Led by Marshall Burke and David Lobell, researchers at the Center on Food Security and the Environment are exploring new analytical techniques to harness data sets with the potential to solve challenges of food security. Voluminous data, including those obtained from satellite sensors, mobile phone carriers, or social media companies, provide opportunities to assess issues such as farm productivity, food prices, nutritional status, household assets, rural infrastructure, fishing patterns, human conflict, and social unrest. FSE is focused on the long-term opportunities in understanding and improving both food security and environmental quality through big data analysis.
Developing and testing remote-sensing based approaches to gather aid-relevant data on household assets, focusing on Sub-Saharan Africa.
Using high-resolution satellite imagery with household survey data to develop algorithms and measure crop yields to improve productivity and eventually reduce hunger.
Using data and spatial analysis to atribute local-level factors to health and child mortality.
Marshall Burke is an Assistant Professor in the Department of Earth System Science and Center Fellow at the Center on Food Security and the Environment at Stanford University, and Research Fellow at the National Bureau of Economic Research. His research focuses on social and economic impacts of environmental change, and on the economics of rural development in Africa.
David Lobell is a Professor at Stanford University in the Department of Earth System Science and Deputy Director of the Center on Food Security and the Environment. He is the William Wrigley Senior Fellow at the Stanford Woods Institute for the Environment, and the Freeman Spogli Institute for International Studies. His research focuses on agriculture and food security, specifically on generating and using unique datasets to study rural areas worldwide.
Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries
Remote Sensing of Environment, May 2017
Satellite-based assessment of yield variation and its determinants in smallholder African systems
Proceedings of the National Academy of Sciences, February 2017
Using remotely sensed temperature to estimate climate response functions
Environmental Research Letters, January 2017
Sources of variation in under-5 mortality across sub-Saharan Africa: a spatial analysis
Lancet Global Health, October 2016
Combining satellite imagery and machine learning to predict poverty
Science, August 2016
Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence
Global Change Biology, November 2015
Stanford researchers have determined that more than 15 million children are living in high-mortality hotspots across 28 Sub-Saharan African countries, where death rates remain high.
Using high-res images taken by the latest generation of compact satellites, Stanford scientists have developed a new capability for estimating crop yields from space. Measuring yields could improve productivity and eventually reduce hunger.
A Stanford-led team has discovered how to estimate crop yields with more accuracy than ever before with satellites that measure a special form of light emitted by plants.
Machine-Learning Software Scans Satellite Images to Find Hidden Poverty
Marshall Burke from Stanford University has used satellite data and a clever computer programme to predict areas of poverty in African countries. The computer programme searches for patterns that indicate poor locations.