资源科学 ›› 2019, Vol. 41 ›› Issue (8): 1526-1540.doi: 10.18402/resci.2019.08.12

• 资源管理 • 上一篇    下一篇

基于FIRMS MODIS与VIIRS的东南亚活跃火频次时空动态分析

李鹏1,2,李文君1,2,封志明1,2,肖池伟1,2,刘怡媛3   

  1. 1.中国科学院地理科学与资源研究所,北京 100101
    2.中国科学院大学资源与环境学院,北京 100049
    3.江西师范大学地理与环境学院,南昌 330022
  • 收稿日期:2018-12-01 修回日期:2019-03-07 出版日期:2019-08-28 发布日期:2019-08-21
  • 作者简介:李鹏,男,江西永新人,博士,副研究员,主要从事资源地理与国土资源遥感研究。E-mail: lip@igsnrr.ac.cn
  • 基金资助:
    中国科学院地理科学与资源研究所“秉维”优秀青年人才计划项目(2018RC201)

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

摘要:

活跃火(含植被火)是影响全球陆地生态系统碳循环的重要因素,其发生类型与成因、时空特征及其影响评价是重要研究内容。利用美国国家航空航天局(NASA)FIRMS发布的MODIS C6和VIIRS V1活跃火(Active fire)位置矢量产品,从不同时间尺度(年际、逐月、分旬与小时)与空间尺度(中南半岛/马来群岛)分析了东南亚2000—2017年活跃火的时空特征与动态变化,并比较了两套活跃火产品的数据差异。结果表明:①2000—2017年东南亚MODIS C6活跃火频次累计达4.42×10 6次,年际呈现显著波动变化特征,年际峰值出现时间与全球厄尔尼诺年较为一致。中南半岛是东南亚活跃火主要分布区,其与马来群岛对厄尔尼诺的响应时间有约1年的差异,且马来群岛国家(如印度尼西亚)活跃火对厄尔尼诺现象响应更为敏感。②近18年间,缅甸、老挝、泰国、柬埔寨、越南与印度尼西亚依次是中南半岛与马来群岛活跃火频发国家,其中以缅甸东部与西部、老挝北部、柬埔寨北部、印度尼西亚的苏门答腊岛东南部与加里曼丹岛南部等地分布较为集中。③中南半岛5国活跃火现象高度集中在旱季,其中以2—4月尤为突出,3月最甚。马来群岛3国的活跃火主要集中在6—11月,其中以8—10月最为明显,9月最强。东南亚8国活跃火现象集中分布于特征月份的下、中旬。活跃火观测时段主要集中在格林威治标准时间5—7时与17—19时,6时最多。④2012—2017年东南亚VIIRS V1活跃火频次监测规模约为同期MODIS C6监测结果的5倍,且二者对活跃火频次年际变化监测具有较好一致性。MODIS C6的优势在于活跃火监测时序长,而VIIRS V1对活跃火监测精度更高,应用潜力更大。

关键词: MODIS C6, VIIRS V1, 活跃火, 时空特征, 动态分析, 东南亚

Abstract:

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