Mapping forest and peat fires using hyperspectral airborne remote-sensing data

被引:0
|
作者
V. V. Kozoderov
T. V. Kondranin
E. V. Dmitriev
V. P. Kamentsev
机构
[1] Moscow State University,
关键词
Smoke; Oceanic Physic; Hyperspectral Image; Burnt Area; Hyperspectral Data;
D O I
暂无
中图分类号
学科分类号
摘要
The characteristic features of airspace hyperspectral remote sensing (RS) are considered in order to develop classification techniques for relevant images. Currently available approaches to constructing classifiers (computational procedures) are described for recognizing natural and anthropogenic objects in hyperspectral images. We confirm that the methods under development are effective enough with the reduced dimensionality of the feature space of original spectra and the decreased sample volumes in supervising procedures for the selected object classes. Data from joint hyperspectral and aerial photography provide examples of the spectral distributions smoke of different intensities from forest and peat fires in the presence and absence of fire sources, for the smoke coverage of water surfaces, and for the forest vegetation without ignition sources within a selected area. The results obtained in the supervising procedures are used for pattern recognition and scene analysis in airborne images obtained for the test areas during forest-fire season.
引用
收藏
页码:941 / 948
页数:7
相关论文
共 50 条
  • [1] Mapping forest and peat fires using hyperspectral airborne remote-sensing data
    Kozoderov, V. V.
    Kondranin, T. V.
    Dmitriev, E. V.
    Kamentsev, V. P.
    [J]. IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS, 2012, 48 (09) : 941 - 948
  • [2] Mapping Forest and Peat Fires Using Hyperspectral Airborne Remote-Sensing Data (vol 48, pg 941, 2012)
    Kozoderov, V. V.
    Kondranin, T. V.
    Dmitriev, E. V.
    Kamentsev, V. P.
    [J]. IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS, 2014, 50 (02) : 224 - 224
  • [3] REMOTE-SENSING OF FOREST STAND AGE USING AIRBORNE SPECTROMETER DATA
    NIEMANN, KO
    [J]. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1995, 61 (09): : 1119 - 1127
  • [4] Regional monitoring of forest vegetation using airborne hyperspectral remote sensing data
    Dmitriev, Egor V.
    Kozoderov, Vladimir V.
    Kondranin, Timophey V.
    Sokolov, Anton A.
    [J]. MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL REMOTE SENSING TECHNOLOGY, TECHNIQUES AND APPLICATIONS V, 2014, 9263
  • [5] TROPICAL FOREST TYPE MAPPING AND MONITORING USING REMOTE-SENSING
    ROY, PS
    RANGANATH, BK
    DIWAKAR, PG
    VOHRA, TPS
    BHAN, SK
    SINGH, IJ
    PANDIAN, VC
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1991, 12 (11) : 2205 - 2225
  • [6] Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data
    Finn, Michael P.
    Lewis, Mark
    Bosch, David D.
    Giraldo, Mario
    Yamamoto, Kristina
    Sullivan, Dana G.
    Kincaid, Russell
    [J]. GISCIENCE & REMOTE SENSING, 2011, 48 (04) : 522 - 540
  • [7] Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data
    Liu, Luxia
    Coops, Nicholas C.
    Aven, Neal W.
    Pang, Yong
    [J]. REMOTE SENSING OF ENVIRONMENT, 2017, 200 : 170 - 182
  • [8] FOREST BIODIVERSITY MAPPING USING AIRBORNE LIDAR AND HYPERSPECTRAL DATA
    Zeng Yuan
    Zhao Yujin
    Zhao Dan
    Wu Bingfang
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 3561 - 3562
  • [9] Forest species mapping using airborne hyperspectral APEX data
    Tagliabue, Giulia
    Panigada, Cinzia
    Colombo, Roberto
    Fava, Francesco
    Cilia, Chiara
    Baret, Frederic
    Vreys, Kristin
    Meuleman, Koen
    Rossini, Micol
    [J]. MISCELLANEA GEOGRAPHICA, 2016, 20 (01): : 28 - 33
  • [10] AIRBORNE REMOTE-SENSING
    SCHWEITZER, GE
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 1982, 16 (06) : A338 - A346