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遥感技术与应用  2021, Vol. 36 Issue (5): 959-972    DOI: 10.11873/j.issn.1004-0323.2021.5.0959
综述     
红树林雷达遥感研究进展
朱彬1,3(),廖静娟1,2(),沈国状1
1.中国科学院空天信息创新研究院 数字地球重点实验室,北京 100094
2.海南省地球观测重点实验室,海南 三亚 572029
3.中国科学院大学,北京 100049
Review on Radar Remote Sensing of Mangrove
Bin Zhu1,3(),Jingjuan Liao1,2(),Guozhuang Shen1
1.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
2.Key Laboratory of Earth Observation,Sanya 572029,China
3.University of Chinese Academy of Sciences,Beijing 100049,China
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摘要:

红树林是海岸带生态系统中重要的植物群落,具有较高的社会、生态和经济价值。遥感技术的发展为红树林监测提供了一种高效便利的手段。雷达遥感由于具有穿透性好、不受云雨影响的特点,在红树林分布地区具有得天独厚的优势。对近几十年来雷达遥感在红树林监测方面的研究进行了回顾,着重分析了红树林散射机制、红树林分类与识别和红树林生物物理参数反演这3个方面的研究进展,对各类方法进行了总结和对比,最后针对存在的问题提出了未来可以改进的方向。

关键词: 红树林雷达散射机制分类反演    
Abstract:

Mangroves are important plant communities in coastal ecosystems, and have enormous social, ecological and economic values. The development of remote sensing technology provides an efficient and convenient way for mangrove monitoring. Radar remote sensing has a unique advantage in mangrove distribution area because it has high penetration and is unaffected by cloud and rain. This paper reviews the study on mangrove monitoring based on radar remote sensing in recent decades in the aspects of mangrove scattering mechanism, mangrove classification and recognition, and mangrove biophysical parameters retrieval. The summary and comparison of different methods in three aspects are also proposed. Finally, the future improvements are discussed according to the existing problems.

Key words: Mangrove    Radar    Scattering mechanism    Classification    Retrieval
收稿日期: 2020-07-02 出版日期: 2021-12-08
ZTFLH:  X37  
基金资助: 海南省重大科技计划项目(ZDKJ2019006);中国科学院战略性先导科技专项(A类)(XDA19030302)
通讯作者: 廖静娟     E-mail: zhubin@aircas.ac.cn;liaojj@aircas.ac.cn
作者简介: 朱彬(1992-),男,北京人,博士研究生,主要从事红树林遥感方面的研究。E?mail: zhubin@aircas.ac.cn
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引用本文:

朱彬,廖静娟,沈国状. 红树林雷达遥感研究进展[J]. 遥感技术与应用, 2021, 36(5): 959-972.

Bin Zhu,Jingjuan Liao,Guozhuang Shen. Review on Radar Remote Sensing of Mangrove. Remote Sensing Technology and Application, 2021, 36(5): 959-972.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.5.0959        http://www.rsta.ac.cn/CN/Y2021/V36/I5/959

传感器波段极化

分辨率

/m

发射

时间

参考文献
AIRSARP C L全极化/1985[15-17]
ERS-1CVV301991.07[15, 18, 19]
SIR-CC L X全极化10~2001991.08[15, 20, 21]
JERS-1LHH181992.02[15, 18, 22]
ERS-2CVV301995.04[23, 24]
Radarsat-1CHH10~1001995.11[25-27]
EnvisatC双极化282002.02[28-30]
ALOSL双极化10~1002006.01[22, 31-33]
TerraSAR-XX双极化1~402007.06[34]
Radarsat-2C全极化1.5~1002007.12[35-37]
Sentinel-1C双极化5~402014.04[38-40]
ALOS-2L全极化3~1002014.05[41-44]
表1  已用于红树林研究的雷达传感器
波段穿透性散射特性
C-band穿过冠层顶约几米体散射(树叶、树枝)
L-band穿透冠层

单次散射(地表)

二次散射(树干-地表)

体散射(树枝间)

P-band穿透冠层到土壤层

单次散射(土壤)

二次散射(树干-地表)

表2  不同波段下微波信号的特征
类别方法
目视解译假彩色、数据融合
传统方法最大似然、Wishart、决策规则、ISODATA、 马尔科夫随机场
面向对象上下文特征、图像分割
机器学习决策树、支持向量机、人工神经网络、随机 森林、旋转森林
表3  已用于红树林分类方法
波段敏感性

生物量饱和值

(t/hm2

VVHHHV
C-band生物量、胸径、树高、密度、胸高断面积生物量、胸径LAI(大角度好)、树高(小角度好)、密度50
L-band--生物量(大角度好)、胸径、树高、密度、胸高断面积100
P-band树高、密度、胸高断面积生物量、密度生物量(大角度最好)、胸径、树高、密度、胸高断面积150
表4  红树林反演的有效性
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