Vertical Error
Difference between DEM-observed and reference height.
Δz=zDEM−zref
Vertical Accuracy Assessment
Assess DEM vertical accuracy by sampling DEM elevation at independent point checkpoints with known reference elevation.
Accuracy & RectificationEnd-User DocumentationTheory + Formula + Parameters1. What This Feature Does
Assess DEM vertical accuracy by sampling DEM elevation at independent point checkpoints with known reference elevation.
Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.
2. Recommended Workflow
Load the DEM and independent point checkpoints.
↓
Select the reference elevation field.
↓
Confirm both DEM and checkpoints use the same vertical datum/unit.
↓
Run the assessment.
↓
Review bias, MAE, RMSEz and empirical LE percentiles.
3. Theory, Methods & Equations
RMSEz
Quadratic vertical error summary.
RMSEz=√[(1/n)ΣΔz²]
MAE
Mean absolute elevation error.
MAE=(1/n)Σ|Δz|
LE50 / LE68 / LE90 / LE95
Empirical percentiles of absolute vertical error.
LEp=Percentilep(|Δz|)
4. Input Data
| Input | Type | Requirement | Description |
|---|---|---|---|
| DEM | raster | Required | DEM raster. Band 1 is sampled automatically. |
| Vertical Check Point Layer | vector | Required | Uploaded POINT checkpoint layer normalized to EPSG:4326. |
5. Parameters Available in the Application
| Parameter | Default | Choices / Range | How to Use It |
|---|---|---|---|
| Reference Elevation Field | — | — | Numeric checkpoint Z/elevation field. Reference Z and DEM Z must use metres and the same vertical datum. |
6. Output & Interpretation
The result should be interpreted according to the selected method and the physical meaning of the input data. Preserve source units, coordinate reference information, NoData meaning, acquisition date, and preprocessing level when comparing results.
Scientific interpretation: an algorithm can produce a numerically valid result even when the input data are unsuitable. Always check masks, units, sensor characteristics, spatial resolution and reference data.
Important: A vertical-datum mismatch can dominate the error statistics. Confirm orthometric/ellipsoidal height conventions before interpreting accuracy.
7. Best Practices
- Use analysis-ready inputs and remove invalid/cloud/noise artifacts that are not part of the target phenomenon.
- Choose parameters from the physical scale of the data, not only from visual appearance.
- Keep categorical and continuous rasters conceptually separate when selecting interpolation/resampling methods.
- Compare the result with the source image and independent reference information.
- Document the settings used when results will be compared across dates, sensors or study areas.