Combining information from one raster with another raster

Combining Information from One Raster with Another: A Human’s Guide

Okay, let’s talk about rasters. If you’re diving into the world of Geographic Information Systems (GIS) or remote sensing, you’re going to bump into raster data sooner or later. Think of rasters as digital images, but instead of just looking pretty, each pixel holds real-world information – elevation, temperature, land cover, you name it.

Now, a single raster can tell you a lot, but things get really interesting when you start combining them. It’s like mixing ingredients in a recipe; suddenly, you’ve got something new and exciting. Combining information from one raster with another is a core skill in GIS that lets you see patterns and connections you’d otherwise miss. By mashing up different raster datasets, you can unlock insights that are invisible when you look at each layer on its own. So, let’s dive into how to do it and why it matters.

Rasters: More Than Just Pixels

Raster data is basically a grid of cells, like a spreadsheet laid over the Earth. Each cell has a value, and that value represents something about that location. It could be how high the land is, what kind of vegetation is growing there, or even the temperature on a given day.

Raster analysis is where the magic happens. It’s how we tease out trends, spot changes, and generally make sense of all that spatial data. Unlike vector data, which uses points, lines, and polygons, rasters are all about those individual pixels, each holding a piece of the puzzle.

You’ll find raster analysis being used everywhere from tracking deforestation to predicting crop yields. It’s a powerful tool for anyone trying to understand our planet.

Ways to Mix and Match Rasters

There are a bunch of ways to combine raster data, and each one has its own strengths. Here are a few of the most common:

• Raster Overlay: Imagine stacking transparent maps on top of each other. That’s essentially what raster overlay does. You’re combining the characteristics of multiple raster layers into a single layer, lining up each cell perfectly. By assigning numbers to each characteristic, you can perform mathematical operations to combine the layers. This is great for finding spots that meet specific requirements.
• Raster Algebra (aka Map Algebra or Band Math): If you’re comfortable with equations, you’ll love raster algebra. It’s all about performing mathematical and logical operations on raster datasets to create new ones. Think addition, subtraction, multiplication, division, and even more complex stuff like conditional statements. I remember one time I used raster algebra to calculate a vegetation index from satellite imagery – it was like unlocking a secret code! In ArcGIS, the “Raster Calculator” tool is your friend here.
• Weighted Overlay: Not all raster layers are created equal. Sometimes, one layer is more important than another. That’s where weighted overlay comes in. You assign weights to each layer based on its importance before combining them. This is super useful for things like suitability modeling, where you’re trying to find the best places for something based on a bunch of different factors.
• Combine: The Combine tool is like a data blender. It takes multiple input rasters and spits out a new raster where each unique combination of input values gets its own value. The cool thing is, it keeps track of the original cell values from each input in the output raster’s attribute table.
• Merging and Mosaicking: Ever seen a map made up of multiple images stitched together? That’s mosaicking in action. Merging is similar, but it’s more about combining raster datasets end-to-end.

Getting Down to Specifics

Okay, let’s get a little more granular. Here are some specific operations you can use when combining raster data:

• Arithmetic Operations: The basics: addition, subtraction, multiplication, and division. You can do these on a pixel-by-pixel basis. I’ve used these to convert units, calculate differences between rasters, and even create cost surfaces for routing analysis.
• Logical Operations: Think of these as “true” or “false” tests for your raster cells. Common ones are AND, OR, NOT, and XOR.
• Conditional Statements: These let you perform different calculations depending on whether a certain condition is met. It’s like saying, “If the elevation is above 1000 meters, do this; otherwise, do that.”
• Reclassification: Sometimes, you need to simplify your raster data. Reclassification lets you group raster values into meaningful categories. For example, you could reclassify a continuous elevation raster into “low,” “medium,” and “high” elevation zones.
• Zonal Statistics: Want to know the average elevation within a specific forest stand? Zonal statistics can do that. It calculates statistics within predefined zones or regions of interest.
• Proximity Analysis: This calculates the distance from each cell to the nearest source, like a road or a river. It’s great for finding areas that are within a certain distance of a feature.

Real-World Examples

So, why bother with all this? Here are a few examples of how combining raster data is used in the real world:

• Suitability Modeling: Finding the perfect spot for a new park, a wind farm, or even a housing development.
• Change Detection: Spotting deforestation, tracking urban growth, or monitoring the spread of invasive species. I once used change detection to map the impact of a wildfire over time.
• Environmental Modeling: Predicting soil erosion, modeling water runoff, or assessing habitat suitability for endangered species.
• Land Use Planning: Making informed decisions about how land should be used.
• Geological Assessments: Creating soil maps and analyzing geological features.
• Remote Sensing Analysis: Extracting information from satellite images and aerial photos.
• Terrain Mapping: Creating digital elevation models (DEMs) and analyzing terrain characteristics.

A Few Things to Keep in Mind

Before you start combining rasters willy-nilly, here are a few things to consider:

• Resolution: High resolution (smaller grid size) requires more computing power and storage, while low resolution (larger grid size) may lose detail.
• Coordinate Reference System (CRS): Make sure all your rasters are in the same CRS. If not, you’ll need to reproject them.
• Cell Size: When combining rasters with different cell sizes, you’ll need to decide on an output cell size.
• Extent: If your rasters have different extents, you may need to crop or pad them to match.
• Data Type: Ensure that the input rasters have the same number of bands and that they represent the same type of data.
• NoData Values: Handle NoData values carefully to avoid messing up your calculations.
• Weighting: If you’re doing a weighted overlay, put some thought into how you assign weights.

Tools of the Trade

You’ve got plenty of software options for combining raster data:

• ArcGIS Pro: A powerhouse with tons of raster analysis tools.
• QGIS: A free and open-source option that’s surprisingly powerful.
• GDAL (Geospatial Data Abstraction Library): A command-line tool for raster manipulation.
• TNTmips: A GIS software that offers raster combination operations.
• ENVI: A remote sensing software with band math tools.

The Bottom Line

Combining raster data is a crucial skill for anyone working with spatial information. By understanding the different methods and keeping the key considerations in mind, you can unlock a world of insights and make better decisions. So, go forth and combine those rasters!