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A Google Earth Engine Wetland Extent Mapping Tool Using Open Source Optical and Radar Satellite Sensors

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A Google Earth Engine Wetland Extent Mapping Tool Using Open Source Optical and Radar Satellite Sensors 1/16/2020 AGU - iPosterSessions.com A Google Earth Engine Wetland Extent Mapping Tool using Open Source Optical and Radar Satellite Sensors Melissa Ferriter, Erica O'Connor, Alice Lin, Christopher Notto, Benjamin Holt, Bruce Chapman NASA Jet Propulsion Laboratory, NASA DEVELOP National Program PRESENTED AT: https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 1/13 1/16/2020 AGU - iPosterSessions.com BACKGROUND Google Earth Engine, a cloud based geospatial processing platform that hosts a multi-petabyte catalog of satellite imagery, allows for large scale processing and analysis It is challenging to map wetlands at a large scale due to their spatial heterogeneity, dynamic nature, and spectral similarity to other landcover types Due to the pivotal role of wetlands in our environmental law and their vulnerability to the changing climate, there is a need for fully automated, standardized, on-demand wetland mapping on national and global scales Our objective was to create an automated, multi-sensor, and seasonal approach for wetland mapping to aid end users in assessing wetland gain and loss across the state of Minnesota https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 2/13 1/16/2020 AGU - iPosterSessions.com METHODS We used an object based random forest approach to classify three landcover types: wetland, upland, and open water The results of the random forest approach were also compared to a manual thresholding classification to see which achieved the highest accuracy Study Period: Growing Seasons of 2017 and 2018 (May - September) Datasets: Landsat 8 OLI Surface Reflectance, Sentinel-1 C-SAR GRD, LIDAR DEM, Reference Data Parameters: Tasseled Cap Wetness Greenness Difference Index (TCGWD), Modified Normalized Difference Water Index (MNDWI), Topographic Wetness Index (TWI), VV VH Backscatter Platforms: Google Earth Engine, ENVI https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 3/13 1/16/2020 AGU - iPosterSessions.com WETLAND EXTENT TOOL (WET) This preliminary tool allows users to define their own date range, region of interest, inputs, and classification method to generate a map of wetland extent Figure 1. Comparison of the National Wetland Inventory (NWI) update completed in 2013 to the random forest and manual classification methods for 2017 and 2018 at the Fond du Lac Reservation in Minnesota https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 4/13 1/16/2020 AGU - iPosterSessions.com RESULTS Figure 2. Pixel values for each index/band were sampled at each field validation point and a distribution was visualized via box-and-whisker plots. Bolded indices are the ones we used in our subsequent analysis. To eliminate inputs that didn’t easily distinguish between land cover classes we generated a distribution visualization via box-and-whisker plots We omitted indices with considerable overlap between land cover classes Figure 3. Confusion matrices obtained from the (A) Random Forest Map compared to field points, (B) Random Forest Map compared to NWI, (C) Threshold Map compared to field points, and (D) Threshold Map compared to NWI An overall classification accuracy of 87% was achieved when comparing the random forest classification to the field data points Our random forest classification confused upland for open water 15% of the time and wetland for open water 10% of the time Our second best classification was the random forest compared to the NWI which achieved an overall accuracy of 55% The threshold classification achieved a 49% and 44% overall accuracy when compared to the field data and the updated NWI respectively https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 5/13 1/16/2020 AGU - iPosterSessions.com https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 6/13 1/16/2020 AGU - iPosterSessions.com CONCLUSION An object based random forest classification that included VV backscatter, VH backscatter, TCWGD, MNDWI and TWI yielded the highest overall accuracy Additional reference points, particularly in the open water class, are needed to avoid confusion with other classes Future work: Hierarchical wetland classification (emergent, shrub, forested, aquatic veg.) Update field validation points Expand tool to neighboring states in the Great Lakes area Investigate how accuracy changes with the inclusion of additional datasets (soil, Sentinel-2) and classification methods (pixel vs. object) [VIDEO] https://www.youtube.com/embed/IaQIMm_CSx4? feature=oembed&fs=1&modestbranding=1&rel=0&showinfo=0 https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 7/13 1/16/2020 AGU - iPosterSessions.com DISCLOSURES This material contains modified Copernicus Sentinel data (2017-2018), processed by ESA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration. This material is based upon work supported by NASA through contract NNL16AA05C. https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 8/13 1/16/2020 AGU - iPosterSessions.com CV Melissa M. Ferriter, B.S. email: [email protected] phone: (818) 216-9407 Education B.S. (2017) Environmental Sciences, University of California, Berkeley Minor: Geospatial Information Science and Technology Dr. Jenny Palomino and Dr. Kelly Easterday, graduate student advisors Thesis: "Quantifying Post-Wildfire Vegetation Regrowth in California since Landsat 5" Service/Societies American Geophysical Union (2019-Present) Ecological Society of America (2019-Present) Contributor, EnceliaAlliance, (2017) Research and Employment Experience Project Lead (06/2019 – Present) NASA DEVELOP National Program, NASA JPL, Pasadena, California, Dr. Natasha Stavros & Dr. Latha Baskaran, advisors. Co-Founder & Geospatial Analyst (05/2017 – Present) Pinecrest Environmental Consulting, Oakland, California. Co-Founder & Web Development (09/2016 – Present) Tru-Breed Technologies, Oakland, California. Remote Sensing Analyst (01/2019 – 04/2019) NASA DEVELOP National Program, NASA JPL, Pasadena, California, Dr. Bruce Chapman, advisor. Research Assistant (05/2016, 09/2016) University of California, Berkeley, Beissinger Lab, Dr. Nathan Van Schmidt, supervisor. Research Assistant (09/2015, 01/2016) University of California, Berkeley, Beissinger Lab, Dr. Kelly Iknayan, supervisor. Research Assistant (05/2015, 08/2015) University of California, Berkeley, Thompson Lab, Dr. Gabrielle Boisrame, supervisor. Research Assistant (03/2015, 09/2015) University of California, Berkeley, Field Station for the Study of Behavior, Ecology, and Reproduction, Dr. Hiromi Uno, supervisor. Research Assistant (03/2014, 08/2014) University of California, Berkeley, Mojave National Preserve, Dr. Christopher DiVittorio, supervisor. Public Media and Outreach Conducted outreach at John Muir High School in Pasadena, California as a resume reviewer during Engineering Week. Academic Presentations M.F. Ferriter. 2019. Analyzing Recent Wildfire Impacts to Assist the Resource Conservation District of the Santa Monica Mountains in Identifying Tree Species to Replant. NASA DEVELOP Closeout Presentation, NASA JPL, August 9, 2019. M.F. Ferriter. 2019. Improving Wetland Change Mapping using Optical and Radar Satellite Sensors to Assess Wetland Gain and Loss Metrics in Minnesota. NASA DEVELOP Closeout Presentation, NASA JPL, April 4, 2019. M.F. Ferriter. 2017. Fire Science Meets Data Science: Mapping the Response of California Ecosystems to Wildfire. Environmental Sciences Senior Thesis Symposium, UC Berkeley, April 23, 2017. Awards/Honors Nominated as DEVELOPer of the Term, NASA DEVELOP National Program https://agu2019fallmeeting-agu.ipostersessions.com/Default.aspx?s=02-1E-D1-3C-F1-F0-4D-AE-39-1A-14-5D-1A-41-19-95&pdfprint=true&guestview=true 9/13 1/16/2020 AGU - iPosterSessions.com ABSTRACT Wetlands are a critical feature of our landscape for the ecological services they provide, including protecting water quality, providing habitat to rare species, mitigating erosion, and providing opportunities for recreation. Despite this, wetlands are facing increasing threats from a variety of anthropogenic sources, including pollution, changes in climate, and commercial development. Although an accurate baseline inventory of wetland extent is essential for addressing these threats and quantifying future wetland change, traditional wetland mapping methods are time intensive, costly, and difficult to implement on a large scale. Here we show that statewide, fully automated wetland mapping is possible to a high degree of accuracy by combining recent advances in remote sensing and cloud computing. Using a multi-source, multi-temporal, object-based random forest classification approach in GEE, we generated 30m resolution maps of wetland extent and change for the growing seasons (May through September) of 2017 and 2018 in Minnesota. In particular, the inclusion
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