Monitoring Saltmarsh Restoration in the Upper Bay of Fundy Using Multi-Temporal Sentinel-2 Imagery and Random Forests Classifier

被引:0
|
作者
Naojee, Swarna M. [1 ]
Larocque, Armand [1 ,2 ]
Leblon, Brigitte [2 ]
Norris, Gregory S. [1 ]
Barbeau, Myriam A. [3 ]
Rowland, Matthew [4 ]
机构
[1] Univ New Brunswick, Fac Forestry & Environm Management, Fredericton, NB E3B 5A3, Canada
[2] Lakehead Univ, Fac Nat Resources Management, Thunder Bay, ON P7B 5E1, Canada
[3] Univ New Brunswick, Dept Biol, Fredericton, NB E3B 5A3, Canada
[4] Environm & Climate Change Canada, Gatineau, PQ K1A 0H3, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
saltmarsh vegetation recovery; remote sensing; Sentinel-2; supervised image classification; random forests; SPARTINA-ALTERNIFLORA; NEW-BRUNSWICK; VEGETATION; PATTERN; CANADA; ROOTS; COLOR; TIME; RED;
D O I
10.3390/rs16244667
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Saltmarshes provide important ecosystem services, including coastline protection, but face decline due to human activities and climate change. There are increasing efforts to conserve and restore saltmarshes worldwide. Our study evaluated the effectiveness of Sentinel-2 satellite imagery to monitor landcover changes using a saltmarsh restoration project undergoing its 9th to 12th year of recovery in the megatidal Bay of Fundy in Maritime Canada. Specifically, in 2019-2022, five satellite images per growing season were acquired. Random Forests classification for 13 landcover classes (ranging from bare mud to various plant communities) achieved a high overall classification accuracy, peaking at 96.43% in 2021. Field validation points confirmed this, with high validation accuracies reaching 93.02%. The classification results successfully distinguished ecologically significant classes, such as Spartina alterniflora-S. patens mix. Our results reveal the appearance of high marsh species in restoration sites and elevational-based zonation patterns, indicating progression. They demonstrate the potential of Sentinel-2 imagery for monitoring saltmarsh restoration projects in north temperate latitudes, aiding management efforts.
引用
收藏
页数:26
相关论文
共 50 条
  • [41] OPTIMIZATION OF A RANDOM FOREST CLASSIFIER FOR BURNED AREA DETECTION IN CHILE USING SENTINEL-2 DATA
    Roteta, E.
    Oliva, P.
    2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS), 2020, : 568 - 573
  • [42] Temporal Analysis of Mangrove Forest Extent in Restoration Initiatives: A Remote Sensing Approach Using Sentinel-2 Imagery
    Farzanmanesh, Raheleh
    Khoshelham, Kourosh
    Volkova, Liubov
    Thomas, Sebastian
    Ravelonjatovo, Jaona
    Weston, Christopher
    FORESTS, 2024, 15 (03):
  • [43] Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
    Feng, Yongyu
    Chen, Bingyao
    Liu, Wei
    Xue, Xiurong
    Liu, Tongqing
    Zhu, Linye
    Xing, Huaqiao
    APPLIED SCIENCES-BASEL, 2024, 14 (09):
  • [44] Detection and Monitoring of Maltese Shoreline Changes using Sentinel-2 Imagery
    Fejjari, Asma
    Valentino, Gianluca
    Briffa, Johann A.
    D'Amico, Sebastiano
    2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR THE SEA; LEARNING TO MEASURE SEA HEALTH PARAMETERS, METROSEA, 2023, : 52 - 56
  • [45] Impact Eichhornia crassipes Cultivation on Water Quality in the Caohai Region of Dianchi Lake Using Multi-Temporal Sentinel-2 Images
    Shen, Jinxiang
    He, Ping
    Sun, Xiaoli
    Shen, Zhanfeng
    Xu, Rong
    REMOTE SENSING, 2023, 15 (09)
  • [46] Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)
    Bigi, Velia
    Vigna, Ingrid
    Pezzoli, Alessandro
    Comino, Elena
    SUSTAINABILITY, 2021, 13 (24)
  • [47] Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks
    Lumban-Gaol, Yustisi
    Ohori, Ken Arroyo
    Peters, Ravi
    MARINE GEODESY, 2022, 45 (06) : 615 - 644
  • [48] A Modified Swin-UNet Model for Coastal Wetland Classification Using Multi-Temporal Sentinel-2 Images
    Wang, Binyu
    Sun, Yuanheng
    Zhu, Xueyuan
    Teng, Senlin
    Li, Ying
    ESTUARIES AND COASTS, 2025, 48 (03)
  • [49] Quantification of forest extent in Germany by combining multi-temporal stacks of Sentinel-1 and Sentinel-2 images
    Suresh, Gopika
    Hovenbitzer, Michael
    SIXTH INTERNATIONAL CONFERENCE ON REMOTE SENSING AND GEOINFORMATION OF THE ENVIRONMENT (RSCY2018), 2018, 10773
  • [50] Monitoring temporal chlorophyll-a using Sentinel-2 imagery in urban retention ponds receiving a biological-chemical treatment
    Chaffee, Matthew
    Mittelstet, Aaron R.
    Comfort, Steven
    Messer, Tiffany
    Shrestha, Nawaraj
    Eskridge, Kent
    McCoy, Jenna
    ECOLOGICAL ENGINEERING, 2023, 197