Understanding digital elevation models: a 3D view of terrain elevation data

DEM Demystified: A practical guide for GEOINT thinking

What is a digital elevation model, really?

If you’ve spent time with maps and 3D terrain, you’ve probably run into the term digital elevation model, or DEM. Here’s the straightforward version: a DEM is a 3D representation of terrain elevation data. In plain words, it’s a grid that tells you how high the ground is at many little points across a region. Think of it as a digital version of contour lines, but instead of lines that connect points of equal height, you’ve got a continuous surface made of tiny squares (cells) with height values.

Why this definition matters

You might wonder why DEMs get all the attention. Because that grid of elevations is the backbone for a lot of geospatial work. When you pull up a DEM in a GIS like ArcGIS or QGIS, you’re seeing the raw surface that can be analyzed for slope, aspect, watersheds, and even flood scenarios. It’s not just a pretty picture; it’s a measurement of the Earth’s surface that enables modelers, planners, and analysts to reason about what the land can do under different conditions.

A quick note on terminology

You’ll also hear about related data types like DSMs and DTMs. Here’s the quick distinction to keep straight, because it matters when you’re building workflows:

• DEM (digital elevation model): a bare-earth representation of terrain elevations. It emphasizes the ground surface, not buildings or trees.

• DSM (digital surface model): a surface that includes everything you’d see from above—the tops of trees, poles, rooftops, and other tall features.

• DTM (digital terrain model): sometimes used interchangeably with DEM, but in many workflows it’s a terrain model refined to remove artifacts and non-ground features, focusing on the land’s actual surface.

In many GIS datasets, people use DEM when they mean the bare-earth surface, but the key is to know what your data actually represents. A mismatch here can throw off slope calculations, water routing, or line-of-sight analyses.

How a DEM is structured under the hood

Let’s pull back the curtain just a notch. The classic DEM is a regular grid: a matrix of cells, each with a single elevation value. The grid covers a defined area, and every cell represents a fixed ground area—its size is the resolution. Common resolutions for DEMs include 30 meters (global, moderate detail) or finer like 1-5 meters (local, high detail, often from LiDAR). Some datasets use triangular networks or irregular grids, especially when they’re derived from point clouds. Either way, the goal is the same: a consistent surface model you can query quickly.

Two practical knobs you’ll notice

• Resolution: What’s the size of each cell? A finer resolution gives more detail but ups the data volume and processing needs.

• Vertical datum: Elevation is tied to a reference surface, like a geoid or ellipsoid. When you move data between datasets, you’ll often see vertical units and datums that need reconciling to keep heights accurate.

Where DEMs come from

DEMs aren’t conjured from thin air. They come from several data streams:

• Satellite altimetry and radar missions (think SRTM, Copernicus DEM). These give broad coverage with moderate detail.

• Photogrammetric methods from aerial imagery (more detail, often used for coastal or mountainous areas where high-res data is crucial).

• LiDAR (light detection and ranging) scanning, which can produce very high-resolution DEMs with excellent vertical precision in smaller, targeted areas.

• Combined workflows that blend these sources, filling gaps and refining the surface.

In the NGA GEOINT ecosystem, you’ll see DEMs used across a lot of layers and analyses. They’re the baseline, the common ground that lets analysts compare terrain across regions, simulate water flow, or model visibility for defense, planning, or environmental work.

From data to insights: what you can do with a DEM

Here are the big practical uses that make DEMs so essential in geospatial workflows:

• Terrain analysis: Slopes tell you how steep the land is, while aspect reveals the direction each slope faces. This matters for everything from erosion risk to solar exposure for planning purposes.

• Hydrologic modeling: Drainage pathways, watershed boundaries, and flood risk all start with a good terrain surface. DEMs feed flow direction algorithms and help simulate water movement across landscapes.

• Shadowing and visibility: Viewshed analyses rely on terrain to determine what can be seen from a vantage point—critical for line-of-sight planning, surveillance, or even search-and-rescue operations.

• 3D visualization: A DEM lets you drape other data (like land cover, infrastructure, or population) over a realistic surface, producing intuitive visuals for reports and briefings.

• Terrain visualization and scenario planning: In urban and environmental contexts, you can explore how terrain changes under different scenarios—like wildfire spread, landslides, or storm surge zones.

A quick tour through a typical workflow

If you’re working with a GPC-like certification in mind, you’ll appreciate how a DEM slides into a real-world workflow. Here’s a simplified path:

• Gather data: Start with a DEM or several data sources like LiDAR-derived high-resolution terrain or global DEMs for broader context.

• Reconcile reference systems: Ensure the horizontal and vertical coordinate systems line up with your map projection and datum. A mismatch here is the kind of small error that ruins big analyses.

• Assess quality: Check vertical accuracy, look for voids (gaps in data), and note where features such as cliffs or dense vegetation might bias elevation values.

• Process and refine: If you have LiDAR, you might filter point clouds to keep ground returns and generate a bare-earth surface. In other cases, you interpolate from scattered elevation points to fill gaps.

• Analyze: Compute slope and aspect, delineate watersheds, run hydrological models, or perform viewshed analyses depending on the task.

• Visualize: Create layered maps and 3D scenes, color ramps that convey elevation changes clearly, and annotations that tell the terrain’s story.

• Validate: Ground-truth if you can. Compare against independent measurements or higher-precision data in critical zones to confirm that the model behaves as expected.

Finding the right DEM for the job

Not every project needs a high-tech, LiDAR-grade DEM. The trick is to match data quality to the decision you’re supporting:

• For regional planning or environmental monitoring, a medium-resolution global DEM often suffices to reveal broad terrain patterns.

• For urban planning or flood simulations in a specific valley or river corridor, you’ll likely want higher-resolution data to capture microtopography and man-made features.

• For rugged, uncharted terrain, you may rely on drone or airborne LiDAR surveys to build a local DEM with excellent vertical accuracy.

DEMs in the real world, beyond the map

Here’s a small digression to keep things human. Have you ever stood on a hillside and tried to imagine how far a road project would cut into the slope, or how floodwater would carve its path through a floodplain? A good DEM helps you translate that gut feeling into numbers you can map, measure, and plan around. It’s one thing to know the terrain looks steep; it’s another to quantify, say, a 25-degree slope over a square kilometer and then simulate whether a particular storm event would overwhelm a culvert. That mix of intuition and data is what makes DEMs so powerful in practice.

Common pitfalls to watch for

As you work with terrain data, a few recurring issues show up:

• Data voids: Gaps in elevation data can create artificial “holes” in your surface that misleadingly flatten or spike the terrain. Always check for gaps and fill them with appropriate interpolation methods or supplementary data.

• Datum drift: If horizontal or vertical references aren’t consistent, your measurements can drift apart as you combine layers. A quick reconciliation step saves a lot of headaches later.

• Oversimplification: A coarse DEM might hide important terrain features like narrow ridges, culverts, or small escarpments. Assess whether a finer resolution is warranted for your objective.

• Misleading color ramps: Elevation visuals are powerful, but color choices can obscure real differences. Use perceptually uniform color ramps and include a legend that makes the surface intelligible at a glance.

A few practical tips to keep your work crisp

• Start with a baseline: Use a widely available, consistent DEM for broad analyses and layer higher-resolution data where precision matters.

• Document your steps: A short notes trail—data sources, resolutions, vertical datums, and processing choices—saves time when others review or reuse your work.

• Compare and combine: Don’t lock into one data source. Overlay multiple DEMs and examine how the surface changes at boundaries or in challenging terrain.

• Stay curious about sources: Global DEMs are great, but local or national datasets—often produced with LiDAR or photogrammetry—can dramatically improve accuracy in your region of interest.

Why this matters for GEOINT and the certification landscape

In the NGA GEOINT field, working with terrain data isn’t just a niche skill; it’s a core capability. DEMs underpin analyses that inform risk assessments, mission planning, environmental stewardship, and infrastructure resilience. Understanding what a DEM represents, its limitations, and how to integrate it with other data layers is a practical, everyday competency for professionals in the field. The more comfortable you are with DEMs, the more smoothly you’ll translate sensor data, imagery, and intelligence into credible, defensible analyses.

A few closing thoughts

If you’re mapping the terrain of knowledge in geospatial intelligence, consider DEMs your first terrain companion. They’re not the flashiest data layer on the shelf, but they’re the sturdy workhorse that makes everything else possible. The next time you open a GIS project, take a moment to ask: What elevation story is the surface trying to tell me? Are my data sources aligned? Is the resolution right for the question I’m asking? Those small questions pay off with clearer maps, more reliable models, and a deeper understanding of the landscape you’re studying.

To keep growing in this space, stay connected with reliable data sources and practical workflows. USGS, NASA, and OpenTopography offer accessible datasets and tutorials, while software ecosystems from Esri, QGIS, and other GIS platforms provide hands-on ways to experiment with terrain analyses. DEMs aren’t just numbers on a screen; they’re a way to feel the lay of the land—and a gateway to smarter decisions in the real world.