FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2016402591> ?p ?o ?g. }

• W2016402591 endingPage "316" @default.
• W2016402591 startingPage "304" @default.
• W2016402591 abstract "Coastal mangroves lose large amounts of water through evapotranspiration (ET) that can be equivalent to the amount of annual rainfall in certain years. Satellite remote sensing can play a crucial role in identifying regional ET trends and surface energy changes after disturbances in isolated and inaccessible areas of coastal mangrove wetlands, like those found in Everglades National Park in southern Florida. Using a combination of long-term datasets acquired from the NASA Landsat 5TM satellite database and the Florida Coastal Everglades Long-Term Ecological Research project, the present study investigates how ET as well as radiation and other energy balance parameters in the Everglades mangrove ecotone have responded to multiple hurricane events and restoration projects over the past two decades. An energy balance model using satellite data was used to estimate latent heat (λE) in tall and scrub mangrove environments. An eddy-covariance tower and weather tower supplied long-term data of multiple environmental and meteorological parameters that were used in calibrating and testing the modeled results from the Landsat images. Results identified significant differences in λE and soil heat flux measurements between the tall and scrub, and fringe and basin mangrove environments. The scrub mangrove site had the lowest λE rates, highest soil heat flux and lowest biophysical index (i.e., Fractional Vegetation Cover (FVC), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI) values. Mangrove damage and mortality associated with two strong hurricanes decreased FVC, NDVI, and SAVI, and increased soil heat flux at the tall mangrove site located in a basin-type environment. Recovery of the spectral characteristics, energy balance parameters and λE following hurricane disturbance was quicker in fringe mangroves than in basin mangroves. Latent heat fluxes (λE) were also relatively high after each storm and may have increased as a result of increasing vapor pressure deficits. Remote sensing of the surface energy balance and λE of mangrove forests can help our understanding of how these environments respond to disturbances to the landscape and the effect that these changes can have on the energy and water budget. Moreover, relationships between energy and water balance components developed for the coastal mangroves of southern Florida could be extrapolated to other mangrove systems in the Caribbean to measure changes caused by natural events and human modifications." @default.
• W2016402591 created "2016-06-24" @default.
• W2016402591 creator A5039668119 @default.
• W2016402591 creator A5048321225 @default.
• W2016402591 creator A5050922013 @default.
• W2016402591 creator A5062808320 @default.
• W2016402591 creator A5087060371 @default.
• W2016402591 date "2015-11-01" @default.
• W2016402591 modified "2023-09-30" @default.
• W2016402591 title "Spatial and temporal variability in spectral-based surface energy evapotranspiration measured from Landsat 5TM across two mangrove ecotones" @default.
• W2016402591 cites W106107486 @default.
• W2016402591 cites W1582568036 @default.
• W2016402591 cites W185560886 @default.
• W2016402591 cites W1964217023 @default.
• W2016402591 cites W1964432931 @default.
• W2016402591 cites W1966664222 @default.
• W2016402591 cites W1969798168 @default.
• W2016402591 cites W1978987129 @default.
• W2016402591 cites W1981072855 @default.
• W2016402591 cites W1983118263 @default.
• W2016402591 cites W1986566787 @default.
• W2016402591 cites W1988998717 @default.
• W2016402591 cites W1989919922 @default.
• W2016402591 cites W1990120830 @default.
• W2016402591 cites W2007477973 @default.
• W2016402591 cites W2012389900 @default.
• W2016402591 cites W2014897524 @default.
• W2016402591 cites W2018413914 @default.
• W2016402591 cites W2023081439 @default.
• W2016402591 cites W2026111105 @default.
• W2016402591 cites W2030097343 @default.
• W2016402591 cites W2032530979 @default.
• W2016402591 cites W2035147677 @default.
• W2016402591 cites W2035546609 @default.
• W2016402591 cites W2045208852 @default.
• W2016402591 cites W2048751762 @default.
• W2016402591 cites W2050023061 @default.
• W2016402591 cites W2051659151 @default.
• W2016402591 cites W2059603114 @default.
• W2016402591 cites W2069396737 @default.
• W2016402591 cites W2069917830 @default.
• W2016402591 cites W2087470097 @default.
• W2016402591 cites W2094040832 @default.
• W2016402591 cites W2094503242 @default.
• W2016402591 cites W2095240796 @default.
• W2016402591 cites W2095802152 @default.
• W2016402591 cites W2096734594 @default.
• W2016402591 cites W2098907952 @default.
• W2016402591 cites W2106141996 @default.
• W2016402591 cites W2108874754 @default.
• W2016402591 cites W2110091522 @default.
• W2016402591 cites W2113116813 @default.
• W2016402591 cites W2113195958 @default.
• W2016402591 cites W2117207425 @default.
• W2016402591 cites W2119130463 @default.
• W2016402591 cites W2123481070 @default.
• W2016402591 cites W2132111528 @default.
• W2016402591 cites W2138361175 @default.
• W2016402591 cites W2139813070 @default.
• W2016402591 cites W2140630543 @default.
• W2016402591 cites W2140853456 @default.
• W2016402591 cites W2155731548 @default.
• W2016402591 cites W2163017019 @default.
• W2016402591 cites W2166841559 @default.
• W2016402591 cites W2168847448 @default.
• W2016402591 cites W2183338940 @default.
• W2016402591 cites W2401109918 @default.
• W2016402591 cites W2482423852 @default.
• W2016402591 cites W2803310770 @default.
• W2016402591 cites W3023041173 @default.
• W2016402591 doi "https://doi.org/10.1016/j.agrformet.2014.11.017" @default.
• W2016402591 hasPublicationYear "2015" @default.
• W2016402591 type Work @default.
• W2016402591 sameAs 2016402591 @default.
• W2016402591 citedByCount "18" @default.
• W2016402591 countsByYear W20164025912015 @default.
• W2016402591 countsByYear W20164025912016 @default.
• W2016402591 countsByYear W20164025912017 @default.
• W2016402591 countsByYear W20164025912018 @default.
• W2016402591 countsByYear W20164025912019 @default.
• W2016402591 countsByYear W20164025912021 @default.
• W2016402591 countsByYear W20164025912022 @default.
• W2016402591 countsByYear W20164025912023 @default.
• W2016402591 crossrefType "journal-article" @default.
• W2016402591 hasAuthorship W2016402591A5039668119 @default.
• W2016402591 hasAuthorship W2016402591A5048321225 @default.
• W2016402591 hasAuthorship W2016402591A5050922013 @default.
• W2016402591 hasAuthorship W2016402591A5062808320 @default.
• W2016402591 hasAuthorship W2016402591A5087060371 @default.
• W2016402591 hasBestOaLocation W20164025911 @default.
• W2016402591 hasConcept C110872660 @default.
• W2016402591 hasConcept C127313418 @default.
• W2016402591 hasConcept C142724271 @default.
• W2016402591 hasConcept C1549246 @default.
• W2016402591 hasConcept C176783924 @default.
• W2016402591 hasConcept C187320778 @default.
• W2016402591 hasConcept C18903297 @default.
• W2016402591 hasConcept C199877563 @default.

• next