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
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 and Veterinary Medicine, University of Lleida, Lleida, Spain.
Ecoresolve, San Francisco, CA, United States; Morobe Development Foundation (via United Nations Volunteering Program), Lae, Papua New Guinea.
Sci Total Environ. 2024 Aug 15;938:173270. doi: 10.1016/j.scitotenv.2024.173270. Epub 2024 May 19.
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.
对红树林进行准确测量、绘图和监测有助于亚太地区红树林蓝碳的可持续管理。遥感与建模相结合能够在更大的时空范围内高效且准确地估算红树林蓝碳储量。本研究旨在确定用于估算红树林蓝碳的遥感/建模趋势、利用遥感估算的红树林碳固存属性/变化情况，梳理研究差距与机遇，随后为未来研究提供建议。我们采用系统文献综述方法，回顾了105篇基于遥感的同行评议文章（1990年至2023年6月）。尽管缅甸、孟加拉国和巴布亚新几内亚的红树林面积很大，但相关研究较少。使用最频繁的传感器是哨兵2号多光谱成像仪（Sentinel-2 MSI），占总使用量的14.5%，其次是陆地卫星8号运行陆地成像仪（Landsat 8 OLI，11.5%）、先进陆地观测卫星2号相控阵L波段合成孔径雷达（ALOS-2 PALSAR-2，7.3%）、先进陆地观测卫星相控阵L波段合成孔径雷达（ALOS PALSAR，7.2%）、陆地卫星7号增强型专题绘图仪（Landsat 7 ETM+，6.1%）、哨兵1号（Sentinel-1，6.7%）、陆地卫星5号专题绘图仪（Landsat 5 TM，5.5%）、航天飞机雷达地形测绘任务数字高程模型（SRTM DEM，5.5%）和无人机激光雷达（UAV-LiDAR，4.8%）。尽管线性回归等参数方法仍然使用最为广泛，但近年来随机森林（RF）和极端梯度提升（XGB）等机器学习回归模型也颇受欢迎，且表现出良好的准确性。在估算的各种属性中，地下红树林蓝碳和碳储量估值的研究较少。因地理位置、物种和森林类型导致的碳固存潜力变化得到了广泛研究。为提高蓝碳测量的准确性，应采用标准化/协调且创新的方法，并辅以可靠信息和可操作数据。借助遥感加强技术监测（每2至5年一次）可为红树林可持续管理提供准确精确的数据，同时为自愿碳市场开辟机会，造福亚太地区发展中国家的环境和当地生计。
