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Floodplain_Resilience (ImageServer)

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Service Description:

Flooded and non-flooded regions were delineated from recent Hurricanes 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 >91% accuracy against an independent withheld samplefor each storm. 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). For additional details regarding the methods, please see the peer-reviewed publication and data and code archives referenced in the Credits below.



Name: Floodplain_Resilience

Description:

Flooded and non-flooded regions were delineated from recent Hurricanes 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 >91% accuracy against an independent withheld samplefor each storm. 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). For additional details regarding the methods, please see the peer-reviewed publication and data and code archives referenced in the Credits below.



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Mensuration Capabilities: Basic

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Copyright Text: 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

Service Data Type: esriImageServiceDataTypeThematic

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Standard Deviation Values: N/A

Object ID Field:

Fields: None

Default Mosaic Method: Center

Allowed Mosaic Methods:

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Mosaic Operator: First

Default Compression Quality: 75

Default Resampling Method: Bilinear

Max Record Count: null

Max Image Height: 4100

Max Image Width: 15000

Max Download Image Count: null

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Allow Raster Function: true

Allow Compute TiePoints: false

Supports Statistics: false

Supports Advanced Queries: false

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Child Resources:   Info   Key Properties   Legend   MultiDimensionalInfo   rasterFunctionInfos

Supported Operations:   Export Image   Identify   Measure   Compute Histograms   Compute Statistics Histograms   Get Samples   Compute Class Statistics   Query Boundary   Compute Pixel Location