Machine learning-based monitoring of mangrove ecosystem dynamics in the Indus Delta

被引:0
|
作者
Zhou, Ying [1 ]
Dai, Zhijun [1 ,2 ]
Liang, Xixing [1 ,3 ]
Cheng, Jinping [4 ]
机构
[1] East China Normal Univ, State Key Lab Estuarine & Coastal Res, Shanghai 200062, Peoples R China
[2] Qingdao Marine Sci & Technol Ctr, Lab Marine Geol, Qingdao 266061, Peoples R China
[3] Beibu Gulf Univ Qinzhou, Guangxi Key Lab Marine Environm Change & Disaster, Qinzhou 200062, Peoples R China
[4] Educ Univ Hong Kong, Dept Sci & Environm Studies, Hong Kong, Peoples R China
关键词
Mangrove expansion; Hydro-sediment dynamic; Machine Learning; Random Forest; Tidal channel; Shoreline erosion; SEA-LEVEL RISE; RANDOM FOREST; CLIMATE-CHANGE; RIVER DELTA; CLASSIFICATION; EXTENT; RECOMMENDATIONS; VULNERABILITY; ADAPTATION; SATELLITE;
D O I
10.1016/j.foreco.2024.122231
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Mangrove forests play a vital role in carbon sequestration, typhoon-induced wave attenuation, and the provision of ecological services. However, mangrove ecosystems have experienced large-scale loss globally due to rising sea levels and anthropogenic activities. This study investigates the dynamic changes in mangrove cover within the mega-Indus delta, the largest delta in Pakistan and Southern Asia, using multi-temporal remote sensing data and machine learning techniques from 1988 to 2023. The results indicate an increasing trend in mangrove areas in the Indus Delta, with an average annual growth rate of 18.72 %. The spatial distribution of mangrove forests tends to concentrate towards the landward areas, extending along tidal channels, while losses primarily occur in the seaward regions. Rising sea levels pose a potential threat to the survival of these mangroves. The strong southwest monsoon-driven waves are the leading cause of shoreline erosion of the Indus Delta mangroves. Meanwhile, the reduction in riverine sediment discharge is not associated with the increase in mangrove area. Instead, the tidal currents influenced by the southwest monsoon carry sediments into the delta's tidal channels, causing them to fill and create suitable habitats for mangroves, which are the primary drivers of the observed mangrove expansion in the Indus Delta. Additionally, afforestation activities observed in the northwest and southwest parts of the study area have contributed to the restoration of mangroves. The loss of mangroves in the northernmost part of the northwest region was attributed to an oil spill incident. This study highlights the dynamic nature of mangrove ecosystems in the Indus Delta, characterized by an arid climate and low population density. The findings provide valuable insights into the factors influencing mangrove gain and loss and can inform management strategies for global mangrove restoration efforts.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Machine Learning-Based Predictive Farmland Optimization and Crop Monitoring System
    Adebiyi, Marion Olubunmi
    Ogundokun, Roseline Oluwaseun
    Abokhai, Aneoghena Amarachi
    SCIENTIFICA, 2020, 2020
  • [22] Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution
    Melesse, Tsega Y.
    Bollo, Matteo
    Di Pasquale, Valentina
    Centro, Francesco
    Riemma, Stefano
    3RD INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING, 2022, 200 : 13 - 20
  • [23] Machine Learning-based System for Monitoring Social Distancing and Mask Wearing
    Naji, Mohammed Faisal
    Joumaa, Chibli
    Alswailem, Yousef
    Alobthni, Abdulrahman
    Albusilan, Rayan
    2022 IEEE WORLD AI IOT CONGRESS (AIIOT), 2022, : 1 - 8
  • [24] Progress of machine learning-based biosensors for the monitoring of food safety: A review
    Hassan, Md Mehedi
    Xu, Yi
    Sayada, Jannatul
    Zareef, Muhammad
    Shoaib, Muhammad
    Chen, Xiaomei
    Li, Huanhuan
    Chen, Quansheng
    BIOSENSORS & BIOELECTRONICS, 2025, 267
  • [25] Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording
    Tu, Zhiyi
    Zhang, Yuehan
    Lv, Xueyang
    Wang, Yanyan
    Zhang, Tingting
    Wang, Juan
    Yu, Xinren
    Chen, Pei
    Pang, Suocheng
    Li, Shengtian
    Yu, Xiongjie
    Zhao, Xuan
    NEUROSCIENCE BULLETIN, 2025, 41 (03) : 449 - 460
  • [26] Monitoring machine learning-based risk prediction algorithms in the presence of performativity
    Feng, Jean
    Petrick, Nicholas
    Gossmann, Alexej
    Sahiner, Berkman
    Pennello, Gene
    Pirracchio, Romain
    INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 238, 2024, 238
  • [27] Motion estimation and machine learning-based wind turbine monitoring system
    Kim B.-J.
    Cheon S.-P.
    Kang S.-J.
    Kang, Suk-Ju (sjkang@sogang.ac.kr), 1600, Korean Institute of Electrical Engineers (66): : 1516 - 1522
  • [28] A dataset of pomegranate growth stages for machine learning-based monitoring and analysis
    Zhao, Jifei
    Almodfer, Rolla
    Wu, Xiaoying
    Wang, Xinfa
    DATA IN BRIEF, 2023, 50
  • [29] Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring
    Zhang, Jingcheng
    He, Yuhang
    Yuan, Lin
    Liu, Peng
    Zhou, Xianfeng
    Huang, Yanbo
    AGRONOMY-BASEL, 2019, 9 (09):
  • [30] Machine learning-based optimal design of groundwater pollution monitoring network
    Xiong, Yu
    Luo, Jiannan
    Liu, Xuan
    Liu, Yong
    Xin, Xin
    Wang, Shuangyu
    ENVIRONMENTAL RESEARCH, 2022, 211