Mohammed Sadek

Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and state-of-the-art remote sensing products and methodologies to address the issue of random urban expansion, which negatively impacts environmental sustainability. The study aims (1) to investigate the land-use/land-cover (LULC) changes over the past 27 years, and to simulate the future LULC dynamics over Gharbia; and (2) to produce an Urbanization Risk Map in order for the decision-makers to be informed of the districts with priority for sustainable planning. Time-series Landsat images were utilized to analyze the historical LULC change between 1991 and 2018, and to predict the LULC change by 2033 and 2048 based on a logistic regression–Markov chain model. The results show that there is a rapid urbanization trend corresponding to a diminution of the agricultural land. The agricultural sector represented 91.2% of the total land area in 1991, which was reduced to 83.7% in 2018. The built-up area exhibited a similar (but reversed) pattern. The results further reveal that the observed LULC dynamics will continue in a like manner in the future, confirming a remarkable urban sprawl over the agricultural land from 2018 to 2048. The cultivated land changes have a strong negative correlation with the built-up cover changes (the R² were 0.73 in 1991–2003, and 0.99 in 2003–2018, respectively). Based on the Fuzzy TOPSIS technique, Mahalla Kubra and Tanta are the districts which were most susceptible to the undesirable environmental and socioeconomic impacts of the persistent urbanization. Such an unplanned loss of the fertile agricultural lands of the Nile Delta could negatively influence the production of premium agricultural crops for the local market and export. This study is substantial for the understanding of future trends of LULC changes, and for the proposal of alternative policies to reduce urban sprawl on fertile agricultural lands.

In the modern era, vegetation dynamics is an important aspect of climate change studies. The present study examined spatiotemporal changes of (NDVI) normalized difference vegetation index in the Korama basin (Southern Zinder of Niger) from 2000 to 2018, and their correlation with climatic factors was predicted. To analyze the change of vegetation cover, geographical information system, MODIS_NDVI, remote sensing, and climate variables (e.g., temperature and precipitation) datasets were used. Further, the correlation was performed for different years of vegetation types during the growing season (June–October). Our results show an increasing trend in average maximum annual NDVI across the Korama River Basin in the years 2000 and 2018. Conversely, significantly increasing trends in most of the areas were reported. Moreover, in downstream the vegetation cover is increased in Matameye and Magaria, but with a smaller increase in the upstream rate in Mirriah. Furthermore, a decrease in the surface water was observed in the Tessaoua, Matameye, and Magaria sections of the study region in 2000 and 2018, while a rise in water surface area was observed in Matameye and Magaria in the years 2006 and 2012. During rainy and dry seasons, NDVI correlated differently with temperature and precipitation with strong seasonal variations, while the mean vegetation period of NDVI does not show any significant change. In addition, moderate increase was observed in years 2000 and 2012 (r: 0.22; P: 0.50; R²: 0.05; r: 0.31; P: 0.34, R²: 0.10, respectively), and weak decrease in 2006 and 2018 (r: 0.61; P: 0.04; R²: 0.37; r: 0.58; P: 0.06, R²:0.33, respectively). The analysis indicates that climatic parameters such as precipitation and temperature are the main limiting factors affecting the vegetation growth. Indeed, the trends calculated by the correlation analysis showed that as climate factors increased (July, August, and September), the NDVI value increased at a rate of 0.16, reflecting the best growth in vegetation and rise in water bodies, although significantly decreased during years. This study would be highly useful in choice-making for sustainable water resource management in the Korama watershed in Southern Zinder, Niger.

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.

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.

Natural hazards are indeed counted as the most critical challenges facing our world, represented in floods, earthquakes, volcanoes, hurricanes, and forest fires. Among these natural hazards, the flash flood is regarded the most frequent. In this work, we utilized two Sentinel‐2 satellite images, before and after the flash flood, SRTM and photos captured by using a helicopter. This paper aims at three prime objectives. Firstly, the flood influence is determined on the city of Ras Ghareb, Egypt, based on analyzing free satellite data (Sentinel‐2 images). Secondly, fuzzy the analytical hierarchy process (FAHP) method and a geographical information system (GIS) are integrated for flood risk analysis and evaluation in the flood‐prone area. Finally, such a flood vulnerability map is used as an index to assist the decision‐makers prepare for probable flooding. FAHP is preferable as it can cater to the uncertainties in data and analysis. As a result, FAHP is appropriate to determine the flood‐vulnerable area in cities especially due to the matching with the most destroyed areas identified by the change detection between the two Sentinel‐2 images. Then, the decision‐maker can depend on Sentinel‐2 images to estimate the flood influence through a regional scale or applying the FAHP on cities susceptible to flash floods in case of unavailable satellite images to contribute in establishing an early warning system enough to the evacuation of the risky areas.