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0. Preface
GIS case, study on the eighth day
1. Text
1.1 Perform terrain elevation analysis on a certain area. Requirements: Randomly distribute sampling areas, and use a circular area with a radius of 100 meters as the statistical unit to calculate the elevation statistics of each sampling area.
[Method] Use ArcGIS in [Buffer] (buffer) and [Zonal Statistics As Table] (display zone statistics in a table) and other tools to achieve. Note: The statistical unit in this example is circular. Therefore, to achieve random distribution sampling, firstly create the spatial position of the random sampling area, that is, the center point of each statistical circle; secondly, extract the buffer from each center point to obtain the circle Statistical unit; Finally, use the regional statistical tool to get statistical table data.
1) Create randomly distributed points. Open [Data Management Tool]|[Feature Class]|[Create Random Point] tool, the output location is set to the storage folder of randomly distributed points, the output point feature class is set to "sample_pts", and the constraint range is set to "dem with layer Same", the number of points is set to "100", and the minimum allowable distance is set to "10 meters". Confirm, get randomly distributed point data. Note: In order to ensure that the randomly distributed points are in the dem area to be analyzed, the constraint range must be the same as the dem; the minimum allowable distance limits the shortest distance between two adjacent points, and ensures that the points can not be concentrated within 10 meters.
2) Create a statistical circle. Open the [Analysis Tools]|[Neighborhood Analysis]|[Buffer] tool, the input feature is set to "sampl_pt", the output feature class is set to "circles", the distance is set to "100m", and the fusion type is set to "NONE", That is, each circle is independent, and overlapping circles are not merged. Determine, get random distribution statistical circle.
3) Count the elevation value with circle as the unit. Open [Spatial Analyst Tools]|[Area Analysis]|[Display Area Statistics in Table] tool, input feature area data is set to "circles", area field is set to "FID", output table is set to "statistics", and statistics type is selected "ALL" means calculating all statistical indicators. Confirm, get the statistical result table.
4) Connect the statistical result table to the statistical circle layer. Because the spatial distribution of table data cannot be viewed intuitively, this example connects the statistical results of the table to the statistical circle layer, and the connection relationship between the table and the statistical circle layer is the field "FID". Right-click the circles layer, open the [Connection and Association]|[Connection] dialog box, select "Table Connection Properties", select "FID" for the field based on, select "statistics" for the table connected to the layer, and connect the basic field Select "FID".
5) Symbolize statistical circles. Double-click the connected statistical circle layer to open the properties dialog box, select "Symbolism", display and select "Category|Unique Value", set the value field to "MEAN", and click "Add all values". Confirm, and display the statistical circle as the average elevation value.
1.2 [Question 2] Existing DEM data, how to extract the hydrological slope length of the DEM data?
[Method] Use the [Flow Length] tool in ArcGIS to achieve. Note: The slope length calculated in this example is a concept in hydrological analysis. The slope length is divided into the upstream slope length and the downstream slope length. The upstream slope length is the horizontal projection length from the current grid to the nearest upstream watershed (the number of grids); the downstream slope length is the horizontal projection length from the current grid to the downstream water outlet (raster data).
[Experimental steps]
1) Extract the flow direction grid. Open [Spatial Analyst Tools]|[Hydrological Analysis]|[Flow Direction] tool, set the input surface raster data to "dem" and the output flow direction raster data to "fdir". OK, get the flow direction raster data. Note: Before calculating the flow direction grid, whether the DEM data needs to be filled and excavated depends on whether the depression is considered. If you need to consider the existence of surface depressions, there is no need to fill and dig operations; if depressions can be ignored, you need to fill and dig the DEM, and then calculate the flow direction raster data. In this example, a depression is considered, so filling and digging operations are not performed.
2) Calculate the upstream slope length grid. Open [Spatial Analyst Tools]|[Hydrological Analysis]|[Water Flow Length] tool, set the input flow direction raster data to "fdir", the output raster data to "upflowlength", and the measurement direction to select "UPSTREAM". Determine, get the upstream slope length data.
3) Calculate the downstream slope length grid. Open [Spatial Analyst Tools]|[Hydrological Analysis]|[Water Flow Length] tool, set the input flow direction raster data to "fdir", the output raster data to "dnflowlength", and the measurement direction to select "DOWNSTREAM". Confirm and get the downstream slope length data.
4) Query the slope length value at any position. Check the upstream/downstream slope length layer, and leave the others unchecked. Click the [Recognition] tool button in the [Tools] toolbar, set the recognition range to "Visible Layer", and click any grid at any position to return the upstream/downstream slope length value of the clicked grid.
1.3 [Question 3] How to extract mountain apex data from existing DEM data?
[Method] Use [Focal Statistics] tool and [Raster Calculator] tool in ArcGIS. Note: In the terrain data expressed in DEM, the peak of the mountain is a grid with the largest local elevation value, that is, if the elevation value of the central grid is larger than that of all the grids in a certain range around it, the central grid is Mountain apex grid. Therefore, to extract mountain vertices from DEM, it needs to be obtained through regional statistical analysis and grid comparison. In this example, only the method of window statistics is used to extract mountain vertices, which is simple and convenient for quick extraction. But to accurately extract all mountain vertices, you need to refer to relevant literature.
[Experimental steps]
1) Perform focus statistics on DEM. Open [Spatial Analyst Tools]|[Neighborhood Analysis]|[Focus Statistics] tool, the input raster is set to "dem", the output raster is set to "max", the neighborhood analysis is "rectangle", and the height and width are both set Is "7", the unit is "pixel", and the statistics type is "MAXIMUM". Confirm, get the focus statistics grid data. Note: The height and width values of the neighborhood determine the number of surrounding grids that are compared with the central grid for elevation values. The larger the neighborhood, the larger the statistical area, that is, the greater the area controlled by the peak of the mountain. The size of the range can be set as needed, in this case the 7X7 range. The statistical type "MAXIMUM" means that the maximum value in the statistical range is assigned to the central grid.
2) Calculate the mountain vertex grid. Open [Spatial Analyst Tools]|[Map Algebra]|[Raster Calculator] tool, enter the formula ["max"-"dem" = = 0], and set the output raster to "peaks". Confirm, get the mountain apex raster data. Note: It can be known from the focus statistics operation that only when the center grid is a mountain vertex grid, its elevation value will not change before and after focus statistics; if the center grid is a non-mountain vertex grid, the grid value will be It is assigned as the elevation value of other mountain vertices in the neighborhood. It can be seen that, before and after the focus statistics, the grid with the same elevation value is the mountain apex grid, and the grid with the elevation value changed is the non-mountain apex grid. Therefore, the input calculation formula is ["max"-"dem" = = 0], and the grid that meets this condition is a mountain vertex, otherwise it is a non-mountain vertex.
3) Generate mountain top data. Right-click the peaks layer, open the attribute table, and select the record with a value of 1 (mountain vertex raster record). This operation is to convert only the record with the value of 1 during the subsequent conversion operation, and ignore the record with the value of 0; open [Convert Tools]|[Raster to Area] tool, the input raster is set to "peaks", the field is "VALUE", the output area element is "area", and the "simplified area" is checked. OK, get the top surface data. Note: As the result of extracting mountain vertices in step 2 has non-single grid area, to obtain single point data, the grid must be converted to surface data.
4) Generate mountain apex data. Open the [Data Management Tool]|[Element]|[Element to Point] tool, the input element is set to "area", and the output feature class is set to "peaks". OK, get the peak data. Note: This operation is to extract the geometric center point of the surface.
