基于工业热源区域识别的邯郸市热异常产品分析

马彩虹; 王大成; 杨进; 关琳琳; 李天柱

doi:10.11873/j.issn.1004-0323.2022.1.0034

閬ユ劅鎶�鏈笌搴旂敤鈥衡�� 2022, Vol. 37 鈥衡�� Issue (1): 34-44.DOI: 10.11873/j.issn.1004-0323.2022.1.0034

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鏀剁鏃ユ湡:2021-07-27 淇洖鏃ユ湡:2021-12-27 鍑虹増鏃ユ湡:2022-02-20 鍙戝竷鏃ユ湡:2022-04-08
閫氳浣滆��: 鐜嬪ぇ鎴�
浣滆�呯畝浠�:椹僵铏癸紙1986-锛夛紝濂筹紝灞变笢涓存矀浜猴紝鍗氬＋锛屽伐绋嬪笀锛屼富瑕佷粠浜嬮仴鎰熷浘鍍忔櫤鑳藉鐞嗕笌妫�绱紝閬ユ劅鏁版嵁鍙戝竷绠＄悊锛岀儹婧愰噸宸ヤ笟璇嗗埆鐮旂┒銆侲?mail锛�mach@aircas.ac.cn
鍩洪噾璧勫姪:
璧勬簮涓庣幆澧冧俊鎭郴缁熷浗瀹堕噸鐐瑰疄楠屽寮�鏀惧熀閲戝拰涓浗绉戝闄㈤潚骞村垱鏂颁績杩涗細璧勫姪

Assessing the Distribution of Active Fire data based on Large Industrial Heat Sources Detection in Handan

Caihong Ma^1,²(), JinYang¹(),Dacheng Wang¹,Linlin Guan¹,Tianzhu Li^1,³

^1.Aerospace Information Research Institute锛孋hinese Academy of Sciences锛孊eijing锛�100094锛孋hina
^2.State Key Laboratory of Resources and Environmental Information System锛孖nstitute of Geographic Science and Natural Resource Research锛孋hinese Academy of Sciences锛孊eijing 100101锛孋hina
^3.University of Chinese Academy of Sciences锛學eihai 101499锛孋hina

Received:2021-07-27 Revised:2021-12-27 Online:2022-02-20 Published:2022-04-08
Contact: JinYang

鎽樿/Abstract

鎽樿锛�

鍒╃敤鐩墠绌洪棿鍒嗚鲸鐜囥�佹椂闂村垎杈ㄧ巼锛�1 d锛夋渶楂樼殑鎴愮啛鐑紓甯镐骇鍝佹暟鎹富鍔ㄥ紡鐑紓甯告暟鎹紙ACF锛孨PP-VIIRS active fire/hotspot data锛夛紝缁撳悎鍩轰簬闀挎椂搴忕儹寮傚父鏁版嵁鏀寔涓嬬殑宸ヤ笟鐑簮鍖哄煙璇嗗埆妯″瀷锛屽彔鍔犲湴琛ㄨ鐩栫被鍨嬫暟鎹紝瀹炵幇閭兏甯備笉鍚岀被鍨嬬儹寮傚父鏁版嵁鐨勮瘑鍒笌鍒嗘瀽銆傗憼娓呮礂閭兏甯�2012.1.20~2020.12.31骞�89 249涓儹寮傚父鐐规暟鎹紝寰楀埌2012~2020骞撮棿鍦ㄨ惀鐨�81涓伐涓氱儹婧愬尯鍩燂紝涓昏鍒嗗竷鍦ㄤ含骞跨嚎锛圙4楂橀�燂級浠ヨタ鐨勪笜闄靛湴鍖猴紝瀵嗛泦鍒嗗竷浜庢秹鍘裤�佹瀹夈�佸嘲宄扮熆鍖猴紝涓庨偗閮稿競鐭夸骇璧勬簮鐨勫垎甯冨叿鏈夎緝濂界殑涓�鑷存�э紱2020骞村湪钀ョ殑宸ヤ笟鐑簮涓�2013骞寸浉姣旓紝鍏冲仠32涓紙42.67%锛夛紝鐑紓甯哥偣鏁扮洰鏄庢樉鍑忓皯鐨�19涓紙25.33%锛夛紝鏂板5涓紒涓氾紱浠庝紒涓氱被鍒笂鐪嬶紝璇嗗埆鍑虹殑宸ヤ笟鐑簮浼佷笟涓昏涓洪挗閾佸強閾搁�犵被銆佺叅鍖栧伐鍙婄劍鍖栥�佹按娉ュ巶绛変紒涓氾紝鍒嗗埆鍗犳瘮杈�53.24%銆�28.16%鍜�0.08%銆傗憽閭兏甯�2012~2020骞村巻骞寸殑鐑紓甯哥偣涓昏鏉ユ簮浜庡伐涓氱儹婧愶紝鍏跺伐涓氱儹寮傚父鐐瑰巻骞村钩鍧囧崰姣斾负91.61%锛�2016骞村伐涓氱儹婧愬尯鍩熷唴鐑紓甯哥偣鍗犳瘮鏈�楂橈紝鍗犳瘮瓒呰繃94.34%锛涢偗閮稿競鑰曞湴鍦拌〃瑕嗙洊涓紝鏈�42.73%鐨勭儹寮傚父鏁版嵁涓哄伐涓氱儹寮傚父鐐癸紱鍦ㄤ汉閫犲湴琛ㄨ鐩栦腑锛屾湁5.5%鐨勭儹寮傚父涓洪潪宸ヤ笟鐑紓甯告暟鎹�傛晠锛屼笌浠呬粎渚濊禆鍦拌〃瑕嗙洊绫诲瀷璇嗗埆绉哥鐕冪儳鏂规硶鐩告瘮锛岃鏂规硶鑳芥湁鏁堢殑鎻愰珮璇嗗埆绮惧害銆傞偗閮稿競宸ヤ笟鐑紓甯哥偣涓昏涓嬪灚闈负浜洪�犲湴琛ㄧ被鍨嬶紙鍗犳瘮89.87%锛夛紝闈炲伐涓氱儹寮傚父鐐逛笅鍨潰涓昏涓鸿�曞湴绫诲瀷锛堝崰姣�75.93%锛夛紱闈炲伐涓氱儹寮傚父鐐�2013~2017骞存寜鏈堢粺璁″浘鍏锋湁鏄庢樉鐨勨�滃弻宄扳�濈幇璞★紝褰撳勾鐨� 6/10鏈堜唤鍑虹幇杈冨ぇ鐨勫嘲鍊肩偣锛屾绉嶇幇璞¤嚜2018骞磋捣娑堝け銆�

鍏抽敭璇�: 宸ヤ笟鐑簮, 涓诲姩寮忕儹寮傚父鏁版嵁, 闀挎椂搴�, 閭兏

Abstract:

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

Key words: Heavy Industry Heat Source, Active Fire data, Long-term, Handan

涓浘鍒嗙被鍙�:

P407

椹僵铏�,鐜嬪ぇ鎴�,鏉ㄨ繘,鍏崇惓鐞�,鏉庡ぉ鏌�. 鍩轰簬宸ヤ笟鐑簮鍖哄煙璇嗗埆鐨勯偗閮稿競鐑紓甯镐骇鍝佸垎鏋怺J]. 閬ユ劅鎶�鏈笌搴旂敤, 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[J]. Remote Sensing Technology and Application, 2022, 37(1): 34-44.

鍙傝�冩枃鐚�

1	Liu Jianbo锛� Ma Caihong锛� Chen Fu锛� et al.Design and implementation of active-based instant remote sensing data service锛籎锛�.Remote Sensing Information锛� 2016锛� 31锛�8锛夛細 61-67.
1	鍒樺缓娉紝椹僵铏癸紝闄堢敨锛� 绛�. 閬ユ劅鍗槦鏁版嵁瀹炴椂涓诲姩鏈嶅姟绯荤粺璁捐涓庡疄鐜帮蓟J锛�. 閬ユ劅淇℃伅锛� 2016锛� 31锛�8锛夛細 61-67.
2	Chen Xingfeng锛� Liu Li锛� Li Jiaguo锛� et al. Application and research progress of fire monitoring using satellite remote sensing锛籎锛�. Journal of Remote Sensing锛�2020锛�24锛�5锛夛細531-542.
2	闄堝叴宄帮紝鍒樻潕锛� 鏉庡鍥斤紝绛�. 鍗槦閬ユ劅鐏偣鐩戞祴搴旂敤鍜岀爺绌惰繘灞曪蓟J锛�. 閬ユ劅瀛︽姤锛�2020锛�24锛�5锛夛細531-542.
3	Yan Junjie锛� Qu Jianhua锛� Ran Maonong锛� et al. Himawari-8 AHI fire decection in clear sky based on time-phase change锛籎锛�. Journal of Remote Sensing锛�2020锛�24锛�5锛夛細571-577.
3	閯繆娲侊紝鐬垮缓鍗庯紝鍐夎寕鍐滐紝绛�. 鍩轰簬鏃剁浉鍙樺寲鐨勬櫞绌烘潯浠朵笅Himawari-8 AHI鐏偣妫�娴嬶蓟J锛�. 閬ユ劅瀛︽姤锛�2020锛�24锛�5锛夛細571-577.
4	Sun Jiaqi锛� Liu Yongxue锛� Dong Yanzhu锛� et al. Classification of urban industrial heat sources based on Suomi-NPP VIIRS nighttime thermal anomaly products锛� A case study of the Beijing-Tianjin-Hebei region锛籎锛�. Geography and Geographic Information Science锛� 2018锛� 34锛�3锛夛細19-25.
4	瀛欎匠鐞紝鍒樻案瀛︼紝钁ｉ泚浼紝绛�. 鍩轰簬Suomi-NPP VIIRS澶滈棿鐑紓甯镐骇鍝佺殑鍩庡競宸ヤ笟鐑簮鍒嗙被鈥斺�斾互浜触鍐�鍦板尯涓轰緥锛籎锛�. 鍦扮悊涓庡湴鐞嗕俊鎭瀛︼紝 2018锛� 34锛�3锛夛細19-25.
5	Liu Y锛� Hu C锛� Zhan W锛� et al. Identifying industrial heat sources using time-series of the VIIRS nightfire product with an object-oriented approach锛籎锛�. Remote Sensing of Environment锛�2018锛�204锛�347-365. DOI锛�10.1016/j.rse. 2017. 10.019 .
6	Ma C锛� Niu Z锛� Ma Y锛� et al. Assessing the distribution of heavy industrial heat sources in india between 2012 and 2018锛籎锛� International Society for Photogrammetry and Remote Sensing锛�2019锛�8锛�12锛夛細568. DOI锛�10.3390/ijgi8120568 .
7	Ma C锛� Yang J锛� Chen F锛� et al. Assessing heavy industry heat source distribution in China using real-time VIIRS active fire/hotspot data锛籎锛�. Sustainability锛�2018锛�10锛�12锛夛細 4419.DOI锛� 10.3390/su10124419 .
8	Chen Xingfeng锛� Liu Li锛� Li Jiaguo锛� et al. Applications and research of fire monitoring using satellite remote sensing锛籎锛�. Journal of Remote Sensing锛�2020锛�24锛�5锛夛細56-67.
8	闄堝叴宄帮紝鍒樻潕锛� 鏉庡鍥斤紝绛�. 鍗槦閬ユ劅鐏偣鐩戞祴搴旂敤鍜岀爺绌惰繘灞曪蓟J锛�. 閬ユ劅瀛︽姤锛�2020锛�24锛�5锛夛細56-67.
9	Giglio L锛� Schroeder W锛� Justice C O. The collection 6 MODIS active fire detection algorithm and fire products锛籎锛�. Remote Sensing of Environment锛�2016锛�178锛�31-41. DOI锛� 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锛籎锛�. Fire Safety Journal锛�2011锛�3锛�004. DOI锛� 10.1016/B978-0-444-53599-3.10005-8 .
11	Yin Zhenzhen锛� Chen Fang锛� Lin Zhengyang锛� et al. Active fire monitoring based on FY-3D MERSI satellite data锛籎锛�. Remote Sensing Technology and Applications锛� 2020锛� 35锛�5锛夛細 1099-1108.
11	娈烽拡閽堬紝闄堟柟锛� 鏋楁斂闃筹紝绛�. 鍩轰簬FY-3D MERSI鏁版嵁鐨勭伀鐐硅瘑鍒柟娉曠爺绌讹蓟J锛�. 閬ユ劅鎶�鏈笌搴旂敤锛� 2020锛� 35锛�5锛夛細 1099-1108.
12	Schroeder W锛� Oliva P锛� Giglio L锛� et al. Active fire detection using Landsat-8/OLI data锛籎锛�. Remote Sensing of Environment锛�2016锛�185锛�210-220. DOI锛�10.1016/j.rse.2015.08.032 .
13	Trifonov G M锛� Zhizhin M N锛� Melnikov D V锛� et al. VIIRS nightfire remote sensing volcanoes锛籎锛�. Procedia Computer Science锛�2017锛�119锛�307-314. DOI锛�10.1016/j.procs.2017.11.189 .
14	Chen Xingfeng锛� Liu Li锛� Li Jiaguo锛� et al. Application and research progress of fire monitoring using satellite remote sensing锛籎锛�. Journal of Remote Sensing锛�2020锛�24锛�5锛夛細56-67.
14	闄堝叴宄帮紝鍒樻潕锛� 鏉庡鍥斤紝绛�. 鍗槦閬ユ劅鐏偣鐩戞祴搴旂敤鍜岀爺绌惰繘灞曪蓟J锛�. 閬ユ劅瀛︽姤锛� 2020锛�24锛�5锛夛細56-67.
15	Xu Benben锛� Fan Meng锛� Chen Liangfu锛� et al. Analysis of temporal and spatial variations of crop residue burning in china from 2013 to 2017锛籎锛�. Journal of Remote Sensing锛�2020锛�24锛�10锛夛細1221-1232.
15	寰愬濂旓紝鑼冭悓锛� 闄堣壇瀵岋紝绛�.2013骞粹��2017骞翠富瑕佸啘涓氬尯绉哥鐒氱儳鏃剁┖鐗瑰緛鍙婂奖鍝嶅洜绱犲垎鏋愶蓟J锛�.閬ユ劅瀛︽姤锛�2020锛�24锛�10锛夛細1221-1232.
16	Zhang Lijuan锛� Li Qing锛� Chen Hui锛� 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锛籎锛�. Environment and Sustainable Development锛�2016锛�41锛�6锛夛細61-65.
16	寮犱附濞燂紝鍘夐潚锛岄檲杈夛紝绛�. 2014-2015骞村绉嬫敹鏈熼棿鍏ㄥ浗绉哥鐒氱儳閬ユ劅鐩戞祴缁撴灉瀵规瘮鍒嗘瀽锛籎锛�. 鐜涓庡彲鎸佺画鍙戝睍锛�2016锛�41锛�6锛夛細61-65.
17	Sun Shuang锛� Li Lingjun锛� Zhao Wenji锛� et al. Industrial pollution emissions based on thermal anomaly remote sensing monitoring锛� A case study of southern Hebei Urban agglomerations锛孋hina锛籎锛�. China Environmental Science锛� 2019锛�7锛夛細3120-3129.
17	瀛欑埥锛屾潕浠ゅ啗锛岃档鏂囧悏锛岀瓑. 鍩轰簬鐑紓甯搁仴鎰熺殑鍐�鍗楀煄甯傜兢宸ヤ笟鑳借�楀強澶ф皵姹℃煋锛籎锛�. 涓浗鐜绉戝锛�2019锛�7锛夛細3120-3129.
18	Qu Ran锛� Zhang Yaqiong锛� Lou Qijia锛� et al. Analysis of distribution characteristics and impact of industrial enterprises in Suzhou City based on thermal anomaly remote sensing data锛籎锛�. Environmental Ecology锛� 2020锛� 2锛�12锛夛細55-60.
18	灞堝唹锛� 寮犻泤鐞硷紝濞勫惎浣筹紝绛�. 鍩轰簬鐑紓甯搁仴鎰熸暟鎹殑鑻忓窞甯傚伐涓氫紒涓氬垎甯冨彉鍖栫壒寰佸強鍏跺奖鍝嶅垎鏋愶蓟J锛�. 鐜鐢熸�佸锛� 2020锛� 2锛�12锛夛細55-60.
19	Gu Yanchun锛� Meng Qingyan锛� Hu D锛� et al. Analysis of environmental effects of industrial thermal anomalies锛籎锛�. Remote Sensing for Land and Resources锛�2020锛�32锛�4锛夛細190-198.
19	璋疯壋鏄ワ紝瀛熷簡宀╋紝绛�. 宸ヤ笟鐑紓甯哥幆澧冩晥搴斿垎鏋愶蓟J锛�. 鍥藉湡璧勬簮閬ユ劅锛�2020锛�32锛�4锛夛細190-198.
20	Gong P锛� Liu H锛� Zhang M N锛宔t al. Stable classification with limited sample锛� transferring a 30 m resolution sample set collected in 2015 To mapping 10 m resolution global land cover in 2017锛籎锛�. Science Bulletin锛�2019锛�64锛�6锛夛細370-373.
21	I-Band VIIRS 375 m Active Fire Data锛籇B/OL锛�. Available online 锛團IRMS锛夛細 .
22	Wang Yanlin锛� Li Na锛� Wu Lifeng. Research on air quality prediction in heavy polluted days in Handan锛籎锛�. Journal of Hebei Engineering University 锛圢atural Science Edition锛夛紝 2020锛�37锛�1锛夛細91-97.
22	鐜嬪溅鏋楋紝鏉庡锛屽惔鍒╀赴.閭兏閲嶆薄鏌撴棩绌烘皵璐ㄩ噺棰勬祴鐮旂┒锛籎锛�. 娌冲寳宸ョ▼澶у瀛︽姤锛堣嚜鐒剁瀛︾増锛夛紝 2020锛�37锛�1锛夛細91-97.
23	Duan Wenjiao锛� Zhou Ying锛� Li Jifeng锛� et al. PM_2.5 pollution characteristics and source apportionment in Handan Urban Area锛籎锛�. China Environmental Science锛�2019锛�39锛�10锛夛細4108-4116.
23	娈垫枃濞囷紝鍛ㄩ锛� 鏉庣邯宄帮紝绛�. 閭兏甯傚尯PM_锛�2.5锛夋薄鏌撶壒寰佸強鏉ユ簮瑙ｆ瀽锛籎锛�. 涓浗鐜绉戝锛� 2019锛� 39锛�10锛夛細 4108-4116.

[1]	鏉庡崥,鑼冧繆鐢�,闊╃暀鐢�,鍛ㄧ帀绉�,寮犲ぇ瀵�. 鍩轰簬VIIRS Nightfire鏁版嵁鐨勫北涓滅渷宸ヤ笟鐑簮鍒嗙被鎻愬彇涓庡彉鍖栫壒寰佸垎鏋�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(4): 919-928.
[2]	鏉庝匠浣�,鏉熻繘鑺�,瑁樺娆�,绗﹀唹杩�,閲戠倻. 闈㈠悜婊ㄦ捣婀垮湴鐨勫叏鑹�/澶氬厜璋卞奖鍍忚瀺鍚堟柟娉曚笌搴旂敤鍒嗘瀽鈥斺�斾互鏉窞婀撅紙1999~2018骞达級涓轰緥[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2021, 36(3): 627-637.

鍩轰簬宸ヤ笟鐑簮鍖哄煙璇嗗埆鐨勯偗閮稿競鐑紓甯镐骇鍝佸垎鏋�

Assessing the Distribution of Active Fire data based on Large Industrial Heat Sources Detection in Handan

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