Assessment of long-term mangrove distribution using optimised machine learning algorithms and landscape pattern analysis

Mangrove ecosystems provide numerous benefits, including carbon storage, coastal protection and food for marine organisms. However, mapping and monitoring of mangrove status in some regions, such as the Red Sea area, has been hindered by a lack of data, accurate and precise maps and technical expert...

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Vydáno v:	Environmental science and pollution research international Ročník 30; číslo 29; s. 73753 - 73779
Hlavní autoři:	Bindajam, Ahmed Ali, Mallick, Javed, Talukdar, Swapan, Shohan, Ahmed Ali A., Alshayeb, Mohammed J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023 Springer Nature B.V
Témata:	Algorithms Aquatic Pollution Area Artificial neural networks Atmospheric Protection/Air Quality Control/Air Pollution Biodiversity Biodiversity hot spots Carbon sequestration Coastal zone management Computer vision Connectivity Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental protection Environmental science habitat fragmentation habitats High resolution Image resolution Land cover Land use land use and land cover maps Landscape landscapes Learning algorithms Machine learning Mangroves Mapping Marine organisms Neural networks Pattern analysis Red Sea Research Article Saudi Arabia Statistical analysis statistics Structural analysis Support vector machines Waste Water Technology Water Management Water Pollution Control Red Sea Saudi Arabia Mangrove habitat Saudi Arabia Optimised machine learning algorithms LULC Landscape structure Red Sea Connectivity modelling
ISSN:	1614-7499, 0944-1344, 1614-7499
On-line přístup:	Získat plný text
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