矿业密集区地表热环境时空异质性驱动机理

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.

The spatiotemporal heterogeneity and driving forces of surfacial thermal environment over an intensive mining region

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.