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  • Product Sheet: Land Cover Characterisation

Land cover & land cover change characterisation


Land cover map, Vienna, Austria 2014 (Source: GeoVille/ESA)



Component products

Land Cover


  • N/A



  • Environmental monitoring – Baseline historic mapping of environment and ecosystems
  • Environmental monitoring – Continuous monitoring of changes throughout the lifecycle
  • Environmental monitoring – Natural Hazard Risk Analysis
  • Logistics planning and operations – Facility siting, pipeline routing and roads development
  • Logistics planning and operations – Monitoring of assets
  • Logistics planning and operations – Support to surveying crews for planning surveys and H&S

Geo-information requirements

  • Critical habitat identification
  • Detailed land cover information


Land cover maps depict the physical characteristics of the Earth’s surface. The maps typically offer information on forest coverage and types, shrubs, grasslands, built-up areas, bare soil, water bodies, rivers, wetlands following standard classification schemes according to FAO Land Cover Classification System (LCCS) or CORINE Land Cover.

Land cover maps can be created from optical or radar satellite data. General cartographic information can be portrayed and feature extraction can be performed to transform image information to discrete land cover features. This is especially relevant to perform statistical GIS analyses or to update existing map data.

Whilst medium resolution satellite data with a resolution of 100 m and below is used to generate overview land cover maps on regional scale (1:1.000.000 and smaller), high (up to 10 m) and very high resolution data (up to 1 m and below) is used to generate detailed land use maps at the licence/project scale of up to 1:5.000).

Land cover can be directly extracted from satellite data.

Examples of product use are the following:

  • Baseline mapping to provide information about a site prior to development
  • Environmental Impact Assessment (EIA) or
  • Monitoring of subtle changes or impacts on ecosystems

By mapping land cover changes based on series of historic satellite images, changes between land cover classes can be calculated and quantified over time. For instance, a typical task is to determine the deforestation rate in a tropical forest region and to examine to which land cover the forest has been converted, e.g. bare soil, grassland or built-up. These analyses can be combined with GIS modelling to predict future scenarios. 

Known restrictions / limitations

In tropical rain forest areas frequent cloud cover can be an issue for the production of the maps but may be mitigated by combing radar and optical satellite images. The achievable size of mapped objects is depending on the used sensor and its resolution.

The number of classes desired by the end user may be challenging to derive from EO data alone, without ground-based on contextual data.

Lifecycle stage and demand











In all life cycle stages land use and land cover change information is important in order to ensure high corporate and social responsibility standards, good public relations and the ability to meet regulatory requirements. Ultimately, this is to ensure that the impact that O&G activities have on the environment is minimized.  Land cover and change information is likely to become increasingly critical to support ever increasing reporting obligations.

Pre-Licensing & Exploration:

  • Baseline information over a large area provides the ability to prove that operations are sustainable and comply with environmental legislation. Maintaining a reputation for being a responsible operator allows the O&G companies to continue to access new reserves.
  • Land cover information and their changes over large areas provide the ability to commence an Environmental Impact Assessment (EIA), if it is required already at an early stage. This helps to ensure that surveys are well planned, time on-the-ground is spent efficiently, and that the baseline is comprehensive.

Development & Production:

  • Land cover information and their changes over a large areas to monitor subtle changes or damages in ecosystems and their impacts on society. Furthermore, information on vegetation encroachment on assets can reduce access and/or damage the integrity of structures.
  • Land cover information and permits consistent change detection over vast areas which can provide critical information to the EIA.


  • Information on re-vegetation of a development site is a good indicator of ecosystem recovery.

Geographic coverage and demand

Demand and coverage is global.


OTM:013 Flagging environmentally sensitive areas prior to seismic surveys

OTM:017 Identification of seasonal environment changes e.g. migration patterns

OTM:019 Reconnaissance survey for EIA

OTM:023 Infrastructure planning

OTM:024 Encroachment on O&G assets

OTM:029 Prelicensing site selection
OTM:030 Ecosystem valuation of potential site
OTM:031 Creating an ecosystem inventory prior to exploration
OTM:032 Detecting ecosystem damages
OTM:033 Mapping of environmental degradation (change)

OTM:036 Geohazard exposure analysis

OTM:038 Planning secondary surveys

OTM:039 Selection of development sites
OTM:041 Vegetation encroachment on O&G asset

OTM:045 Identifying soft ground for seismic vehicles

OTM:047 Logistics planning for emergency events (emergency response planning)
OTM:057 Fire mapping
OTM:062 Monitoring re-vegetation

OTM:065 Floodplain mapping
OTM:069 Change detection for competitor intelligence

OTM:070 Understanding security situations
OTM:071 Planning around protected sites
OTM:072 Monitoring flash floods

OTM:073 Identifying sources of building resources

OTM:075 Creating basemaps in politically challenging regions

OTM:077 Validating co-ordinates of old wells

OTM:078 Remote supervision of operations

OTM:079 Identification of archaeological or burial sites


HC:1101 Identify areas with soft sediments to avoid strong attenuation

HC:1103 Identify soft and hard ground as areas of potentially poor source and receiver coupling

HC:1105 Identify permafrost zone for data analysis

HC:1203 Identify areas with soft sediments to plan access and assess hazards

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

HC:1211 Planning bridging through a tropical forest

HC:1301 Identify sensitive habitat to minimise and manage impacts of activities

HC:1302 Assess and map forest fire risk and provide situational awareness of fire occurrence.

HC:1303 Planning heliports, camps, and drop zones in forested areas

HC:2201 Identify geological structure through landform

HC:2401 Identify geohazards and landscape change rates

HC:3204 Monitor stability of surface reservoirs such as settling ponds

HC:3301 Monitoring carbon capture storage reservoir leaks

HC:4101 Assess fragmentation of natural habitat and cumulative disturbance

HC:4102 Land cover and land use for environmental baseline and/or impact assessment

HC:4103 Social baseline information to support compensation and/or resettlement

HC:4104 Mapping of forest extent and quality for environmental baseline and/or impact assessment

HC:4201 Remediation and reclamation monitoring

HC:4202 Map coastal habitat and built environment/settlement sensitivity to strengthen tactical oil spill response and preparedness>

HC:4203 Monitor "induced access" corridors to assess indirect impacts or effects as a result of project development.

HC:4204 Monitoring local communities and land use in the project area

HC:4205 Remediation monitoring related to agriculture impacts

HC:4207 Understanding and predicting changes in hydrological processes

HC:4209 Monitor onshore pipeline right of way (RoW) to evaluate successions of vegetation communities

HC:4302 Floodplain mapping and understanding flood extent and flood frequency.

HC:4306 Assess and manage forest fire risk to facilities and infrastructure

HC:5101 Obtaining baseline land use for pipeline route planning

HC:5303 Mapping land cover trends over the project area

HC:5307 Assess coastal environment for infrastructure planning

HC:5401 Monitor pipeline corridor hazards

HC:5402 Detection of oil contamination and oil seeps


Input data sources

Optical: VHR1, VHR2, HR1, HR2

Radar: VHR1, VHR2, HR1, HR2 (supporting optical data)

Supporting data:

  • Existing land cover information for calibration and validation (hydrographic features, irrigation areas, road infrastructure etc.)

Spatial resolution and coverage

Spatial resolution: 0.5 m to 1 km pixel size

Minimum Mapping Unit (MMU)

Minimum mapping unit (MMU) is depending on the used input data. For optical satellite data with 0.5 m spatial resolution this can be for example a MMU of 0.1-1 ha.

Accuracy / constraints

Thematic accuracy: 80-90%

Spatial accuracy: The goal would be 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, producer’s accuracy and kappa statistics for land cover. In-situ measurements.

Frequency / timeliness

Observation frequency: The frequency is constrained by satellite revisit and acquisition, 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. Typically, long-term changes are detected in 2 years or longer intervals.

Timeliness of deliverable: Depends on the size of the mapped area, resolution, MMU and number of mapped classes required.


Freely available or commercially acquired depending on the sensor selected.

Delivery / output format

Data type:

  • Vector formats
  • Raster formats (depending on customers’ needs)

File format:

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


Download product sheet.


Lead Author:OTM
Peer Reviewer:Hatfield Consultants


Maria Lemper, Jan Militzer

Document Title:

Land cover & land cover change characterisation

# of Pages:



Internal – Project consortium and science partners


External – ESA

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