High-Resolution Satellite Imagery to Assess Sargassum Inundation Impacts to Coastal Areas
Hernandez, W. J.; Morell, J. M.; Armstrong, R. A.
Show abstract
A change detection analysis utilizing Very High-resolution (VHR) satellite imagery was performed to evaluate the changes in benthic composition and coastal vegetation in La Parguera, southwestern Puerto Rico, attributable to the increased influx of pelagic Sargassum spp and its accumulations in cays, bays, inlets and near-shore environments. Satellite imagery was co-registered, corrected for atmospheric effects, and masked for water and land. A Normalized Difference Vegetation Index (NDVI) and an unsupervised classification scheme were applied to the imagery to evaluate the changes in coastal vegetation and benthic composition. These products were used to calculate the differences from 2010 baseline imagery, to potential hurricane impacts (2018 image), and potential Sargassum impacts (2020 image). Results show a negative trend in Normalized Difference Vegetation Index (NDVI) from 2010 to 2020 for the total pixel area of 24%, or 546,446 m2. These changes were also observed in true color images from 2010 to 2020. Changes in the NDVI negative values from 2018 to 2020 were higher, especially for the Isla Cueva site (97%) and were consistent with the field observations and drone surveys conducted since 2018 in the area. The major changes from 2018 and 2020 occurred mainly in unconsolidated sediments (e.g. sand, mud) and submerged aquatic vegetation (e.g. seagrass, algae), which can have similar spectra limiting the differentiation from multi-spectral imagery. Areas prone to Sargassum accumulation were identified using a combination of 2018 and 2020 true color VHR imagery and drone observations. This approach provides a quantifiable method to evaluate Sargassum impacts to the coastal vegetation and benthic composition using change detection of VHR images, and to separate these effects from other extreme events.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Suitability of resampled multispectral datasets for mapping flowering plants in the Kenyan savannah 96%
- Beyond Traditional Methods: Innovative Integration of LISS IV and Sentinel 2A Imagery for Unparalleled Insight into Himalayan Ibex Habitat Suitability 96%
- Spatio-temporal modelling for the evaluation of an altered Indian saline Ramsar site and its drivers for ecosystem management and restoration 96%
Similar papers in this journal
- Developing snakebite risk model using venomous snake habitat suitability as an indicating factor: An application of species distribution models in public health research 93%
- Assessment of Environmental Factors Associated with Antibiotic Resistance Genes (ARGs) in the Yangtze Delta, China 92%
- Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change 91%
Similar papers in this journal
- Urban Vulnerability Assessment for Pandemic surveillance: The COVID-19 case in Bogotá, Colombia 93%
- The effects of biodegradable mulch film on the growth, yield, and water use efficiency of cotton and maize in an arid region 91%
- Education influences knowledge about environmental issues in Washington, DC, USA 91%
Similar papers in this journal
- High-resolution three-dimensional mapping of eelgrass (Zostera marina) habitat and blue carbon using drone-borne LiDAR 94%
- Locating and dating land cover change events in the Renosterveld, a Critically Endangered shrubland ecosystem 91%
- Classification of daily crop phenology in PhenoCams using deep learning and hidden markov models 89%
Similar papers in this journal
- Using cost-effective surveys from platforms of opportunity to assess cetacean occurrence patterns for marine park management in the heart of the Coral Triangle 94%
- Whale shark residency and small-scale movements around oil and gas platforms in Qatar 92%
- Modelling plastics exposure for the marine biota: Risk maps for fin whales in the Pelagos Sanctuary (North-Western Mediterranean) 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.