article · Global Challenges
Satellite imagery from Landsat 4, 5, 7, and 8 was analysed using a random forest algorithm to track land use and land cover shifts in the Upper Blue Nile River Basin between 1983 and 2022. The landscape was categorised into seven classes: cultivated lands, bare lands, built-up areas, forests, grazing lands, shrublands, and waterbodies. The classifications achieved agreement accuracies of 83, 85, and 91 percent according to the Kappa index. Over the evaluated period, agricultural lands expanded by 47,541 square kilometres and built-up regions grew by 1,777 square kilometres, driven primarily by population growth. This expansion occurred through the depletion of forests, shrublands, and grazing areas. Concurrently, water bodies increased by 662 square kilometres due to the construction of hydroelectric and irrigation dams.
Long-term monitoring of land cover dynamics provides essential evidence for tackling food security, habitat loss, and climate change. Quantifying the rate at which agriculture and settlement encroach upon forests and shrublands allows regional planners to design better conservation measures, manage vital water resources, and establish sustainable development strategies in sensitive river basins.
The demonstrated methodology operates as an applied remote sensing workflow suitable for environmental consultancies, agricultural planning bodies, and water resource authorities. While the underlying research remains at an applied analytical stage rather than a packaged software tool, it offers an established basis for developing automated land monitoring services and decision-support systems for infrastructure planning.
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Monitoring land use change dynamics is critical for tackling food security, climate change, and biodiversity loss on a global scale. This study is designed to classify land use and land cover in the upper Blue Nile River Basin (BNRB) using a random forest (RF) algorithm. The Landsat images for Landsat 45, Landsat 7, and Landsat 8 are used for classification purposes. The study area is classified into seven land use/land cover classes: cultivated lands, bare lands, built-ups, forests, grazing lands, shrublands, and waterbodies. The accuracy of classified images is 83%, 85%, and 91% using the Kappa index of agreements. From 1983 to 2022 periods, cultivated lands and built-up areas increased by 47541 and 1777 km2, respectively, at the expense of grazing lands, shrublands, and forests. Furthermore, the area of water bodies has increased by 662 km2 due to the construction of small and large-scale irrigation and hydroelectric power generation dams. The main factors that determine agricultural land expansion are related to population growth. Therefore, land use and land cover change detection using a random forest is an important technique for multispectral satellite data classification to understand the optimal use of natural resources, conservation practices, and decision-making for sustainable development.
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DOI: 10.1002/gch2.202300155
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