Land degradation assessment with multi-sensor data and machine learning algorithms

Land degradation is the temporary or permanent decline in the productive capacity of the land, and the diminution of the productive potential, including its major land uses (e.g., rain-fed arable, irrigation, forests, rangelands), its farming systems (e.g., smallholder subsistence), and its value as an economic resource. Land degradation is the single most pressing current global problem, causing a reduction in productivity, loss of ecosystem services, and threatens the survival and development of humankind for present and future populations.

It is caused by a complex of interacting environmental and anthropogenic factors, such as competing socio-economic activities, poor land management practices, and climate change. The global goal is to achieve Land Degradation Neutrality (LDN), which is defined as ‘a state whereby the amount and quality of land resources, necessary to support ecosystem functions and services and enhance food security, remains stable or increases within specified temporal and spatial scales and ecosystems’.

The aims of the project is to:

  • Assess the status and impacts of land degradation induced by various environmental and socio-economic factors using satellite data from various sensors and machine learning algorithms at various geographical scales; and
  • Develop cutting-edge methodologies relevant for characterising various aspects of land degradation, towards LDN and support reporting for Sustainable Development Goals (SGDs) indicator 15.3.1 - Proportion of land that is degraded over total land area.