Exploring drivers of land use/land cover transformations in Goang watershed Ethiopia: Integrating local community perceptions with remote sensing data

Land use/land cover (LULC) changes driven by human activities are major environmental challenges in many developing regions. This study assessed local community perceptions and household-level drivers of LULC change in northwest Ethiopia. The study combined remote sensing analysis of LULC change with household surveys to understand local perspectives and the socioeconomic, biophysical, and institutional factors influencing household engagement in deforestation activities. The household survey data was collected from 384 randomly selected household heads, as well as from focus group discussions and key informant participants. The remote sensing component used ERDAS IMAGINE 2014 software to classify satellite images and assess LULC changes over time. The quantitative socio-economic data was analyzed using descriptive statistics and econometric methods of the multivariate probit (MVP) model. The study revealed that population growth, poverty, and food insecurity were the major proximate driving factors, while agricultural land expansion, settlement growth, fuelwood collection, overgrazing, and forest fires were the major underlying causes of LULC change. The MVP model result indicated that gender, off-farm income, access to training, family size, educational level, and agroecology were key determinants of households’ involvement in deforestation drivers such as forestland clearing for agriculture, fuelwood collection, timber harvesting, and overgrazing. These findings highlight the need for integrated land use policies and programs that address the socioeconomic and biophysical root causes of LULC change. This study recommends supporting alternative livelihoods, promoting fuel-efficient technologies, and tailoring interventions to different agroecological contexts of sustainable land use planning and natural resource management in the study site. Future studies should compare perceptions and drivers of land use change across different regions to identify common patterns and unique challenges. This approach will enhance understanding and inform targeted interventions for sustainable land management.