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Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.
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Space-Based Detection of Significant Water-Depth Increase Induced by Hurricane Irma in the Everglades Wetlands Using Sentinel-1 SAR Backscatter Observations
Extreme rainfall, induced by severe weather events, such as hurricanes, impacts wetlands because rapid water-depth increases can lead to flora and fauna mortality. This study developed an innovative algorithm to detect significant water-depth increases (SWDI, defined as water-depth increases above a threshold) in wetlands, using Sentinel-1 SAR backscatter. We used Hurricane Irma as an example that made landfall in the south Florida Everglades wetlands in September 2017 and produced tremendous rainfall. The algorithm detects SWDI for during- and post-event SAR acquisition dates, using pre-event water-depth as a baseline. The algorithm calculates Normalized Difference Backscatter Index (NDBI), using pre-, during-, and post-event backscatter, at a 20-m SAR resolution, as an indicator of the likelihood of SWDI, and detects SWDI using all NDBI values in a 400-m resolution pixel. The algorithm successfully detected large SWDI areas for the during-event date and progressive expansion of non-SWDI areas (water-depth differences less than the threshold) for five post-event dates in the following two months. The algorithm achieved good performance in both ‘herbaceous dominant’ and ‘trees embedded within herbaceous matrix’ land covers, with an overall accuracy of 81%. This study provides a solution for accurate mapping of SWDI and can be used in global wetlands, vulnerable to extreme rainfall.
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- PAR ID:
- 10381571
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 14
- Issue:
- 6
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 1415
- 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/10.3390/rs14061415
