Earth Observations for Detecting and Characterizing LSLAs
Dr. Evan Ellicott
UMD Geographical Sciences
Monday May 6, 2019, 12:00-1:00 PM
ESSIC Conference Room 4102, 5825 University Research Ct, College Park, MD 20740
Abstract:
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.
Numerous organizations monitor land transactions and offer details about investments to increase awareness and transparency. Perhaps the largest source of such information is provided by the Land Matrix (landmatrix.org). The Land Matrix Initiative (LMI) monitors and describes thousands of land transactions across the globe for various intentions, from agriculture to mining. The LMI has developed a methodology to collect data from a variety of sources including NGOs, international organizations, company websites, and media reports. In addition, partnerships with national-level organizations lend insight and credibility to the data collection. However, the question we posed is what information can be added from remote sensing (RS), particularly when the geospatial location of a deal is not certain or unknown.
The application of remote sensing for land cover and land use change (LCLUC) delivers a perspective unachievable on the ground. Satellites improve the use of RS through 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 studies.
Here, I present LSLAs as ‘telecoupled’ processes with distinct archetypical causal pathways through which agricultural commodity production leads to direct and indirect land-use changes. Using the case of Economic Land Concessions (ELCs) in Cambodia, we employ novel synthesis approaches combining remote sensing, spatio-temporal statistics, and case study meta-analysis to construct archetypical pathways of the causes, timing, and consequences of ELC-driven land change. I will then follow up with a developing framework to monitor, identify, and characterize LSLAs using a combination of different spatial, temporal, and radiometric resolutions for a variety of investment intentions.
Bio-sketch:
Dr. Evan Ellicott is an Associate Research Professor in the Department of Geographical Sciences at the University of Maryland. His research interests include the exploration of geospatial data to describe and characterize biophysical phenomena and the interactions with society. A primary focus of his research is biomass burning, specifically wildland fires, in a changing physical and social environment. He explores methods to estimate biomass burning emissions and fuel consumption using satellite and in-situ measurements. Another component of his research is focused on investigating land-cover and land-use change as a result of globalized teleconnections of commodities trade and the phenomenon referred to as large-scale land acquisitions (LSLA).
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