Determining Changes in Mangrove Cover Using Remote Sensing with Landsat Images: a Review

被引:6
|
作者
Vasquez, Juan [1 ]
Acevedo-Barrios, Rosa [1 ,2 ]
Miranda-Castro, Wendy [1 ]
Guerrero, Milton [2 ]
Meneses-Ospina, Luisa [1 ]
机构
[1] Univ Tecnol Bolivar, Fac Ciencias Basicas, Grp Estudios Quim & Biol, Cartagena 130010, Colombia
[2] Univ Tecnol Bolivar, Fac Ingn, Grp Sistemas Ambientales & Hidraul, Cartagena 130010, Colombia
来源
WATER AIR AND SOIL POLLUTION | 2024年 / 235卷 / 01期
关键词
Coastal ecosystem; Estuarine ecosystems; Landscape ecology; GIS; Forest change; MONITORING CHANGES; FOREST; PROVINCE; EXTENTS;
D O I
10.1007/s11270-023-06788-6
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Mangroves are ecosystems within the intertidal zone of tropical and subtropical coasts; they offer ecosystem services such as protection from coastal erosion and storms and flood control, act as carbon sinks and are also sources of income by providing various forest products. However, their cover is rapidly disappearing worldwide, which makes the diagnosis and monitoring of the state of these important ecosystems, as well as their restoration and conservation, a challenge. Remote sensing is a promising technique that provides accurate and efficient results in the mapping and monitoring of these ecosystems. The Landsat sensor provides the most used medium-resolution images for this type of study. The main objective of this article is to provide an updated review of the main remote sensing techniques, specifically Landsat satellite imagery, used in the detection of changes and mapping of mangrove forests, as well as a review of climatic and/or chemical factors related to changes in the spatial distribution of these ecosystems.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images
    Lv, Zhiyong
    Wang, FengJun
    Liu, Tongfei
    Kong, XiangBin
    Benediktsson, Jon Atli
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [42] Determination of Land Use And Land Cover Changes in Canakkale Province Using Remote Sensing
    Sayi, O.
    Genc, L.
    JOURNAL OF TEKIRDAG AGRICULTURE FACULTY-TEKIRDAG ZIRAAT FAKULTESI DERGISI, 2013, 10 (03): : 64 - 73
  • [43] Changes in the Danube Delta According to Remote Sensing Data by Landsat Satellite
    Starodubtsev, V. M.
    ARID ECOSYSTEMS, 2013, 3 (04) : 258 - 262
  • [44] AN OIL SLICK DETECTION INDEX BASED ON LANDSAT 8 REMOTE SENSING IMAGES
    Zhao, Dong
    Cheng, Xinwen
    Zhang, Hongping
    Zhang, Haitao
    2018 INTERNATIONAL WORKSHOP ON BIG GEOSPATIAL DATA AND DATA SCIENCE (BGDDS 2018), 2018,
  • [45] Cloud Segmentation of Remote Sensing Images on Landsat-8 by Deep Learning
    Zeng, Xiaoshuang
    Yang, Jungang
    Deng, Xinpu
    PROCEEDINGS OF THE 2018 2ND INTERNATIONAL CONFERENCE ON BIG DATA RESEARCH (ICBDR 2018), 2018, : 174 - 177
  • [46] Cloud Detection Of Remote Sensing Images On Landsat-8 By Deep Learning
    Zeng, Xiaoshuang
    Yang, Jungang
    Deng, Xinpu
    An, Wei
    Li, Jun
    TENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2018), 2018, 10806
  • [47] Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review
    Thyagharajan, K. K.
    Vignesh, T.
    ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2019, 26 (02) : 275 - 301
  • [48] Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review
    K. K. Thyagharajan
    T. Vignesh
    Archives of Computational Methods in Engineering, 2019, 26 : 275 - 301
  • [49] Review: Monitoring of land cover changes and plant phenology by remote-sensing in East Asia
    Shin, Nagai
    Saitoh, Taku. M. M.
    Takeuchi, Yayoi
    Miura, Tomoaki
    Aiba, Masahiro
    Kurokawa, Hiroko
    Onoda, Yusuke
    Ichii, Kazuhito
    Nasahara, Kenlo Nishida
    Suzuki, Rikie
    Nakashizuka, Tohru
    Muraoka, Hiroyuki
    ECOLOGICAL RESEARCH, 2023, 38 (01) : 111 - 133
  • [50] Mangrove LAI estimation based on remote sensing images and machine learning algorithms
    Fu B.
    Sun J.
    Li Y.
    Zuo P.
    Deng T.
    He H.
    Fan D.
    Gao E.
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2022, 38 (07): : 218 - 228