Matthew Duckett

Dr. Matthew Duckett


Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 July 2025 in Remote Sensing
Verified
Journal Article
Open access
PDF file
MRMichael Routhier
Michael Routhier
GMGregg Emery Moore
Gregg Emery Moore
BRBarrett Rock
Barrett Rock
SGStanley Glidden
Stanley Glidden
MDMatthew Duckett
Matthew Duckett
SZSusan Zaluski
Susan Zaluski
Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope.