This proposal directly addresses the first component of the NASA lcluc announcement: identifying hotpot areas using multi-source data approaches to quantify land-cover and land-use changes (lclucs)…

The University of Vermont & State Agricultural College has been awarded a $797,605 research grant by NASA to identify land-cover and land-use change hotspots in critical agricultural regions using multi-source data approaches and machine learning algorithms. The project directly addresses NASA's Land Cover/Land Use Change program announcement and aims to quantify changes including cropland expansion, intensification, and abandonment across rangelands. By integrating socioeconomic and environmental datasets, the research team—comprised of social and natural scientists with over four decades of combined experience in Brazilian land-use development—will analyze farmer decision-making processes under climate change conditions in globally significant breadbasket regions. The grant was awarded on January 25, 2023, with an ultimate completion date of January 24, 2027, and carries no set-aside designation.

This research effort leverages the interdisciplinary expertise of senior investigators including Galford, Fisher, Spera, Macedo, and Rattis, who bring established track records in generating novel land-cover/land-use change datasets and conducting socioeconomic analyses in Brazil. The project will employ statistical, empirical, and process-based modeling approaches to produce integrated datasets and analytical frameworks that advance understanding of human-environment interactions in response to climatic variability. As a public land-grant research institution and Vermont's primary research university, UVM is well-positioned to execute this work, having demonstrated substantial federal contracting experience across multiple agencies including NASA, the U.S. Department of Agriculture, and the National Science Foundation.

This proposal directly addresses the first component of the NASA lcluc announcement: identifying hotpot areas using multi-source data approaches to quantify land-cover and land-use changes (lclucs) including the expansion, intensification, and abandonment of croplands and rangelands. We will leverage machine learning algorithms to develop novel data sets of lcluc and land use transitions (luts) with our ultimate goal being to integrate socioeconomic and environmental datasets to better understand farmers decision making under a changing climate in a critically endangered global breadbasket. Below we detail the key objectives, methods, and significance of the work. The project personnel, which include social and natural scientists, have a proven history of producing deeply integrated work on environment and socioeconomics. Galford, fisher, spera, macedo and rattis have a combined experience of > 4 decades working on brazilian land-use development, including generating novel lcluc datasets applying statistical, empirical and process-based models analyzing socioeconomic data and surveys and collaborating with researchers and students.