Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data

被引:0
作者
Babu, K. V. Suresh [1 ]
Singh, Swati [2 ]
Kabdulova, G. [1 ]
Gulnara, Kabzhanova [1 ]
Baktybekov, G. [1 ]
机构
[1] Kazakhstan Natl Co, Astana, Kazakhstan
[2] CSIR Natl Bot Res Inst, Lucknow, India
关键词
burned area; KazEOSat-1; satellite; GEMI; AVI; BAI; QUANTIFYING BURN SEVERITY; SPECTRAL INDEXES; VEGETATION; MODIS; MOUNTAINS; BIOMASS;
D O I
10.3389/ffgc.2024.1296100
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.
引用
收藏
页数:7
相关论文
共 51 条
  • [1] [Anonymous], 2015, FAO Forestry Paper No. 1
  • [2] Ashburn P., 1979, NASA JOHNS SPAC CTR
  • [3] Modeling Fire Danger in Galicia and Asturias ( Spain) from MODIS Images
    Bisquert, Mar
    Manuel Sanchez, Juan
    Caselles, Vicente
    [J]. REMOTE SENSING, 2014, 6 (01) : 540 - 554
  • [4] Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination
    Chuvieco, E
    Martín, MP
    Palacios, A
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2002, 23 (23) : 5103 - 5110
  • [5] Chuvieco E., 1988, GEOCARTO INT, V3, P41, DOI [DOI 10.1080/10106048809354180, 10.1080/10106048809354180]
  • [6] Chuvieco Emilio, 2008, P109, DOI 10.1007/978-1-4020-6358-9_6
  • [7] Fire science for rainforests
    Cochrane, MA
    [J]. NATURE, 2003, 421 (6926) : 913 - 919
  • [8] Positive feedbacks in the fire dynamic of closed canopy tropical forests
    Cochrane, MA
    Alencar, A
    Schulze, MD
    Souza, CM
    Nepstad, DC
    Lefebvre, P
    Davidson, EA
    [J]. SCIENCE, 1999, 284 (5421) : 1832 - 1835
  • [9] Leaf traits and litter flammability: evidence for non-additive mixture effects in a temperate forest
    de Magalhaes, Rita M. Q.
    Schwilk, Dylan W.
    [J]. JOURNAL OF ECOLOGY, 2012, 100 (05) : 1153 - 1163
  • [10] directory.eoportal, 2014, NAOMI-1. KazEOSat-1