Resources Science ›› 2019, Vol. 41 ›› Issue (8): 1526-1540.doi: 10.18402/resci.2019.08.12

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Spatiotemporal dynamics of active fire frequency in Southeast Asia with the FIRMS Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) data

Peng LI1,2,Wenjun LI1,2,Zhiming FENG1,2,Chiwei XIAO1,2,Yiyuan LIU3   

  1. 1.Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3.School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
  • Received:2018-12-01 Revised:2019-03-07 Online:2019-08-28 Published:2019-08-21


Active fire (including vegetation fire) influences the carbon cycle of global terrestrial ecosystem, and its occurrence types, ignition causes, spatiotemporal features, and impacts are important research questions. Currently, remote sensing is the main way to obtain the spatial and temporal information and occurrence frequency of active fires. With the two standard active fire data products, including MODIS C6 (Moderate Resolution Imaging Spectroradiometer Collection 6) and VIIRS V1 (Visible infrared Imaging Radiometer Version 1) provided by the US NASA’s Fire Information for Resource Management System (FIRMS), the distribution characteristics and dynamic changes of satellite-based fire occurrence (frequency) in Southeast Asia (SEA) were quantitatively examined with ArcGIS 10.5 platform at hourly, ten-day, monthly, and annual levels and national to regional (Mainland Southeast Asia (MSEA) and Island Southeast Asia (ISEA)) scales. The differences in the two active fire datasets were compared accordingly. The results show that: (1) Active fire occurrence frequencies were 4.42×10 6 in SEA based on the MODIS C6 products of 2000-2017, showing clear annual fluctuations. Temporal consistency between the occurrence frequency of maximum active fire and global El Niño events during the same period was detected. MSEA, in comparison with ISEA, was the primary region for active fires in SEA, displaying about one year gap in response to El Niño. However, active fire in ISEA countries (for example, Indonesia) was more sensitive to El Niño. (2) In the past near two decades, Myanmar, Laos, Thailand, Cambodia, and Vietnam from MSEA and Indonesia in ISEA were the leading countries for active fire occurrence, especially in regions such as eastern and western Myanmar, northern Laos, northern Cambodia, northwest Vietnam, and the southern parts of Sumatra and Kalimantan of Indonesia. (3) Active fires showed high occurrences in the five MSEA countries during the dry season, especially from February to April, and mostly in March. Similar results in the three ISEA countries (Indonesia, Malaysia, and the Philippines) were reported between June and November, particularly from August to October, and mostly in September. Within these months, active fires were primarily seen in mid-to-late month, and typically observed at five to seven a.m. and five to seven p. m. in Greenwich Mean Time (GMT), mostly at six a.m. (GMT). (4) Active fire counts derived from VIIRS V1 were about five times that of MODIS C6 during the same period (2012-2017), showing similar trends of annual changes. The latter has the advantage of longer time series since 2000, while the former has higher accuracy of detection with more detailed information and greater potential for application.

Key words: MODIS C6, VIIRS V1, active fire, spatiotemporal characteristics, dynamic analysis, Southeast Asia