摘要
工业热源活动的精细提取是我国大气污染防治和工业经济预测的重要前提,然而因热源特征不明、类型判定不准等问题,导致工业热源遥感监测目前难以大范围推广应用。该文选取湖南省作为研究区,基于2015—2021年Suomi-NPP VIIRS夜火数据,结合空间滤波与具有噪声的基于分层密度空间聚类(hierarchical density-based spatial clustering of applications with noise,HDBSCAN)算法提取夜间工业热源,利用高斯混合模型构建不同类别的工业热源温度特征模板函数,根据相同类别的温度相似性判断工业热源子类别,总体分类精度达86.31%。并分析湖南省工业热源布局情况,结果表明:①全省工业以石化厂为主,煤化厂数量最少;②冶金企业的热辐射强度最大,集中在娄底市-湘潭市-株洲市一带;③2015—2019年间,全省工业热源数量呈减少趋势,说明政府部门在“十三五”期间对省内“散乱污”工厂进行有效整治,新冠疫情期间热源数量整体变化不大,在政府有效调控下已逐步复工复产;④通过分析热辐射排放强度与能源消耗量、工业污染物排放的相关指标的灰色关联度,以工业综合能源消费量、工业二氧化硫排放量和热辐射排放强度为标准,探究湖南省“能耗-污染-热排”的相关情况,并将各市州划分为7种类型。研究可为地方政府动态监测当地重点工业企业的生产活动状况提供信息来源与数据支持,摸清该地区不同产业类型的工业企业空间分布格局及演变趋势,有利于政府以及相关部门制定产业转型政策,践行可持续发展理念。
Precisely extracting the information of industrial heat source activities serves as a significant prerequisite for the prevention and control of air pollution and the prediction of industrial economy in China.However,due to unclear heat source characteristics and inaccurate type determination,the remote sensing monitoring of industrial heat sources fails to be widely applied.This study investigated Hunan Province based on the Suomi-NPP VIIRS Nightfire data from 2015 to 2021.First,this study extracted nighttime industrial heat sources from the data using spatial filtering and the hierarchical density-based spatial clustering of applications with noise(HDBSCAN)algorithm.Second,this study constructed temperature characteristic template functions for different industrial heat sources using the Gaussian mixture model.Third,this study determined the subcategories of industrial heat sources according to temperature similarity of the same categories,achieving an overall classification accuracy of 86.31%.Finally,this study obtained the layout of industrial heat sources in Hunan Province.The results indicate that the industry in Hunan Province was dominated by petrochemical plants,with the smallest number of coal-to-chemical plants.Metallurgical enterprises,showing the highest heat radiation intensity,were primarily distributed in the Loudi-Xiangtan-Zhuzhou area.From 2015 to 2019,industrial heat sources in Hunan Province showed a decreasing trend,indicating that relevant government departments effectively rectified the scattered,non-compliant,and polluting factories in Hunan Province during the 13th Five-Year Plan period.During the COVID-19 pandemic,the number of heat sources changed slightly since work and production were gradually resumed under the effective regulation of the government.This study analyzed the grey relational degrees between the heat radiation emission intensity and the relevant indicators of energy consumption and industrial pollutant emissions.Based on the comprehensive industrial energy consumption,industrial sulfur dioxide emissions,and heat radiation emission intensity,this study explored the relevant situation of energy consumption,pollution,and heat emissions in Hunan Province,dividing the cities and prefectures into seven types accordingly.Overall,this study provides information sources and data support for local governments to dynamically monitor the production activities of local key industrial enterprises.Ascertaining the spatial distribution patterns and evolutionary trends of different industrial enterprises will contribute to the formulation of industrial transformation policies by the government and relevant departments and the practice of sustainable development.
作者
李乐林
王文曦
杨文涛
陈浩
彭焕华
赵茜
LI Lelin;WANG Wenxi;YANG Wentao;CHEN Hao;PENG Huanhua;ZHAO Qian(National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology,Hunan University of Science and Technology,Xiangtan 411201,China;Institute for Local Sustainable Development Goals,Hunan University of Science and Technology,Xiangtan 411201,China;School of Earth Sciences and Spatial Information Engineering,Hunan University of Science and Technology,Xiangtan 411201,China)
出处
《自然资源遥感》
北大核心
2025年第4期173-183,共11页
Remote Sensing for Natural Resources
基金
国家科技基础资源调查专项项目“全球地表覆盖时空变化数字地图编研”(编号:2019FY202502)
湖南省自然科学基金项目“后扶贫时代基于多源遥感与深度学习的乡村防返贫动态监测关键技术研究”(编号:2023JJ30232)
国家级大学生创新训练计划项目“面向乡村振兴的融合多源遥感数据返贫监测研究”(编号:202210534038)共同资助。
关键词
Suomi-NPP
VIIRS夜火数据
工业热源
温度特征
高斯混合模型
遥感识别
Suomi-NPP VIIRS Nightfire data
industrial heat source
temperature characteristics
Gaussian mixture model
remote sensing identification
