Assessing cyclone disturbances (1988–2016) in the Sundarbans mangrove forests using Landsat and Google Earth Engine

被引:2
|
作者
Mohammad Shamim Hasan Mandal
Tetsuro Hosaka
机构
[1] Hiroshima University,Department of Development Technology, Graduate School for International Development and Cooperation (IDEC)
来源
Natural Hazards | 2020年 / 102卷
关键词
NDVI; GEE; Wind damage; Hurricane damage;
D O I
暂无
中图分类号
学科分类号
摘要
Cyclone disturbances can cause significant damage to forest vegetation. The Sundarbans spreading across Bangladesh and India, the world’s largest mangrove forest, is frequently exposed to cyclones of various magnitudes. However, the extent and pattern of forest disturbances caused by cyclones in the Sundarbans (both parts) remain poorly understood, and a long-term dataset focused on cyclones and forest disturbances is required. In this study, Google Earth Engine and Landsat images were used to evaluate changes in the normalized difference vegetation index (NDVI) before versus after 21 cyclones that occurred between 1988 and 2016. Supervised classification successfully classified the forest area with an overall accuracy of 86% and Kappa coefficient of 0.80. The percentage of affected forest area (i.e., the area that exhibited negative changes in NDVI values following a cyclone) ranged from 0.5 to 24.1% of the total forest area. Of the 21 focal cyclones, 18 affected less than 10% of the forest area, while two cyclones, Sidr in 2007 (category H5) and a cyclone in 1988 (category H3), affected 24.1% and 20.4%, respectively. Among the cyclone parameters (i.e., maximum wind speed, distance from the Sundarbans, and river water level), wind speed was significantly and positively correlated with affected forest area. Wind speed and affected forest area were nonlinearly related indicated by the piecewise linear regression and cubic regression. The piecewise model estimated a threshold point, suggesting that wind speed had little effects below a breakpoint of 101.9 km h−1. Our analyses, based on a 29-year dataset, suggest that, although the region experienced cyclones almost every year, only the largest cyclones (i.e., in the H3 category or higher) affected 20% or more of the mangrove forest area, and these occurred around once per 7- to 12-year period. Trees with broken stems or uprooted canopies as a result of strong winds are likely to contribute to the reduction in NDVI in the aftermath of a cyclone. From a long-term perspective, such rare yet intense cyclones may have a significant effect on regeneration and species composition in the Sundarbans mangrove forest. Since previous studies only focused on a few cyclones, our results based on 21 cyclones will certainly help better understanding of the effects of cyclones on mangrove forest disturbance.
引用
收藏
页码:133 / 150
页数:17
相关论文
共 50 条
  • [41] Continuous monitoring of lake dynamics on the Mongolian Plateau using all available Landsat imagery and Google Earth Engine
    Zhou, Yan
    Dong, Jinwei
    Xiao, Xiangming
    Liu, Ronggao
    Zou, Zhenhua
    Zhao, Guosong
    Ge, Quansheng
    SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 689 : 366 - 380
  • [42] Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine
    Somasundaram, Deepakrishna
    Zhang, Fangfang
    Ediriweera, Sisira
    Wang, Shenglei
    Yin, Ziyao
    Li, Junsheng
    Zhang, Bing
    REMOTE SENSING, 2021, 13 (11)
  • [43] Tracking dynamics characteristics of tidal flats using landsat time series and Google Earth Engine cloud platform
    Chen, Chao
    Sun, Weiwei
    Yang, Zhaohui
    Yang, Gang
    Jia, Mingming
    Zhang, Zhijiang
    Liang, Jintao
    Chen, Yankun
    Ren, Taohua
    Hu, Xingbai
    Liu, Zhisong
    RESOURCES CONSERVATION AND RECYCLING, 2024, 209
  • [44] Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
    Midekisa, Alemayehu
    Holl, Felix
    Savory, David J.
    Andrade-Pacheco, Ricardo
    Gething, Peter W.
    Bennett, Adam
    Sturrock, Hugh J. W.
    PLOS ONE, 2017, 12 (09):
  • [45] Historical mapping of rice fields in Japan using phenology and temporally aggregated Landsat images in Google Earth Engine
    Carrasco, Luis
    Fujita, Go
    Kito, Kensuke
    Miyashita, Tadashi
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2022, 191 : 277 - 289
  • [46] Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine
    Nyland, Kelsey E.
    Gunn, Grant E.
    Shiklomanov, Nikolay I.
    Engstrom, Ryan N.
    Streletskiy, Dmitry A.
    REMOTE SENSING, 2018, 10 (08)
  • [47] Using Landsat observations (1988-2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation
    Xie, Zunyi
    Phinn, Stuart R.
    Game, Edward T.
    Pannell, David J.
    Hobbs, Richard J.
    Briggs, Peter R.
    McDonald-Madden, Eve
    REMOTE SENSING OF ENVIRONMENT, 2019, 232
  • [48] Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine
    DeVries, Ben
    Huang, Chengquan
    Armston, John
    Huang, Wenli
    Jones, John W.
    Lang, Megan W.
    REMOTE SENSING OF ENVIRONMENT, 2020, 240
  • [49] Building a mangrove ecosystem monitoring tool for managers using Sentinel-2 imagery in Google Earth Engine
    Kotikot, Susan M.
    Spencer, Olivia
    Cissell, Jordan R.
    Connette, Grant
    Smithwick, Erica A. H.
    Durdall, Allie
    Grimes, Kristin W.
    Stewart, Heather A.
    Tzadik, Orian
    Canty, Steven W. J.
    OCEAN & COASTAL MANAGEMENT, 2024, 256
  • [50] Loss of Relict Oak Forests along Coastal Louisiana: A Multiyear Analysis Using Google Earth Engine
    Thakore, Paurava
    Raut, Parusha
    Bhattacharjee, Joydeep
    FORESTS, 2022, 13 (07):