It has known that grain production is declining globally, leading to food insecurity becoming increasingly apparent in tropical countries, particularly in Sub-Saharan Africa. Countries in Sub-Saharan Africa must concentrate on indigenous agricultural methods to mitigate the impact of climate change on grain production while preserving ecological balances and achieving sustainable goals. Matengo/Ngolo pits, practised on steep slopes in the Matengo highlands, southern Tanzania, are indigenous knowledge invented by local communities over the past 300 years. Despite its effectiveness in increasing agricultural productivity, soil moisture retention, and other environmental advantages, Matengo/Ngolo agricultural technique has resulted in severe land cover changes that substantially influence other producing sectors. Understanding the agro-ecological zones is essential for enhancing policy development for the expansion and restrictive of Matengo/Ngolo pits practice that intercepting by decreasing its influence on the shrinkage of other ecological services, achieving sustainable agricultural practice in the Matengo highlands. Therefore, this study employed the multi-criteria parameters under the fuzzy logic algorithm in ArcGIS 10.8 for modelling the Matengo/Ngolo pits agro-ecological zone to realize sustainable land management in Matengo highlands.
As countries in Sub-Saharan Africa strive to reduce deforestation in Miombo woodlands, it is essential to use the appropriate, reliable, and cost-effective tools for assessing land cover changes. This study employed Remote Sensing and GIS techniques to assess land use and its changes in the Litumbandyosi-Gesimasowa Game Reserve between 1990 and 2020. The tools employed were GEE and Collect Earth. The study employed Sentinel-2 and Landsat-5 TM imagery and also incorporated the Atmospheric Resistant Vegetation Index (ARVI) for improving classification by overcoming the effects of Non-Photosynthetic Vegetation (NPV) and phenology. The study produced highly accurate land cover maps, with an overall accuracy of 99.53% and a kappa coefficient of 98.11% in 1990, 99.84% and a kappa coefficient of 98.69% in 2011, and 92.10% and an 89.62% kappa coefficient in 2020. The findings of the post-classification revealed an alarming change in land cover over the last 30 years, with heavy forestland decreasing by 10.77%, shrubland increasing by 12.19%, and grassland increasing by 13.35%. Furthermore, farmland expanded by 4.58%, barren land grew by 5.82%, and wetlands grew by 0.74%. Significant agents of change have been identified as forest fires, overgrazing, crop farming, and mining.
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