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.
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页数:7
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共 51 条
  • [31] Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)
    Miller, Jay D.
    Thode, Andrea E.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2007, 109 (01) : 66 - 80
  • [32] Intensive land use in the Swedish mountains between AD 800 and 1200 led to deforestation and ecosystem transformation with long-lasting effects
    Ostlund, Lars
    Hornberg, Greger
    DeLuca, Thomas H.
    Liedgren, Lars
    Wikstrom, Peder
    Zackrisson, Olle
    Josefsson, Torbjorn
    [J]. AMBIO, 2015, 44 (06) : 508 - 520
  • [33] A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio
    Parks, Sean A.
    Dillon, Gregory K.
    Miller, Carol
    [J]. REMOTE SENSING, 2014, 6 (03) : 1827 - 1844
  • [34] EFFECTS OF AEROSOL FROM BIOMASS BURNING ON THE GLOBAL RADIATION BUDGET
    PENNER, JE
    DICKINSON, RE
    ONEILL, CA
    [J]. SCIENCE, 1992, 256 (5062) : 1432 - 1434
  • [35] A comparative evaluation of NOAA/AVHRR vegetation indexes for burned surface detection and mapping
    Pereira, JMC
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1999, 37 (01): : 217 - 226
  • [36] GEMI - A NONLINEAR INDEX TO MONITOR GLOBAL VEGETATION FROM SATELLITES
    PINTY, B
    VERSTRAETE, MM
    [J]. VEGETATIO, 1992, 101 (01): : 15 - 20
  • [37] The collection 5 MODIS burned area product - Global evaluation by comparison with the MODIS active fire product
    Roy, D. P.
    Boschetti, L.
    Justice, C. O.
    Ju, J.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2008, 112 (09) : 3690 - 3707
  • [38] Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products
    Roy, David P.
    Boschetti, Luigi
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2009, 47 (04): : 1032 - 1044
  • [39] Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data
    Roy, DP
    Jin, Y
    Lewis, PE
    Justice, CO
    [J]. REMOTE SENSING OF ENVIRONMENT, 2005, 97 (02) : 137 - 162
  • [40] Burned area mapping using multi-temporal moderate spatial resolution data - a bi-directional reflectance model-based expectation approach
    Roy, DP
    Lewis, PE
    Justice, CO
    [J]. REMOTE SENSING OF ENVIRONMENT, 2002, 83 (1-2) : 263 - 286