矿业开发密集区地表热环境分异效应遥感评价模型构建研究
投稿时间:2019-11-17  修订日期:2020-08-19  点此下载全文
引用本文:侯春华,何宝杰,宋文,李富平,谷海红.矿业开发密集区地表热环境分异效应遥感评价模型构建研究[J].红外与毫米波学报,2020,39(5):637~651].Hou Chunhua,He Baojie,Song Wen,Li Fuping,Gu Haihong.Study on Surface Thermal Environment Differentiation Effect in Mining Intensive Area through Developing Remote Sensing Assessment Model[J].J.Infrared Millim.Waves,2020,39(5):637~651.]
摘要点击次数: 13
全文下载次数: 0
作者单位E-mail
侯春华 华北理工大学矿业工程学院 houchunhua5288@163.com 
何宝杰 澳大利亚新南威尔士大学建筑环境学院  
宋文 华北理工大学矿业工程学院  
李富平 华北理工大学矿业工程学院 lhxtsg@stu.ncst.edu.cn 
谷海红 华北理工大学矿业工程学院  
基金项目:河北省自然科学基金-钢铁联合基金项目 E2015209300;河北省高等学校青年拔尖人才计划项目 BJ2014029;河北省引进留学人员资助项目 CL201633;唐山市科学技术研究与发展计划重点项目 19150247E;唐山市科技创新团队培养计划项目 19130206C河北省自然科学基金-钢铁联合基金项目(E2015209300),河北省高等学校青年拔尖人才计划项目(BJ2014029),河北省引进留学人员资助项目(CL201633),唐山市科学技术研究与发展计划重点项目(19150247E),唐山市科技创新团队培养计划项目(19130206C)
中文摘要:矿产资源的开发推动了社会经济的迅速发展,但同时也使矿区成为地表高温聚集区,对生态环境带来不利影响。本文基于2000-2018年研究区的Landsat卫星遥感影像,利用辐射传输方程法(Radiative Transfer Equation,RTE)反演地表温度(Land Surface Temperature,LST);基于NDVI-DFI像元三分模型反演植被覆盖度(Vegetation Fractional Coverage,VFC);借助回归分析法定量分析4个陆表生物物理指标(光合植被覆盖度(Fractional Cover of Photosynthetic Vegetation,fPV),土壤湿度(Normalized Difference Moisture Index,NDMI),建筑指数(Normalized Difference Build-Up Index,NDBI),裸土指数(bare soil index,BSI))对地表温度的驱动机制;利用主成分分析方法(Principal Component Analysis,PCA)耦合以上4个生态参数,提出一种能够综合分析矿业开发密集区地表热环境分异效应的遥感综合生态模型(Remote Sensing Integrated Ecological Index,RSIEI),利用时空分析法定量化和可视化分析矿业开发密集区地表热环境时空分异的影响机理;借助热场变异指数(Heat Index,HI)分析研究区地表热环境分异效应与生态环境质量之间的关系。结果表明:4个生态参数对地表热环境分异效应具有不同的驱动作用,定量回归分析表明,fPV和NDMI与LST均呈线性负相关关系,并通过了p<0.01的显著性检验,说明光合植被覆盖度和土壤湿度的增加,对地表均具有降温效应;NDBI和BSI与LST均呈线性正相关关系,并通过了p<0.01的显著性检验,说明建筑用地和裸土面积的增加,对地表起升温效应。4个镇域矿业开发密集区RSIEI影像与LST影像的空间光谱分布特征表明,二者具有空间逆关联特点,即RSIEI值高(生态环境质量好)的像元对应于LST值低的像元,反之亦然。对4个矿业开发密集区3个年份的RSIEI与LST的定量回归分析表明,RSIEI的值每上升10%,LST的值相应下降0.67–0.77°C。经验证基于主成分分析方法建立的RSIEI模型适用于矿业开发密集区地表热环境分异效应的综合评估。
中文关键词:遥感  地表温度  生物物理指标  矿业开发密集区  RSIEI模型
 
Study on Surface Thermal Environment Differentiation Effect in Mining Intensive Area through Developing Remote Sensing Assessment Model
Abstract:The exploitation of mineral resources has promoted rapid economic growth, but it has also caused mining areas to have increased surface thermal flux, which has a negative impact on the ecological environment. In this study, using on Landsat satellite remote sensing images of the study area from 2000 to 2018, the radiative transfer equation method was used to invert Land Surface Temperature (LST). VFC in the study area was inverted based on the Normalized Difference Vegetation Index (NDVI)-Dry Fuel Index (DFI) three-component pixel model. Mixed pixels were decomposed into Photosynthetic Vegetation (PV), Non-Photosynthetic Vegetation (NPV), and Bare Soil (BS). Based on the four ecological parameters, Factional Cover of Photosynthetic Vegetation (fPV), Normalized Difference Moisture Index (NDMI), Normalized Difference Built-up Index (NDBI), and Bare Soil Index (BSI), a remote sensing integrated ecological index (RSIEI) model which can comprehensively evaluate the differentiation effect of the surface thermal environment in mining intensive areas is proposed using Principal Component Analysis (PCA). The relationship between the differentiation effect of the surface thermal environment and the quality of the ecological environment was studied using the heat island variation index. The results showed that the NDVI-DFI feature space of the study area conforms to the basic assumption of the three-component pixel model. And the four ecological parameters are closely related to the differentiation effect of the surface thermal environment. From the regression equation of the four ecological parameters and LST in study area over three years, it can be seen that fPV and NDMI has a significant linear negative correlation with LST (p<0.01); NDBI and BSI have a significant linear positive correlation with LST (p<0.01). The spatial distribution of normalized RSIEI images and normalized LST images of study area showed an inverse spatial correlation, i.e., the areas with high RSIEI (good ecological quality) in the study area correspond to the areas with low LST and vice versa. The quantitative regression analysis of RSIEI and LST in 3 years in 4 mining intensive areas shows that, when RSIEI is increased by 10%, LST was decreased by 0.67–0.77°C. It is proved that the RSIEI model based on Principal Component Analysis (PCA) is suitable for the comprehensive evaluation of the surface thermal environment differentiation effect in mining intensive areas.
keywords:Remote sensing  Land surface temperature  Biophysical parameters  Mining intensive area  RSIEI model
查看全文  HTML  查看/发表评论  下载PDF阅读器

版权所有:《红外与毫米波学报》编辑部