Golly GEE Wetlands! Forecasting Wetland Extent in Rwanda

EarthzineDevelop Summer 2017, DEVELOP Virtual Poster Session

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Project Team: Rwanda Ecological Forecasting

Team Location: NASA Marshall Space Flight Center at NSSTC ‰ÛÒ Huntsville, Alabama

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Nicholas McVey, Julia Bayer, Jennifer Gelmis, Dashiell Cruz


Dr. Jeffrey Luvall (NASA at NSSTC), Dr. Robert Griffin (University of Alabama in Huntsville), Emily Adams (NASA SERVIR Science Coordination Office), Leigh Sinclair (University of Alabama in Huntsville/Information Technology and Systems Center)

Land use classification divided into five categories (Water, Land, Forest, Urban, and Wetland) for western Rwanda derived from Landsat 8 Operational Land Imager Data from 2016. Image Credit: Rwanda Ecological Forecasting Team


Balancing the demands of economic development and environmental protection is a challenge which requires policymakers to be well-informed about the extent and value of key natural ecosystems. Wetlands have long been known to regulate hydrological processes, reduce erosion and flooding, safeguard local biodiversity, mitigate changes in climate, and contribute to food security. Nonetheless, due to the coupled threat of increasing population and lack of land use classification, east African wetlands face eradication if a concerted effort to identify, map, and protect these areas is not rapidly undertaken. To advance the wetland conservation efforts of the Rwanda Environmental Management Authority (REMA) and the UN’s Sustainable Development Goals (SDG), a time series and land change extent model were generated to provide a baseline inventory of Rwandan wetlands and pinpoint critical areas that should be targeted for enhanced research and protection. Wetlands were identified by a supervised maximum likelihood classification conducted in Google Earth Engine API using Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) data. The TerrSet Land Change Modeler software was used to generate a forecast of the land changes predicted to occur by the year 2030. These predictive models will optimize REMA’s conservation efforts of wetlands, and provide researchers with a replicable methodology that can be used to continue monitoring global wetland extent.

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