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.

Assessing desertification vulnerability and mitigation strategies in northern Nigeria: A comprehensive approach.

Desertification constitutes a grave threat to the environmental and socio-economic stability of desertification frontline states in Northern Nigeria. From 2003 to 2020, this research comprehensively analyzes desertification vulnerability, integrating parameters such as NDVI, LST, TVDI, MSAVI, and Albedo. Key factors contributing to land degradation are identified, along with the spatial patterns and trends of desertification over the two-decade period. The consequences are profound, with Northern Nigeria's ecosystem experiencing a steady decline in vegetation cover. Agriculture, vital to the region's economy, faces increased aridity and reduced arable land, jeopardizing food security. Diminishing water resources exacerbates scarcity issues, placing additional strain on communities. These environmental changes lead to severe socio-economic implications, including displacement, loss of livelihoods, and heightened vulnerability to climate-related risks. Urgent, comprehensive, and strategic interventions are imperative. Policy recommendations underscore revising and enforcing land use regulations, promoting sustainable agricultural practices, and establishing monitoring systems to guide decision-making. This research contributes practical strategies to enhance the resilience of desertification frontline states, safeguard livelihoods, and align with Nigeria's sustainable development objectives. Findings from the study indicate that only a tiny percentage (6.7 %) of the study area remains unaffected by desertification. Moreover, 13.3 % exhibit light vulnerability, 20 % demonstrate moderate exposure, and 60 % fall into the severe (26.7 %) and compelling (33.3 %) vulnerability categories. These statistics underscore the gravity of desertification in the study area, emphasizing the urgent need for effective mitigation measures to address its impact comprehensively.

Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing.

Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002-2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983-2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia.

With the progress of urbanization, atmospheric pollution and physical health issues caused by the increase of aerosol optical depth (AOD) become more and more prominent. Hence, population exposure risk to AOD becomes a research hotspot. The arid Central Asia (ACA) has a generally high AOD and is a major source area for dust aerosols in the world. Only few studies have discussed population exposure risk to AOD in ACA. Based on multisource remote sensing data, and used population exposure risk model, this study evaluated population exposure risk to AOD in six ecological zones (Northern steppe region of ACA (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert area (JBDA), Tarim Basin desert area (TBDA) and Hexi corridor desert area (HCDA)). Generally, AOD in ACA was kept increasing from 2000 to 2015, and it increased mostly in HCDA and areas near the Aral Sea (p < 0.001). With respect to seasonal variations, the maximum AOD was observed in spring and autumn, and the minimum was in winter. Considering land use changes, AOD was mainly manifested by the reduction of water bodies and expansion of construction lands. This was the mostly significant in NSCA and ASDA (p < 0.01). The population exposure risk to AOD in ACA was increasing continuously from 2000 to 2015, and high-value regions (>9) concentrated in oases, specifically, in the Aral Sea basin and Tarim River basin.The Aral Sea basin became the major AOD source region in ACA due to the shrinking water area after unreasonable development and utilization of water resources. These further increase population exposure risk to AOD in the Aral Sea area. Hence, ecological restoration in terminal lakes of ACA will become the key to lower population exposure risk to AOD practically.

Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa.

Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km2) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors.