Cold-Water Coral Habitat Mapping: Trends and Developments in Acquisition and Processing Methods

被引:18
|
作者
Lim, Aaron [1 ]
Wheeler, Andrew J. [1 ,2 ]
Conti, Luis [3 ]
机构
[1] Univ Coll Cork, Environm Res Inst, Earth & Environm Sci, Sch Biol, Cork T23 TK30, Ireland
[2] Univ Coll Cork, Marine & Renewable Energy Ctr, Irish Ctr Res Appl Geosci, Cork P43 C573, Ireland
[3] Univ Sao Paulo, Escola Artes Ciencias & Humanidades, BR-01000000 Sao Paulo, Brazil
基金
爱尔兰科学基金会; 欧盟地平线“2020”; 巴西圣保罗研究基金会;
关键词
cold water corals; mapping; multibeam bathymetry; side-scan sonar; habitats; machine learning; geographic information systems; CARBONATE-MOUND DEVELOPMENT; ROCKALL TROUGH MARGIN; HIGH-RESOLUTION BATHYMETRY; DEEP-WATER; LOPHELIA-PERTUSA; NE ATLANTIC; PORCUPINE SEABIGHT; ROV VIDEO; SEABED CLASSIFICATION; SOUTHERN GULF;
D O I
10.3390/geosciences11010009
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Cold-water coral (CWC) habitats are considered important centers of biodiversity in the deep sea, acting as spawning grounds and feeding area for many fish and invertebrates. Given their occurrence in remote parts of the planet, research on CWC habitats has largely been derived from remotely-sensed marine spatial data. However, with ever-developing marine data acquisition and processing methods and non-ubiquitous nature of infrastructure, many studies are completed in isolation resulting in large inconsistencies. Here, we present a concise review of marine remotely-sensed spatial raster data acquisition and processing methods in CWC habitats to highlight trends and knowledge gaps. Sixty-three studies that acquire and process marine spatial raster data since the year 2000 were reviewed, noting regional geographic location, data types ('acquired data') and how the data were analyzed ('processing methods'). Results show that global efforts are not uniform with most studies concentrating in the NE Atlantic. Although side scan sonar was a popular mapping method between 2002 and 2012, since then, research has focused on the use of multibeam echosounder and photogrammetric methods. Despite advances in terrestrial mapping with machine learning, it is clear that manual processing methods are largely favored in marine mapping. On a broader scale, with large-scale mapping programs (INFOMAR, Mareano, Seabed2030), results from this review can help identify where more urgent research efforts can be concentrated for CWC habitats and other vulnerable marine ecosystems.
引用
收藏
页码:1 / 20
页数:20
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