In This Space
Image credit: Hatfield Consultants
EO data offer a cost efficient method for identifying and monitoring wet areas.
Optical sensors can effectively delineate herbaceous/open wet areas based on distinctive spectral characteristics and spatial heterogeneity.
Radar sensors can effectively identify wet areas, and may compliment optical data, or provide unique information. Mapping marshes or wet areas under tree canopy can benefit from radar data. Radar backscatter is sensitive to dielectric properties (soil and vegetation moisture content), geometric (surface roughness) attributes of wet area surfaces, and presence of standing water. Longer L-band wavelength provide better penetration of forest cover to determine wet or flooded status of the ground surface. Shorter C-band based wavelengths provided information for herbaceous wet area detection. L- and C-band data are needed to separate forest from herbaceous wetland as well as from other land cover.
Archived optical and/or radar data can be used for historical mapping of wet areas in comparison with current conditions. Taking into account that wet areas may be ephemeral and not present during baseline data acquisition, multi-temporal data (particularly radar) may reveal seasonally wet areas.
Modelling of the terrain and prediction of potentially wet areas is valuable. Depending on the scale required this can be achieved using satellite-derived elevation datasets of various resolutions. The use of elevation data derived drainage provides improved scope for localisation of ephemeral wet areas, which appear after long period of rain or with higher than usual groundwater levels.
The wet areas product delivers maps showing the location and extent of wet areas (e.g., marshes, ephemeral wet areas).
Known restrictions / limitations
Historical wet area mapping depends on the available archive of images, and optical images may be affected by cloud cover. A potential limitation with wet area extent products is the presence of dense forest vegetation within the water/wet areas, and in some systems where water levels and extent can change considerably (e.g., marshes). Longer wavelength radar sensors (e.g., PALSAR-2 with L-band) mitigate this issue to some extent.
The real resolution of radar data and products may be reduced due to effects of image speckle and the need to filter images and output products.
Prediction of ephemeral wet areas is dependent on the availability and quality of DEMs – mapping of subtle changes in elevation may require elevation data with accuracy that can be obtained using LiDAR.
Wet areas extent is connected to surface hydrology and groundwater levels; wet area modelling should take these factors into account.
Lifecycle stage and demand
Pre-License: Information on existing wet areas is important early in the evaluation of a prospect.
Exploration: Wet area mapping supports the effective management of environmental sensitivities as well as the movement of equipment and people. Knowledge of wet areas can also contribute to more effective land seismic survey.
Development: Information on wet areas extent (wetlands, lakes, etc.) supports the environmental impact assessment for development and effective management and mitigation of impacts.
Production: Information supports water management for operations, including appropriate environmental setback distances.
Decommissioning: Information supports water management for decommissioning.
Geographic coverage and demand
Coverage and demand is global, but of particular utility in less developed countries or for remote areas.
OTM:036 Geohazard exposure analysis
OTM:038 Planning secondary surveys
OTM:065 Floodplain mapping
Input data sources
Optical: VHR1, VHR2, HR1
Radar: HR1, HR2, MR1
Spatial resolution and coverage
Spatial resolution: 0.5–10 m
VHR1, VHR2, HR1, HR2 depending on the desired coverage (Regional, Basin, or Project level).
DEM vertical resolution should fit to the reflectance data, which in the case of VHR1 and VHR2 may require obtaining it from other sources (local databases, SAR, LiDAR).
Minimum Mapping Unit (MMU)
Variable, depending on source data resolution.
If raster-based products are delivered the product is constrained by the elevation model pixel resolution.
Accuracy / constraints
Thematic accuracy: 80-90% in areas of low vegetation cover and density.
Spatial accuracy: The goal would be 1 pixel, but depends on reference data.
Accuracy assessment approach & quality control measures
Comparison with ground based observations.
Frequency / timeliness
Observation frequency: Dependent on the radar sensor and beam mode (resolution/extent) selected. Frequency of historical maps is highly variable depending on the archive.
Timeliness of delivery: Delivery time need not be rapid, but data will be required before specific project phases, e.g., before exploration and development, and prior to or coincident with decommissioning.
On-demand availability from commercial suppliers.
New acquisitions can be requested globally.
Archived products available for public search. Availability may be limited for specific dates and locations.
Delivery / output format
# of Pages:
Internal – Project consortium and science partners
External – ESA
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