GC068
Quantifying impacts in socio-environmental systems through the application of remote sensing and machine learning

Session ID#: 77863

Session Description:
Increases in the quantity and resolution of earth observing satellites are rapidly transforming our ability to monitor and manage global challenges from environmental degradation to poverty alleviation. At the same time, the recent development and application of machine learning techniques to remote sensing enables us to extract more complex social and environmental information from satellite imagery. Together, these trends present novel opportunities to quantify important relationships in coupled socio-environmental systems, from estimating the economic impacts of climate change to evaluating the ecological impacts of environmental policy. We invite two categories of submission to this session. First, research that leverages remote sensing and machine learning to answer policy-relevant questions within environmental science, economics, and particularly at the intersection of these two fields. Second, research that integrates machine learning with remotely sensed information to advance methodologies for monitoring key social and environmental indicators.
Co-Organized:
  • GH - GeoHealth
  • SI - Societal Impacts and Policy Sciences
Index Terms:

0230 Impacts of climate change: human health [GEOHEALTH]
1630 Impacts of global change [GLOBAL CHANGE]
1640 Remote sensing [GLOBAL CHANGE]
4337 Remote sensing and disasters [NATURAL HAZARDS]

Primary Convener:  Ian Wesley Bolliger, University of California Berkeley, Energy and Resources Group, Berkeley, CA, United States
Conveners:  Tamma Carleton, University of Chicago, Economics, Chicago, IL, United States and Jonathan Proctor, University of California Berkeley, Agricultural and Resource Economics, Berkeley, CA, United States
Primary Liaison:  Ian Wesley Bolliger, University of California Berkeley, Energy and Resources Group, Berkeley, CA, United States
 
Estimation of Poverty Residents Distribution Using Satellite Images and Household Survey Data (498030)
Kohei Okuda, Univ. of Tokyo, Civil Engineering Department, Tokyo, Japan and Akiyuki Kawasaki, University of Tokyo, Department of Civil Engineering, Tokyo, Japan
 
Generalized Planetary Remote Sensing (499003)
Esther Rolf, University of California Berkeley, Berkeley, United States, Jonathan Proctor, University of California Berkeley, Agricultural and Resource Economics, Berkeley, CA, United States, Tamma Carleton, University of California, Berkeley, Berkeley, CA, United States, Ian Wesley Bolliger, University of California Berkeley, Energy and Resources Group, Berkeley, CA, United States, Vaishaal Shankar, University of California Berkeley, Electrical Engineering and Computer Science, Berkeley, CA, United States, Miyabi Ishihara, University of California Berkeley, Statistics, Berkeley, CA, United States, Ben Recht, University of California Berkeley, Berkeley, CA, United States and Solomon M Hsiang, University of California Berkeley, Goldman School of Public Policy, Berkeley, CA, United States
 
Quantifying Drought Effects on Waterfowl Food Resources in California’s Central Valley Through Time-Series Remote Sensing and Machine Learning (502575)
Kristin B Byrd, USGS Western Regional Offices Menlo Park, Menlo Park, CA, United States, Austen Lorenz, U.S. Geological Survey, Dixon, United States, James Anderson, U.S. Geological Survey, Menlo Park, CA, United States, Cynthia Wallace, USGS Arizona Water Science Center, Tucson, AZ, United States, Kara A Moore-O'Leary, US Fish and Wildlife Service, Pacific Southwest Region, Sacramento, CA, United States, Jennifer E Isola, Sacramento National Wildlife Refuge Complex, Willows, CA, United States, Ric Ortega, Grassland Water District, Los Banos, United States and Matthew Edward Reiter, Point Blue Conservation Science, Petaluma, CA, United States
 
A Bayesian Modeling Approach for Improved Land Use Classification (503037)
Rachel Ulrich, Bozeman, MT, United States, Scott L Powell, Montana State University Bozeman, Bozeman, MT, United States, Mark C Greenwood, Katharine Banner, Montana State University, Mathematical Sciences, Bozeman, MT, United States, David Theobald, Los Alamos, NM, United States and Andrew Hansen, Montana State University, Bozeman, MT, United States
 
Beyond Night Light: Using High-Resolution Daytime Satellite Data in Economics (507850)
Yue Huang, University of California, Berkeley, Agricultural and Resource Economics, Berkeley, CA, United States
 
The spatial and temporal patterns of impervious surface and their socioeconomic impacts in a Chinese rapid urbanized area—A hybrid of remote sensing and machine learning approaches. (512458)
Liangji Kong, Peking University, Beijing, China, Runzi Wang, Michigan State University, East Lansing, MI, United States and Chong Liu, Jiangxi Normal University, Nanchang, China
 
Monitoring Shifts in Flowering Phenology Using Satellite Imagery and Deep Learning (521974)
Joséphine Gantois, Columbia University of New York, Sustainable Development, New York, NY, United States
 
Voluntary, Permanent Land Protection Reduces Forest Loss and Development in a Rural-Urban Landscape (523498)
Christoph Nolte, Boston University, Boston, MA, United States, Katharine Sims, Amherst College, Amherst, United States, Spencer Meyer, Highstead Foundation, Redding, CT, United States and Jonathan Thompson, Harvard University, Harvard Forest, Cambridge, United States
 
Vulnerability and resilience in California forests (Invited) (552250)
Philip Brodrick, Arizona State University, Center for Global Discovery and Conservation Science, Tempe, AZ, United States
 
Using satellite imagery to understand and promote sustainable development (Invited) (569069)
Marshall Burke, Stanford University, Earth System Science, Stanford, CA, United States and David B Lobell, Stanford University, Stanford, CA, United States
 
Quantifying the economic effects of narcotics trafficking in Mexico using remote-sensed measures of illicit crop production (592065)
Jay Sayre and Alejandro J. Favela Nava, University of California Berkeley, Agricultural and Resource Economics, Berkeley, CA, United States
 
US Environmental Protection Agency EnviroAtlas Meter-Scale Urban Land Cover (MULC) data, machine learning and ecosystem goods and services (EGS): information for healthy communities (603906)
Andrew N Pilant1, Shanti Shrestha2, Gillian Gundersen2, Daniel Rosenbaum3, Megan Van Fossen4, John Lovette2 and Keith Endres5, (1)US Environmental Protection Agency, Durham, NC, United States, (2)Oak Ridge Associated Universities, Research Triangle Park, NC, United States, (3)Oak Ridge Institute for Science and Education, Research Triangle Park, NC, United States, (4)US Environmental Protection Agency, Research Triangle Park, NC, United States, (5)US Environmental Protection Agency, Sensing and Spatial Analysis Branch, Research Triangle Park, NC, United States
 
Classifying the Complexity of Coupled Energy-Water-Land Systems in the United States (607924)
Jennie Rice1, Chris R Vernon2, Sean William Donald Turner3, Casey D. Burleyson1 and Ryan McManamay4, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Pacific Northwest National Lab, Richland, WA, United States, (3)Pacific Northwest National Laboratory, Richland, United States, (4)Baylor University, Waco, United States
 
Mapping health indicators from remote sensing imagery: machine learning approaches to scaling up sparse survey data (611616)
Mollie Van Gordon1, Guillaume Chabot-Couture2 and Joshua L. Proctor2, (1)Organization Not Listed, Washington, DC, United States, (2)Organization Not Listed, Bellevue, WA, United States
 
Harnessing Machine Learning and Citizen Science Data to Improve Remotely Sensed Estimates of Lake Water Clarity and Document Regional Clarity Trends (612770)
David A Lutz1, Hannah Rubin2, Bethel Steele3, Mark J Ducey4, Kathryn L Cottingham1, Michael W Palace5, Kathleen C Weathers3 and Jonathan W Chipman1, (1)Dartmouth College, Hanover, NH, United States, (2)Dartmouth College, Geography, Hanover, NH, United States, (3)Cary Institute of Ecosystem Studies, Millbrook, NY, United States, (4)University of New Hampshire, Durham, NH, United States, (5)University of New Hampshire, Dept. of Earth Sciences and Earth Systems Research Center, Durham, NH, United States
 
Virtual Sensors for Air Quality (AQ) Measurements in Urban Environments (628502)
Adwait Sahasrabhojanee, NASA Ames Research Center, Moffett Field, CA, United States, Michael von Pohle, NASA Ames Research Center, USRA, Moffet Field, CA, United States, Meytar Sorek-Hamer, NASA Ames Research Center, USRA, Moffett Field, United States, Kamalika Das, NASA Ames Research Center, USRA, Mountain View, CA, United States, Bryan Matthews, NASA Ames Research Center, SGT, Greenbelt, MD, United States, Nikunj Oza, NASA Ames Research Center, Mountain View, CA, United States and Michael Brauer, University of British Columbia, School of Population and Public Health, Vancouver, BC, Canada
 
Statewide, Tree-Scale Ecology for Forest Management, Planning & Monitoring (628882)
David Marvin and Christopher Anderson, Salo Sciences, Inc., San Francisco, CA, United States
 
Remote Sensing Algorithm Development of Identifying Aeolian Surface Erosion Over Dust Storm Source Area: Case Study in Kubuqi Desert, China (629366)
Cheng-Wei Lin1, Jungrack Kim2, Shih-Yuan Lin1 and YunSoo Choi3, (1)National Chengchi University, Taipei, Taiwan, (2)SOU, Seoul, South Korea, (3)University of Seoul, Seoul, Korea, Republic of (South)
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