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snippet: This data was created as part of a NatureNet Science Fellowship project initiated in 2018 by The Nature Conservancy and the Arizona State University Center for Biodiversity Outcomes to assess risks and identify solutions for nutrient pollution under extreme events. This dataset identifies regions where flooding was detected from either Hurricane Matthew or Hurricane Florence, where buyouts or nature-based solutions could be implemented to reduce flooding and water quality impacts on sensitive human and ecological communities. This is part of a collection of datasets produced as part of a study of the potential implications of repeated hurricanes for water quality in North Carolina. Complementary datasets include flood extent estimated using remote sensing from Hurricane Matthew (2016) and Hurricane Florence (2018). Note: Do not download this raster using the map at the top of the page. Instead, click Download then "Download Floodplain Resilience raster." The link will take you to a page where the raster can be downloaded.
summary: This data was created as part of a NatureNet Science Fellowship project initiated in 2018 by The Nature Conservancy and the Arizona State University Center for Biodiversity Outcomes to assess risks and identify solutions for nutrient pollution under extreme events. This dataset identifies regions where flooding was detected from either Hurricane Matthew or Hurricane Florence, where buyouts or nature-based solutions could be implemented to reduce flooding and water quality impacts on sensitive human and ecological communities. This is part of a collection of datasets produced as part of a study of the potential implications of repeated hurricanes for water quality in North Carolina. Complementary datasets include flood extent estimated using remote sensing from Hurricane Matthew (2016) and Hurricane Florence (2018). Note: Do not download this raster using the map at the top of the page. Instead, click Download then "Download Floodplain Resilience raster." The link will take you to a page where the raster can be downloaded.
accessInformation: Schaffer-Smith, D., Myint, S.W., Muenich, R.L., Tong, D., and DeMeester, J.E. 2020. Repeated hurricanes reveal risks and opportunities for social-ecological resilience to flooding and water quality problems. Environmental Science & Technology. Schaffer-Smith, D. 2020. Hurricanes Matthew and Florence: impacts and opportunities to improve floodplain management. Knowledge Network for Biocomplexity. doi:10.5063/F1SB443J. https://knb.ecoinformatics.org/ Schaffer-Smith, D. 2020. Supporting code for: Schaffer-Smith, D., Myint, S.W., Muenich, R.L., Tong, D., & DeMeester, J.E. 2020. Repeated hurricanes reveal risks and opportunities for social-ecological resilience to flooding and water quality problems. Environmental Science & Technology. https://github.com/dschaffersmith/repeatFloodingNC
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maxScale: 136056.670397262
typeKeywords: ["Data","Service","Image Service","ArcGIS Server"]
description: <div style='text-align:Left;'><div><div><p style='margin:0 0 7 0;'><span>Flooded and non-flooded regions were delineated </span><span>from recent Hurricanes </span><span>using a random forest classification model leveraging pre- and post-storm synthetic aperture radar from the European Space Agency's Sentinel-1 sensor, in addition to topography, floodplain, and landcover data. The classification model was trained with USGS and NCDEMS high-water marks, in addition to flooded and non-flooded regions delineated from high-resolution NOAA aerial photography; the model achieved </span><span>&gt;91</span><span>% accuracy against an independent withheld sample </span><span>for each storm</span><span>. </span><span>Within regions where flooding was detected, opportunities for buyouts, conservation of forests and wetlands, or lands where restoration or easements could be implemented were identified using the National Land Cover Dataset (2011). </span><span>For additional details regarding the methods, please see the peer-reviewed publication and data and code archives referenced in the Credits below. </span></p><p><span>Note: Do not download this raster using the map at the top of the page. Instead, click Download then "Download Floodplain Resilience raster." The link will take you to a page where the raster can be downloaded.</span></p></div></div></div>
licenseInfo: <div style='text-align:Left;'><div><div><p style='margin:0 0 7 0;'><span>Please provide attribution for use of this dataset by citing the following resources:</span></p><p style='margin:0 0 7 0;'><span>Schaffer-Smith, D., Myint, S.W., Muenich, R.L., Tong, D., and DeMeester, J.E. 2020. Repeated hurricanes reveal risks and opportunities for social-ecological resilience to flooding and water quality problems. Environmental Science &amp; Technology. </span></p><p style='margin:0 0 7 0;'><span>Schaffer-Smith, D. 2020. Hurricanes Matthew and Florence: impacts and opportunities to improve floodplain management. Knowledge Network for Biocomplexity. doi:10.5063/F1SB443J. https://knb.ecoinformatics.org/ </span></p><p style='margin:0 0 7 0;'><span>Schaffer-Smith, D. 2020. Supporting code for: Schaffer-Smith, D., Myint, S.W., Muenich, R.L., Tong, D., &amp; DeMeester, J.E. 2020. Repeated hurricanes reveal risks and opportunities for social-ecological resilience to flooding and water quality problems. Environmental Science &amp; Technology. https://github.com/dschaffersmith/repeatFloodingNC</span></p><p><span>https://www.nconemap.gov/pages/terms</span></p></div></div></div>
catalogPath:
title: Floodplain Resilience
type: Image Service
url: https://services.gis.nc.gov/secure
tags: ["The Nature Conservancy","environment","geoscientificInformation","inlandWaters","flood","hurricane","nature-based solutions","NC","North Carolina","Department of Information Technology","DIT","Center for Geographic Information and Analysis","CGIA","NC OneMap","imagery","orthoimagery","aerial","imageryBaseMapsEarthCover"]
culture: en-US
name: Floodplain_Resilience
guid:
minScale: 4353813.45271238
spatialReference: NAD_1983_StatePlane_North_Carolina_FIPS_3200_Feet