“Unexploded ordnance and agricultural productivity: Using remote sensing to detect the long-term impact of U.S. bombing of Cambodia”❯×
Erin Lin, PI; Assistant Professor, Department of Political Science, College of Arts and Science
TDA affiliate Rongjun Qin, Co-PI; Assistant Professor, Department of Civil, Environmental and Geodetic Engineering and Department of Electrical and Computer Engineering, College of Engineering
The weapons left behind from war—carpet bombs, cluster munitions, landmines—pose a significant risk to the communities left to live on that land. However, unexploded ordnance (UXO) are difficult to find, oftentimes hidden underneath several inches of ground. It remains a challenge for farmers, clearance teams, and aid organizations to identify contaminated areas.
Instead of traditional, high-risk in-person surveying, we will develop a remote-sensing method to identify the location of unexploded ordnance. This method combines declassified U.S. Air Force data on payload drop sites from the secret bombing of Cambodia with high-resolution maps of terrain features and contemporary vegetation. By developing machine-learning methods on the images, we can identify an important element of post-bombardment geography: bomb craters, which provide a physical evidence of the number of bombs that have detonated within each drop zone. The U.S. Air Force data provide the number of bombs that were dropped in each payload, with the coordinates of each drop site. Since, for each payload, we know the total number of bombs dropped and the number of bombs detonated, we can estimate the number and density of unexploded ordnance left within each drop zone.
As part of the process, we will use moderate resolution multispectral imagery to identify crop activity and yield, a new method that employs both temporal and reflective spectral statistics to generate granular data on cultivation and crop productivity. These remotely sensed images have been captured, rendered, and disseminated every eight days since 1998 through a U.S. government initiative, so we can develop a rich sense of land use patterns for the past two decades.
The impact of UXO on the quantity of arable land can be estimated from these data using maximum likelihood techniques or a threshold regression model. By the end of data collection, we will be able to estimate the degree to which UXO change the total area of active agricultural land, and how persistent this effect is, from 1998 to present day. The impact of UXO on arable land will be reported to raise the awareness of the importance of de-mining.
Award amount: $29,960