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Retreat of coastal forests in relation to sea level rise has been widely documented. Recent work indicates that coastal forests on the Delmarva Peninsula, United States, can be differentiated into persistence and regenerative zones as a function of sea-level rise and storm events. In the lower persistence zone trees cannot regenerate because of frequent flooding and high soil salinity. This study aims to verify the existence of these zones using spectral remote sensing data, and determine whether the effect of large storm events that cause damage to these forests can be detected from satellite images. Spectral analysis confirms a significant difference in average Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values in the proposed persistence and regenerative zones. Both NDVI and NDWI indexes decrease after storms triggering a surge above 1.3 m with respect to the North American Vertical Datum of 1988 (NAVD88). NDWI values decrease more, suggesting that this index is better suited to detect the effect of hurricanes on coastal forests. In the regenerative zone, both NDVI and NDWI values recover three years after a storm, while in the persistence zone the NDVI and NDWI values keep decreasing, possibly due to sea level rise causing vegetation stress. As a result, the forest resilience to storms in the persistence zone is lower than in the regenerative zone. Our findings corroborate the ecological ratchet model of coastal forest disturbance.
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This content will become publicly available on August 16, 2026
Monitoring mangrove responses to seasonal changes, hurricane-induced disturbance, and recovery in the South Florida Everglades: A spatio-temporal analysis of decade-long (2013-2023) Landsat-8 observations
Mangrove forests play a critical role in coastal ecosystems by buffering shorelines against the destructive forces of storms and storm surges, but in doing so, they often endure significant structural damage, including defoliation, tree snapping, and branch loss. Using decade-long remote sensing Landsat 8 data, we calculated the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to assess patterns and trends within the decade-long time series for each index in mangrove forests of southwestern Florida Everglades. Before calculating NDVI and NDMI, we cloud-filtered and calculated the monthly spectral means of the study region from March 2013 to March 2023. Using both NDVI and NDMI, we found seasonal variations in the value of both indices, in which the value increased during the wet season and decreased during the dry season of the Everglades. We also detected the impact of Hurricane Irma on mangroves in 2017 due to a sudden drop in the indices’ values right after the storm. The time series showed a slow recovery of indices values compared to pre-storm values. Using an exponential recovery model, we calculated that most mangrove areas recovered within two to four years. However, some small mangrove areas show no recovery, which we attribute to saltwater ponding and areas without freshwater flow and hydrological connectivity.
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- PAR ID:
- 10643729
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Remote Sensing Applications: Society and Environment
- ISSN:
- 2352-9385
- Subject(s) / Keyword(s):
- Mangroves Florida Everglades Recovery Hurricane Irma Remote sensing Long-term data Landsat
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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