# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 4 Solar Potential Assessment Using GISs
### Spatial Analysis Tool
For this purpose, point data for three different types of marginal land (i.e., Brownfield, Landfill, and abandoned mine lands) and grid cell data for five different renewable energy sources were spatially joined to map energy resources to regions of interest in a GIS environment. Many researchers examined surface characteristics, including slope, aspect, orientation, and curvature. [PERSON] et al. [90] analyzed the slope of terrain using the surface toolset and DEM data in ArcGIS to identify regions with slopes of less than 10\({}^{\circ}\) for a fixed-type PV system. The purpose of setting this terrain condition was to minimize the environmental damage caused by constructing a solar power plant in an abandoned mine area. [PERSON] et al. [91] computed the slope, orientation, and horizontal angles of each building, using a DSM coupled with building footprints, to assess solar irradiation on building rooftops, because shadows occur when the elevation angle of the sun is less than the angle of the local horizon of the observer. [PERSON] and [PERSON] [92] analyzed the slope in Oman to calculate the yearly electricity generation potential for different CSP technologies, such as parabolic trough, parabolic dish, tower, and concentrated PV. It was determined that if only the land of Oman with a slope less than 1%, which constitutes 10% of the total land area, is considered to be exploitable for parabolic trough CSP technology, then the total potential yearly electricity generation would be about 7.6 million GWh. He and [PERSON] [93] calculated the available land for solar development in each province by applying the following filters in the GIS modeling: DEM with an elevation less than 3000 m and a slope less than 1% and land use categorized as barren land, as defined in the land use data available for solar development. Forests, cropland, wetlands, water, woody savannas, shrub lands, savannas, grasslands, snowy and glacial areas, and protected land were excluded from this study for stationary PV. In a few studies, distance or buffer analysis was performed to derive specific regions or values related to distance. [PERSON] et al. [94] generated two worldwide maps showing the cost of electricity by PV systems with and without transmission lines to analyze the effects of the cost of building new transmission lines to the nearest load center on the cost of PV electricity. At this point, distance analysis and map algebra were employed to estimate the cost derived from the length of the transmission line (distance to the nearest load center). [PERSON] et al. [95] identified regions with DNI of more than 18 MJ/m\({}^{2}\)/day to determine the most promising regions for CSP systems. In addition, transmission lines with more than 66 kV were considered as they can technically absorb the output of 5-60 MWe CSP-biomass hybrid plants. In addition, 50 km buffer analysis was conducted around transmission lines as biomass transport for 50 km is possible. These two conditions were integrated to generate a map showing the areas within 50 km of the existing transmission lines, overlapped with different DNI values.