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Southeastern United States frequently experience tornadoes, necessitating rapid response and recovery efforts by state and federal agencies. Accurate information about the extent and severity of tornado-induced damage, especially debris volume and locations, is crucial for these efforts. This study, therefore, focuses on post-tornado debris assessment in Leon County, Florida, which was hit by two EF-2 and an EF-1 tornadoes in May 2024. Using satellite imagery from the Planetscope satellite and Geographic Information Systems (GIS), a macro-level evaluation of tornado debris impact was conducted, particularly on roadways and impacted communities. The proposed approach includes an evaluation of the overall post-tornado debris impact across the entire county and its population, and a detailed analysis of debris impact on roadways and its effect on accessibility. Spectral indices from satellite images, specifically the Normalized Difference Vegetation Index (NDVI), were utilized to derive assessment parameters. By comparing NDVI values from pre- and post-tornado images, we analyzed changes in vegetation and debris accumulation along roadway segments leading to possible roadway closures. This integrated method provides critical insights for enhancing disaster response and recovery operations in tornado-prone regions. Findings indicate that high volumes of vegetative debris were present in the south-central parts of the county, which is occupied by the highest population of county residents. The roadway segments in this region also recorded highest debris volumes, which is a critical information for agencies that need to know highly impacted locations. Comparing the results to ground truth damage data, the accuracy recorded was 74%.
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Radar and optical remote sensing for near real‐time assessments of cyclone impacts on coastal ecosystems
Rapid impact assessment of cyclones on coastal ecosystems is critical for timely rescue and rehabilitation operations in highly human-dominated landscapes. Such assessments should also include damage assessments of vegetation for restoration planning in impacted natural landscapes. Our objective is to develop a remote sensing-based approach combining satellite data derived from optical (Sentinel-2), radar (Sentinel-1), and LiDAR (Global Ecosystem Dynamics Investigation) platforms for rapid assessment of post-cyclone inundation in nonforested areas and vegetation damage in a primarily forested ecosystem. We apply this multi-scalar approach for assessing damages caused by the cyclone Amphan that hit coastal India and Bangladesh in May 2020, severely flooding several districts in the two countries, and causing destruction to the Sundarban mangrove forests. Our analysis shows that at least 6821 sq. km. land across the 39 study districts was inundated even after 10 days after the cyclone. We further calculated the change in forest greenness as the difference in normalized difference vegetation index (NDVI) pre- and post-cyclone. Our findings indicate a <0.2 unit decline in NDVI in 3.45 sq. km. of the forest. Rapid assessment of post-cyclone damage in mangroves is challenging due to limited navigability of waterways, but critical for planning of mitigation and recovery measures. We demonstrate the utility of Otsu method, an automated statistical approach of the Google Earth Engine platform to identify inundated areas within days after a cyclone. Our radar-based inundation analysis advances current practices because it requires minimal user inputs, and is effective in the presence of high cloud cover. Such rapid assessment, when complemented with detailed information on species and vegetation composition, can inform appropriate restoration efforts in severely impacted regions and help decision makers efficiently manage resources for recovery and aid relief. We provide the datasets from this study on an open platform to aid in future research and planning endeavors.
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- Award ID(s):
- 1757353
- PAR ID:
- 10331391
- Editor(s):
- Sankey, Temuulen; Van Den Broeke, Matthew
- Date Published:
- Journal Name:
- Remote Sensing in Ecology and Conservation
- ISSN:
- 2056-3485
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/rse2.257
