Special Issue Overview: Advances in Remote Sensing and Mapping for Integrated Studies of Reef Ecosystems in Oceania (Great Barrier Reef and Beyond)

被引:1
|
作者
Devlin, Michelle J. [1 ,2 ]
Petus, Caroline [2 ]
Oubelkheir, Kadija [3 ]
机构
[1] Ctr Environm Fisheries & Aquaculture Sci Cefas, Pakefield Rd, Lowestoft NR33 0HT, England
[2] James Cook Univ, Catchment Reef Res Grp, TropWater, Townsville, Qld 4811, Australia
[3] ERG Educ Res Growth Ctr, Tamellalt 85000, Morocco
关键词
habitat mapping; coral reefs; water quality; river plumes; ocean colour; bio-optics; sensors; GIS; monitoring; WATER-QUALITY; CORAL-REEFS; CLIMATE-CHANGE; RESILIENCE; MANAGEMENT; POLLUTION; CONSERVATION; VARIABILITY; IMPACTS;
D O I
10.3390/rs15102505
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The recent widespread and recurrent coral bleaching events over the Great Barrier Reef, the largest coral reef system on Earth and a hotspot of marine biodiversity, are a reminder of the vulnerability of reef ecosystems to human activities and a warming world. Protection of the Great Barrier Reef and similar reef ecosystems across Oceania requires a better understanding of environmental and socio-economic pressures, as well as the development of integrated management strategies. The rapid expansion of Earth Observation technologies and data has greatly advanced our capability to map and monitor reef habitats, ecological processing and exposure risk, providing spatially rich data essential to support and evaluate management and conservation strategies. However, these technologies are proportionally still under-utilized, and it is important to synthesise remote-sensing-derived tools and methods currently available for mapping reef ecosystems in Oceania to facilitate their intake in coral reefs studies. Publications in this Special Issue contribute toward filling this gap and explore recent advances in remote sensing of the Great Barrier Reef and other reef ecosystems in Oceania, from novel methodological approaches (sensors, algorithm development and improved thematic classification) to applications for environmental monitoring and management.
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页数:13
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