Detection of Coral Reef Bleaching Based on Sentinel-2 Multi-Temporal Imagery: Simulation and Case Study

被引:5
|
作者
Xu, Jingping [1 ]
Zhao, Jianhua [1 ]
Wang, Fei [1 ,2 ]
Chen, Yanlong [1 ]
Lee, Zhongping [3 ]
机构
[1] Natl Marine Environm Monitoring Ctr, Dalian, Peoples R China
[2] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Peoples R China
[3] Univ Massachusetts, Sch Environm, Boston, MA 02125 USA
基金
中国国家自然科学基金;
关键词
coral reef bleaching; remote sensing; Sentinel-2; multi-temporal; change detection; Lizard Island; ATMOSPHERIC CORRECTION; SHALLOW WATERS; REMOTE; LANDSAT; BENTHOS; ISLAND;
D O I
10.3389/fmars.2021.584263
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Sentinel-2 mission has been shown to have promising applications in coral reef remote sensing because of its superior properties. It has a 5-day revisit time, spatial resolution of 10 m, free data, etc. In this study, Sentinel-2 imagery was investigated for bleaching detection through simulations and a case study over the Lizard Island, Australia. The spectral and image simulations based on the semianalytical (SA) model and the sensor spectral response function, respectively, confirmed that coral bleaching cannot be detected only using one image, and the change analysis was proposed for detection because there will be a featured change signal for bleached corals. Band 2 of Sentinel-2 is superior to its other bands for the overall consideration of signal attenuation and spatial resolution. However, the detection capability of Sentinel-2 is still limited by the water depth. With rapid signal attenuation due to the water absorption effect, the applicable water depth for bleaching detection was recommended to be less than 10 m. The change analysis was conducted using two methods: one radiometric normalization with pseudo invariant features (PIFs) and the other with multi-temporal depth invariant indices (DII). The former performed better than the latter in terms of classification. The bleached corals maps obtained using the PIFs and DII approaches had an overall accuracy of 88.9 and 57.1%, respectively. Compared with the change analysis based on two dated images, the use of a third image that recorded the spectral signals of recovered corals or corals overgrown by algae after bleaching significantly improved the detection accuracy. All the preliminary results of this article will aid in the future studies on coral bleaching detection based on remote sensing.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Forest stand segmentation with multi-temporal Sentinel-2 imagery and superpixels
    Demirpolat, Caner
    Leloglu, Ugur Murat
    JOURNAL OF APPLIED REMOTE SENSING, 2024, 18 (01)
  • [2] Automating field boundary delineation with multi-temporal Sentinel-2 imagery
    Watkins, Barry
    Van Niekerk, Adriaan
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 167
  • [3] Mapping Shrub Coverage in Xilin Gol Grassland with Multi-Temporal Sentinel-2 Imagery
    Gan, Liqin
    Cao, Xin
    Chen, Xuehong
    He, Qian
    Cui, Xihong
    Zhao, Chenchen
    REMOTE SENSING, 2022, 14 (14)
  • [4] Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery
    Cuypers, Suzanna
    Nascetti, Andrea
    Vergauwen, Maarten
    REMOTE SENSING, 2023, 15 (10)
  • [5] Mono-temporal and multi-temporal approaches for burnt area detection using Sentinel-2 satellite imagery (a case study of Rokan Hilir Regency, Indonesia)
    Afira, Natasya
    Wijayanto, Arie Wahyu
    ECOLOGICAL INFORMATICS, 2022, 69
  • [6] MULTI-TEMPORAL DATA AUGMENTATION FOR HIGH FREQUENCY SATELLITE IMAGERY: A CASE STUDY IN SENTINEL-1 AND SENTINEL-2 BUILDING AND ROAD SEGMENTATION
    Ayala, C.
    Aranda, C.
    Galar, M.
    XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, 2022, 43-B3 : 25 - 32
  • [7] Mapping an Urban Boundary Based on Multi-Temporal Sentinel-2 and POI Data: A Case Study of Zhengzhou City
    Wang, Zhe
    Wang, Haiying
    Qin, Fen
    Han, Zhigang
    Miao, Changhong
    REMOTE SENSING, 2020, 12 (24) : 1 - 19
  • [8] Quantifying Temperate Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery
    Ren, Chunying
    Jiang, Hailing
    Xi, Yanbiao
    Liu, Pan
    Li, Huiying
    REMOTE SENSING, 2023, 15 (02)
  • [9] Deep Seasonal Network for Remote Sensing Imagery Classification of Multi-Temporal Sentinel-2 Data
    Cheng, Keli
    Scott, Grant J.
    REMOTE SENSING, 2023, 15 (19)
  • [10] Spatio-Temporal Classification Framework for Mapping Woody Vegetation from Multi-Temporal Sentinel-2 Imagery
    Kovacevic, Jovan
    Cvijetinovic, Zeljko
    Lakusic, Dmitar
    Kuzmanovic, Nevena
    Sinzar-Sekulic, Jasmina
    Mitrovic, Momir
    Stancic, Nikola
    Brodic, Nenad
    Mihajlovic, Dragan
    REMOTE SENSING, 2020, 12 (17) : 1 - 23