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遥感技术与应用  2022, Vol. 37 Issue (1): 34-44    DOI: 10.11873/j.issn.1004-0323.2022.1.0034
青促会十周年专栏     
基于工业热源区域识别的邯郸市热异常产品分析
马彩虹1,2(),王大成1(),杨进1,关琳琳1,李天柱1,3
1.中国科学院空天信息创新研究院,北京 100094
2.中国科学院地理科学与资源研究所,北京 100094
3.中国科学院大学,北京 101499
Assessing the Distribution of Active Fire data based on Large Industrial Heat Sources Detection in Handan
Caihong Ma1,2(), JinYang1(),Dacheng Wang1,Linlin Guan1,Tianzhu Li1,3
1.Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,100094,China
2.State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Science and Natural Resource Research,Chinese Academy of Sciences,Beijing 100101,China
3.University of Chinese Academy of Sciences,Weihai 101499,China
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摘要:

利用目前空间分辨率、时间分辨率(1 d)最高的成熟热异常产品数据主动式热异常数据(ACF,NPP-VIIRS active fire/hotspot data),结合基于长时序热异常数据支持下的工业热源区域识别模型,叠加地表覆盖类型数据,实现邯郸市不同类型热异常数据的识别与分析。①清洗邯郸市2012.1.20~2020.12.31年89 249个热异常点数据,得到2012~2020年间在营的81个工业热源区域,主要分布在京广线(G4高速)以西的丘陵地区,密集分布于涉县、武安、峰峰矿区,与邯郸市矿产资源的分布具有较好的一致性;2020年在营的工业热源与2013年相比,关停32个(42.67%),热异常点数目明显减少的19个(25.33%),新增5个企业;从企业类别上看,识别出的工业热源企业主要为钢铁及铸造类、煤化工及焦化、水泥厂等企业,分别占比达53.24%、28.16%和0.08%。②邯郸市2012~2020年历年的热异常点主要来源于工业热源,其工业热异常点历年平均占比为91.61%,2016年工业热源区域内热异常点占比最高,占比超过94.34%;邯郸市耕地地表覆盖中,有42.73%的热异常数据为工业热异常点;在人造地表覆盖中,有5.5%的热异常为非工业热异常数据。故,与仅仅依赖地表覆盖类型识别秸秆燃烧方法相比,该方法能有效的提高识别精度。邯郸市工业热异常点主要下垫面为人造地表类型(占比89.87%),非工业热异常点下垫面主要为耕地类型(占比75.93%);非工业热异常点2013~2017年按月统计图具有明显的“双峰”现象,当年的 6/10月份出现较大的峰值点,此种现象自2018年起消失。

关键词: 工业热源主动式热异常数据长时序邯郸    
Abstract:

In this study, different types of NPP VIIRS 375 m active fire product (ACF,NPP-VIIRS active fire/hotspot data) in Handan were analysed based on heavy industry heat sources detection model base on long-term data. (1) 81 heavy industry heat sources worked between 2012 and 2020 were detected based on 89 249 ACF data. They were mainly distributed in the hilly area (the west of Beijing Guangzhou line and G4 Expressway), especially in Shexian, Wu'An and Fengfeng mining areas. It was consistent with the distribution of mineral resources in Handan city. And, compared the num of working heavy industrial heat sources between 2020 and 2013, 32 (42.67%) industrial heat sources were shut down, 19 (25.33%) industrial heat sources which thermal abnormal points were significantly reduced, also 5 new enterprises appeared. And heavy industrial heat sources detected were maily belong to iron and steel and foundry, coal chemical and coking, cement plants, accounting for 53.24%, 28.16% and 0.08% respectively. (2) Betwwen 2012 and 2020, the average annual proportion of thermal anomaly points from industrial heat sources was 91.61%, especially more than 94.34% in 2016. There was 42.73% of ACF data on the cultivated land surface coverage belonging to industrial thermal anomaly points, 5.5% data on the artificial surface coverage belonging to non industrial thermal anomaly points. And, the most of industrial heat anomaly points in Handan were on the artificial land (accounting for 89.87%), while the most of non industrial heat anomaly points were on the cultivated land (accounting for75.93%).There was obvious "double peak" phenomenon (with a larger peak point in June / October) for the monthly statistical chart, which has disappeared since 2018.

Key words: Heavy Industry Heat Source    Active Fire data    Long-term    Handan
收稿日期: 2021-07-27 出版日期: 2022-04-08
ZTFLH:  P407  
基金资助: 资源与环境信息系统国家重点实验室开放基金和中国科学院青年创新促进会资助
通讯作者: 王大成     E-mail: mach@aircas.ac.cn;wangdc@aircas.ac.cn
作者简介: 马彩虹(1986-),女,山东临沂人,博士,工程师,主要从事遥感图像智能处理与检索,遥感数据发布管理,热源重工业识别研究。E?mail:mach@aircas.ac.cn
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引用本文:

马彩虹,王大成,杨进,关琳琳,李天柱. 基于工业热源区域识别的邯郸市热异常产品分析[J]. 遥感技术与应用, 2022, 37(1): 34-44.

Caihong Ma, JinYang,Dacheng Wang,Linlin Guan,Tianzhu Li. Assessing the Distribution of Active Fire data based on Large Industrial Heat Sources Detection in Handan. Remote Sensing Technology and Application, 2022, 37(1): 34-44.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0034        http://www.rsta.ac.cn/CN/Y2022/V37/I1/34

图1  研究区域图
图2  邯郸区域2012年1月20日~2020年12月31日主动式热异常点区域分布图
图3  邯郸区工业热源区域分布图
本文识别的非工业热异常点本文识别的工业热 异常点
非固定热异常点6 912593
固定热异常点21277 312
表1  邯郸市本文识别的工业热异常点与ACF数据自带的热异常点类型的混淆矩阵
图4  邯郸区2012-2020年间不同类型热异常点时序图
耕地林地草地灌木地湿地水体苔原人造地表裸地冰雪其他总计
1020304050607080901000
工业热异常点4 1978341 69263798073 6357690081 934
非工业热异常点5 5542591132301201 30645037 315
总计9 7511 0931 805293810074 9418140389 249
表2  邯郸市不同地表覆盖类型的热异常点类型统计表
图5  邯郸区2012-2020年间工业热源区域热异常点时序图
图6  邯郸区2012~2020年间非工业热异常点时序图
1 Liu Jianbo, Ma Caihong, Chen Fu, et al.Design and implementation of active-based instant remote sensing data service[J].Remote Sensing Information, 2016, 31(8): 61-67.
1 刘建波, 马彩虹, 陈甫, 等. 遥感卫星数据实时主动服务系统设计与实现[J]. 遥感信息, 2016, 31(8): 61-67.
2 Chen Xingfeng, Liu Li, Li Jiaguo, et al. Application and research progress of fire monitoring using satellite remote sensing[J]. Journal of Remote Sensing,2020,24(5):531-542.
2 陈兴峰, 刘李, 李家国, 等. 卫星遥感火点监测应用和研究进展[J]. 遥感学报,2020,24(5):531-542.
3 Yan Junjie, Qu Jianhua, Ran Maonong, et al. Himawari-8 AHI fire decection in clear sky based on time-phase change[J]. Journal of Remote Sensing,2020,24(5):571-577.
3 鄢俊洁,瞿建华,冉茂农,等. 基于时相变化的晴空条件下Himawari-8 AHI火点检测[J]. 遥感学报,2020,24(5):571-577.
4 Sun Jiaqi, Liu Yongxue, Dong Yanzhu, et al. Classification of urban industrial heat sources based on Suomi-NPP VIIRS nighttime thermal anomaly products: A case study of the Beijing-Tianjin-Hebei region[J]. Geography and Geographic Information Science, 2018, 34(3):19-25.
4 孙佳琪, 刘永学, 董雁伫, 等. 基于Suomi-NPP VIIRS夜间热异常产品的城市工业热源分类——以京津冀地区为例[J]. 地理与地理信息科学, 2018, 34(3):19-25.
5 Liu Y, Hu C, Zhan W, et al. Identifying industrial heat sources using time-series of the VIIRS nightfire product with an object-oriented approach[J]. Remote Sensing of Environment,2018,204:347-365. DOI:10.1016/j.rse. 2017. 10.019 .
doi: 10.1016/j.rse. 2017. 10.019
6 Ma C, Niu Z, Ma Y, et al. Assessing the distribution of heavy industrial heat sources in india between 2012 and 2018[J] International Society for Photogrammetry and Remote Sensing,2019,8(12):568. DOI:10.3390/ijgi8120568 .
doi: 10.3390/ijgi8120568
7 Ma C, Yang J, Chen F, et al. Assessing heavy industry heat source distribution in China using real-time VIIRS active fire/hotspot data[J]. Sustainability,2018,10(12): 4419.DOI: 10.3390/su10124419 .
doi: 10.3390/su10124419
8 Chen Xingfeng, Liu Li, Li Jiaguo, et al. Applications and research of fire monitoring using satellite remote sensing[J]. Journal of Remote Sensing,2020,24(5):56-67.
8 陈兴峰, 刘李, 李家国, 等. 卫星遥感火点监测应用和研究进展[J]. 遥感学报,2020,24(5):56-67.
9 Giglio L, Schroeder W, Justice C O. The collection 6 MODIS active fire detection algorithm and fire products[J]. Remote Sensing of Environment,2016,178:31-41. DOI: 10.1016/j.rse.2016.02.054 .
doi: 10.1016/j.rse.2016.02.054
10 Zhao W. Research and evaluation of the algorithm of land surface fire detection based on FY3-VIRR data[J]. Fire Safety Journal,2011,3:004. DOI: 10.1016/B978-0-444-53599-3.10005-8 .
doi: 10.1016/B978-0-444-53599-3.10005-8
11 Yin Zhenzhen, Chen Fang, Lin Zhengyang, et al. Active fire monitoring based on FY-3D MERSI satellite data[J]. Remote Sensing Technology and Applications, 2020, 35(5): 1099-1108.
11 殷针针, 陈方, 林政阳, 等. 基于FY-3D MERSI数据的火点识别方法研究[J]. 遥感技术与应用, 2020, 35(5): 1099-1108.
12 Schroeder W, Oliva P, Giglio L, et al. Active fire detection using Landsat-8/OLI data[J]. Remote Sensing of Environment,2016,185,210-220. DOI:10.1016/j.rse.2015.08.032 .
doi: 10.1016/j.rse.2015.08.032
13 Trifonov G M, Zhizhin M N, Melnikov D V, et al. VIIRS nightfire remote sensing volcanoes[J]. Procedia Computer Science,2017,119:307-314. DOI:10.1016/j.procs.2017.11.189 .
doi: 10.1016/j.procs.2017.11.189
14 Chen Xingfeng, Liu Li, Li Jiaguo, et al. Application and research progress of fire monitoring using satellite remote sensing[J]. Journal of Remote Sensing,2020,24(5):56-67.
14 陈兴峰, 刘李, 李家国, 等. 卫星遥感火点监测应用和研究进展[J]. 遥感学报, 2020,24(5):56-67.
15 Xu Benben, Fan Meng, Chen Liangfu, et al. Analysis of temporal and spatial variations of crop residue burning in china from 2013 to 2017[J]. Journal of Remote Sensing,2020,24(10):1221-1232.
15 徐奔奔, 范萌, 陈良富,等.2013年—2017年主要农业区秸秆焚烧时空特征及影响因素分析[J].遥感学报,2020,24(10):1221-1232.
16 Zhang Lijuan, Li Qing, Chen Hui, et al. Analysis and comparison of straw burning based on remote sensing monitoring data during summer and autumn harvest season from 2014 to 2015 in China[J]. Environment and Sustainable Development,2016,41(6):61-65.
16 张丽娟,厉青,陈辉,等. 2014-2015年夏秋收期间全国秸秆焚烧遥感监测结果对比分析[J]. 环境与可持续发展,2016,41(6):61-65.
17 Sun Shuang, Li Lingjun, Zhao Wenji, et al. Industrial pollution emissions based on thermal anomaly remote sensing monitoring: A case study of southern Hebei Urban agglomerations,China[J]. China Environmental Science, 2019(7):3120-3129.
17 孙爽,李令军,赵文吉,等. 基于热异常遥感的冀南城市群工业能耗及大气污染[J]. 中国环境科学,2019(7):3120-3129.
18 Qu Ran, Zhang Yaqiong, Lou Qijia, et al. Analysis of distribution characteristics and impact of industrial enterprises in Suzhou City based on thermal anomaly remote sensing data[J]. Environmental Ecology, 2020, 2(12):55-60.
18 屈冉, 张雅琼, 娄启佳, 等. 基于热异常遥感数据的苏州市工业企业分布变化特征及其影响分析[J]. 环境生态学, 2020, 2(12):55-60.
19 Gu Yanchun, Meng Qingyan, Hu D, et al. Analysis of environmental effects of industrial thermal anomalies[J]. Remote Sensing for Land and Resources,2020,32(4):190-198.
19 谷艳春,孟庆岩,等. 工业热异常环境效应分析[J]. 国土资源遥感,2020,32(4):190-198.
20 Gong P, Liu H, Zhang M N,et al. Stable classification with limited sample: transferring a 30 m resolution sample set collected in 2015 To mapping 10 m resolution global land cover in 2017[J]. Science Bulletin,2019,64(6):370-373.
21 I-Band VIIRS 375 m Active Fire Data[DB/OL]. Available online (FIRMS): .
22 Wang Yanlin, Li Na, Wu Lifeng. Research on air quality prediction in heavy polluted days in Handan[J]. Journal of Hebei Engineering University (Natural Science Edition), 2020,37(1):91-97.
22 王彦林,李孥,吴利丰.邯郸重污染日空气质量预测研究[J]. 河北工程大学学报(自然科学版), 2020,37(1):91-97.
23 Duan Wenjiao, Zhou Ying, Li Jifeng, et al. PM2.5 pollution characteristics and source apportionment in Handan Urban Area[J]. China Environmental Science,2019,39(10):4108-4116.
23 段文娇, 周颖, 李纪峰, 等. 邯郸市区PM_(2.5)污染特征及来源解析[J]. 中国环境科学, 2019, 39(10): 4108-4116.
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