Method Development for device-level industrial heat source identification using Medium and High Resolution Satellite Images

SUN Shuang; JIANG Lei; LIU Baoxian; LU Haifeng; REN Huazhong; WANG Xinhui; LI Jinxiang

doi:10.11834/jrs.20232486

Views : 0 下载量: 253 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Method Development for device-level industrial heat source identification using Medium and High Resolution Satellite Images
Pages: 1-12(2023)
Published Online： 14 March 2023 ，
DOI： 10.11834/jrs.20232486

扫描看全文

孙爽，姜磊，刘保献，鹿海峰，任华忠，王新辉，李金香.XXXX.基于中高分辨率卫星影像的装置级别工业热源识别方法研究.遥感学报，XX（XX）： 1-12

SUN Shuang，JIANG Lei，LIU Baoxian，LU Haifeng，REN Huazhong，WANG Xinhui，LI Jinxiang. XXXX. Method Development for device-level industrial heat source identification using Medium and High Resolution Satellite Images. National Remote Sensing Bulletin， XX（XX）：1-12
孙爽，姜磊，刘保献，鹿海峰，任华忠，王新辉，李金香.XXXX.基于中高分辨率卫星影像的装置级别工业热源识别方法研究.遥感学报，XX（XX）： 1-12 DOI： 10.11834/jrs.20232486.

SUN Shuang，JIANG Lei，LIU Baoxian，LU Haifeng，REN Huazhong，WANG Xinhui，LI Jinxiang. XXXX. Method Development for device-level industrial heat source identification using Medium and High Resolution Satellite Images. National Remote Sensing Bulletin， XX（XX）：1-12 DOI： 10.11834/jrs.20232486.

摘要

针对精细化工业热源遥感监测困难与误差较大等问题，本文聚焦单个规模较大的工厂内部热场分布情况，提出一种基于中高分辨率卫星影像（Landsat -8）的装置级别工业热源的多层级识别方法。该方法基于双通道非线性劈窗算法反演的地表温度，利用多种空间统计分析方法分别对单期影像开展热源装置范围的识别检测，筛选出识别位置准确、识别能力稳定的方法，开展多时相热源装置识别分析，确定厂区范围内稳定出现的热源装置的位置，并结合高分辨率卫星影像进一步确定热源装置的边界范围。在某石油化工企业厂区的实验研究结果如下：5种方法均具有识别热源装置的能力。其中，温度1.5倍标准差分级与焦点统计分析方法适合捕捉厂区内最主要的热源装置，冷热点分析、温度1倍标准差分级方法以及聚类与异常值分析方法更适用于识别厂区内一般的热源装置。聚类与异常值分析方法识别结果正确检出率在80%以上，整体遗漏率低于10%，且各期识别结果受季节变化影响较小，更适用于识别装置级别（主要包括生产装置、循环水、锅炉装置、储存库、火炬装置以及罐区）的热源对象。识别的热源装置中，生产装置占比约55%，是厂区主要的热源装置。研究结果表明，中高分辨率卫星遥感影像数据源可以有效监测工业企业的精细尺度发热单元，为环境保护与管理、工业去产能监测提供技术支撑。

Abstract

Objective In view of the problems of insufficient refinement and large errors in the existing remote sensing monitoring of industrial heat sources

this paper focuses on the internal thermal field distribution of a single large-scale factory

and proposes a device-level industrial heat source identification method based on medium and high-resolution satellite images. .Method That is

the surface temperature is first obtained based on the dual-channel nonlinear split-window algorithm

and then a variety of spatial statistical analysis methods are used to identify candidate high-temperature areas

and the location of high-temperature devices in the plant is determined by multi-temporal superposition analysis of the identification results

and then combined with high-resolution satellite images. Determine the range of the high temperature device

compare and analyze the difference in the accuracy of the recognition results of several methods

and determine the method with the best recognition effect.Results (1) The study shows that each method has the ability to identify heat source devices. The boundary of the standard deviation grading of temperature 1.5x and the focal statistical analysis method is clearer

and the accuracy rate and fall rate of the cell point are more than 85%

but the omission rate of the general heat source device is also high

more than 50%

so it is suitable for capturing the most important heat source device in the factory area; The identification results of the cold and hot spot analysis method are relatively stable

but the range is larger than that of the actual heat source device

and the non-falling rate is 23%

and the result is more misidentified pixels

but more heat source devices can be captured. The identification results of the temperature 1 standard deviation grading method and the clustering and outlier analysis method are close

the accuracy rate is about 82%

and the omission rate is within 10%

which is more suitable for identifying heat source devices in the plant area.(2) Comprehensive analysis of the identification effect of the five methods

cluster and outlier analysis methods have good identification ability

the correct detection rate of identification results is more than 80%

the overall omission rate is less than 10%

and the identification results of each period can be obtained This method is less affected by seasonal changes

and is more suitable as a method for identifying general heat source devices in the factory.(3) Judging by the characteristics of high-resolution satellite images

the heat source device mainly includes production device

circulating water

boiler device

storage warehouse

flare device and tank farm

of which the production device accounts for about 55%

which is the main heat source device in the factory. And the difference between the average temperature of each type of device and the factory area shows that the production equipment is also the main high temperature area.Conclution The results show that the medium and high resolution satellite remote sensing image data sources can effectively monitor the fine-scale heating units of industrial enterprises

and provide technical support for environmental protection and managemen and provide technical support for industrial overcapacity reduction monitoring.

关键词

工业热源装置高温单元识别地表温度空间统计分析

Keywords

Industrial heat source deviceHigh temperature unit identificationSurface temperatureThermal infrared remote sensing

references

Chen S L,Wang T X. 2009. Comparison analyses of equal interval method and mean-standard deviation method used to delimitate urban heat island Journal of geo-information science, 11(2): 145-150.(陈松林, 王天星. 2009. 等间距法和均值标准差法界定城市热岛的对比研究. 地球信息科学学报, 11(02), 145-150) [http://www.dqxxkx.cn/CN/Y2009/V11/I2/145http://www.dqxxkx.cn/CN/Y2009/V11/I2/145]

Du C., Ren H Z., Qin Q M., Meng J J, and Zhao S H. 2015. A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data. Remote Sensing, 7(1): 647-665 [DOI:10.3390/rs70100647http://dx.doi.org/10.3390/rs70100647].

Franklin M, Chau K, Cushing L J, and Johnston J E. 2019. Characterizing flaring from unconventional oil and gas operations in south texas using satellite observations. Environmental Science and Technology, 53(4): 2220-2228. [DOI:10.1021/acs.est.8b05355].

Gu Y C, Meng Q Y, Hu D, Zhou X C. 2020.Analysis of environmental effects of industrial thermal anomalies.Remote Sensing for Natural Resources, 32(04): 190-198.

谷艳春, 孟庆岩, 胡蝶, 周小成. 2020. 工业热异常环境效应分析. 国土资源遥感, 32(04), 190-198[DOI: 10.6046/gtzyyg.2020.04.24http://dx.doi.org/10.6046/gtzyyg.2020.04.24].

Gu Y C, Meng Q Y, Hu D. 2021. An adaptive industrial thermal anomaly detection method. Journal of Guizhou University(Natural Sciences). 38(05), 54-63.

谷艳春, 孟庆岩, 胡蝶. 2021. 一种自适应的工业热异常检测方法. 贵州大学学报(自然科学版), 38(05), 54-63 [DOI: 10.15958 /j.cnki.gdxbzrb.2021.05.10http://dx.doi.org/10.15958/j.cnki.gdxbzrb.2021.05.10].

Hao L C, Meng Q Y, Ge X S, Zhang Y, Hu D, Zhang L L,Tang. Z X. 2020. Extraction method of industrial heat pollution area based on octave method. Remote Sensing Technology and Application, 35(2): 469-477.

郝丽春, 孟庆岩, 葛小三, 张颖, 胡蝶, 张琳琳, 唐子欣. 2020. 一种基于八分位法的工业热污染区提取方法. 遥感技术与应用, 35(02), 469-477 [DOI: 10.11873/j.issn.1004-0323.2020.2.046http://dx.doi.org/10.11873/j.issn.1004-0323.2020.2.046].

He X H, Zhai W M, Guo H L, Tian Z H. 2022. Study on population exposure risk to AOD in China based on multitemporalre‐mote sensing data. Journal of Henan Polytechnic University（Natural Science）.41（5）:68-76.

赫晓慧,翟伟民,郭恒亮,田智慧. 2022. 基于多时相遥感数据的中国AOD人口暴露风险研究. 河南理工大学学报(自然科学版), 41(05), 68-76. [DOI:10.16186/j.cnki.1673-9787.2020100009].

Hu Y C, Tao F, Zhou T, Yan J W, Liu R R.2021. Urban heat island assessment method integrated by multi-source remote sensing data. Remote Sensing Information. 36(01), 61-68.

胡宇宸, 陶菲, 周侗, 闫金伟, 刘润瑞. 2021. 融合多源遥感数据的城市热岛评估方法. 遥感信息, 36(01), 61-68 [DOI: 10.3969/j.issn.1000-3177.2021.01.10http://dx.doi.org/10.3969/j.issn.1000-3177.2021.01.10].

Hyndman R J, Fan Y A. 1996. Sample quantiles in statistical packages. The American Statistician, 50(4), 361-365 [DOI: 10.1080/00031305.1996.10473566http://dx.doi.org/10.1080/00031305.1996.10473566].

Li J, Sun Q Q, Zhang P, Sun D F, Wen L, Li X W. 2019. A study of auxiliary monitoring in iron and steel plant based on multi-temporal thermal infrared remote sensing.Remote Sensing for Natural Resources, 31(01): 220-228.

李静, 孙强强, 张平, 孙丹峰, 温礼, 李宪文. 2019. 基于多时相热红外遥感的钢铁企业生产状态辅助监测. 国土资源遥感, 31(01), 220-228 [DOI:10.6046/gtzyyg.2019.01.29http://dx.doi.org/10.6046/gtzyyg.2019.01.29].

Li X. 2019. Spatial-temporal pattern change and ecological security assessment of thermal environment in Hangzhou. Chengdu :Chengdu University of Technology.

李雪. 2019. 杭州市热环境的时空格局变化与生态安全评价. 硕士, 成都理工大学. [DOI:10.26986/d.cnki.gcdlc.2019.000578http://dx.doi.org/10.26986/d.cnki.gcdlc.2019.000578].

Liu Y X, Hu C M, Zhan W F, Sun C, Murch B, and Ma L. 2018. Identifying industrial heat sources using time-series of the VIIRS Nightfire product with an object-oriented approach. Remote Sensing of Environment, 204,47-365 [DOI: 10.1016/j.rse.2017.10.019http://dx.doi.org/10.1016/j.rse.2017.10.019].

Luc A. 1995. Indicators of spatial association—LISA. Geographical Analysis, 27(2), 93-115 [DOI: 10.1111/j.1538-4632.1995.tb00338.xhttp://dx.doi.org/10.1111/j.1538-4632.1995.tb00338.x].

Meng Q Y, Hu D, Zhang Y, Chen X, Zhang L L, Wang Z A. 2022.Do industrial parks generate intra-heat island effects in cities? New evidence, quantitative methods, and contributing factors from a spatiotemporal analysis of top steel plants in China. Environmental Pollution, 292, Part B, 118383. [DOI: 10.1016/j.envpol.2021.118383].

Ord J K, and Getis A. 1995. Local Spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4), 286-306 [DOI: 10.1111/j.1538-4632.1995.tb00912.xhttp://dx.doi.org/10.1111/j.1538-4632.1995.tb00912.x].

Peng Y J, He W T, Zheng Z G, Pan P J, Ju Y, Lu Z W, Liao Y Y, Wang H L, Zhang C, Wang J, Jiang L N, Yang Y Q, Liang H, Chen M M, Ye L.2022 Spatial epidemiological analysis of severe hand, foot and mouth disease in Guangxi, 2014- 2018. China Tropical Medicine 1-15[2023-02-03].

彭远军,何为涛,郑志刚,潘沛江,居昱,卢振伟,廖艳研,王海龙,张超,王晶,蒋丽娜,杨雅倩,梁浩,陈敏玫,叶力．2022. 广西2014—2018 年重症手足口病空间流行特征．中国热带医学, 1-15[2023-02-03] http://kns.cnki.net/kcms/detail/46.1064.R.20221102.1834.006.htmlhttp://kns.cnki.net/kcms/detail/46.1064.R.20221102.1834.006.html

Ren H Z, Du C, Liu R Y, Qin Q M, Yan G J, Li Z L, and Meng J J. 2015. Atmospheric water vapor retrieval from landsat 8 thermal infrared images. Journal of Geophysical Research Atmospheres, 120(5), 1723-1738. [DOI:10.1002/2014jd022619http://dx.doi.org/10.1002/2014jd022619]

Sun S, Li L J, Wu Z H, Gautam A, Li J X, and Zhao W J. 2020. Variation of industrial air pollution emissions based on VIIRS thermal anomaly data. Atmospheric Research, 244, 105021 [DOI: 10.1016/j.atmosres.2020.105021].

Wan Z M. 2014. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sensing of Environment, 140, 36-45. [DOI: 10.1016/j.rse.2013.08.027].

Wang T X. 2008. Quantitative estimation of land surface parameters and their application in urban thermal environment studies. Fuzhou: Fujian Normal University.

王天星. 2008. 地表参数遥感定量反演及其在城市热环境研究中的应用. 硕士, 福建师范大学.

Yu C, Hu D Y, Cao S S, Zhang Y, Zhang Y N, Duan X. 2019. The spatial characteristics and changes of ISP-LST of Beijing in recent 30 years. Geographical Research. 38(9): 2346-2356.

于琛, 胡德勇, 曹诗颂, 张旸, 张亚妮, 段欣. 2019. 近30年北京市ISP-LST空间特征及其变化. 地理研究, 38(09), 2346-2356 [DOI: 10.11821/dlyj020180621http://dx.doi.org/10.11821/dlyj020180621].

Zhang C J, Zhang Z Y. 2017. Agglomeration effect and radiation effect of provincial carbon emission intensity in China. Acta Sctentiae Circumstantiae. 37(3): 1178-1184.

张翠菊,张宗益．2017．中国省域碳排放强度的集聚效应和辐射效应研究．环境科学学报,37(3), 1178-1184 [DOI:10.13671/j.hjkxxb.2016.0254http://dx.doi.org/10.13671/j.hjkxxb.2016.0254].

Zhang L L, Meng Q Y, Sun Z H, Sun Y X. 2017. Spatial and Temporal Analysis of the Mitigating Effects of Industrial Relocation on the Surface Urban Heat Island over China. ISPRS International Journal of Geo-Information, 6(4):121. [DOI: 10.3390/ijgi6040121].

Zhang P, Yuan C C, Sun Q Q, Liu A X, You S C, Li X W, Zhang Y P, Jiao X, Sun D F, Sun M X, Liu M, and Lun F. 2019. Satellite-based detection and characterization of industrial heat sources in China. [Journal Article]. Environment Science Technology, 53(18), 11031-11042. [DOI: 10.1021/acs.est.9b02643].

Zhao F. 2019.Research on a production capacity monitoring method for iron and steel plants based on remote sensing. Beijing: Aerospace Information Research Institute, Chinese Academy of Science.

赵菲. 2019. 钢铁企业产能状况遥感监测方法研究. 博士,中国科学院大学(中国科学院空天信息创新研究院).[DOI:10.44231/d.cnki.gktxc.2019.000008http://dx.doi.org/10.44231/d.cnki.gktxc.2019.000008].

Alert me when the article has been cited

提交

暂无数据