Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce and sensitive nature of the plant cover. Due to expected changes in vegetation cover, land productivity and biodiversity might be affected. Thus, early detection of vegetation cover changes and the assessment of their extent and severity at the local and regional scales become very important in preventing future biodiversity loss. Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions. Remote sensing data, such as satellite images, can be obtained from satellite-based and aircraft-based sensors to monitor and detect vegetation cover changes. By combining remotely sensed images, e.g., from satellites and aircraft, with ground truth data, it is possible to improve the accuracy of monitoring and mapping techniques. Additionally, satellite imagery data combined with ancillary data such as slope, elevation, aspect, water bodies, and soil characteristics can detect vegetation cover changes at the species level. Using analytical methods, the data can then be used to derive vegetation indices for mapping and monitoring vegetation.

Saskatchewan is one of Canada’s highest emitters of greenhouse gases, largely due to the burning of lignite coal to generate electricity. The province is also the world’s second largest producer of uranium. This research was intended to establish a process for evaluating geographical considerations in site selection for small modular reactors (SMRs) in Saskatchewan. SMRs are the next generation of electrical power, producing less than 300 megawatts (MW) and featuring a basic design that offers enhanced safety, health, and environmental benefits compared to traditional reactors. Selecting an SMR site is a two-stage process: (i) Identifying candidate site locations based solely on available geographical, economic, and logistical data—an objective process—and (ii) refining the potential locations based on public perceptions, social conventions, and political will—a subjective process. This study focused on the objective geographical considerations in SMR site selection in Saskatchewan. The study areas were subjected to a multi-criteria decision analysis based on specific criteria drawn from various Canadian federal regulation documents. Criteria weights were assigned using the analytical hierarchy process, with results for two different types of criteria weights applied for the purpose of demonstration. Three distinct cases of criteria fuzzy standardization were conducted to assign spatial suitability values for all the criteria. Spatial decision-making models were implemented in a geographic information system to identify candidate sites. Geographical maps constructed from the findings showed suitable sites for SMRs, ranging from very suitable to unsuitable based on the geographical analysis of the study area.

The assessment of ecosystem quality and the maintenance of optimal ecosystem function require understanding vegetation area dynamics and their relationship with climate variables. This study aims to detect vegetation area changes downstream of the Hali dam, which was built in 2009, and to understand the influence of the dam as well as climatic variables on the region’s vegetation areas from 2000 to 2020. The case study is located in an arid area with an average rainfall amount from 50 to 100 mm/year. An analysis of seasonal changes in vegetation areas was conducted using the Normalized Difference Vegetation Index (NDVI), and supervised image classification was used to evaluate changes in vegetation areas using Landsat imagery. Pearson correlation and multivariate linear regression were used to assess the response of local vegetation areas to both hydrologic changes due to dam construction and climate variability. The NDVI analysis revealed a considerable vegetation decline after the dam construction in the dry season. This is primarily associated with the impoundment of seasonal water by the dam and the increase in cropland areas due to dam irrigation. A significantly stronger correlation between vegetation changes and precipitation and temperature variations was observed before the dam construction. Furthermore, multivariant linear regression was used to evaluate the variations in equivalent water thickness (EWT), climate data, and NDVI before and after the dam construction. The results suggested that 85 percent of the variability in the mean NDVI was driven by climate variables and EWT before the dam construction. On the other hand, it was found that only 42 percent of the variations in the NDVI were driven by climate variables and EWT from 2010 to 2020 for both dry and wet seasons.

The assessment of ecosystem quality and the maintenance of optimal ecosystem function requires understanding vegetation area dynamics and its relationship with climate variables. This study aims to detect vegetation area changes over a downstream dam and to understand the influence of the dam as well as climatic variables on the region’s vegetation areas. The case study is located in an arid area with an average rainfall amount of 50 to 100 mm/year. An analysis of seasonal changes in vegetation areas was conducted using the Normalized Difference Vegetation Index (NDVI), and supervised image classification was used to evaluate changes in vegetation areas using Landsat imagery. Pearson correlation and multivariate linear regression were used to assess the response of local vegetation areas to both hydrologic changes due to dam construction and climate variability. The NDVI analysis reveals a considerable vegetation decline after the dam construction in the dry season. This is primarily associated with the impoundment of the seasonal water by the dam and the increase in cropland areas due to dam irrigation. A significantly stronger correlation between vegetation changes and precipitation and temperature variations is observed before the dam construction. Furthermore, multivariant linear regression was used to evaluate the variations of equivalent water thickness (EWT), climate data, and NDVI before and after the dam construction. The results suggest that 85 percent of the variability in the mean NDVI was driven by climate variables and EWT before the dam construction. On the other hand, it was found that only 42 percent of the variations in NDVI were driven by climate variables and EWT after the dam construction for both dry and wet seasons.