Earth Observations for Detecting and Characterizing LSLAs

Authors: Evan Ellicott*, University of Maryland - College Park, Kaspar Hurni, Centre for Development and Environment (CDE), Ariane de Bremond, Global Land Programme (GLP), Nicholas Magliocca, University of Alabama
Topics: Land Use and Land Cover Change, Remote Sensing, Human-Environment Geography
Keywords: remote sensing, land deals, acquisitions
Session Type: Paper
Day: 4/5/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded


The acquisition of land by domestic and foreign agents is not a new phenomenon, but the pace and extent to which land investment is occurring has drawn attention to linkages between global economies and local issues of land tenure, water rights, and social justice.
The Land Matrix Initiative (LMI) monitors and describes thousands of land transactions across the globe for various intentions and is perhaps the largest source of information about large-scale land acquisitions (LSLAs). The LMI has developed a robust methodology to collect data and verify deals.
However, the question we pose is what information can be added from satellite-based remote sensing (RS). The application of remote sensing for land cover and land use change (LCLUC) delivers a perspective unachievable on the ground, such as regular, large-area observations. The challenge in applying RS for LCLUC analysis is first understanding biophysical conditions for the region of interests and then choosing the appropriate spatial and temporal scales for the change being studied.
We present a framework to monitor, identify, and characterize LSLAs using a combination of spatial, temporal, and radiometric resolutions for a variety of investment intentions. LSLA data, as well as area potential areas of investment to monitor, are provided by the LMI and their national partners. We conclude that RS offers early detection through regular monitoring, as well as sufficient information to characterize the timing, extent, and type of change. Drawing on several exemplars, we discuss the contextual nature of LSLAs and the considerations needed when applying RS.

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