Land use & land use change characterisation
Land cover and land use mapping, Sullom Voe Terminal, Shetland Islands 2014 (Source: GeoVille)
Land use maps describe not only the physical characteristics of the Earth’s surface (land cover), but also the actual anthropogenic usage on the ground. The product differentiates basic land cover types (e.g. built-up areas) into different use classes, such as residential areas, industrial complexes, roads, buildings, or cropland.
Land use maps can be created from optical or radar satellite data, but often require additional reference information to characterise the actual land use (e.g. if vegetated areas are grown naturally or under a crop rotation cycle). Features can be extracted from the data and converted to GIS vector formats. The precision and detail of land use information is dependent on the input data resolution. This is especially relevant for features such as roads, buildings and other structures in order to perform GIS analyses or to update existing map data.
Land use cannot be clearly extracted from satellite data in a direct way. That means some forms of utilisation must be derived with the help of multi-temporal analysis, like agricultural areas for example, whilst extraction of road features depends on visual interpretation. For precise land use information additional in-situ information are mandatory.
Whilst medium resolution satellite data with a resolution of 100 meters and below is used to generate overview maps on regional scale (1:1.000.000 and smaller), high (up to 10m) and very high resolution data (up to 1m and below) is used to generate detailed land use maps at the licence/project scale of up to 1:5.000).
Examples of use include the following:
Known restrictions / limitations
In the inner and outer tropics 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.
Further limitations of mapping land use classes are given by the spectral information of satellite data. Meaning land use cannot be clearly extracted from satellite data in a direct way. Some forms of utilisation can be derived with the help of multi-temporal analysis such as agricultural areas for example, whilst others like roads depend on visual interpretation. For precise land use information additional in-situ information are mandatory.
Lifecycle stage and demand
In all life cycle stages land use and land use change information are important, for both the lead organisation’s reputation and obligation to the environment, to ensure that the impact O&G activity has on the environment is minimized.
Development & Production:
Geographic coverage and demand
Demand and coverage is global. Areas with high wave energy, proximate to human populations or high value ecological systems will be of special concern.
OTM:023 Infrastructure planning
OTM:024 Encroachment on O&G assets
OTM:029 Prelicensing site selection
OTM:036 Geohazard exposure analysis
OTM:078 Remote supervision of operations
HC:1215 Identify UXO related hazards
Input data sources
Optical: VHR1, VHR2, HR1, HR2
Radar: VHR1, VHR2, HR1, HR2 (supporting optical)
Supporting data: Spatial thematic data (use information); Existing land cover information
Spatial resolution and coverage
Spatial resolution: 0.5-30 m pixel size
Minimum Mapping Unit (MMU)
MMU is depending on the used input data. For 0.5m input data it is between 25 m² and 50 m² for example.
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 use.
Frequency / timeliness
Observation frequency: The frequency is constrained by satellite revisit and acquisition, but also processing requirements. While the minimum frequency is technically driven by the revisit cycle of the satellite, the maximum frequency is defined be the customer. Depending on the requirements of the customer the most suitable satellite sensor has to be chosen considering spatial / spectral resolution as well as revisit frequency. Typically, long-term changes are detected on a yearly basis or longer intervals (frequency can be lower depending on demand).
Timeliness of deliverable: Depending on size of the mapped area, resolution, MMU and number of mapped classes.
Freely available or commercially acquired depending on the sensor selected.
Delivery / output format
Maria Lemper, Jan Miltizer
Land use & land use change characterisation
# of Pages:
Internal – Project consortium and science partners
External – ESA
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