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遥感技术与应用  2022, Vol. 37 Issue (1): 73-84    DOI: 10.11873/j.issn.1004-0323.2022.1.0073
青促会十周年专栏     
2001~2018年我国热异常点时空分布特征研究
葛强1,3(),沈文举2,3,李冉4,李莘莘5(),蔡坤1,3,左宪禹2,3,乔保军1,3,张云舟6
1.河南大学 河南省大数据分析与处理重点实验室,河南 开封 475004
2.河南大学 河南省空间信息处理工程实验室,河南 开封 475004
3.河南大学 计算机与信息工程学院,河南 开封 475004
4.国家航天局对地观测与数据中心成果转化部,北京 100101
5.中国科学院空天信息创新研究院,遥感科学国家重点实验室,北京 100101
6.国家文物局,北京 100010
Research on the Temporal and Spatial Cistribution Characteristics of Thermal Anomalies in China from 2001 to 2018
Qiang Ge1,3(),Wenju Shen2,3,Ran Li4,Shenshen Li5(),Kun Cai1,3,Xianyu Zuo2,3,Baojun Qiao1,3,Yunzhou Zhang6
1.Henan Key Laboratory of Big Data Analysis and Processing,Kaifeng 475004,China
2.Henan Engineering Laboratory of Spatial Information Processing,Kaifeng 475004,China
3.School of Computer and Information Engineering,Henan University,Kaifeng 475004,China
4.Earth Observation System and Data Center,CNSA,Department of Achievement Transformation,Beijing 100101,China
5.State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China
6.State Administration of Cultural Heritage of China,Beijing 100010,China
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摘要:

基于2001~2018年MODIS标准产品,研究了我国及七大区域热异常点的时空分布特征。结果表明:空间分布上,热异常点主要分布在除西北、西南之外的大部分地区;年际趋势上,2001~2014年间热异常点数量持续上升,年均增长率为15.01%,2015年后逐年下降,年均下降率为14.96%。月季尺度上,热异常点在春、秋季节出现最为频繁(春:551 716个,秋:416 698个),春、秋季相对在东北地区分布最多(春:164 898个,秋:186 727个),东北地区月均数量10月最高(118 274个);夏季热异常点数量最低(290 793个),多分布于华东地区(120 455个),华东地区月均数量6月最高(76 465个);冬季数量为358 483个,且在华南地区分布最多(108 209个),华南地区月均数量1月最高(37 770个)。研究有助于掌握我国典型区域的森林、草原火灾,以及由于秸秆焚烧、工业排放等引起热异常的变化情况,进而为区域灾害防治和环境监测提供技术支撑。

关键词: 热异常点秸秆焚烧时空分布MODIS    
Abstract:

In recent years, environmental pollution problems caused by straw burning and industrial emissions have become more serious. The use of satellite thermal abnormal products to analyze the temporal and spatial distribution of thermal abnormalities plays an important role in environmental monitoring. Based on MODIS standard products from 2001 to 2018, the temporal and spatial distribution characteristics of thermal anomalies in China and seven major regions are studied. The results showed that: in terms of spatial distribution, thermal anomalies are mainly distributed in most areas except Northwest and East China. In terms of inter-annual trends, the number of thermal anomalies continued to increase from 2001 to 2014 years, with an average annual growth rate of 15.01%, 2015 years After that, it decreased year by year, with an average annual decline rate of 14.96%. On month and season scales, thermal anomalies occur most frequently in spring and autumn (spring: 551 716, autumn: 416 698), Spring and autumn are relatively most distributed in Northeast China (spring: 164 898, autumn: 186 727). The highest in October (118 274); the lowest number of hot anomalies in summer (290 793), mostly distributed in East China (120 455), the average monthly number in East China is the highest in June (76 465); the number in winter is 358 483, South China has the most distribution (108 209), and South China has the highest monthly average number in January (37 770). This research is helpful to master forest and grassland fires in typical regions of China, as well as changes in thermal abnormalities caused by straw burning and industrial emissions, and then provide technical support for regional disaster prevention and environmental monitoring.

Key words: Thermal anomaly    Straw burning    Temporal and distribution    MODIS
收稿日期: 2020-09-13 出版日期: 2022-04-08
ZTFLH:  X87  
基金资助: 国家自然科学基金项目(U1704122)
通讯作者: 李莘莘     E-mail: gq@henu.edu.cn;lishenshen@126.com
作者简介: 葛强(1977-),男,河南泌阳人,教授,主要从事空间信息处理、大数据分析研究。E?mail:gq@henu.edu.cn
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引用本文:

葛强,沈文举,李冉,李莘莘,蔡坤,左宪禹,乔保军,张云舟. 2001~2018年我国热异常点时空分布特征研究[J]. 遥感技术与应用, 2022, 37(1): 73-84.

Qiang Ge,Wenju Shen,Ran Li,Shenshen Li,Kun Cai,Xianyu Zuo,Baojun Qiao,Yunzhou Zhang. Research on the Temporal and Spatial Cistribution Characteristics of Thermal Anomalies in China from 2001 to 2018. Remote Sensing Technology and Application, 2022, 37(1): 73-84.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0073        http://www.rsta.ac.cn/CN/Y2022/V37/I1/73

图1  2001~2018年中国大陆地区热异常点空间分布
区域东北华北华中华东华南西南西北
区域面积/km21 520 000838 100560 000798 300452 9002 340 6003 080 100
热异常/个388 180224 297172 231320 732225 724204 74381 783
单位面积热异常/(个/km2)0.2550.2680.3080.4020.4980.0880.027
表1  我国七大区域热异常点的数量分布
图2  2001~2018年我国大陆地区热异常点季节和年际数量变化趋势
图3  2001~2018年我国大陆地区热异常点年均值空间分布
图4  2001~2018年我国大陆地区热异常点数量季均值分布
区域1月2月3月4月5月6月7月8月9月10月11月12月
东北1 4229 22661 94582 00220 9515 9407 08911 80919 456118 27448 9971 069
华北3 21611 59941 04027 14216 22922 40914 96514 12417 04434 96818 2703 291
华中15 61223 37924 80611 7309 57131 3307 4375 7084 96212 9649 61715 115
华东23 15729 45430 92020 39625 03776 46523 99619 9949 51119 55319 81322 436
华南37 77033 84431 93819 3109 6014 1904 5674 3545 66815 14922 73836 595
西南24 21449 83146 78731 67815 4364 5035 4366 2852 6283 4035 9408 602
西北2 3344 4849 52310 2355 4398 4106 5175 2656 59414 1107 0391 833
表2  2001~2018年我国大陆七大局部地区热异常点数量月均变化
图5  2001~2018年我国大陆七大地区热异常点数量季节占比
图6  2001~2018年我国大陆七大局部地区热异常点季节和年际数量变化趋势
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