Fine identification and biomass estimation of mangroves based on UAV multispectral and LiDAR

被引：0
|
作者：
Wu P. [1 ]
Ren G. [1 ]
Zhang C. [1 ,2 ]
Wang H. [1 ]
Liu S. [3 ]
Ma Y. [1 ]
机构：
[1] First Institute of Oceanography, Ministry of Natural Resources, Qingdao
[2] College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao
[3] College of Oceanography and Space Information, China University Petroleum (East China), Qingdao
来源：
National Remote Sensing Bulletin | 2022年 / 26卷 / 06期
关键词：
aboveground biomass; LiDAR; mangrove; multispectral; remote sensing; tree species classification; UAV;
D O I：
10.11834/jrs.20221484
中图分类号：
学科分类号：
摘要：
Mangroves are special types of woody plants that grow exclusively in the intertidal zones of the tropics and the subtropics. With respect to environmental and ecological values, mangroves protect the shoreline from tides, winds, and storms and act as the first line of defense against extreme weather in coastal areas. Moreover, mangroves have a continuous carbon fixation capacity, which is much higher than those of peat swamp and coastal salt marsh. Mangroves are an important part of the earth carbon cycle system and considered an important blue carbon sink on the sea and land margin. However, mangrove habitat is threatened all over the world due to human development and utilization activities. Therefore, monitoring the spatial distribution of mangrove types and biomass will help guide policy-makers in taking effective utilization and protection measures. In this paper, on the basis of UAV multispectral images and LiDAR point cloud data, the support vector machine classification method is used for mangrove identification. Furthermore, mangrove species are distinguished according to different heights and distribution areas using the elevation information in the UAV LiDAR point cloud data. The structure characteristics of mangrove single wood are extracted by the point-cloud-based cluster segmentation method, and an estimation model of the tree height, canopy, and aboveground biomass obtained by LiDAR remote sensing is constructed, Finally, the aboveground biomass of mangrove in the study area is calculated, and its spatial distribution information is analyzed. The classification result of the species types of mangroves, which is combined with the multi spectrum and LiDAR point cloud data, can reach 90.69% in total accuracy. The kappa coefficient is 0.88. The accuracies of the algorithm in identifying single trees of Kandelia candel and Aegiceras corniculata are 86.71% and 60.21%, respectively. Among them, the middle errors of the heights of K. candel and A. corniculata are 0.36 and 0.18 m, respectively, and the crown width extraction precision of K. candel is higher than that of A. corniculata. The regression models of the aboveground biomass of K. candel and A. corniculata are constructed. The accuracy of the fusion model is the highest, and the respective decision coefficients (R²) are 0.678 and 0.832 for K. candel and A. corniculata. Mangroves are mostly planted artificially in the study area and distributed in a belt perpendicular to the dam: K. candel—Cyperus malaccensis Lam. and Acanthus ilicifolius L. —A. corniculata. The area of A. corniculata is the largest, which is approximately 8.91 hm2 and distributed on both sides of the tidal ditch far from the dam. The area of K. candel is 4.69 hm2, which is distributed in the area near the dam. C. malaccensis Lam. and A. ilicifolius L. are scattered in small areas among the different types of objects. The aboveground biomass of mangrove is calculated by the estimation model of above ground biomass. The aboveground biomass follows the order Sonneratia apetala > A. corniculata > C. malaccensis Lam. > K. candel > A. ilicifolius L. The distribution range of mangrove’s aboveground biomass is 1.24—3.6 kg/m2 © 2022 National Remote Sensing Bulletin. All rights reserved.
引用
收藏
页码：1169 / 1181
页数：12
相关论文
共 38 条
  • [1] Chadwick J, Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties, International Journal of Remote Sensing, 32, 21, pp. 6765-6781, (2011)
  • [2] Duarte C M, Middelburg J J, Caraco N, Major role of marine vegetation on the oceanic carbon cycle, Biogeosciences, 2, 1, pp. 1-8, (2005)
  • [3] Duke N C, Meynecke J O, Dittmann S, Ellison A M, Anger K, Berger U, Cannicci S, Diele K, Ewel K C, Field C D, Koedam N, Lee S Y, Marchand C, Nordhaus I, Dahdouh-Guebas F, A world without mangroves?, Science, 317, 5834, pp. 41-42, (2007)
  • [4] Fatoyinbo T, Feliciano E A, Lagomasino D, Lee S K, Trettin C, Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta, Environmental Research Letters, 13, 2, (2018)
  • [5] Feliciano E A, Wdowinski S, Potts M D, Assessing mangrove above-ground biomass and structure using terrestrial laser scanning: a case study in the Everglades National Park, Wetlands, 34, 5, pp. 955-968, (2014)
  • [6] Feliciano E A, Wdowinski S, Potts M D, Lee S K, Fatoyinbo T E, Estimating mangrove canopy height and above-ground biomass in the Everglades National Park with airborne LiDAR and TanDEM-X data, Remote Sensing, 9, 7, (2017)
  • [7] He Q F, Zheng W, Huang X R, Liu X, Shen W H, He F, Carbon storage and distribution of mangroves at Qinzhou bay, Journal of Central South University of Forestry and Technology, 37, 11, pp. 121-126, (2017)
  • [8] Hickey S M, Callow N J, Phinn S, Lovelock C E, Duarte C M, Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: a remote sensing height-biomass-carbon approach, Estuarine, Coastal and Shelf Science, 200, pp. 194-201, (2018)
  • [9] Hu T Y, Zhang Y Y, Su Y J, Zheng Y, Lin G H, Guo Q H, Mapping the global mangrove forest aboveground biomass using multisource remote sensing data, Remote Sensing, 12, 10, (2020)
  • [10] Huang Y Q, Wu X F, Han W D, Liu X T, A study on biomass of Sonneratia apetala artificial stands, Acta Agriculturae Universitatis Jiangxiensis, 24, 4, pp. 533-536, (2002)
1234