Tips For Seamless Raster Reprojection After Reclassification

The Core Problem of Raster Reprojection

When working with raster datasets in Geographic Information Systems (GIS), users often need to reproject data from one coordinate system to another. This allows the raster to be viewed, analyzed, and integrated properly with other geospatial data layers that may have different projections.

However, directly reprojecting a raster can cause undesired distortions, warping, and resampling issues if the raster contains reclassified categorical data. Reclassification refers to the GIS operation of remapping or condensing the pixel values in a raster into new categorical codes or numeric ranges. For example, land cover values of 1 to 100 might be reclassified into new codes of 1 for urban, 2 for agriculture, 3 for forest, and so on.

The problem arises when a reclassified raster is projected to a different coordinate system using standard projection tools. The resampling process can scramble up the carefully defined categorical pixels, introducing unwanted new values through interpolation. This undermines the categorical integrity of the reclassified raster.

Choosing the Right Projection System

To avoid scrambling reclassified categorical values during projection, the first key step is choosing an appropriate projection system for the target output raster:

Geographic Coordinate Systems

Geographic coordinate systems (GCS) use a spherical or ellipsoidal model of the Earth to define locations with latitude and longitude values. Common GCS include WGS 1984 and NAD 1983.

GCS preserve angular relationships and shapes across the dataset, meaning they are spatially accurate globally. However, they can distort area and distance calculations in some locations. They are best suited for data spanning large regions.

Projected Coordinate Systems

Projected coordinate systems (PCS) transform the spherical GCS coordinates into planar or cartesian coordinates using mathematical projections. This projection process introduces some distortions but allows for more accurate area, distance, and directional analysis locally.

Common PCS used in GIS include Universal Transverse Mercator (UTM), State Plane, and Lambert Conformal Conic. PCS are optimized for regional-scale mapping and analysis in specific geographies.

For reprojection of reclassified rasters, a PCS tailored to the study area is recommended to minimize overall distortion effects.

Performing Reclassification Carefully

Before reprojecting, the raster should first be carefully reclassified into the desired categorical schema. This involves:

Using Proper Data Types

Declare the output reclassified raster dataset as an integer or categorical data type. Avoid using floating point numbers for the pixel values. This allows the discrete categorical classes to be clearly defined in the data structure, facilitating the later reprojection.

Checking for Null Values

Identify and address any null values coded as zeroes or unusual numbers like -9999. Define a specific “No Data” value to represent any null or missing pixels instead. This ensures all coded pixels represent legitimate class values.

Setting Resampling Technique for Reprojection

The projection process requires resampling the raster from source to target coordinate space. The choice of resampling technique strongly influences how pixel values are interpolated:

Nearest Neighbor

Nearest neighbor assigns pixels the value of the closest neighboring cell in the source raster. It provides precise preservation of original values without interpolation, ideal for categorical rasters.

Bilinear Interpolation

Bilinear interpolation averages the values of the four nearest pixel centers in the source raster. It can introduce unwanted new averages not matching the classification schema.

Cubic Convolution

Cubic convolution fits a smooth curve using weighted cubic polynomials based on surrounding 16 pixel values. This can scatter spurious new values across class boundaries if used injudiciously.

For reclassified categorical rasters, nearest neighbor resampling provides the most appropriate and conservative projection method.

Verifying Integrity of Reclassified Raster

After reprojection, systematically examine the reclassified categorical raster to ensure no errors were introduced in the process:

Comparing Cell Values

Compare the distribution of cell values before and after reprojection using frequency analysis tools. Verify the same set of properly defined class codes are present without any additional vestigial values.

Checking Histogram

Visually inspect the histograms of the raster before and after reprojection. The essential categorical form of the value distribution should remain consistent if the process preserved integrity.

Example Code for Reprojecting in Python

Using a scripting language like Python enables automating the process of carefully reprojecting a reclassified raster. Key steps include:

Importing Modules

Import necessary modules like gdal, osr, and numpy to handle geospatial data operations.

Opening Raster Dataset

Access the source raster using gdal and define key parameters like bands, dimensions, and cell values.

Reclassifying Values

Apply reclassify operation to compress cell values into target categorical schema.

Defining Target Projection

Create a SpatialReference object defining destination PCS using epsg code.

Resampling and Writing Out

ReprojectDataset and write out to new raster using nearest neighbor resampling. Inspect results carefully.

Proper reprojection methodology helps retain the categorical integrity within reclassified rasters, enabling further seamless spatial analysis.

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