Citation:
Citation_Information:
Originator:
United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)
Publication_Date: 20221128
Title:
USDA NASS Disaster Analysis 2022 Event - Hurricane Nicole Florida Inundation - November 2022
Edition:
USDA NASS Disaster Analysis 2022 Event - Hurricane Nicole Florida Inundation - November 2022
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place:
USDA, NASS Marketing and Information Services Office, Washington, D.C.
Publisher: USDA, NASS
Other_Citation_Details:
The USDA NASS monitors the impact of natural disasters on US agriculture in near real-time using remotely sensed data and geospatial techniques. The first test case is detailed in the following papers <https://www.nass.usda.gov/Research_and_Science/Disaster-Analysis/2018/Hurricane-Michael/Flood_Monitoring_Methodology_Paper.pdf> and <https://www.nass.usda.gov/Research_and_Science/Cropland/docs/Boryan_IGARSS2018%20_Flood.pdf>.
Online_Linkage:
<https://www.nass.usda.gov/Research_and_Science/Disaster-Analysis/>
Description:
Abstract:
Hurricane Nicole was a late-season hurricane that made landfall in Florida in early November 2022. The United States Department of Agriculture's National Agricultural Statistics Service (NASS) responded to inquiries regarding damage to agricultural land in Florida, specifically citrus areas in Indian River and St. Lucie Counties. Datasets used for flood detection for this event were from very limited collections of descending Sentinel-1A SAR images (path 84), with pre-event images from August 4, 2020, and post-event from November 10, 2022. The flood inundation layers were derived from before and after event VH polarization bands using a threshold differencing algorithm under the Google Earth Engine App "Flood Detection and Monitoring System V1.0". These data are not considered official NASS estimates. For this event, inundation raster files for the areas of interest are included for download. The official website <https://www.nass.usda.gov/Research_and_Science/Disaster-Analysis/> provides disaster assessments in geospatial data format, reports, and metadata as available. These data are not official NASS estimates.
Purpose:
Agricultural disaster monitoring is important for food security and economic stability. Using remotely sensed data and geospatial techniques provides an additional tool for the USDA NASS to use in evaluating a disaster's impact on US agriculture.
Supplemental_Information:
LIST OF INPUTS
Event: Hurricane Nicole - November 2022
Last Updated: November 28, 2022
--------------------
Inundation Data
Raster Zip File: 2022_Hurricane_Nicole_Inundation_Raster_20221111.zip
Inundation derived from Sentinel-1A SAR dated November 10, 2022 and the 2021 Cropland Data Layer.
Value 0 = NoData
Value 2 = Inundated Other Land
Value 3 = Inundated Cropland
--------------------
Land Cover Inputs
NASS 2021 Cropland Data Layer (planted acres)
Obtained From: https://croplandcros.scinet.usda.gov/
Disclaimer: These data are not official NASS estimates.
--------------------
Official NASS Estimates
Florida Citrus Statistics Report 2020-2021
https://www.nass.usda.gov/Statistics_by_State/Florida/Publications/Citrus/Citrus_Statistics/2020-21/fcs2021b.pdf
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 20220804
Ending_Date: 20221112
Currentness_Reference: November 2022
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -87.6435
East_Bounding_Coordinate: -80.0490
North_Bounding_Coordinate: 30.9950
South_Bounding_Coordinate: 24.5432
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: farming, 001
Theme_Keyword: environment, 007
Theme_Keyword: imageryBaseMapsEarthCover, 010
Theme:
Theme_Keyword_Thesaurus: Global Change Master Directory (GCMD) Science Keywords
Theme_Keyword:
Earth Science > Biosphere > Terrestrial Ecosystems > Agricultural Lands
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: crop cover
Theme_Keyword: cropland
Theme_Keyword: agriculture
Theme_Keyword: farming
Theme_Keyword: land cover
Theme_Keyword: disaster monitoring
Theme_Keyword: crop estimates
Theme_Keyword: ESA SENTINEL-1
Place:
Place_Keyword_Thesaurus: Global Change Master Directory (GCMD) Location Keywords
Place_Keyword: Continent > North America > United States of America > Florida
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Florida
Place_Keyword: FL
Temporal:
Temporal_Keyword_Thesaurus: None
Temporal_Keyword: 2022
Access_Constraints: None
Use_Constraints:
The USDA NASS Disaster Analysis data are provided to the public as is and are considered public domain and free to redistribute. The USDA, NASS does not warrant any conclusions drawn from these data. These data are not official NASS estimates.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA, NASS, Spatial Analysis Research Section
Contact_Person: USDA, NASS, Spatial Analysis Research Section staff
Contact_Address:
Address_Type: mailing and physical address
Address: 1400 Independence Avenue, SW, Room 5029 South Building
City: Washington
State_or_Province: District of Columbia
Postal_Code: 20250-2001
Country: USA
Contact_Voice_Telephone: 800-727-9540
Contact_Facsimile_Telephone: 855-493-0447
Contact_Electronic_Mail_Address: SM.NASS.RDD.GIB@usda.gov
Data_Set_Credit: USDA, National Agricultural Statistics Service
Security_Information:
Security_Classification_System: None
Security_Classification: Unclassified
Security_Handling_Description: None
Native_Data_Set_Environment:
Microsoft Windows 10 Enterprise; ERDAS Imagine Version 2018 <https://www.hexagongeospatial.com/>; ESRI ArcGIS Version 10.7.0.10450 <https://www.esri.com/>. ERDAS Imagine is used in the analysis and post-processing of all raster-based data. ESRI ArcGIS is used to prepare all vector-based data and to create graphics. Some analysis is conducted within Google Earth Engine (GEE). The use of GEE is a proof of concept for a modified processing methodology.