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

doi:10.17520/biods.2018067

Abstract

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

Le Wang, Chen Shi, Jinyan Tian, Xiaonan Song, Mingming Jia, Xiaojuan Li, Xiaomeng Liu, Ruofei Zhong, Dameng Yin, Shanshan Yang, Xianxian Guo. Researches on mangrove forest monitoring methods based on multi-source remote sensing[J]. Biodiv Sci, 2018, 26(8): 838-849.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2018067

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

Figures/Tables 5

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

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).

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.

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.

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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