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• W4313443465 endingPage "101964" @default.
• W4313443465 startingPage "101964" @default.
• W4313443465 abstract "Seagrasses, a unique group of marine flowering plants, profoundly influence the marine environment by providing an array of critical ecological functions. They serve as the foundational habitat for several endangered and charismatic species, including sea cows, sea turtles, and sea horses, and are often referred to as coastal canaries. In comparison to boreal and tropical forests, they have an amazing ability for carbon storage. Despite their long evolutionary history, they are threatened by rapid environmental changes caused by climate change and human activity. Long-term monitoring is required to comprehend the changes in this fragile ecosystem. Conventional field survey methods for collecting long-term data are laborious, time-consuming, and expensive. Hence, this work builds a time-series dataset of the seagrass coverage in the Kalpeni lagoon from 2003 to 2020 by analysing Landsat data on Google Earth Engine. We also evaluated the temporal changes in the seagrass coverage of the study area and studied the influence of selected environmental factors on the seagrass coverage. We observed a negative relationship between sea surface temperature and seagrass coverage. The results revealed a decline in more than 99% of seagrass coverage, indicating an alarming threat to this seagrass ecosystem of the region. With such a drastic shrinkage in the seagrass coverage, the hysteresis must be strong, and the recovery of these meadows may require intensive interventions. By establishing a long-term time series database of seagrass coverage, our study also opens up new avenues for future ecological research on the seagrass meadows." @default.
• W4313443465 created "2023-01-06" @default.
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• W4313443465 date "2023-05-01" @default.
• W4313443465 modified "2023-10-01" @default.
• W4313443465 title "Dwindling seagrasses: A multi-temporal analysis on Google Earth Engine" @default.
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• W4313443465 doi "https://doi.org/10.1016/j.ecoinf.2022.101964" @default.
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