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  • Product Sheet: Tree Cover Density

Tree Cover Density


Tree cover density, Turkey 2012 (Source: GeoVille/EEA)




Component products

Land Cover


  • N/A



  • Seismic Planning – Identification of adverse terrain for trafficability
  • Environmental monitoring – Baseline historic mapping of environment and ecosystems
  • Environmental monitoring – Continuous monitoring of changes throughout the lifecycle
  • Environmental monitoring – Natural hazard risk analysis

Geo-information requirements

  • Detailed land cover information
  • Critical habitat identification


Tree cover density, also referred to as canopy coverage or crown cover, is defined as the proportion of the forest floor covered by the vertical projection of the tree crowns. Tree cover density products display the level of tree cover density in a range from 0 (no trees) to 100% (full canopy coverage).

Tree cover density is a major factor in evaluating the forest status and is an important indicator for forest management interventions. Furthermore, tree cover density information is an important parameter to determine forest above-ground biomass.

The analyses can be conducted with very high resolution optical satellite images or LiDAR– each having certain advantages.

Tree cover densities are modelled on a continuous scale combining with two different methods. For one method, spectral information is used to calculate the probability of each pixel belonging to a certain class (0-100%), whilst the other method utilities density functions from sample plots (based on in-situ reference data or very high resolution imagery) are used for site-specific calibration.

Known restrictions / limitations

In tropical forest areas frequent cloud cover can be an issue for the production of imagery. Density samples are required to generate tree cover density. Thus either sample plots are needed or (even though the density can be derived from lower resolution data (HR2)) the availability of VHR1 or VHR2 data must be ensured for the generation of density samples based on visual interpretation.

Lifecycle stage and demand












  • Forest density information as a component to estimate biomass and thereby the value of the ecosystem.


  • Information about how forested areas may impact the planning of a seismic survey e.g. access limitations, potential ground conditions.


  • Identify and assess tree cover density to map important habitat and manage potential environmental impacts.
  • Mapping forest quality in a consistent manner is challenging, e.g. pristine vs. degraded forests and naturally closed to open or sparse forests. This decreases the uncertainties and risks that all important habitats have been identified and characterized.

Geographic coverage and demand

Demand and coverage is global, especially in heavily forested areas.


OTM:029 Prelicensing site selection
OTM:030 Ecosystem valuation of potential site
OTM:032 Detecting ecosystem damages
OTM:033 Mapping of environmental degradation (change)

HC:1204 Assess forest characteristics to plan access and assess hazards

HC:1205 Identify steep slopes to assess potential constraints to access in forested areas


Input data sources

Optical: HR1, HR2  

Supporting data:

  • VHR1, VHR2 (optical) for density sample generation
  • Digital elevation models (DEM) – LIDAR nDSM or optical or radar based VHR1 nDSM
  • In-situ information for calibration and validation

Spatial resolution and coverage

Spatial resolution: 30- 100 m pixel size

Minimum Mapping Unit (MMU)

n/a (the product is directly based on the input data; the smallest unit is one pixel)

The minimum mapping width is between 10 to 30 m.

Accuracy / constraints

The accuracy of the maps is related to the annual/seasonal variability on ground as well as the possibility to use ground data to validate and update the map contents. The geometric accuracy is less than 1 pixel which in the case of tree cover density is on the order of 4-30 m (related to pixel resolution) and typically accuracies of 80–90% are reached for the classifications.

Thematic accuracy: 80-90%

Spatial accuracy: one pixel, but depends on reference data

Accuracy assessment approach & quality control measures

Stratified random points sampling approach utilizing VHR reference or other geospatial in-situ data. Statistical confusion matrix with user’s and producer’s accuracy as well as kappa statistics for tree cover density.

Frequency / timeliness

Observation frequency: The frequency is constrained by satellite revisit and acquisition timeframes, but also processing requirements. Depending on the requirements of the customer the best suitable satellite sensor has to be chosen considering spatial / spectral resolution as well as revisit frequency. For change monitoring, a baseline assessment is needed, which then can be followed by annual monitoring of revegetation/regrowth. Typically, long-term changes are detected on a 3 to 5 year basis (frequency can be lower or higher depending on demand).

Timeliness of deliverable: Depending on size of the mapped area, resolution, MMU.


VHR1 data must be commercially acquired.

Delivery / output format

Data type:

  • Vector formats
  • Raster formats (depending on customer needs)

File format:

  • Geotiff or shapefile (standard - any other OGC standard file formats)

 Download product sheet.


Lead Author:GeoVille
Peer Reviewer:Hatfield Consultants


Maria Lemper, Jan Militzer

Document Title:

Tree cover density

# of Pages:



Internal – Project consortium and science partners


External – ESA



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