Quantifying land change dynamics, resilience and feedback: A comparative analysis of the lake Chad basin in Africa and Aral Sea basin in Central Asia.

The intricate interaction of natural and anthropogenic factors drives changes in land and water in response to societal demands and climate change. However, there has been insufficient information on the feedback effects in dryland hotspots altered by land change dynamics. This research compared two transboundary inland lakes, the Lake Chad basin (LCB) in Africa and the Aral Sea basin (ASB) in Central Asia, using remote sensing and geographic information system techniques to analyze and quantify present and future land cover dynamics, resilience, and their feedback effects. The study integrated Cellular Automata, Markov Chain, and Multilayer Perceptron models to predict LULC changes up to 2030. Descriptive statistics, ordinary least squares regression, hotspot Gi-Bin, trend analysis, and advanced geostatistical methods were utilized to identify relationships, patterns, magnitudes, and directions of observed changes in the feedback effects. From 2000 to 2030, the analysis unveils intriguing trends, including an increase in cropland from 48% to 51% and a decrease in shrubland from 18% to 15% in the LCB. The grassland increased from 21% to 22%, and the settlement expanded from 0.10 to 0.60% in the ASB. Water bodies remained stable at 1.60 % in LCB, while in ASB, it declined from 3% to 2%. These changes were significantly influenced by population, elevation, and temperature in both basins, with irrigation also playing a significant role in the ASB and slope in LCB. The study further revealed discernible shifts in normalized difference vegetation index, temperature, and precipitation linked to specific land cover conversions, suggesting alterations in surface properties and vegetation health. This study underscores the complex interplay between land cover dynamics, resilience, climate variability, and feedback mechanisms in LCB and ASB.

Remote sensing approach in evaluating anthropogenic impacts on the spatiotemporal changes in net primary productivity of the Niger river basin, from 2000 to 2020.

Deterioration of the environment can be examined by utilizing a statistical evaluation of the effects of anthropogenic activities (beneficial or detrimental) on net primary productivity. The Niger River Basin's net primary productivity is significant both theoretically and practically for the management of the natural environment. It is important for her member countries to understand vegetation dynamics, maintain carbon balance, and ensure food security in the region. The research applied remote sensing to determine the relative impact of human activities on the net primary productivity of the Niger River Basin from 2000 to 2020. The study simulated the actual and potential net primary productivity using the Carnegie Ames Stanford Approach and Thornthwaite's Memorial Model respectively, while the result of the simulations was used to calculate human-influenced net primary productivity. The slope of the three simulations was calculated and merged in several scenarios using ArcGIS 10.8 to determine the impact of human activities on net primary productivity of the study area. The negative impacts of human activities were recorded in 89.88 % of the investigated area, while 10.12 % of the NRB had signs of positive impacts. Amongst the biomes, urban areas and bare land experienced the largest negative impacts (97.2 % and 99.8 %, respectively). The study advised the effectiveness of ecological restoration programs, through sound scientific and technical methods, such as those used in rural development, nomadic herding, environmental protection, and natural resource management policies.