Land cover mapping using remote sensing data in the Apure River Flood Plain (Venezuela)

被引:1
|
作者
Guzman, Rosiris [1 ]
Bezada, Maximiliano [2 ,3 ]
Rodriguez-Santalla, Inmculada [4 ]
机构
[1] Univ Alcala, Dept Geol Geog & Medio Ambiente, Calle Colegios 2, Alcala de Henares 28801, Madrid, Spain
[2] Univ Pedagog Expt Libertador, Inst Pedagog Caracas, Dept Ciencias Tierra, Av Jose Antonio Paez, Caracas 1020, Venezuela
[3] Univ Minnesota, Coll Sci & Engn, Minnesota Geol Survey, Minneapolis, MN 55455 USA
[4] Univ Rey Juan Carlos, Dept Biol Geol Fis & Quim Inorgan, Calle Tulipan, Mostoles 28933, Madrid, Spain
来源
CUADERNOS DE INVESTIGACION GEOGRAFICA | 2023年 / 49卷 / 01期
关键词
supervised classification; soil cover; Landsat; 8; Sentinel; 2; IMAGE CLASSIFICATION; RIPARIAN VEGETATION; SENTINEL-2; MIGRATION; LANDSAT-8; ACCURACY; SYSTEM;
D O I
10.18172/cig.5607
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
The soil cover is a fundamental indicator to identify the factors that act in the development of the geomorphology of an alluvial plain. This coverage is characterized by the control exercised by the vegetation in the hydromorphological processes, as well as the maintenance and stability of the fluvial channels. A record on the distribution of land cover in the middle course of the anastomosed system of the Apure River is presented. The distribution of geomorphological environments in an area of 65 km2 is analyzed from a combination of data from Landsat-8 and Sentinel-2 images, integrated into a Geographic Information System (GIS). A supervisedclassification was established using the Support Vector Machine and Maximum Likelihood algorithms. The Landsat image was processed through an atmospheric correction, to later calculate the spectral signatures. Six covers were found: a) wooded savannah, b) forest, c) open savannah, d) crops, e) bodies of water, and f) scrub. There are no substantial differences in the reliability achieved with the Support Vector Machines and Maximum Likelihood classification algorithms. It was shown that the woodland cover is the most representative in the study area with a total extension of 5,717.26 ha (39%), out of 14,658.77 ha. The classification presented a global thematic accuracy of 98.08% and a Kappa index of 0.98. As a result, a soil cover cartography was generated from the best classifier, based on the Kappa index. These findings serve as a reference to increase the records of soil cover characterization and can be useful in studies on management and use of the territory, to identify places more susceptible to degradation and propose measures for the management and conservation of water resources, which can be potentially applicable in similar fluvial environments in other latitudes.
引用
收藏
页码:113 / 137
页数:25
相关论文
共 50 条
  • [31] High-Resolution Flood Hazard Mapping Using Remote Sensing Data
    Dhamodaran, S.
    Shrthi, A.
    Thomas, Adline Suresh
    2016 INTERNATIONAL CONFERENCE ON COMPUTATION OF POWER, ENERGY INFORMATION AND COMMUNICATION (ICCPEIC), 2016, : 276 - 282
  • [32] Remote sensing of land cover and land cover change
    Wyatt, BK
    OBSERVING LAND FROM SPACE: SCIENCE, CUSTOMERS AND TECHNOLOGY, 2000, 4 : 127 - 136
  • [33] Land Cover Change Detection Using Multispectral and Multitemporal Remote Sensing Data
    Hashim, Ummi Kalsom Mohd
    Ahmad, Asmala
    Abu Sari, Mohd Yazid
    Mohd, Othman
    Sakidin, Hamzah
    Rasib, Abd Wahid
    PROCEEDINGS OF INNOVATIVE RESEARCH AND INDUSTRIAL DIALOGUE 2018 (IRID'18), 2019, : 176 - 177
  • [34] Land surface temperature in response to land use/cover change based on remote sensing data in Sangong River
    Cao, Xiaoming
    Bao, Anming
    Chen, Xi
    Xia, Yun
    REMOTE SENSING AND MODELING OF ECOSYSTEMS FOR SUSTAINABILITY V, 2008, 7083
  • [35] The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data
    ZHANG Xinchang
    SHI Qian
    SUN Ying
    HUANG Jianfeng
    HE Da
    Journal of Geodesy and Geoinformation Science, 2024, 7 (03) : 1 - 23
  • [36] Subpixel Mapping Based on Multisource Remote Sensing Fusion Data for Land-Cover Classes
    Wang, Peng
    Wang, Yulan
    Zhang, Lei
    Ni, Kang
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [37] Remote sensing: land cover
    Aplin, P
    PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT, 2004, 28 (02): : 283 - 293
  • [38] Analysis of Land Use and Land Cover Changes in Gombak, Selangor Using Remote Sensing Data
    Asnawi, Nur Hakimah
    Choy, Lam Kuok
    SAINS MALAYSIANA, 2016, 45 (12): : 1869 - 1877
  • [39] Rivne City Land Cover and Land Surface Temperature Analysis Using Remote Sensing Data
    Shumilo, Leonid
    Yailymov, Bohdan
    Kussul, Nataliia
    Lavreniuk, Mykola
    Shelestov, Andrii
    Korsunska, Yuliia
    2019 IEEE 39TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY (ELNANO), 2019, : 813 - 816
  • [40] Using hyperspectral remote sensing for land cover classification
    Zhang, W
    Sriharan, S
    MULTISPECTRAL AND HYPERSPECTRAL REMOTE SENSING INSTRUMENTS AND APPLICATIONS II, 2005, 5655 : 261 - 270