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<p>Understanding change in land use land cover (LULC) and their implications is vital for effective land management and for mitigating negative impacts on future use. Various efforts have been made to restore Ethiopia’s forest cover through afforestation programs over the last 50 years, including the Wof-Washa Forest area, Amhara Region, Ethiopia. These efforts are expected to contribute to LULC changes and their associated impacts. However, little is known about the dynamics and implications of these efforts. Therefore, this study aimed to analyze the drivers of LULC dynamics and their implications for the Wof-Washa Forest areas between 1973 and 2022. The main input data for this study were obtained from remotely sensed images and the Normalized Difference Vegetation Index (NDVI) data derived from Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Landsat Operational Land Imager (OLI) datasets. The geospatial data were supplemented by household, focus group discussions and key-informant interviews data collected through survey. The geospatial data were analyzed using geographic information systems (GIS) techniques, primarily through machine learning (ML) based Random Forest (RF) algorithm analysis. NDVI analysis was used to validate the results of the RF analysis. The quantitative and qualitative socioeconomic survey data were analyzed using descriptive statistics and thematic analysis respectively. The analysis results revealed a net increases 3455&#xa0;ha of forest cover between 1973 and 2022. NDVI analysis indicate a shift from − 0.7 to 0.8 in 1973, to 0.025–0.6 in 2022, reflecting moderate to high vegetation cover and reduced degradation as negative values were no longer observed. However, forest cover change was non-linear with interim losses of 409&#xa0;ha (2%) between 1973 and 1985, 0.230&#xa0;ha (1.0%) between 1985 and 2000, and a significant decline of 951&#xa0;ha (4.2%) between 2000 and 2022, reflecting fluctuating forest dynamics. These observed LULC dynamics were driven by both underlying and proximate causes. Government policies and development interventions promoting afforestation and the conservation of forest remnants drove increases in forest cover, while harvesting activities contributed to declines. Regular monitoring and analysis of LULC using robust GIS methods such as the RF technique and high resolution remote sensing data is essential to generate evidence-based insights for effective decision-making. Promoting alternative energy sources can reduce dependence on biomass and ease pressure on the reaming forests. Additionally, preventing agricultural encroachment into forests and promoting eco-tourism through park establishment could support sustainable resource use, particularly in restoring and sustaining the forest cover.</p>

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Drivers and implications of land use land cover change in Wof–Washa forest and its surroundings, Amhara region, Ethiopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • Aklilu Assefa Hake,
  • Shimeles Damene,
  • Almaz Deche

摘要

Understanding change in land use land cover (LULC) and their implications is vital for effective land management and for mitigating negative impacts on future use. Various efforts have been made to restore Ethiopia’s forest cover through afforestation programs over the last 50 years, including the Wof-Washa Forest area, Amhara Region, Ethiopia. These efforts are expected to contribute to LULC changes and their associated impacts. However, little is known about the dynamics and implications of these efforts. Therefore, this study aimed to analyze the drivers of LULC dynamics and their implications for the Wof-Washa Forest areas between 1973 and 2022. The main input data for this study were obtained from remotely sensed images and the Normalized Difference Vegetation Index (NDVI) data derived from Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Landsat Operational Land Imager (OLI) datasets. The geospatial data were supplemented by household, focus group discussions and key-informant interviews data collected through survey. The geospatial data were analyzed using geographic information systems (GIS) techniques, primarily through machine learning (ML) based Random Forest (RF) algorithm analysis. NDVI analysis was used to validate the results of the RF analysis. The quantitative and qualitative socioeconomic survey data were analyzed using descriptive statistics and thematic analysis respectively. The analysis results revealed a net increases 3455 ha of forest cover between 1973 and 2022. NDVI analysis indicate a shift from − 0.7 to 0.8 in 1973, to 0.025–0.6 in 2022, reflecting moderate to high vegetation cover and reduced degradation as negative values were no longer observed. However, forest cover change was non-linear with interim losses of 409 ha (2%) between 1973 and 1985, 0.230 ha (1.0%) between 1985 and 2000, and a significant decline of 951 ha (4.2%) between 2000 and 2022, reflecting fluctuating forest dynamics. These observed LULC dynamics were driven by both underlying and proximate causes. Government policies and development interventions promoting afforestation and the conservation of forest remnants drove increases in forest cover, while harvesting activities contributed to declines. Regular monitoring and analysis of LULC using robust GIS methods such as the RF technique and high resolution remote sensing data is essential to generate evidence-based insights for effective decision-making. Promoting alternative energy sources can reduce dependence on biomass and ease pressure on the reaming forests. Additionally, preventing agricultural encroachment into forests and promoting eco-tourism through park establishment could support sustainable resource use, particularly in restoring and sustaining the forest cover.