Remote sensing-based mangrove blue carbon assessment in the Asia-Pacific: A systematic review

红树林 环境科学 蓝炭 亚太地区 遥感 碳纤维 地理 海洋学 生态学 固碳 地质学 生物 计算机科学 业务 二氧化碳 算法 复合数 国际贸易
作者
Abhilash Dutta Roy,Pavithra S. Pitumpe Arachchige,Michael S. Watt,Apoorwa Kale,M. E. Davies,Joe Eu Heng,Redeat Daneil,G. A. Pabodha Galgamuwa,Lara G. Moussa,Kausila Timsina,Ewane Basil Ewane,Kerrylee Rogers,Ian Hendy,Andrew Edwards‐Jones,Sergio de‐Miguel,John A. Burt,Tarig Ali,Frida Sidik,Meshal M. Abdullah,P. Pandi Selvam,Wan Shafrina Wan Mohd Jaafar,Isuru Alawatte,Willie Doaemo,Adrián Cardíl,Midhun Mohan
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:938: 173270-173270 被引量:1
标识
DOI:10.1016/j.scitotenv.2024.173270
摘要

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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