From Remote Sensing to Artificial Intelligence in Coral Reef Monitoring

被引:2
|
作者
Pineros, Victor J. [1 ]
Reveles-Espinoza, Alicia Maria [2 ,3 ]
Monroy, Jesus A. [4 ]
机构
[1] Univ Guadalajara, Dept Estudios Desarrollo Sustentable Zona Costera, Ctr Univ Costa Sur, Gomez Farias 82, San Patricio Melaque 48980, Jalisco, Mexico
[2] Inst Politecn Nacl CIDETEC IPN, Ctr Innovac & Desarrollo Tecnol Computo, Ave Juan Dios Batiz s-n,Gustavo A Madero, Mexico City 07700, Mexico
[3] Univ Autonoma Queretaro, Fac Quim, Cerro Campanas s-n, Santiago De Queretaro 76010, Queretaro, Mexico
[4] CONAHCYT Ctr Invest Ciencias Informac Geoespacial, Sierra Papacal Chuburna,Km 5, Merida 97302, Yucatan, Mexico
关键词
coral reefs; ecological analysis; molecular biomarkers; underwater vehicles; remote sensing; artificial intelligence; AUTONOMOUS UNDERWATER VEHICLE; NITRIC-OXIDE PRODUCTION; INTERVENTION MISSIONS; MANIPULATION; DESIGN; SYSTEM; IMPLEMENTATION; BIODIVERSITY; ARCHITECTURE; METHODOLOGY;
D O I
10.3390/machines12100693
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This review comprehensively covers the development of viable unmanned underwater vehicles based on their technical capabilities, in particular those designed to conduct research exploration in underwater ecosystems and address environmental issues through analysis of coral reef vulnerability. The most important elements to be obtained are in situ data samples for analysis and characterization, supported by molecular biomarkers and marine ecology indicators. The following aspects are considered in this study: first, the geographic distribution of coral reefs for the study of marine ecology and molecular biological approaches for the detection of biomarkers to evaluate the vulnerability of coral reefs are detailed; then, the technologies currently available for the study of coral reefs are briefly described, ranging from large-scale capture to local-scale capture directly in the study region, taking advantage of remote sensing systems assisted by aerial technologies, marine vehicles, and artificial intelligence for the mapping, monitoring, and management of coral reefs as well as the characterization of their vulnerability; following this, existing marine vehicle technologies are generally explained, including a categorical description and an updated and highlighted list of innovative and robust marine vehicles that have been used in coral reef applications; the technical capabilities of such vehicle throughout the missions they have been subjected to are presented based on bibliographic references; finally, this review promotes multidisciplinary work to integrate the developments in the associated knowledge areas in order to obtain synergies in the face of challenges related to the massive scale of coral reef degradation worldwide.
引用
收藏
页数:32
相关论文
共 50 条
  • [21] Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing
    Goodman, James A.
    Lay, Mui
    Ramirez, Luis
    Ustin, Susan L.
    Haverkamp, Paul J.
    REMOTE SENSING, 2020, 12 (03)
  • [22] Coral reef ecosystem monitoring using remote sensing data: case study in Owi Island, Biak, Papua
    Awak, Dominggus Samuel H. L. M. K.
    Gaol, Jonson Lumbar
    Subhan, Beginer
    Madduppa, Hawis H.
    Arafat, Dondy
    2ND INTERNATIONAL SYMPOSIUM ON LAPAN-IPB SATELLITE (LISAT) FOR FOOD SECURITY AND ENVIRONMENTAL MONITORING, 2016, 33 : 600 - 606
  • [23] Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence
    Liu, Yun
    Jing, Yuqin
    Lu, Yinan
    JOURNAL OF CHEMISTRY, 2020, 2020
  • [24] Reef Cover, a coral reef classification for global habitat mapping from remote sensing (vol 8, 196, 2021)
    Kennedy, Emma V.
    Roelfsema, Chris M.
    Lyons, Mitchell B.
    Kovacs, Eva M.
    Borrego-Acevedo, Rodney
    Roe, Meredith
    Phinn, Stuart R.
    Larsen, Kirk
    Murray, Nicholas J.
    Yuwono, Doddy
    Wolff, Jeremy
    Tudman, Paul
    SCIENTIFIC DATA, 2021, 8 (01)
  • [25] Monitoring Coral Reefs Death Causes with Artificial Intelligence
    Pooloo, Nabeelah
    Aumeer, Wafiik
    Khoodeeram, Raj Eev
    2021 IST-AFRICA CONFERENCE (IST-AFRICA), 2021,
  • [26] Remote Sensing of Coral Reefs for Monitoring and Management: A Review
    Hedley, John D.
    Roelfsema, Chris M.
    Chollett, Iliana
    Harborne, Alastair R.
    Heron, Scott F.
    Weeks, Scarla J.
    Skirving, William J.
    Strong, Alan E.
    Eakin, C. Mark
    Christensen, Tyler R. L.
    Ticzon, Victor
    Bejarano, Sonia
    Mumby, Peter J.
    REMOTE SENSING, 2016, 8 (02)
  • [27] Remote Monitoring and Artificial Intelligence: Outlook for 2050
    Feinstein, Max
    Katz, Daniel
    Demaria, Samuel
    Hofer, Ira S.
    ANESTHESIA AND ANALGESIA, 2024, 138 (02): : 350 - 357
  • [28] The remote sensing composite information entropy and types of Nansha coral reef atolls
    刘宝银
    王岩峰
    高俊国
    郝庆祥
    Acta Oceanologica Sinica, 1999, (03) : 389 - 400
  • [29] Bottom Reflectance in Ocean Color Satellite Remote Sensing for Coral Reef Environments
    Reichstetter, Martina
    Fearns, Peter R. C. S.
    Weeks, Scarla J.
    McKinna, Lachlan I. W.
    Roelfsema, Chris
    Furnas, Miles
    REMOTE SENSING, 2015, 7 (12) : 16756 - 16777
  • [30] Identification of Coral Reef feature using Hyper-spectral remote sensing
    Mohanty, P. C.
    Panditrao, Satej
    Mahendra, R. S.
    Kumar, H. Shiva
    Kumar, T. Srinivasa
    MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL REMOTE SENSING TECHNOLOGY, TECHNIQUES AND APPLICATIONS VI, 2016, 9880