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How to Calculate Raster Statistics of a DEM in QGIS |

Raster statistics refer to the quantitative information derived from analyzing the values of a raster dataset. A raster dataset consists of a grid of cells or pixels, where each cell contains a value representing a specific attribute or measurement. Raster statistics provide insights into the distribution, variation, and characteristics of these values within the raster. Common raster statistics include: 1. Minimum: The smallest value within the raster dataset. 2. Maximum: The largest value within the raster dataset. 3. Mean: The average value calculated by summing all the pixel values and dividing by the total number of pixels. 4. Median: The middle value when all pixel values are sorted in ascending order. 5. Mode: The most frequently occurring value within the raster dataset. 6. Standard Deviation: A measure of the dispersion or spread of the pixel values around the mean. 7. Range: The difference between the maximum and minimum values. 8. Sum: The total sum of all pixel values in the raster dataset. 9. Count: The total number of pixels in the raster dataset. These statistics can be calculated for an entire raster dataset or within specific regions or zones of interest. They are commonly used in various applications, including remote sensing, geographic information systems (GIS), environmental monitoring, and spatial analysis, to understand and analyze the characteristics of raster data. Count: to count the number of pixels Sum: to sum the pixel values Mean: to get the mean of pixel values Median: to get the median of pixel values StDev: to get the standard deviation of pixel values Min: to get the minimum of pixel values Max: to get the maximum of pixel values Range: to get the range (max - min) of pixel values Minority: to get the less represented pixel value Majority: to get the most represented pixel value Variety: to count the number of distinct pixel values Let's consider an example of terrain analysis using raster statistics with a digital elevation model (DEM) in GIS. A DEM is a raster dataset that represents the elevation of the Earth's surface as a grid of cells or pixels. It provides information about the topography of an area. Here's how raster statistics can be applied to a DEM: 1. Minimum and Maximum Elevation: By calculating the minimum and maximum values from the DEM, we can determine the lowest and highest points in the area of interest. These statistics are helpful for identifying valleys, peaks, or areas of extreme elevation. 2. Slope: Slope represents the steepness of the terrain. It can be calculated from the DEM by determining the rate of change in elevation between neighboring pixels. Raster statistics such as mean, standard deviation, or range of slope values can provide insights into the variability or distribution of slope across the area. 3. Aspect: Aspect describes the direction in which a slope faces. It can be derived from the DEM by calculating the azimuth or angle of the steepest downhill slope. Raster statistics such as mean or mode of aspect values can reveal the predominant slope directions in the area. 4. Curvature: Curvature quantifies the shape or convexity of the terrain surface. It can be computed from the DEM by analyzing the rate of change in slope. Raster statistics like mean, standard deviation, or range of curvature values can help identify areas with varying curvatures, such as ridges, valleys, or flat areas. 5. Local Relief: Local relief measures the difference in elevation between a pixel and its neighboring pixels. It can be calculated from the DEM by subtracting the elevation of each pixel from the average elevation of its surrounding neighbors. Raster statistics such as mean or standard deviation of local relief values can indicate areas of high or low relief variation. 6. Profile Statistics: Profile statistics involve analyzing the elevation values along a specific line or transect. By extracting a profile across the DEM, statistics such as minimum, maximum, or average elevation along the profile can provide information about changes in elevation over distance, such as for studying river profiles or terrain cross-sections. These raster statistics derived from the DEM can be used for various applications in GIS, such as landform identification, hydrological modeling, visualization of terrain features, or suitability analysis for specific activities or features. They help in understanding the characteristics of the terrain and support decision-making processes related to land management, infrastructure planning, environmental assessment, and more.