A Canopy Height Model (CHM) is a raster product created by subtracting the DSM from the DTM; this result leaves only the height above ground. In non-forest areas, this is also called a normalized DSM (nDSM), showing the elevation values normalized to the underlying terrain. A common use for this is identifying and classifying tree canopies. You can see the green polygons generated that delineate the tree canopy extents. Tree point features can also be generated.

This is a type of raster that shows the compass direction that each slope in a terrain faces, measured in degrees from 0° (north) to 360° (north again). This information is useful for understanding sunlight exposure, wind patterns, and other environmental factors affecting the terrain. For example, if you are planting vegetation for remediation and want to identify a specific exposure, this product can be used to help identify build a planting prescription.

This derivative distinguishes topographic features such as uplands from other lower depressions or valleys. This is calculated by comparing the elevation of each pixel to its surrounding neighbors. This product can be used to analyze terrain for identifying landscape types that are common in certain areas; for example, lowland features have a higher likelihood of containing wetlands. This can also be a helpful visualization tool on maps.

TPI can be adjusted based on the size of the neighboring data. Decreasing this window increases the detail of the results but reduces the relative results across the study area, causing more intense differences to be identified.

With a small neighborhood value and high-resolution data, the results can become less valuable when working across a large study area; still it can be valuable when looking for small area changes.

This is a measurement of the gradient or steepness of each cell of the raster dataset. The example provided is showing slope in degrees, but it can also be converted to percent slope depending on the needs of the client. This information can be used to identify hazard areas or areas of potential slope instability when paired with soil and vegetation coverage information. Slope can also be a critical layer when calculating riparian potential (another CARD Stack tool).

This product uses surface elevation data to identify landscape depressions. This can be used to highlight areas where flow might accumulate and to identify depressions that may be hard to see from a visual review of a more basic derivative like a hillshade.

The TWI incorporates elements of flow accumulation and depression detection to create a data layer for identifying low-lying areas. It also separates a low area with less upstream catchment from a low area with a larger upstream catchment, making it a strong tool for analysis on wetlands and riparian identification.

Volumetrics can be calculated using a mesh dataset generated from the source point data. Once a delineation of the target features is generated (either manually or through deep learning), a cut and fill attribute is calculated representing the area volume in m³.

This point data can be captured from LiDAR or imagery, and we offer photogrammetry processing as a service. Point cloud data can also be used to render 3D models and visualizations of features or landscapes. By generating mesh surfaces from the points, we can help provide a new perspective for marketing, planning, and public engagement projects.