Objective
Today, more than 50% of the Earth’s surface is covered by areas prone to land degradation and desertification (LDD). It is widely recognized that an understanding of LDD is central to the debate of global environmental change and sustainability. However, for all its importance to scientists and policy-makers confronting the complexities of environmental change, LDD is still poorly understood.
One of the most seriously affected regions in the world is South Africa (SA) with ~440,000km2 vulnerable to some extent. Through desertification, soil in SA has lost 25% or more of its fertility and the process is ongoing; moreover, large scale erosion and desertification have led to food insecurity in several areas.
Quantifying the processes of LDD is still very much a formidable, if not an impossible, task due to its complex nature and the use of desertification indices has become a common practice as a way around this hurdle. Remote sensing data adhere to the principles of repetitiveness, objectivity and consistency, which are prerequisites in the frame of monitoring and surveillance. However, no indicator of LDD is directly inferable from satellite-based data. Suitable indirect indicators need to be chosen, which can be related to LDD processes that operate in the specific area and scale of study. Moreover, studies of LDD must simultaneously consider both human- and environmental-based variables.
In this context, this research will seek to assess LDD in the pilot-study area of North West Province of South Africa over the last twelve years. An operational, nested indicator appraisal system, LanDDApp, will be developed comprising of quantitative and qualitative LDD indices estimated using satellite imagery, field measurements, socio-economic data and geocomputation techniques. LanDDApp shall be used for identifying degradation ‘hot-spots’ where mitigation measures are required and thus provide a management tool for the prioritisation of such measures.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringsatellite technology
- social sciencessociologydemographyfertility
- engineering and technologyenvironmental engineeringremote sensing
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Call for proposal
FP7-PEOPLE-2012-CIG
See other projects for this call
Coordinator
M15 6BX MANCHESTER
United Kingdom