Yasmine is one of the #DigitAg co-funded PhDs
From satellite images to land-use systems: detection and characterization of large-scale land acquisitions from Earth Observation data
- Start Date: 1st November 2020
- University: University of Montpellier
- PhD School: GAIA
- Fields: Geomatic – Remote sensing
- Doctoral Thesis Advisors: Anne-Elisabeth Laques (IRD, UMR Espace-Dev), Agnès Bégué (CIRAD, UMR Tetis)
- Co-supervisor: Valentine Lebourgeois (CIRAD, UMR Tetis)
- Funding: Contrat doctoral University of Montpellier
- #DigitAg: Funded PhD – Axis 5: Data Mining, Data Analysis and Knowledge Discovery – Challenge 0: cross-cutting subject & Challenge 6: ICT and agricultural territory management
Keywords: LSLA, land use and land cover change, SITS, NDVI, change detection
Abstract: Large scale land acquisitions (LSLAs), often referred to as “land grabbing”, refers to the control of large pieces of land by individuals, states or companies for agricultural purposes, logging, tourism, conservation, mining, urban expansion or large infrastructural works. This study deals with agricultural LSLAs, the most common type of LSLAs. Given the availability of favourable biophysical resources and the lack of strong land tenure regulations, those investments are most prevalent in developed countries (75% in Africa). Because information of those acquisitions is scarce and difficult to obtain, systems allowing LSLAs detection, characterization and monitoring in space and time are needed.
With the increasing availability of global satellite data products, technological development in cloud computing, image and data mining analysis, remote sensing (RS) has appeared to be an interesting tool. Their repetitive coverage at short intervals and consistent image quality, combined with the free-of-cost availability of dense temporal series of satellite images, have explained their wide use in land use and land cover change detection studies. However, because LSLAs are the manifestation of complex human-environment dynamics in a given place, they are not directly observable from RS images. While their detection is often impossible based on land cover observations only, these land-use systems may be inferred from observable activities, structural elements in the landscape or spatiotemporal characteristics at different scales. This research aims to explore the potential of RS data to detect and characterize agricultural LSLAs at different scales. The challenge here is to relate the radiometric signal, which is sensitive to the biophysical properties of the surface, to the land use system in place. In this research, RS indicators and methods will be reviewed and a conceptual approach will be proposed and tested on a set of study cases in Senegal.
Social Media: ResearchGate