Major driving forces can alter Land use/Land cover (LULC) dynamics and affect landscape sustainability around the Nile Delta of Egypt. The present study aims at evaluating and mapping changes in LULC and assessing the dynamics of LULC and Land Surface Temperature (LST) around the two branches of the Nile Delta, Egypt using Landsat data and GIS. Calibrated Landsat images were acquired on 2000, 2014 and 2019 and processed to produce LULC, environmental indices and LST, respectively, using ENVI 5.3. ArcGIS 10.1 was used to extract a transition map from 2000 to 2019 around the two branches. The results displayed that five classes of LULC were extracted around Damietta and Rosetta branches; water, urban, bare, dense and spare vegetation. A continuous increase in water was recorded around Damietta branch; 13.66 km² (197%), 14.21 km² (2.04%) and 16.54 km² (2.30%) in 2000, 2014 and 2019, respectively. Also, urban area was increased around Damietta and Rosetta branch as follows: 53.6 km² (7.72%), 58.34 (8.37%) and 90.37 km² (13.70%) in 2000, 2014 and 2019, 59.55 km² (6.809%), 104.16 (11.90%) and 149.77 km² (17.11%) in 2000, 2014 and 2019, respectively. Urban achieved the highest gain of 24.807 and 85.70 km² at the expense of dense vegetation around Damietta and Rosetta branch, respectively. The results showed that the decrease in vegetation and the increase in urban density lead to increasing LST of the study area. The changes in LST can be monitored depending on the construction materials such as the presence of green areas and topography. Urban and bare lands have the highest LST while the water bodies and vegetation temperature showed a tendency to decrease. It can be concluded that urban areas increased with annual rate 0.27 and 0.54 km² and vegetation decreased with annual rate −0.57 and−0.55 km² around Damietta and Rosetta branches from 2000 to 2019. Results showed that comprehensive index was 321.14 and 330.03 around Damietta and Rosetta branch, the higher the degree of development and exploitation. There has been a significant land use change which was due to an increase in population. Overall, this research provides valuable data about changes in LU/LC around the Nile Delta branches, it is very important for decision maker and stockholders for proper management.

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 (E_SV) 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 km²and 24.86 km²was converted from grassland to industrial lands and irrigated lands, respectively. E_SV 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 E_SVI 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.

Due to the increases in agriculture and industry sector as well as high population, lack of water is becoming a major problem in the Middle East especially in arid regions. Saudi Arabia needs more groundwater research and explorations because of its higher water use and no source of freshwater. Assessing groundwater zonation in semi-arid locations is essential due to the significant degree of variation in groundwater depth, aquifer features, topographical characteristics, and insufficient precipitation. Mapping prospective groundwater zones in Al Qunfudhah region of southwestern Saudi Arabia has utilized the capability of the multi-criteria decision approaches (MCDA), and the Geographic information system (GIS). We have used the analytical hierarchy process (AHP) as one of the MCDA that is applied to achieve the objective of the current study by integrating twelve controlling factors. These factors are represented by the thematic layers; slope, precipitation, soil type, land use/cover (LULC), drainage density (DD), normalized difference vegetation index (NDVI), curvature, topographic position index (TPI), Terrain Ruggedness Index (TRI), drainage density (DD), and Lineament Density (LD). These thematic layers are combined with GIS to delineate the zones of groundwater potentialities. All factors were classified and weighted according to their importance and its effect on groundwater zones. Their normalized weights were evaluated using a pairwise comparison matrix. The present study shows that the groundwater potential zones (GWPZs) map is represented by five groups ranging between a very high zone with an area of 23781.06 Km² that represents 4.04 % of the studied area, and a very poor GWPZ with an area of 182944.4 Km² that represents 31.09 % of the studied area. The AHP model suggests that lineament density, slope, and drainage density are more important for determining the groundwater potentiality than other physiographic factors. The study's findings will be helpful in developing practical strategies for the region's groundwater supply. This analysis shows how the methodology may be used to study a broad coastal groundwater basin. The current study will give the decision makers to select suitable sites with a high groundwater potential.

Soil salinization affects negatively on agricultural productivity in the semiarid region of Ningxia. In this study, the performance of inversion model to determine soil salinization was assessed using some analysis and reflectance of wavelength. About 42 vegetation samples and 42 soil samples were collected for model extraction. Hyper-spectral data processing method was used to analyze spectral characteristics of different levels of salinization area vegetation. Spectral data were transformed in 16 different approaches, including root mean squares, logarithm, inversion logarithm, and first-order differentiation. After the transformation, the obtained soil and vegetation characteristics spectra correlate well with soil salt content, built soil index, and many vegetation indices. Nonlinear regression was employed to establish soil salinization remote sensing monitoring model. By comparing various spectral transformations, the first-order differential of soil spectral was the most sensitive to soil salinization degrees. The model of the current research was based on salinity index (SI) and improved soil-adjusted vegetation index (MSAVI). The correlation between simulated values and measured values was 0.758. Therefore, remote sensing monitoring derived from MSAVI–SI can greatly improve the dynamic and periodical monitoring of soil salinity in the study area.