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在亚太地区基于遥感的红树林蓝色碳评估:系统性审查

Abhilash Dutta Roy¹, Pavithra S Pitumpe Arachchige², Michael S Watt³

¹Ecoresolve, 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.

The Science of the total environment

|May 21, 2024

概括

在亚太地区,精确的红树林蓝碳监测依赖于遥感和建模. 研究显示碳捕获的变化,需要标准化的方法和更多的研究在代表性不足的地区.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
碳封存是一种碳封存技术.

背景情况:

红树林对于蓝色碳的捕获至关重要,支持亚太地区的可持续管理.
遥感和建模为大规模的红树林蓝色碳估计提供了有效的方法.
准确的数据对于保护,碳市场和当地生计至关重要.

研究的目的:

为了确定远程传感/模拟的趋势,以估计红树林的蓝色碳.
为了分析估计红树林碳捕获量的变化.
收集研究缺口和机会,并为未来的研究提供建议.

主要方法:

对105篇基于遥感的同行评审文章 (1990年 - 2023年6月) 的系统文献综述.
对传感器使用情况的分析 (Sentinel-2 MSI,Landsat 8 OLI,ALOS-2 PALSAR-2等). ) 的情况.
评估估计方法,包括参数 (线性回归) 和机器学习 (随机森林,XGB).

主要成果:

哨兵-2 MSI是最常用的传感器,其次是Landsat 8 OLI.
像Random Forest和XGBoost这样的机器学习模型越来越受欢迎和准确.
地下碳和碳库存估值被研究不足;位置,物种和森林类型的变化被广泛检查.
缅甸,孟加拉国和巴布亚新几内亚的研究很少,尽管红树林的面积很大.

结论:

需要标准化,协调和创新的方法来提高蓝色碳测量准确度.
可信的信息和可操作的数据对于有效的红树林管理和碳市场至关重要.
使用遥感的定期技术监测 (2-5年) 可以支持可持续的管理并使当地社区受益.

关键词:

在地面上方的红树林生物质.沿海生态系统的生态系统印度-太平洋地区机器学习是机器学习.红树林的碳是红树林的碳.多谱传感器多谱传感器

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