Source: http://www.dhi-gras.com/news/2015/12/18/globwetland-africa-implementing-sustainable-eo-based-wetland-monitoring-in-africa

Studentinformatie

MSc thesis subject: Mapping Wetland Areas using Sentinel 1 and 2 Data

Wetlands provide valuable services for the ecosystem and as well as food security. Due to human and natural factors, wetlands are endangered. It is estimated that globally, more than 50% of wetlands were lost during the twentieth century.

To protect wetland ecosystem, the 1971 Ramsar Convention aims to support the maintenance of wetland ecology. To maintain wetlands ecological functions as well as food security functions, spatially explicit information on wetland distribution and extent is vital. It is further needed for a variety of purposes from regional wetland and biodiversity management to understanding wetland roles on climate change at continental or global scales.

Remote sensing provides opportunities to characterize and monitor wetland areas and many studies previously attempted to characterize wetland areas at different scales. However, there are considerable uncertainty about their distribution and extent as existing wetland area estimations at large scales differ significantly while increasing the uncertainty of its effect on climate change.

Wetland areas can have different types of natural vegetation such as grassland, shrubs and forests and it can differ in terms flooding frequency such as waterlogged or regularly flooded. While different vegetation types can be mapped with optical remote sensing data, flooding frequency can be better mapped using radar data.

ESA’s Sentinel satellite program has significant potential uses. The Sentinel-1 satellite includes a radar system with the capability of collecting cloud-free water- and moisture-specific data. Sentinel-2 incorporates a fine spatial resolution 10-meter multispectral sensor and will orbit with a short, five-day revisit time. The combination of these radar and optical systems holds great promise for wetland mapping. With the use of Sentinel 1 and 2 data, mapping different types of wetland areas will be tested in an African region. A successful implementation of wetland mapping can be fed into an operational and large scale mapping activities.

Objectives
To map different wetland types in an African region using Sentinel 1 and 2 data.

Literature

Requirements

  • R or python scripting skills
  • Affinity to work with optical and radar remote sensing data

Theme(s): Modelling & visualisation, Integrated Land Monitoring