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The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

Land use/land cover change (LUCC) and climate changes are responsible for degradation of any ecosystem in arid and semi-arid regions. Studying the ecological variations is particularly essential for any type of sustainable development, in which LUCC considers as one of the chief inputs. The main objective is to evaluate the impacts of LUCC and climatic changes on the Ecosystem Vulnerability (ESV) using remote sensing and some statistical models around the Yellow River, Ningxia, China. Eleven classes of LUCC were identified during 1995 and 2019: village land, bare land, grassland, industrial land, irrigated land, swamp land, tidal flat, transportation land, urban land, water bodies, and water channels. Grassland may be decreased annually with percentage − 5.873% due to some human activities and environmental changes in climate from one season to another. About 24.23 km2 and 24.86 km2 was converted from grassland to industrial lands and irrigated lands, respectively. ESV has been calculated using LULC, DEM, slope, soil, and geology. About 45% and 60% of 1995 and 2019, respectively, undergone moderate vulnerability. The annual rate of ESVI decreased in low and reasonable but it was increased in moderate, high, and extreme showing – 4.166% as a total percentage of annual vulnerability. High vulnerability area needs proper management. Majority of vegetation area is located in zone under the moderate vulnerability zone; in contrast, grasslands were subjected to high vulnerability. Areas around the Yellow River were subjected to drought and flooding due to climatic change affecting negatively on the production of crops. Also, the desert lands of the study area have been turned to agriculture according to statistical model. Population growth, industrial development, and governmental policies for ecosystem protection were responsible for major changes. This study is more beneficial for decision-making in eco-environmental protecting and planning. Results of this study could help planners in formulating effective strategies for better management of ecosystem.

Flash floods are among the most common natural hazards in Egyptian and Arabian deserts. In this work, we utilized two Sentinel-1 and Sentinel-2 satellite images, before and after the flash flood, SRTM, and geolocated terrestrial photos captured by volunteers. This paper aims to three substantial objectives: (1) monitoring the flash flood impacts on Wadi El-Natrun region based on free satellite data and mapping the destroyed vegetation cover; (2) the integration of the free remote sensing data, geolocated terrestrial photos, and GIS techniques, along with hydrologic and hydraulic modeling, to evaluate the impact of flash flood hazards on the study area; and (3) assistance of the decision-makers in planning the required protective works to avoid the probable flooding. Two scenarios have been applied to estimate the flash flood effect. The first scenario has relied on Sentinel-1/2 data fusion before and after the flash flood, while the second scenario has been implemented based on the integration of the Sentinel-2 images and hydrologic and hydraulic flood modeling with the help of ArcGIS software to simulate the flash flood route. The results demonstrated that although the first scenario is an efficient solution for continuous monitoring of the change in the water bodies, it is limited in the detection of the submerged vegetation area. On the other hand, the second scenario provided the flash flood route and hydrological parameters, which determine the hazard degree of the basins, thus helping the decision-maker to manage the flood risk. Moreover, the second scenario surpasses the first one by estimating the destroyed infrastructure. Consequently, the second scenario is appropriate to assess the flash flood impacts and mitigate its influence in the future.

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