矿业密集区地表热环境时空异质性驱动机理

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摘要基于Landsat遥感影像热红外波段数据，利用大气校正法反演地表水热因子中的地表温度值，基于Landsat遥感影像可见光波段，从生物地球物理效应角度提取下垫面地表扰动类型和4个生物物理参数（光合植被覆盖度，Fractional Cover of Photosynthetic Vegetation，f_PV；土壤湿度监测指数，Soil Moisture Monitoring Index，SMMI；增强型裸土指数，Enhanced Bare Soil Index，EBSI；归一化不透水面指数，Normalized Difference Impervious Surface Index，NDISI），借助叠加分析、相关分析和回归分析，定量分析了河北省迁安市马兰庄镇地表热环境时空异质性特征并可视化，以及4个生物地球物理参数的响应规律.结果表明：矿业用地地表温度最高，属热岛效应聚集区；地表下垫面扰动类型以及4个驱动因子的时空变化，导致研究区夏季午间地表热环境异质性较大，且5期影像下垫面地表温度均呈现出工矿用地>居民地>耕地>林地>水域的分异特征；单因素回归分析表明，f_PV和SMMI指数与归一化地表温度（Normalized Land Surface Temperature，NLST）呈线性负相关（P<0.01），EBSI和NDISI与NLST呈线性正相关（P<0.01）；多元回归分析表明，利用4个生物物理参数综合考量地表热环境异质性，可全面反映下垫面生物物理参数与地表温度的真实关系，且5期影像与LST呈负相关关系的SMMI回归系数均大于f_PV，说明地表温度的下降与土壤湿度的增加有明显的关系；与LST呈正相关关系的EBSI回归系数均大于NDISI，说明地表温度的上升与裸土的增加有明显的关系.研究结果可为矿业密集区地表热环境异质性的评估和优化提供定量参考.

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关键词 ：遥感, 地表温度, 生物物理参数, 时空异质性, 矿业密集区

Abstract：Based on the Landsat thermal imagery, land surface temperature (LST) of the mining-intensive areas in Manlanzhuang, Qian'an, Hebei Province, China, was retrieved using atmospheric correction method. Meanwhile, the land surface disturbance type and four surface biophysical parameters including Fraction of Photosynthetic Vegetation (f_PV), Soil Moisture Monitoring Index (SMMI), Enhanced Bare Soil Index (EBSI) and Normalized Difference Impervious Surface Index (NDISI) were analysed from the perspective of biogeophysical effects. Afterwards, the spatiotemporal heterogeneity of surface thermal environment was quantified and visualised by overlay analysis. To uncover the driving mechanism behind such spatiotemporal heterogeneities, the relationship between the four biophysical parameters and LST was assessed by correlation and regression analysis. The results show that the mining land had the highest LST, characterised as the severest heat island cluster. Surface disturbance types and four surface biophysical parameters drove the spatiotemporal heterogeneity of surface thermal environment, where the LST followed the order of mining land> residential land> cultivated land> forest land> water area. The single factor regression analysis indicates that f_PV and SMMI had a significant negative linear relationship with the normalised LST (NLST), while EBSI and NDISI had a significant positive linear relationship with NLST. The multivariate regression analysis indicates that using the four biophysical parameters could holistically characterize spatiotemporal heterogeneity of surface thermal environment and better present the actual relationships between biophysical parameters and NLST. The regression coefficient of SMMI was larger than that of f_PV, indicating surface moisture content had a stronger effect on surface temperature reduction. The regression coefficient of EBSI was larger than that of NDISI, indicating bare soil contributed more to surface warming. The findings of this study will provide a quantitative reference for the assessment and optimisation of surface thermal environment in mining-intensive regions.

Key words： remote sensing land surface temperature biophysical parameters spatiotemporal heterogeneity intensive mining areas

收稿日期: 2020-07-14

PACS:

X87

基金资助:河北省重点研发计划项目（19224204D）；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）；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）；唐山市科技研发平台培养计划（2020TS003b）

通讯作者: 李富平,教授,18603158003@163.com E-mail: 18603158003@163.com

作者简介: 侯春华(1981-),女,河北唐山人,华北理工大学博士研究生,主要研究方向为矿区生态恢复与重建.发表论文10余篇.

引用本文:

侯春华, 李富平, 谷海红, 何宝杰, 马朋坤, 宋文. 矿业密集区地表热环境时空异质性驱动机理[J]. 中国环境科学, 2021, 41(2): 872-882. HOU Chun-Hua, LI Fu-Ping, GU Hai-Hong, He Bao-Jie, MA Peng-Kun, SONG Wen. The spatiotemporal heterogeneity and driving forces of surfacial thermal environment over an intensive mining region. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(2): 872-882.

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[1]	Li M S. Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China:A review of research and practice[J]. Science of the Total Environment, 2006,357:38-53.
[2]	Wan Z M. New refinements and validation of the MODIS land surface temperature/emissivity products[J]. Remote Sensing of Environment, 2008,112(1):59-74.
[3]	Zhao Z Q, He B J, Li L G, et al. Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature:Case study of Shenyang, China[J]. Energy and Buildings, 2017,155:282-295.
[4]	雷金睿,陈宗铸,吴庭天,等.1989~2015年海口城市热环境与景观格局的时空演变及其相互关系[J]. 中国环境科学, 2019,39(4):1734-1743.Lei J R, Chen Z Z, Wu T T, et al. Spatio-temporal evolution and interrelationship between thermal environment and landscape patterns of Haikou City, 1989~2015[J]. China Environmental Science, 2019, 39(4):1734-1743.
[5]	李富平,袁怀雨.矿业开发密集地区景观生态重建潜力评价[J]. 金属矿山, 2004,1:54-57.Li F P, Yuan H Y. 2004. Potential assessment of landscape ecological rehabilitation in mining concentrated District[J]. Chinese Journal of Metal Mine, 2004,1:54-57.
[6]	孙铁钢,肖荣波,蔡云楠,等.城市热环境定量评价技术研究进展及发展趋势[J]. 应用生态学报, 2016,27(8):2717-2728.Sun T G, Xiao R B, Cai Y N, et al. Research progress and development trend of quantitative assessment techniques for urban thermal environment[J]. Chinese Journal of Applied Ecology, 2016,27(8):2717-2728.
[7]	康悦,文军,张堂堂,等.卫星遥感数据评估黄土高原陆面干湿程度研究[J]. 地球物理学报, 2014,57(8):2473-2483.Kang Y, Wen J, Zhang T T, et al. Assessment of the land surface wetness by using satellite remote sensing data over the Loess Plateau[J]. Chinese J. Geophys, 2014,57(8):2473-2483.
[8]	刘英,岳辉.基于矿井尺度的神东矿区地表温度分布研究[J]. 煤炭工程, 2018,50(7):101-106.Liu Y, Yue H. Study on surface temperature distribution in Shendong Mining Area based on mine scale[J]. Coal Engineering, 2018,50(7):101-106.
[9]	朱星,吴志杰,张维伟,等.永定矿区植被覆盖度变化及其热环境效应研究[J]. 闽西职业技术学院学报, 2016,18(4):110-116.Zhu X, Wu Z J, Zhang W W, et al. Analysis on the change of fractional vegetation cover and its thermal environmental effects in Yongding mining area[J]. Journal of Minxi Vocational and Technical College, 2016,18(4):110-116.
[10]	Liu K, Su H B, Li X K. Comparative assessment of two vegetation fractional cover estimating methods and their impacts on modeling urban latent heat flux using landsat imagery[J]. Remote Sensing, 2017,9(5):455.
[11]	Duncan J M A, Boruff B, Saunders A, et al. Turning down the heat:An enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale[J]. Science of the Total Environment, 2019,656:118-128.
[12]	Cai Y B, Zhang H, Zheng P, et al. Quantifying the impact of land use/land cover changes on the urban heat island:a case study of the natural wetlands distribution area of Fuzhou city, China[J]. Wetlands, 2016,36(2):285-298.
[13]	梁萍萍,刘绥华,贺中华,等.基于地表裸露度和地表反照率影响的喀斯特地区热环境变化分析[J]. 科学技术与工程, 2020,20(6):2376-2383.Liang P P, Liu S H, He Z H, et al. 2020. Analysis of thermal environment changes in karst area based on exposed bare soils and albedo[J]. Science Technology and Engineering, 2020,20(6):2376-2383.
[14]	曹琦,师满江,周亮,等.山地城市裸地时空变化的热环境响应特征研究[J]. 国土资源遥感, 2019,31(4):190-198.Cao Q, Shi M J, Zhou L, et al. Study of the response characteristics of thermal environment with spatial and temporal changes of bare land in the mountain city[J]. Remote Sensing for Land and Resources, 2019, 31(4):190-198.
[15]	李富平.矿业开发密集程度的模糊聚类分析[J]. 化工矿物与加工, 2003,10:9-11,8.Li F P. Fuzzy cluster analysis for concentration of abundant mines district[J]. Industrial Minerals & Processing, 2003,10:9-11,8.
[16]	徐涵秋.新型Landsat8卫星影像的反射率和地表温度反演[J]. 地球物理学报, 2015,58(3):741-747.Xu H Q. Retrieval of the reflectance and land surface temperature of the newly-launched Landsat 8satellite[J]. Chinese J. Geophys, 2015, 58(3):741-747.
[17]	Sobrino J A, Jiménez-Muoz J C, Paolini L. Land surface temperature retrieval from LANDSAT TM 5[J]. Remote Sensing of Environment, 2004,90(4):434-440.
[18]	覃志豪,Zhang M H,Karnieli A,et al.用陆地卫星TM6数据演算地表温度的单窗算法[J]. 地理学报, 2001,56(4):456-466.Qin Z H, Zhang M H, Karnieli A, et al. 2001. Mono-window algorithm for retrieving land surface temperature from landsat TM6data[J]. Acta Geographica Sinica, 2001,56(4):456-466.
[19]	覃志豪,Zhang M H,Karnieli A.用NOAA-AVHRR热通道数据演算地表温度的劈窗算法[J]. 国土资源遥感, 2001,(2):33-42.Qin Z H, Zhang M H, Karnieli A. 2001. Split window algorithms for retrieving land surface temperature from NOAA-AVHRR data[J]. Remote Sensing for Land and Resources, 2001,(2):33-42.
[20]	Rozenstein O, Qin Z H, Derimian Y, et al. Derivation of land surface temperature for Landsat-8TIRS using a split window algorithm[J]. Sensors, 2014,14(4):5768-5780.
[21]	毛克彪,唐华俊,陈仲新,等.一个从ASTER数据中反演地表温度的劈窗算法[J]. 遥感信息, 2006,(5):7-11.Mao K B, Tang H J, Chen Z X, et al. 2006. A split-window algorithm for retrieving land-surface temperature from ASTER data[J]. Remote Sensing Information, 2006,(5):7-11.
[22]	Aliihsan S, Stefania B. Land surface temperature retrieval from Landsat 5, 7, and 8over rural areas:assessment of sifferent eetrieval algorithms and emissivity models and toolbox implementation[J]. Remote Sensing, 2020,12(2):294.
[23]	Mengdi L, Xiaobin Y, Qing X, et al. Monitoring of fine-scale warm drain-off water from nuclear power stations in the daya bay based on Landsat 8data[J]. Remote Sensing, 2020,12(4):627.
[24]	Liu C G, Shi J H, Wang T X, et al. Mathematical assessment of the effects of substituting the band radiative transfer equation (RTE) for the spectral RTE in the applications of earth's surface temperature retrievals from spaceborne infrared imageries[J]. Remote Sensing, 2019,11(3):226.
[25]	Scarano, Sobrino. On the relationship between the sky view factor and the land surface temperature derived by Landsat-8images in Bari, Italy[J]. International Journal of Remote Sensing, 2015,36(19/20):4820-4835.
[26]	Palade B and Serrano M. Long-term high-frequency validation of MODIS LST products in a Mediterranean mountain environment[J]. International Journal of Remote Sensing, 2014,35(3):818-829.
[27]	Smith R M. Comparing traditional methods for selecting class intervals on Choropleth maps[J]. The Professional Geographer, 1986, 38(1):62-67.
[28]	Liu H, Weng Q. Seasonal variations in the relationship between landscape pattern and land surface temperature in Indianapolis, USA[J]. Environmental Monitoring and Assessment, 2008,144:199-219.
[29]	Zhang Y, Wang X, Balzter H, et al. 2019. Directional and zonal analysis of urban thermal environmental change in Fuzhou as an indicator of urban landscape transformation[J]. Remote Sensing, 11(23):2810.
[30]	Cui Y, Xu X, Dong J, et al. Influence of urbanization factors on surface urban heat island intensity:A comparison of countries at different developmental phases[J]. Sustainability, 2016,8:706.
[31]	Yang C, He X, Yan F, et al. Mapping the influence of land use/land cover changes on the urban heat island effect-a case study of Changchun, China[J]. Sustainability, 2017,9(2):312.
[32]	黄初冬,李丹君,陈前虎,等.海绵城市建设缓解热岛的效应与机理——以浙江省嘉兴市为例[J]. 生态学杂志, 2020,39(2):625-634.Huang C D, Li D J, Chen Q H, et al. The effect and mechanism of sponge city construction on heat island mitigation:A case study of Jiaxing[J]. Chinese Journal of Ecology, 2020,39(2):625-634.
[33]	Cai G, Du M, Gao Y. City block-based assessment of land cover components' impacts on the urban thermal environment[J]. Remote Sensing Applications:Society and Environment, 2019,13:85-96.
[34]	Hua L, Zhang X, Nie Q, et al. The impacts of the expansion of urban impervious surfaces on urban heat islands in a coastal city in China[J]. Sustainability, 2020,12(2):475.
[35]	黄木易,岳文泽,何翔.巢湖流域地表热环境与景观变化相关分析及其尺度效应[J]. 中国环境科学, 2017,37(8):3123-3133.Huang M Y, Yue W Z, He X, et al. Correlation analysis between land surface thermal environment and landscape change and its scale effect in Chaohu Basin[J]. China Environmental Science, 2017,37(8):3123-3133.
[36]	彭璐,李小梅,傅佳丽,等.福建省平潭岛植被覆盖变化及其对地表温度影响的定量研究[J]. 福建师范大学学报:自然科学版, 2016, 32(1):78-85.Peng Lu, Li Xiaomei, Fu Jiali, et al. A quantitative research of vegetation cover change and its effect on the land surface temperature in Pingtan Island of Fujian Province[J]. Journal of Fujian Normal University:Natural Science Edition, 2016,32(1):78-85.
[37]	Amiri R, Weng Q, Alimohammadi A, et al. Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran[J]. Remote Sensing of Environment, 2009,113(12):2606-2617.
[38]	Guerschman J P, Hill M J, Renzullo L J, et al. Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1Hyperion and MODIS sensors[J]. Remote Sensing of Environment, 2009,113(5):928-945.
[39]	Wang G Z, Wang J P, Zou X Y, et al. Estimating the fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil from MODIS data:Assessing the applicability of the NDVI-DFI model in the typical Xilingol grasslands[J]. International Journal of Applied Earth Observation and Geoinformation, 2019,76:154-166.
[40]	杨婷,陈秀万,万玮,等.基于光学与被动微波遥感的青藏高原地区土壤水分反演[J]. 地球物理学报, 2017,60(7):2556-2567.Yang T, Chen X W, Wan W, et al. Soil moisture retrieval in the Tibetan plateau using optical and passive microwave remote sensing data[J]. Chinese Journal of Geophysics, 2017,60(7):2556-2567.
[41]	刘英,吴立新,马保东.基于TM/ETM+光谱特征空间的土壤湿度遥感监测[J]. 中国矿业大学学报, 2013,42(2):296-301.Liu Y, Wu L X, Ma B D. Remote sensing monitoring of soil moisture on the basis of TM/ETM+ spectral space[J]. Journal of China University of Mining and Technology, 2013,42(2):296-301.
[42]	Rikimaru A. Landsat T M data processing guide for forest canopy density mapping and monitoring model Workshop on Utilization of Remote Sensing in Site Assessment and Planning for Rehabilitation of Logged-over Forest[J]. Bangkok, Thailand, 1996:1-8.
[43]	Xu H. Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI)[J]. Photogrammetric Engineering and Remote Sensing, 2010,76(5):557-565.