Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine

被引:1
|
作者
Yailymov, Bohdan [1 ]
Shelestov, Andrii [1 ,2 ]
Yailymova, Hanna [1 ,2 ]
Shumilo, Leonid [3 ]
机构
[1] NAS Ukraine, Dept Space Informat Technol & Syst, Space Res Inst, UA-03187 Kiev, Ukraine
[2] Natl Tech Univ Ukraine, Igor Sikorsky Kyiv Polytech Inst, Dept Math Modelling & Data Anal, UA-03056 Kiev, Ukraine
[3] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
来源
FIRE-SWITZERLAND | 2023年 / 6卷 / 11期
基金
欧盟地平线“2020”; 新加坡国家研究基金会;
关键词
wildfire monitoring; burned area mapping; normalized burn ratio; fire potential index; Google Earth Engine; informational technology; cloud computing; FIRE DETECTION ALGORITHM; FOREST-FIRE; POTENTIAL INDEX; RISK-ASSESSMENT; TIME-SERIES; VEGETATION; SEVERITY; SYSTEM; NDVI;
D O I
10.3390/fire6110411
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Wildfires cause extensive damage, but their rapid detection and cause assessment remains challenging. Existing methods utilize satellite data to map burned areas and meteorological data to model fire risk, but there are no information technologies to determine fire causes. It is crucially important in Ukraine to assess the losses caused by the military actions. This study proposes an integrated methodology and a novel framework integrating burned area mapping from Sentinel-2 data and fire risk modeling using the Fire Potential Index (FPI) in Google Earth Engine. The methodology enables efficient national-scale burned area detection and automated identification of anthropogenic fires in regions with low fire risk. Implemented over Ukraine, 104.229 ha were mapped as burned during July 2022, with fires inconsistently corresponding to high FPI risk, indicating predominantly anthropogenic causes.
引用
收藏
页数:28
相关论文
共 50 条
  • [31] Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas
    Hird, Jennifer N.
    Kariyeva, Jahan
    McDermid, Gregory J.
    REMOTE SENSING, 2021, 13 (23)
  • [32] Oil Spill Monitoring in Norilsk, Russia Using Google Earth Engine and Sentinel-2 Data
    Kim, Minju
    Hyun, Chang-Uk
    KOREAN JOURNAL OF REMOTE SENSING, 2023, 39 (03) : 311 - 323
  • [33] PhenoApp. A Google Earth Engine based tool for monitoring phenology
    Garcia-Diaz, Diego
    Diaz-Delgado, Ricardo
    REVISTA DE TELEDETECCION, 2023, (61): : 73 - 81
  • [34] Monitoring of subsidence water body change based on Google Earth Engine
    Zhao Y.
    Ding B.
    He T.
    Xiao W.
    Ren H.
    Meitan Xuebao/Journal of the China Coal Society, 2022, 47 (07): : 2745 - 2755
  • [35] Prediction of engine demand with a data-driven approach
    Francis, Hudson
    Kusiak, Andrew
    XII INTERNATIONAL SYMPOSIUM INTELLIGENT SYSTEMS 2016, (INTELS 2016), 2017, 103 : 28 - 35
  • [36] A data-driven model for large wildfire behaviour prediction in Europe
    Rodriguez-Aseretto, Dario
    de Rigo, Daniele
    Di Leo, Margherita
    Cortes, Ana
    San-Miguel-Ayanz, Jesus
    2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, 2013, 18 : 1861 - 1870
  • [37] A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine
    Arevalo, Paulo
    Bullock, Eric L.
    Woodcock, Curtis E.
    Olofsson, Pontus
    FRONTIERS IN CLIMATE, 2020, 2
  • [38] Invasive buffelgrass detection using high-resolution satellite and UAV imagery on Google Earth Engine
    Elkind, Kaitlyn
    Sankey, Temuulen T.
    Munson, Seth M.
    Aslan, Clare E.
    REMOTE SENSING IN ECOLOGY AND CONSERVATION, 2019, 5 (04) : 318 - 331
  • [39] Cropland data fusion and correction using spatial analysis techniques and the Google Earth Engine
    Li, Kewei
    Xu, Erqi
    GISCIENCE & REMOTE SENSING, 2020, 57 (08) : 1026 - 1045
  • [40] CataEx: A multi-task export tool for the Google Earth Engine data catalog
    Domej, Gisela
    Pluta, Kacper
    Ewertowski, Marek
    ENVIRONMENTAL MODELLING & SOFTWARE, 2025, 183