Reprojecting Rasters: Understanding the Role of Aggregate Operation

Reprojecting rasters is a crucial step in geospatial data processing, allowing different datasets to be aligned and combined. However, when reprojecting rasters with or without aggregating values, seemingly different results can occur. In this article, we’ll delve into the world of raster reprojection and explore the effects of aggregating values on the output.

Introduction

Raster reprojection is a process that transforms one spatial reference system (SRS) to another while maintaining the same coordinate space. This allows us to align rasters with different projections, ensuring they can be stacked or merged for further analysis. The projectRaster function from the R raster package is commonly used for this purpose.

The Problem: Different Results

A question on Stack Overflow highlights a common issue in reprojection: differences in results when aggregating values before reprojecting. The user attempts to reproject rasters with or without aggregating values, but experiences inconsistent results. To understand the root cause of this discrepancy, let’s examine each approach:

Direct Reprojection

The first approach is to directly reproject one raster onto another using projectRaster. This method assumes that both rasters have the same cell size and resolution.

# Load necessary libraries
library(raster)

# Define two example rasters
sp <- raster("sp.tif")
pf <- raster("pftnc.tif")

# Directly reproject pf onto sp
pf1 <- projectRaster(pf, sp)

Aggregating Values Before Reprojection

Another approach involves aggregating values from one raster before reprojecting. This method can help reduce differences in results.

# Aggregate values from pf by averaging 4 cells
pfa <- aggregate(pf, fact=4)

# Reproject pfa onto sp
pf2 <- projectRaster(from=pfa, to=sp)

Masking for Strict Cell Comparison

To further analyze the effects of aggregating values, we can mask corresponding cells between pf2 and pf1.

# Create a masked raster with pf2 and pf1
pf2m <- mask(pf2, pf1)

cellStats(pf2m, mean)

Discussion

The provided example demonstrates that the order of operations significantly impacts the results. When aggregating values before reprojecting, we get more cells due to NA removal (na.rm=TRUE), which might affect the accuracy of subsequent calculations.

However, if we use masking (mask) to compare strictly identical cells between pf2 and pf1, the differences in results become negligible.

Generating Example Data

To further investigate this issue, let’s create example data using raster() with a custom SRS.

# Define an example raster with CRS +proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +units=m

sp <- raster(nrow=142, ncol=360, ext=extent(-17367529, 17367529, -6356742, 7348382), crs="+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +y_0=0 +datum=WGS84 +units=m")

# Define another example raster with different resolution
pf <- raster(ncol=1440, nrow=720, xmn=-180, xmx=180, ymn=-90, ymx=90, crs='+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0')

# Assign values to pf
values(pf) = rep(c(1:4, NA), ncell(pf)/5)

Conclusion

Reprojecting rasters is a crucial step in geospatial data processing. However, when aggregating values before reprojecting, seemingly different results can occur due to the role of NA values or differences in cell distribution. Masking corresponding cells between pf2 and pf1 helps reduce these discrepancies.

By understanding the effects of aggregating values on raster reprojection, we can improve our data processing workflows and ensure accurate results for further analysis.

Last modified on 2025-04-24