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Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review.
Dutta Roy, Abhilash; Pitumpe Arachchige, Pavithra S; Watt, Michael S; Kale, Apoorwa; Davies, Mollie; Heng, Joe Eu; Daneil, Redeat; Galgamuwa, G A Pabodha; Moussa, Lara G; Timsina, Kausila; Ewane, Ewane Basil; Rogers, Kerrylee; Hendy, Ian; Edwards-Jones, Andrew; de-Miguel, Sergio; Burt, John A; Ali, Tarig; Sidik, Frida; Abdullah, Meshal; Pandi Selvam, P; Jaafar, Wan Shafrina Wan Mohd; Alawatte, Isuru; Doaemo, Willie; Cardil, Adrián; Mohan, Midhun.
Afiliação
  • Dutta Roy A; Ecoresolve, San Francisco, CA, United States; Mediterranean Forestry and Natural Resources Management, School of Agriculture, University of Lisbon, Portugal; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea; School of Agrifood and Forestry Engineering an
  • Pitumpe Arachchige PS; Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
  • Watt MS; Scion, Christchurch, New Zealand.
  • Kale A; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
  • Davies M; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
  • Heng JE; Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
  • Daneil R; Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
  • Galgamuwa GAP; Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea; The Nature Conservancy, Maryland/DC Chapter, Cumberland, MD, United States.
  • Moussa LG; Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
  • Timsina K; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
  • Ewane EB; Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea; BlueForests, San Francisco, CA, United States; Department of Geography, Faculty of Social and Management Sciences, University of Buea, Buea, Cameroon.
  • Rogers K; Faculty of Science, Medicine and Health, School of Earth, Atmospheric and Life Sciences (SEALS), Wollongong, NSW, Australia.
  • Hendy I; Institute of Marine Sciences, University of Portsmouth, Portsmouth, United Kingdom.
  • Edwards-Jones A; Plymouth Marine Laboratory, Plymouth, United Kingdom.
  • de-Miguel S; Department of Agricultural and Forest Sciences and Engineering, University of Lleida, Lleida, Spain; Forest Science and Technology Centre of Catalonia (CTFC), Solsona, Spain.
  • Burt JA; Center for Interacting Urban Networks (CITIES) and Mubadala Arabian Center for Climate and Environmental Sciences (Mubadala ACCESS), New York University Abu Dhabi, 129188, Abu Dhabi, United Arab Emirates.
  • Ali T; Department of Civil Engineering, College of Engineering, American University of Sharjah (AUS), Sharjah, United Arab Emirates.
  • Sidik F; Research Centre for Oceanography, National Research and Innovation Agency, Jakarta, Indonesia.
  • Abdullah M; Ecoresolve, San Francisco, CA, United States; Department of Geography, College of Arts and Social Sciences, Sultan Qaboos University, Muscat, Oman; Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, United States.
  • Pandi Selvam P; GAIT Global, Singapore.
  • Jaafar WSWM; Ecoresolve, San Francisco, CA, United States; Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
  • Alawatte I; Department of Forest Conservation, Ministry of Wildlife and Forest Resources Conservation, Sri Lanka.
  • Doaemo W; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea; Department of Civil Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea.
  • Cardil A; Department of Agricultural and Forest Sciences and Engineering, University of Lleida, Lleida, Spain; Forest Science and Technology Centre of Catalonia (CTFC), Solsona, Spain; Tecnosylva, León, Spain.
  • Mohan M; Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea; BlueForests, San Francisco, CA, United States; Department of Civil Engineering, College of Engineering, American University of Sharjah (AUS), Sharjah, United
Sci Total Environ ; 938: 173270, 2024 Aug 15.
Article em En | MEDLINE | ID: mdl-38772491
ABSTRACT
Accurate measuring, mapping, and monitoring of mangrove forests support the sustainable management of mangrove blue carbon in the Asia-Pacific. Remote sensing coupled with modeling can efficiently and accurately estimate mangrove blue carbon stocks at larger spatiotemporal extents. This study aimed to identify trends in remote sensing/modeling employed in estimating mangrove blue carbon, attributes/variations in mangrove carbon sequestration estimated using remote sensing, and to compile research gaps and opportunities, followed by providing recommendations for future research. Using a systematic literature review approach, we reviewed 105 remote sensing-based peer-reviewed articles (1990 - June 2023). Despite their high mangrove extent, there was a paucity of studies from Myanmar, Bangladesh, and Papua New Guinea. The most frequently used sensor was Sentinel-2 MSI, accounting for 14.5 % of overall usage, followed by Landsat 8 OLI (11.5 %), ALOS-2 PALSAR-2 (7.3 %), ALOS PALSAR (7.2 %), Landsat 7 ETM+ (6.1 %), Sentinel-1 (6.7 %), Landsat 5 TM (5.5 %), SRTM DEM (5.5 %), and UAV-LiDAR (4.8 %). Although parametric methods like linear regression remain the most widely used, machine learning regression models such as Random Forest (RF) and eXtreme Gradient Boost (XGB) have become popular in recent years and have shown good accuracy. Among a variety of attributes estimated, below-ground mangrove blue carbon and the valuation of carbon stock were less studied. The variation in carbon sequestration potential as a result of location, species, and forest type was widely studied. To improve the accuracy of blue carbon measurements, standardized/coordinated and innovative methodologies accompanied by credible information and actionable data should be carried out. Technical monitoring (every 2-5 years) enhanced by remote sensing can provide accurate and precise data for sustainable mangrove management while opening ventures for voluntary carbon markets to benefit the environment and local livelihood in developing countries in the Asia-Pacific region.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Total Environ Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Total Environ Ano de publicação: 2024 Tipo de documento: Article