Please wait a minute...
img

官方微信

遥感技术与应用  2022, Vol. 37 Issue (4): 919-928    DOI: 10.11873/j.issn.1004-0323.2022.4.0919
灯光遥感专栏     
基于VIIRS Nightfire数据的山东省工业热源分类提取与变化特征分析
李博1(),范俊甫1(),韩留生1,周玉科2,张大富1
1.山东理工大学 建筑工程学院,山东 淄博 255000
2.中国科学院地理科学与资源研究所 生态系统网络观测与模拟院重点实验室,北京 100101
Classification Extraction and Variation Characteristics Analysis of Industrial Heat Sources in Shandong Province based on VIIRS Nightfire Data
Bo Li1(),Junfu Fan1(),Liusheng Han1,Yuke Zhou2,Dafu Zhang1
1.School of Civil and Architectural Engineering,Shandong University of Technology,Zibo 255049,China
2.Key Laboratory of Ecosystem Network Observation and Modeling,Institute of Geographic Sciences andNature Resources Research,Chinese Academy of Sciences,Beijing 100101,China
 全文: PDF(6370 KB)   HTML
摘要:

卫星遥感的多传感器技术为大尺度上实现工业热源的快速分类、识别提供了可能。利用2012—2019年VIIRS(可见光红外成像辐射仪)Nightfire数据,以山东省为研究区,首先,根据工业热异常点的空间聚集性特征和统计特征,使用DBSCAN聚类和时间序列聚类提取确认工业热源对象。其次,基于工业温度特征模板,使用K最近邻分类算法实现了工业子类热源的分类。研究表明:①使用该方法提取工业热源对象精度达到99.81%,比“空间—时间—温度”维度的面向对象方法提高了1.47%,在提取工业热源对象数量上提高了8.99%;工业子类热源对象分类总体精度为84.54%,可较好地对山东省工业热源对象进行提取与分类。②山东省工业热源对象主要分布于潍坊市、滨州市、临沂市和东营市4个城市(占比43.30%);工业热源对象集中分布于淄博市、滨州市和东营市交界处,聊城市东部,枣庄市中部及临沂市中部,呈现显著的空间集聚态势;根据工业转型升级政策提出前后工业热源对象数量的变化(减少13.63%),表明山东省推进工业转型升级政策取得一定成效。因此,利用该方法可较好地提取山东省工业热源,为工业相关政策的制定和评价提供客观依据。

关键词: 工业热源VIIRS NightfireDBSCAN聚类时间序列聚类温度特征模板    
Abstract:

Multi-sensor technology of satellite remote sensing provides a possibility for rapid classification and identification of industrial heat sources on a large scale. the Nightfire data of VIIRS (Visible Infrared Imaging Radiometer Suite) from 2012 to 2019 are used, taking Shandong Province as the research area. Firstly, according to the spatial aggregation characteristics and statistical characteristics of industrial heat anomalies, extract and confirm the industrial heat source objects by the DBSCAN clustering and time series clustering. Secondly, based on the industrial temperature feature template, realize the classification of industrial sub-class heat sources by the K-nearest neighbor classification algorithm. The research shows that: ①The precision of extracting industrial heat source objects using the method in this paper reaches 99.81%, which is 1.47% higher than the object-oriented method in the dimension of “space-time-temperature”, and 8.99% higher than the number of industrial heat source objects. The overall precision of industrial sub-class heat source object classification is 84.54%, which can better extract and classify industrial heat source objects in Shandong Province. ②The industrial heat sources in Shandong Province are mainly distributed in Weifang, Binzhou, Linyi, and Dongying (accounting for 43.30%). Industrial heat source objects are concentrated in the junction of Zibo, Binzhou and Dongying, eastern Liaocheng, central Zaozhuang, and central Linyi, showing a significant spatial agglomeration trend. The change in the number of industrial heat sources before and after the industrial transformation and upgrading policy was proposed (13.63% reduction), indicating that Shandong Province has made some achievements in promoting industrial transformation and upgrading policy. Therefore, the method in this paper can be used to better extract the industrial heat source in Shandong Province and provide an objective basis for the formulation and evaluation of industrial policies.

Key words: Industrial heat source    VIIRS Nightfire    DBSCAN clustering    Time series clustering    Temperature feature template
收稿日期: 2021-07-25 出版日期: 2022-09-28
:  TP79  
基金资助: 国家自然科学基金项目(42171413);资源与环境信息系统国家重点实验室开放基金项目资助,国家重点研发计划项目(2017YFB0503500);山东省自然科学基金项目(ZR2020MD0115);山东理工大学青年教师发展支持计划项目(4072-115016)
通讯作者: 范俊甫     E-mail: 823009251@qq.com;fanjf@sdut.edu.cn
作者简介: 李 博(1989-),男,山东菏泽人,硕士研究生,主要从事城市环境遥感研究。E?mail: 823009251@qq.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
李博
范俊甫
韩留生
周玉科
张大富

引用本文:

李博,范俊甫,韩留生,周玉科,张大富. 基于VIIRS Nightfire数据的山东省工业热源分类提取与变化特征分析[J]. 遥感技术与应用, 2022, 37(4): 919-928.

Bo Li,Junfu Fan,Liusheng Han,Yuke Zhou,Dafu Zhang. Classification Extraction and Variation Characteristics Analysis of Industrial Heat Sources in Shandong Province based on VIIRS Nightfire Data. Remote Sensing Technology and Application, 2022, 37(4): 919-928.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.4.0919        http://www.rsta.ac.cn/CN/Y2022/V37/I4/919

图1  工业热源对象提取技术路线
图2  工业子类热源高分影像及温度特征模板
算法疑似工业热源对象算法工业热源对象精度 /%召回率 /%
空间滤波197空间滤波+ 时间滤波18198.3493.68
DBSCAN228DBSCAN+时间序列聚类19499.8192.38
表1  两种方法提取工业热源对象数量
工业类型非金属矿物钢铁煤化油气炼化用户精度/%
非金属矿物2880175.68
钢铁1423091.30
煤化1346092.00
油气炼化11114878.69
制图精度/%90.3265.6392.0097.96总体精度:84.54
表2  工业子类热源对象分类结果
图3  山东省工业热源对象空间分布图及核密度图审图号:GS(2021)3715、鲁SG(2021)013
图4  两段时期工业热源对象空间分布及变化审图号:GS(2021)3715、鲁SG(2021)013
1 Zeng Chao, Zeng Zhen, Cao Zhenyu, et al. Forest fire dynamic monitoring based on time series and multi-source satellite images: A case study of the muli county forest areas in Sichuan province[J]. Remote Sensing Technology and Application. 2021, 36(3): 521-532.曾超, 曾珍, 曹振宇, 等. 多源时序国产卫星影像的森林火灾动态监测——以四川省木里县及其周边林区为例[J]. 遥感技术与应用, 2021, 36(3): 521-532.
2 Rao Yueming, Wang Chuan, Huang Huaguo. Forest fire Monitoring based on multisensor remote sensing techniques in Muli county,Sichuan province[J].Journal of Remote Sensing, 2020, 24(5): 559-570.
2 饶月明, 王川, 黄华国. 联合多源遥感数据监测四川木里县森林火灾[J]. 遥感学报, 2020, 24(5): 559-570.
3 Wang Weiguo, Pan Jinghu, Feng Yaya, et al. Model and zoning of fire risk in Gansu province based on GWLR and MODIS imagery[J]. Remote Sensing Technology and Application, 2017, 32(3): 514-523.
3 王卫国, 潘竟虎, 冯娅娅, 等. 基于MODIS数据和GWLR的甘肃省火灾风险模型与区划[J]. 遥感技术与应用, 2017, 32(3): 514-523.
4 Cheng Yuting, Liu Zhaohua, Lu Linlin, et al. Spatio-temporal dynamics of surface urban heat island in coastal Mega cities along the Belt and Road from remote sensing data[J]. Remote Sensing Technology and Application,2020,35(5):1197-1205.
4 程雨婷, 刘昭华, 鹿琳琳, 等. 一带一路沿海超大城市热岛时空特征遥感分析[J]. 遥感技术与应用, 2020, 35(5): 1197-1205.
5 Lin Zhongli, Xu Hanqiu. Comparative study on the urban heat island effect in "Stove Cities" during the last 20 years[J]. Remote Sensing Technology and Application,2019,34(3): 521-530.
5 林中立, 徐涵秋. 近20年来新旧“火炉城市”热岛状况对比研究[J]. 遥感技术与应用, 2019, 34(3): 521-530.
6 Yin Cuijing, Feng Kai, Wang Qi, et al. Analysis of influence factors of urban heat island effect based on remote sensing[J]. Remote Sensing Technology and Application, 2021, 36(3): 673-681.
6 尹翠景, 封凯, 王奇, 等. 遥感城市热岛提取的影响因素分析[J]. 遥感技术与应用, 2021, 36(3): 673-681.
7 Cheng Tong, Zhu Shanyou, Zhang Guixin, et al. Seasonal variation of PM2.5 in the Beijing-Tianjin-Hebei region in 2018 and its relationship with land surface temperature[J]. Remote Sensing Technology and Application,2020,35(6): 1457-1466.
7 成通, 祝善友, 张桂欣, 等. 2018年京津冀地区PM2.5季节变化及其与地表温度的关系分析[J]. 遥感技术与应用, 2020, 35(6):1457-1466.
8 Tian Huihui, Feng Li, Zhao Menmen, et al. Analysis of meticulous features of urban surface temperature based on UAV thermal thermography[J]. Remote Sensing Technology and Application, 2019, 34(3): 553-563.
8 田慧慧, 冯莉, 赵璊璊, 等. 无人机热红外城市地表温度精细特征研究[J]. 遥感技术与应用, 2019, 34(3): 553-563.
9 Yang Yuting, Tang Jiafa, Bian Jinhu,et al. Seasonal variations in the relationship between land surface temperature and impervious surface percentage in Kolkata[J]. Remote Sensing Technology and Application, 2021, 36(1): 79-89.
9 杨玉婷, 汤家法, 边金虎, 等. 加尔各答市地表温度与不透水面比例季相相关性研究[J]. 遥感技术与应用, 2021, 36(1): 79-89.
10 Li Zaijun, Hu Meijuan, Zhou Nianxing. The spatial pattern and influencing factors of industrial eco-efficiency in Chinese prefecture-level cities[J].Ecomonic Geography,2018,38(12): 126-134.
10 李在军,胡美娟,周年兴.中国地级市工业生态效率空间格局及影响因素[J]. 经济地理,2018,38(12):126-134.
11 Du Zhiwei, Lachang Lü, Huang Ru. Spatial pattern of industrial innovation efficiency for Chinese cities at prefecture level and above[J]. Geographical Science, 2016, 36(3): 321-327.
11 杜志威, 吕拉昌, 黄茹. 中国地级以上城市工业创新效率空间格局研究[J]. 地理科学, 2016, 36(3): 321-327.
12 Liu Youjin, Zeng Xiaoming. China's industrial spatial pattern evolution and the concentration difference-based on the EDSA and urban panel data of spatial econometrics[J]. Regional Economic Review, 2016(1):80-88.
12 刘友金, 曾小明. 中国工业空间格局的演变与集聚差异——基于EDSA和城市面板数据的空间计量研究[J]. 区域经济评论, 2016(1):80-88.
13 Jeff D. A method for satellite identification of surface temperature fields of subpixel resolution[J]. Elsevier,1981,11(none):221-229. DOI: 10.1016/0034-4257(81)90021-3 .
doi: 10.1016/0034-4257(81)90021-3
14 Kaufman Y J, Justice C O, Flynn L P, et al. Potential global fire monitoring from EOS‐MODIS[J]. Journal of Geophysical Research: Atmospheres,1998,103(D24). DOI: 10.1029/98JD01644 @10.1002/(ISSN)2169-8996.EOSAM1.
doi: 10.1029/98JD01644
15 Ge Qiang, Shen Wenju, Li Ran, et al. Research on the temporal and spatial cistribution characteristics of thermal anomalies in China from 2001 to 2018[J]. Remote Sensing Technology and Application, 2022, 37(1): 73-84.
15 葛强, 沈文举, 李冉, 等. 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.
16 Ma Caihong, Wang Dacheng, Yang Jin, et al. Assessing the distribution of active fire data based on large industrial heat sources detection in Handan[J]. Remote Sensing Technology and Application, 2022, 37(1): 34-44.
16 马彩虹, 王大成, 杨进, 等. 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.
17 Wang Kang. Thermal infrared remote sensing technology for geothermal resources detection based on multi-source & multi-temporal data in Dandong Liaoning[D]. Changchun: Jilin University, 2020.
17 王康. 基于多源多时相热红外遥感技术的丹东地热资源探测方法研究[D]. 长春: 吉林大学, 2020.
18 Zhou Y, Fei Z, Wang S,et al.A method for monitoring iron and steel factory economic activity based on satellites[J]. Sustainability,2018,10(6):1935-1956. DOI:10.3390/su10061935 .
doi: 10.3390/su10061935
19 Xia H, Chen Y, Quan J. A simple method based on the thermal anomaly index to detect industrial heat sources[J]. International Journal of Applied Earth Observations and Geoinformation,2018,73(8):627-637. DOI:10.1016/j.jag. 2018. 08.003 .
doi: 10.1016/j.jag. 2018. 08.003
20 Zhang P, Yuan C, Sun Q, et al. Satellite-based detection and characterization of industrial heat sources in China[J]. Environmental Science & Technology,2019,53(18). DOI:10.1021/ acs.est.9b02643 .
doi: 10.1021/ acs.est.9b02643
21 Ma Y, Ma C, Liu P . et al. Spatial-temporal distribution analysis of industrial heat sources in the US with geocoded, tree-based,large-scale clustering[J]. Remote Sensing,2020,12(18): 3069. DOI: 10.3390/rs12183069 .
doi: 10.3390/rs12183069
22 Elvidge C D, Zhizhin M, Hsu F C, et al. Methods for global survey of natural gas flaring from visible infrared imaging radiometer suite data[J].Energies,2015,9(1):1-15. DOI: 10.3390/ en9010014 .
doi: 10.3390/ en9010014
23 He Junxia, Yan Wei, Duan Xuejun, et al. Location identification and spatial evolution of industrial heat sources along Yangtze river in Jiangsu province[J]. Resources and Environment in the Yangtze Basin, 2022, 31(5): 995-1005.
23 何俊霞, 颜蔚, 段学军, 等. 江苏沿江地区工业热源区位识别与空间演变[J]. 长江流域资源与环境, 2022, 31(5): 995-1005.
24 Li Bo, Fan Junfu, Han Liusheng, et al. An industrial heat source extraction method: BP neural network using temperature feature template[J]. Journal of Geo-information Science, 2022, 24(3): 533-545.
24 李博, 范俊甫, 韩留生, 等. 一种工业热源提取方法: 利用温度特征模板的BP神经网络[J]. 地球信息科学学报, 2022, 24(3): 533-545.
25 Ma C H, Yang J, Chen F, et al. Assessing heavy industrial 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
26 Ma C, Niu Z, Ma Y, et al. Assessing the distribution of heavy industrial heat sources in India between 2012 and 2018[J]. ISPRS International Journal of Geo-Information, 2019, 8(12): 568. DOI: 10.3390/ijgi8120568 .
doi: 10.3390/ijgi8120568
27 Sun Jiaqi, Liu Yongxue, Dong Yanzhu, et al. Classifycation 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 Geo-Information Science, 2018, 34(3):13-19.
27 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): 13-19.
28 Lai Jianbo. Study on remote sensing identification and spatial distribution pattern of heat source in heavy industry[D]. Lanzhou: Northwest Normal University, 2020.
28 赖建波. 重工业热源的遥感识别及空间分布格局研究[D]. 兰州: 西北师范大学, 2020.
29 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. DOI: 10.1016/j.rse.2017.10.019 .
doi: 10.1016/j.rse.2017.10.019
30 Elvidge C D, Zhizhin M, Hsu F C, et al. VIIRS Nightfire: satellite pyrometry at night[J]. Remote Sensing, 2013, 5(9): 4423-4449. DOI: 10.3390/rs5094423 .
doi: 10.3390/rs5094423
31 Elvidge C D, Zhizhin M, Ghosh T, et al. Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019[J]. Remote Sensing, 2021, 13(5): 922. DOI: 10.3390/rs13050922 .
doi: 10.3390/rs13050922
32 Cui Xuan. A study on the economic development of the resource-based cities in Shandong province[D]. Changchun: Jilin University, 2015.
32 崔璇. 山东省资源型城市经济发展研究[D]. 长春: 吉林大学, 2015.
33 Wang Beibei. Research on evaluation of green transformation and development of resource-based cities in Shandong province[D]. Beijing: China University of Geosciences, 2019.
33 王贝贝. 山东省资源型城市绿色转型发展评价研究[D]. 北京: 中国地质大学, 2019.
34 Wang Dong. An empirical analysis of industrial structure and economic growth in Shandong province[D]. Shenyang: Liaoning University, 2018.
34 王栋. 山东省产业结构与经济增长的实证分析[D]. 沈阳: 辽宁大学, 2018.
[1] 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.