QGIS Raster Analysis – How to Split a Single Raster Layer into Multiple Layers Based on Raster Values in QGIS

qgisrasterzonal statistics

I am working with a raster layer in QGIS, and within this layer, the raster values are as follows: 10, 11, 12, 13, 21, 22, 23, and 30. I need the number of pixel of each value in city borders. I have a vector layer for cities.

Raster layer unique values report is useful but I need a city by city statistics like zonal statistics. I thought that by splitting each value as a different raster than I can use zonal statistics. Do you know an easier method for that? And do you know how can I split the pixels with specific band values as a different raster?

Best Answer

You dont need to split your raster. Zonal Histogram will create a column for each pixel value with the pixel count.

Appends fields representing counts of each unique value from a raster layer contained within polygon features.

Related Solutions

[GIS] How to calculate raster statistics for each values in a band

You can use Zonal Raster Statistics (also called Zonal Grid) tool to count the number of pixel in each class. The tool will create a new CSV file with the number of pixels for each category.

The only required parameter is the classified image (Zonal Grid) that contains the categories you want to calculate.

In the following example, I have a land use raster with 8 classes. When I run Zonal Raster Statistics tool, I got the following result:

Here is the output:

The tool can be found from Processing toolbox -> SAGA -> Geostatistics -> Zonal Raster Statistics

Python – Rasterising Multiple Shapefiles into a Single Raster Using GDAL and Rasterio

I would recommend a combination of geopandas and geocube.

Here is some untested set of code that should get you pretty close to what you want to do.

Step 1: Combine the shapefiles

import pandas
import geopandas

gpd1 = geopandas.read_file("Shapefiles/shp1.shp")
gpd2 = geopandas.read_file("Shapefiles/shp2.shp")
gpd3 = geopandas.read_file("Shapefiles/shp3.shp")
gpd4 = geopandas.read_file("Shapefiles/shp4.shp")

merged_gpd = pandas.concat([gpd1, gpd2, gpd3, gpd4]).set_geometry("geometry")

Step 3: Rasterize the data

from geocube.api.core import make_geocube

grid = make_geocube(
    vector_data=merged_gpd,
    measurements=["shp_V"],
    resolution=(-0.3, 0.3),
)

Step 4: Export to raster

grid.shp_V.rio.to_raster("Rasters/raster1234.tif", compress="DEFLATE", bigtiff="YES")

Best Answer

Related Solutions

[GIS] How to calculate raster statistics for each values in a band

Python – Rasterising Multiple Shapefiles into a Single Raster Using GDAL and Rasterio

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