基于多源遥感的红树林监测

doi:10.17520/biods.2018067

生物多样性 ›› 2018, Vol. 26 ›› Issue (8): 838-849. DOI: 10.17520/biods.2018067

所属专题：生物入侵

基于多源遥感的红树林监测

王乐^1,^3,^*(), 时晨¹, 田金炎^1,², 宋晓楠¹, 贾明明⁴, 李小娟^1,², 刘晓萌¹, 钟若飞^1,², 殷大萌³, 杨杉杉¹, 郭先仙¹

1 首都师范大学资源环境与旅游学院, 北京 100048
2 首都师范大学北京成像技术高精尖创新中心, 北京 100048
3 Department of Geography, University at Buffalo, The State University of New York, 105 Wilkeson Quad, Buffalo, NY 14261, USA
4 中国科学院东北地理与农业生态研究所, 长春 130102

收稿日期:2018-03-02 接受日期:2018-08-05 出版日期:2018-08-20 发布日期:2018-09-27
通讯作者: 王乐
作者简介:# 共同第一作者
基金资助:
科技创新服务能力建设——基本科研业务费(025185305000/199;025185305000/211)和国家自然科学基金(41601363)

Researches on mangrove forest monitoring methods based on multi-source remote sensing

Le Wang^1,^3,^*(), Chen Shi¹, Jinyan Tian^1,², Xiaonan Song¹, Mingming Jia⁴, Xiaojuan Li^1,², Xiaomeng Liu¹, Ruofei Zhong^1,², Dameng Yin³, Shanshan Yang¹, Xianxian Guo¹

1 College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
2 Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
3 Department of Geography, University at Buffalo, The State University of New York, 105 Wilkeson Quad, Buffalo, NY 14261, USA
4 Laboratory for Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Received:2018-03-02 Accepted:2018-08-05 Online:2018-08-20 Published:2018-09-27
Contact: Wang Le
About author:# Co-first authors

摘要/Abstract

摘要：

红树林是生长在热带以及亚热带海岸潮间带上的生态群落, 其生产力高, 固碳能力强, 对保持海岸带生物多样性具有十分重要的价值。本文介绍了利用多源遥感数据监测红树林的一些主要研究内容, 分为3个方面: (1)在时空模式研究方面, 利用高空间分辨率影像像素和对象结合的方法对红树林树种进行分类以及利用Landsat影像对红树林进行动态变化监测并分析其驱动因素; (2)在结构参数研究方面, 利用无人机多光谱数据及地面激光雷达数据对红树林叶面积指数进行反演; (3)在生理生化参数研究方面, 探讨了红树林叶绿素含量对淹没状况的响应、互花米草(Spartina alterniflora)入侵是否影响红树林光能利用率, 以及光化学反射指数(photochemical reflectance index, PRI)与光能利用率(light use efficiency, LUE)的关系。上述系列研究为提取红树林相关信息要素时如何选择合适的分析方法提供了有力的参考, 强调了遥感在研究红树林时空模式, 提取结构参数和生物生化参数监测的有效性, 从而更好地促进红树林生态系统的生物多样性保育工作。

关键词: 红树林, 生物多样性, 多源遥感, 树种分类, 入侵物种

Abstract

Mangrove forests are ecological communities growing in the intertidal zone of tropical and subtropical coastlines. Due to their high productivity, mangrove forests are essential to persistence of biodiversity along coastlines and have high carbon sequestration ability. In this article we review aspects of monitoring mangrove forests using recent multi-source remote sensing data. First, we reviewed studies on monitoring mangrove dynamics. By integrating object-based and pixel-based classification, high spatial resolution images were used to classify different mangrove species. Landsat images were then used to monitor the dynamics of mangrove forests and analyze factors driving them. Second, we reviewed studies measuring structural parameters of mangroves. Specifically, unmanned aerial vehicle multispectral data and ground-based Light Detection and Ranging (LiDAR) data were used to compute leaf area index of mangrove forests. Finally, we reviewed studies examining physiology and biochemistry parameters. These studies explored adaption of chlorophyll content in mangrove forests under different submergence conditions, whether the invasive species Spartina alterniflora affects the light use efficiency and changed the response of photochemical reflectance index (PRI) to LUE. Our review provides a useful reference for selecting appropriate analytical methods when extracting information of mangroves from remotely sensed data. We emphasize the effectiveness of remote sensing in studying mangrove spatiotemporal patterns, extracting structural parameters, monitoring biochemical parameters, thus aiding efforts to conserve mangrove ecosystems.

Key words: mangrove, biodiversity, multi-source remote sensing, species classification, invasive species

王乐, 时晨, 田金炎, 宋晓楠, 贾明明, 李小娟, 刘晓萌, 钟若飞, 殷大萌, 杨杉杉, 郭先仙 (2018) 基于多源遥感的红树林监测. 生物多样性, 26, 838-849. DOI: 10.17520/biods.2018067.

Le Wang, Chen Shi, Jinyan Tian, Xiaonan Song, Mingming Jia, Xiaojuan Li, Xiaomeng Liu, Ruofei Zhong, Dameng Yin, Shanshan Yang, Xianxian Guo (2018) Researches on mangrove forest monitoring methods based on multi-source remote sensing. Biodiversity Science, 26, 838-849. DOI: 10.17520/biods.2018067.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2018067

https://www.biodiversity-science.net/CN/Y2018/V26/I8/838

图/表 5

表1 通过不同范围的植被遮挡指数(VOI)筛选出的样本个数(plot)以及通过该样本数据估测的叶面积指数(LAI)与实测LAI之间的R2, RMSE, %RMSE

Table 1 The R2, RMSE (root-mean-square error), % RMSE (a percentage between RMSE and the mean value of the field LAI) between LAI estimated from terrestrial laser scanning (TLS) and LAI from LAI2200 PCA, and the number of plots under the different threshold of vegetation occlusion index (VOI)

VOI≤	0.10	0.20	0.25	0.30	0.35	0.40	0.50	0.60	0.70	0.80	0.90
R²	0.73	0.70	0.72	0.67	0.50	0.35	0.31	0.21	0.21	0.22	0.15
RMSE	0.277	0.292	0.27	0.264	0.288	0.312	0.329	0.347	0.346	0.349	0.374
% RMSE	16.77	17.88	17.20	17.23	18.54	20.12	21.72	22.56	22.68	23.06	24.47
Plot	9	16	19	25	35	46	65	85	93	97	102

图1 白骨壤、互花米草影响下的白骨壤(简称互花-白骨壤)、桐花树、互花米草影响下的桐花树(互花-桐花树)等4种研究对象的光化学发射指数(PRI)与光能利用率(LUE)关系(引自Yang et al, 2018)

Fig. 1 LUE-PRI relationship of the four types of mangrove trees, i.e. Avicennia marina (Am), Avicennia marina affected by Spartina alterniflora (Am-Sa), Aegiceras corniculatum (Ac), Aegiceras corniculatum affected by Spartina alterniflora (Ac-Sa). Adopted from Yang et al (2018).

图2 4种类型红树林的光能利用率(LUE) (A)与光化学发射指数(PRI) (B)

Fig. 2 The light use efficiency (LUE) (A) and photochemical reflectance index (PRI) (B) of four types of mangrove trees. Am, Avicennia marina; Sa, Spartina alterniflora; Ac, Aegiceras corniculatum; Am-Sa, Avicennia marina affected by Spartina alterniflora; Ac-Sa, Aegiceras corniculatum affected by Spartina alterniflora.

图3 分别利用数据集A、B和C构建的PLSR模型的独立验证图。实线表示拟合线, 虚线表示1:1线

Fig. 3 Independence verification of PLSR based on dataset a, b and c. The solid line represents the regression line, and the broken black line represents the 1:1 line.

图4 基于种类和季节的不同淹没状况的绿色叶绿素指数(GCI)分布箱体图。每个箱体包含第一四分位数到第三四分位数; 箱体中的粗线表示中位数, 星点表示平均值, 而空白点表示异常值。

Fig. 4 Green chlorophyll index (GCI) distribution of the submerged level based on two mangrove species for four seasons. Each box embodies the first as well as the third quartile, and whiskers are located at 1.5 times the interquartile range. The bold line represents the median in the middle of the box. OFA, Often flooded area; RFA, Rare flooded area.

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基于多源遥感的红树林监测

Researches on mangrove forest monitoring methods based on multi-source remote sensing

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