Raster Processing in QGIS

Last updated on 2026-10-20 | Edit this page

Overview

Questions

  • How do I combine and reclassify rasters cell by cell?
  • How do I summarize a raster inside polygons such as census tracts?
  • How can land cover improve population estimates?

Objectives

  • Explain when rasters line up, and choose a resampling method for measurements and categories
  • Make Boolean rasters with comparisons, reclassify categories with a lookup table, and combine rasters
  • Compute zonal statistics for measurements and class shares for categories
  • Use the Raster Calculator, Reclassify by table, Zonal statistics and Zonal histogram in QGIS

What raster analysis adds

Vector operations ask where features are. Raster analysis asks what is everywhere: the elevation of every 30 m cell, the land cover of every cell, the temperature or vegetation index across a whole city. Two families of operations cover most everyday work:

  • Map algebra combines rasters cell by cell to make new rasters: arithmetic (elevation in feet), comparisons (land below 170 m), logic (low and developed) and reclassification (16 land cover codes into 5 groups).
  • Zonal statistics summarize the cells inside polygons and write the result into the polygons’ table: the mean elevation of each tract, or how many of its cells are developed. This is how raster data meets Census data.
Diagram of eight panels. Top row: a 4 by 4 grid of elevations from 162 to 182 meters; the same grid after the comparison elevation less than 170, showing 1 in seven cells on the lower, right side and 0 elsewhere; a grid of NLCD land cover codes such as 82, 22, 23, 11, 41 and 90; and the same grid reclassified into groups 1 to 5, with group 2 (developed) in red. Bottom row: the cells that are both low and developed, five cells marked 1, with the note 5 cells times 900 square meters equals 4,500 square meters; the reclassified grid with a tract polygon drawn over it and a dot at the center of each cell, black where the center falls inside the polygon; a summary saying that of 8 cells whose centers are inside, 5 are developed, 1 forest and 2 agriculture, a developed share of 62 percent, and that the mean elevation of the same cells is 171.8 meters; and a short note that map algebra works cell by cell on rasters that share a grid while zonal statistics summarize the cells inside each polygon.
Map algebra and zonal statistics on a 4 by 4 grid.

This episode asks two questions about Tippecanoe County, Indiana:

  1. How much developed land lies close to river level, below 170 m? (A rough stand-in for land near the Wabash River. It is not a flood map; FEMA flood zones are the real source for that.)
  2. How developed is each census tract, and how does that relate to where people live?

Rasters must line up

Map algebra compares cell (row 10, column 20) of one raster with cell (row 10, column 20) of another, so both must describe the same piece of ground: the same CRS, the same cell size and the same extent and alignment. The two rasters in this episode were prepared that way (EPSG:26916, 30 m cells, one grid). Real data rarely is, and aligning it means resampling:

Data Resampling method Why
Measurements (elevation, temperature) Bilinear or cubic Averages neighboring values smoothly
Categories (land cover, zoning codes) Nearest neighbor Averaging codes would invent classes that do not exist (the mean of 21 and 41 is 31, “barren”)

Resample as few times as possible, and to the coarser of the two resolutions: resampling to finer cells adds no information.

Map algebra in four kinds

Map algebra operations are often grouped by how many cells feed each output cell:

Kind Each output cell uses Example
Local The same cell in one or more rasters elev < 170, low AND developed, reclassification
Focal A neighborhood around the cell Slope, hillshade, a 3 by 3 average (Workshop Session 2A’s DEM analysis)
Zonal All cells in the same zone Mean elevation per tract
Global The whole raster Distance to the nearest river cell

This episode uses local and zonal operations.

Reclassification

Reclassification maps old values to new ones with a table. For categories it groups detailed classes; for measurements it cuts a continuous range into classes (0 to 2% slope = flat, and so on). Two checks prevent silent errors:

  • Every value must be covered. A code missing from the table becomes no data (or keeps its old value, depending on the software), and the totals no longer add up.
  • Range boundaries matter. “11 to 12” can mean 11 < value ≤ 12 or 11 ≤ value ≤ 12. For whole-number codes include both ends.

The groups used here:

Group NLCD codes Meaning
1 Water 11, 12 Open water, ice
2 Developed 21 to 24 Open space (lawns, parks) to high intensity (downtown, industry)
3 Forest and natural 31, 41 to 43, 52, 71 Barren, forest, shrub, grassland
4 Agriculture 81, 82 Pasture and crops
5 Wetlands 90, 95 Woody and herbaceous wetlands

Zonal statistics

Zonal statistics need the polygons and the raster in the same CRS (software may reproject on the fly, but doing it yourself avoids surprises). Choose the statistic by the kind of raster:

  • Measurements: mean, minimum, maximum, range, standard deviation.
  • Categories: count of each class (a zonal histogram), then shares. Never average land cover codes.

Most tools count a cell if its center falls inside the polygon. For large polygons this hardly matters; for polygons only a few cells wide (a small park, a narrow tract) the result can be off by a lot. Use finer data, an “all touched” option, or report the limitation.

From land cover to people: dasymetric estimation

In the Geoprocessing episode, areal weighting spread each tract’s population evenly over its area. Land cover lets us do better: people live on developed land, so a piece of a tract that is all farmland should get few of its residents. Dasymetric estimation splits each tract’s population in proportion to its developed cells rather than its area. For Lafayette and West Lafayette it raises the estimated city population from about 114,000 to about 122,000 (edge tracts are partly farmland outside the city limits), while the share within 1 mile of a supermarket barely moves (68% either way). Comparing two methods like this shows how much a conclusion depends on an assumption.

Pitfalls

  • No data. Cells outside the county hold a no-data value (here 0 for land cover, -9999 for elevation). Count or average them by mistake and every statistic shifts.
  • Units. Areas come from cell counts × cell area, which is only meaningful in a CRS measured in meters.
  • Resolution versus polygon size. A 30 m raster says little about a 50 m wide parcel.
  • Classification error. Land cover maps come from satellite classification and contain errors, especially between similar classes (pasture and grassland, low and medium intensity developed). Check suspicious results against imagery.
  • Date. NLCD 2021 and ACS 2020-2024 are close in time; mixing data decades apart is not.

Hands-on

These steps were checked in QGIS 4.0 (QGIS 3.34 and later are nearly identical). Save a new project in its own folder first; raster results are written there.

Callout

If QGIS asks you to choose a transformation

When layers in latitude and longitude (EPSG:4326) are drawn in a project in UTM (EPSG:26916), QGIS may open a window that begins “Multiple operations are possible for converting coordinates”. For this county any of the options is accurate to a few meters: keep the first (highlighted) one and click OK.

Part 1: Load the rasters from the web

  1. Layer > Add Layer > Add Raster Layer. Source Type: Protocol: HTTP(S), cloud, etc. Type: HTTP/HTTPS/FTP. URI: https://raw.githubusercontent.com/SpatialTurn/Foundations/main/data/tippecanoe_dem.tif. Click Add.
  2. Repeat with https://raw.githubusercontent.com/SpatialTurn/Foundations/main/data/tippecanoe_landcover_2021.tif.
  3. Load the tracts as a vector layer: https://raw.githubusercontent.com/SpatialTurn/Foundations/main/data/tippecanoe_tracts_acs.geojson.

Check: the land cover appears in the official NLCD colors (the file carries its color table), and the elevation in gray, dark in the Wabash valley. Right-click the elevation layer > Properties > Information: CRS EPSG:26916, pixel size 30 by 30.

Part 2: Land below 170 m (Raster Calculator)

  1. Raster > Raster Calculator.
  2. In Raster Bands, double-click tippecanoe_dem@1 to insert it, then type < 170 so the expression reads "tippecanoe_dem@1" < 170.
  3. Output layer: click … and save as low.tif in your project folder (or tick Create on-the-fly raster to skip saving). OK stays gray until an output is set.
  4. Click OK.
Screenshot of the QGIS Raster Calculator. The Raster Bands list shows tippecanoe_dem@1 and tippecanoe_landcover_2021@1 (and a landcover_groups@1 layer from a later step). The Result Layer section has an empty Output layer box with a browse button, Output format GeoTIFF, and the extent of the layer. Operator buttons include plus, minus, less than, AND, OR, IF, min, max and math functions. The expression box reads tippecanoe_dem@1 in double quotes, less than 170, and the message below says Expression valid.
The Raster Calculator with the comparison expression.

The result has 1 where the comparison is true and 0 where it is false.

Check: in the Processing Toolbox, run Raster layer unique values report on low. Value 1 covers 96,559 cells, about 86.9 km².

Part 3: Group the land cover (Reclassify by table)

  1. Processing Toolbox: Reclassify by table. Raster layer: tippecanoe_landcover_2021.
  2. Reclassification table: click … and add eight rows (Add Row) with Minimum, Maximum and Value as in the table above: 11, 12, 1 / 21, 24, 2 / 31, 31, 3 / 41, 43, 3 / 52, 52, 3 / 71, 71, 3 / 81, 82, 4 / 90, 95, 5. Click the back arrow to return.
  3. Open Advanced Parameters. Range boundaries: min <= value <= max (the default min < value <= max would skip codes 11, 21, 31 and so on). Tick Use NoData when no range matches value. Output data type: Byte.
  4. Save the output as landcover_groups.tif and click Run.
Screenshot of the QGIS Reclassify by table tool showing the Reclassification table panel. Eight rows list Minimum, Maximum and Value: 11, 12, 1; 21, 24, 2; 31, 31, 3; 41, 43, 3; 52, 52, 3; 71, 71, 3; 81, 82, 4; 90, 95, 5. Buttons on the right are Add Row, Remove Row(s), Remove All, OK and Cancel.
The reclassification table in Reclassify by table.

Check: the unique values report on landcover_groups shows five values: 1 about 10.8 km², 2 about 196.8 km², 3 about 167.1 km², 4 about 904.0 km², 5 about 23.4 km². If group 1 or 2 is much smaller, check the range boundaries.

Part 4: Developed land below 170 m

  1. Raster > Raster Calculator again, with the expression ("low@1" = 1) AND ("landcover_groups@1" = 2), saved as low_developed.tif.
  2. Run the unique values report on it.

Check: value 1 covers about 10.7 km², about 5% of the county’s 196.8 km² of developed land.

Part 5: Mean elevation per tract (Zonal statistics)

  1. Reproject the tracts to the rasters’ CRS: Vector > Data Management Tools > Reproject Layer, target EPSG:26916, and rename the result tracts_utm.
  2. Processing Toolbox: Zonal statistics (in the Raster analysis group). Input layer: tracts_utm. Raster layer: tippecanoe_dem. Output column prefix: elev_. Statistics to calculate: tick Mean, Minimum and Maximum. Run.

Check: the new layer has elev_mean, elev_min and elev_max. Census Tract 1 has a mean of about 165.6 m, one of the lowest, close to the river.

Part 6: Percent developed per tract (Zonal histogram)

  1. Processing Toolbox: Zonal histogram. Raster layer: landcover_groups. Vector layer containing zones: the output of Part 5 (or tracts_utm). Leave the prefix as HISTO_. Run.
Screenshot of the QGIS Zonal histogram tool. Raster layer is landcover_groups in EPSG:26916, Band number is Band 1, Vector layer containing zones is tracts_utm in EPSG:26916, Output column prefix is HISTO_, and the output is a temporary layer. The help panel says the algorithm appends fields with counts of each unique value from a raster layer within zones defined as polygons.
The Zonal histogram tool.

  1. The output has one column per group: HISTO_1 to HISTO_5 (cell counts), plus HISTO_NODATA.

  2. In its attribute table, open the Field Calculator and create pct_developed, Decimal Number (real):

    "HISTO_2" / ("HISTO_1" + "HISTO_2" + "HISTO_3" + "HISTO_4" + "HISTO_5") * 100

Check: the lowest value is Census Tract 110 at about 4.8%; the median is about 82%, and several downtown tracts are 100%.

Part 7: Map it

Style the result with Graduated symbology on pct_developed, Natural Breaks (Jenks), 5 classes, a sequential ramp such as Reds.

Choropleth map of Tippecanoe County census tracts titled Developed land by census tract. Five classes in shades of red from 4.8 to 17.1 percent (palest) to 83.5 to 100 percent (darkest). The large rural tracts that fill most of the county are palest; the tracts of Lafayette and West Lafayette in the center are darkest, and a ring of tracts around them is in the middle classes.
Percent developed land by tract.

Compare it with a map of population density (total_pop divided by the tract area): the patterns are very similar. That is why land cover is useful for placing people within tracts, the idea behind dasymetric estimation. The Python version (notebook 2b) carries out the dasymetric estimate.

Challenge

Challenge 1: Which resampling?

You need to combine a 10 m land cover raster with a 30 m elevation raster. Which raster would you resample, to what cell size, and with which method? (In QGIS: Raster > Projections > Warp (Reproject) has a Resampling method option.)

Resample the land cover to 30 m (the coarser resolution), with nearest neighbor (Nearest Neighbour in the Warp dialog), because it holds categories: bilinear would average codes into classes that do not exist. Resampling the elevation to 10 m would create a finer grid without adding information.

Challenge

Challenge 2: A different threshold

Repeat Parts 2 and 4 with 175 m. How much developed land lies below it? Is the answer sensitive to the threshold?

"tippecanoe_dem@1" < 175, then the AND step: about 15.3 km² (compared with 10.7 km² below 170 m). Five meters of threshold add about 40%, so the result is quite sensitive, which is one more reason to use a real flood map for decisions.

Challenge

Challenge 3: Mean or count?

For each tract you want (a) its average elevation, (b) its share of forest, and (c) its most common land cover class. Which QGIS tool and statistic gives each?

  1. Zonal statistics, Mean, on the elevation raster. (b) Zonal histogram on the land cover (or the groups), then forest cells divided by all cells in the Field Calculator. (c) Zonal statistics, Majority, on the land cover raster. Never the Mean of land cover codes.
Key Points
  • Rasters combined cell by cell must share CRS, cell size and extent; resample categories with nearest neighbor
  • The Raster Calculator makes comparisons and combines conditions (AND, OR); results are 1 and 0
  • Reclassify by table groups codes; set the range boundaries to min <= value <= max for whole-number codes
  • Zonal statistics summarize measurements per polygon; Zonal histogram counts categories per polygon
  • Check results with the unique values report: cells times 900 m² gives areas