULC dynamics through landscape restoration.