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遥感技术在蓝碳科学中的应用趋势。

Trends in the application of remote sensing in blue carbon science.

作者信息

Araya-Lopez Rocio, de Paula Costa Micheli Duarte, Wartman Melissa, Macreadie Peter I

机构信息

Centre for Integrative Ecology, School of Life and Environmental Sciences Deakin University Burwood Victoria Australia.

出版信息

Ecol Evol. 2023 Sep 23;13(9):e10559. doi: 10.1002/ece3.10559. eCollection 2023 Sep.

DOI:10.1002/ece3.10559
PMID:37745789
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10517596/
Abstract

Blue carbon ecosystems (BCEs), such as mangroves, saltmarshes, and seagrasses, are increasingly recognized as natural climate solutions. Evaluating the current extent, losses, and gains of BCEs is crucial to estimating greenhouse gas emissions and supporting policymaking. Remote sensing approaches are uniquely suited to assess the factors driving BCEs dynamics and their impacts at various spatial and temporal scales. Here, we explored trends in the application of remote sensing in blue carbon science. We used bibliometric analysis to assess 2193 published papers for changes in research focus over time (1990 - June 2022). Over the past three decades, publications have steadily increased, with an annual growth rate of 16.9%. Most publications focused on mangrove ecosystems and used the optical spaceborne Landsat mission, presumably due to its long-term, open-access archives. Recent technologies such as LiDAR, UAVs, and acoustic sensors have enabled fine-scale mapping and monitoring of BCEs. Dominant research topics were related to mapping and monitoring natural and human impacts on BCEs, estimating vegetation and biophysical parameters, machine and deep learning algorithms, management (including conservation and restoration), and climate research. Based on corresponding author affiliations, 80 countries contributed to the field, with United States (27.2%), China (15.0%), Australia (7.5%), and India (6.0%) holding leading positions. Overall, our results reveal the need to increase research efforts for seagrasses, saltmarshes, and macroalgae, integrate technologies, increase the use of remote sensing to support carbon accounting methodologies and crediting schemes, and strengthen collaboration and resource sharing among countries. Rapid advances in remote sensing technology and decreased image acquisition and processing costs will likely enhance research and management efforts focused on BCEs.

摘要

蓝碳生态系统(BCEs),如红树林、盐沼和海草床,越来越被视为自然气候解决方案。评估BCEs的当前范围、损失和增加情况对于估算温室气体排放和支持政策制定至关重要。遥感方法特别适合评估驱动BCEs动态变化的因素及其在不同空间和时间尺度上的影响。在此,我们探讨了遥感在蓝碳科学中的应用趋势。我们使用文献计量分析来评估2193篇已发表论文随时间(1990年 - 2022年6月)研究重点的变化。在过去三十年中,出版物数量稳步增加,年增长率为16.9%。大多数出版物聚焦于红树林生态系统,并使用光学卫星陆地卫星任务,这可能是由于其长期的、开放获取的档案资料。激光雷达、无人机和声传感器等最新技术已能够对BCEs进行精细尺度的测绘和监测。主要研究主题涉及测绘和监测自然和人类对BCEs的影响、估算植被和生物物理参数、机器学习和深度学习算法、管理(包括保护和恢复)以及气候研究。根据通讯作者的所属机构,80个国家对该领域做出了贡献,美国(27.2%)、中国(15.0%)、澳大利亚(7.5%)和印度(6.0%)占据领先地位。总体而言,我们的研究结果表明,需要加大对海草床、盐沼和大型藻类的研究力度,整合技术,增加使用遥感来支持碳核算方法和信用机制,并加强各国之间的合作与资源共享。遥感技术的快速发展以及图像采集和处理成本的降低可能会加强针对BCEs的研究和管理工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/6cedc7d27f53/ECE3-13-e10559-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/47461e0ef080/ECE3-13-e10559-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/583f04ffb978/ECE3-13-e10559-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/655c13ccf00e/ECE3-13-e10559-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/4617b151456a/ECE3-13-e10559-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/9b84d4a75a24/ECE3-13-e10559-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/3f232eb36647/ECE3-13-e10559-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/6cedc7d27f53/ECE3-13-e10559-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/47461e0ef080/ECE3-13-e10559-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/583f04ffb978/ECE3-13-e10559-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/655c13ccf00e/ECE3-13-e10559-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/4617b151456a/ECE3-13-e10559-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/9b84d4a75a24/ECE3-13-e10559-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/3f232eb36647/ECE3-13-e10559-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10517596/6cedc7d27f53/ECE3-13-e10559-g005.jpg

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