Ecology and Environment ›› 2023, Vol. 32 ›› Issue (1): 110-122.DOI: 10.16258/j.cnki.1674-5906.2023.01.012
• Research Articles • Previous Articles Next Articles
FU Rong1(), WU Xinmei2, CHEN Bin3,*
Received:
2022-09-19
Online:
2023-01-18
Published:
2023-04-06
Contact:
CHEN Bin
通讯作者:
陈斌
作者简介:
付蓉(1992年生),女,硕士研究生,研究方向为资源环境遥感与GIS分析。E-mail: 1025780235@qq.com
基金资助:
CLC Number:
FU Rong, WU Xinmei, CHEN Bin. Analysis on the Spatial Stratified Heterogeneity and Driving Factors Differences of the Urban Land Surface Temperature: A Case Study of Hefei[J]. Ecology and Environment, 2023, 32(1): 110-122.
付蓉, 武新梅, 陈斌. 城市地表温度空间分异及驱动因子差异性分析——以合肥市为例[J]. 生态环境学报, 2023, 32(1): 110-122.
数据 | 数据简记 | 时间分辨率 | 空间分辨率 | 数据来源 |
---|---|---|---|---|
地表温度 | LST | 8 d | 1 km | NASA ( |
归一化植被指数 | NDVI | 16 d | 1 km | NASA ( |
数字高程模型 | DEM | — | 30 m | USGS ( |
人口密度 | DP | — | 1 km | 美国能源部橡树岭国家实验室 ( |
夜间灯光 | NL | 1 m | 0.5 km | 国家青藏高原科学数据中心 ( |
相对湿度 | RH | 1 m | 1 km | 国家科技基础条件平台—国家地球系统科学数据中心 ( |
气压 | AP | 1 d | 0.0625(°) | 国家气象科学数据中心 ( CLDAS-V2.0数据集 |
短波辐射 | SR | 1 d |
Table 1 Research data
数据 | 数据简记 | 时间分辨率 | 空间分辨率 | 数据来源 |
---|---|---|---|---|
地表温度 | LST | 8 d | 1 km | NASA ( |
归一化植被指数 | NDVI | 16 d | 1 km | NASA ( |
数字高程模型 | DEM | — | 30 m | USGS ( |
人口密度 | DP | — | 1 km | 美国能源部橡树岭国家实验室 ( |
夜间灯光 | NL | 1 m | 0.5 km | 国家青藏高原科学数据中心 ( |
相对湿度 | RH | 1 m | 1 km | 国家科技基础条件平台—国家地球系统科学数据中心 ( |
气压 | AP | 1 d | 0.0625(°) | 国家气象科学数据中心 ( CLDAS-V2.0数据集 |
短波辐射 | SR | 1 d |
指标 | 地表温度 | 气压 | 相对湿度 | 短波辐射 | 夜间灯光 | 人口密度 | 归一化植被指数 | 高程 |
---|---|---|---|---|---|---|---|---|
q | 0.575 | 0.554 | 0.397 | 0.758 | 0.605 | 0.32 | 0.65 | 0.337 |
P | <0.01 |
Table 2 The q values of LST and each factor
指标 | 地表温度 | 气压 | 相对湿度 | 短波辐射 | 夜间灯光 | 人口密度 | 归一化植被指数 | 高程 |
---|---|---|---|---|---|---|---|---|
q | 0.575 | 0.554 | 0.397 | 0.758 | 0.605 | 0.32 | 0.65 | 0.337 |
P | <0.01 |
模型 | 修正决定r2 | 偏差解释率 | GCV值 | AIC值 |
---|---|---|---|---|
g(x) | 0.772 | 77.7% | 0.739 | 6198.62 |
g1(x) | 0.771 | 77.4% | 0.742 | 6208.95 |
g2(x) | 0.759 | 76.3% | 0.782 | 6335.66 |
g3(x) | 0.770 | 77.4% | 0.746 | 6222.04 |
g4(x) | 0.763 | 76.7% | 0.768 | 6290.55 |
g5(x) | 0.688 | 69.3% | 1.011 | 6962.92 |
g6(x) | 0.769 | 77.3% | 0.748 | 6227.12 |
g7(x) | 0.737 | 74.2% | 0.852 | 6545.90 |
Table 3 Fitting analysis results of models
模型 | 修正决定r2 | 偏差解释率 | GCV值 | AIC值 |
---|---|---|---|---|
g(x) | 0.772 | 77.7% | 0.739 | 6198.62 |
g1(x) | 0.771 | 77.4% | 0.742 | 6208.95 |
g2(x) | 0.759 | 76.3% | 0.782 | 6335.66 |
g3(x) | 0.770 | 77.4% | 0.746 | 6222.04 |
g4(x) | 0.763 | 76.7% | 0.768 | 6290.55 |
g5(x) | 0.688 | 69.3% | 1.011 | 6962.92 |
g6(x) | 0.769 | 77.3% | 0.748 | 6227.12 |
g7(x) | 0.737 | 74.2% | 0.852 | 6545.90 |
平滑效应项 | 估计自由度 | 参考自由度 | F值 | P值 | 显著性排序 |
---|---|---|---|---|---|
AP | 8.058 | 8.771 | 2.725 | 0.00696 | 7 |
SR | 8.181 | 8.809 | 17.772 | <2×10-16 | 3 |
RH | 3.129 | 4.000 | 6.415 | 0.00004 | 5 |
DP | 7.023 | 8.100 | 12.481 | <2×10-16 | 4 |
NL | 6.029 | 7.213 | 129.351 | <2×10-16 | 1 |
DEM | 6.67 | 7.664 | 4.700 | 0.00002 | 6 |
NDVI | 5.691 | 6.902 | 55.814 | <2×10-16 | 2 |
Table 4 GAM fitting results of high LST and 7 factors in the high temperature region
平滑效应项 | 估计自由度 | 参考自由度 | F值 | P值 | 显著性排序 |
---|---|---|---|---|---|
AP | 8.058 | 8.771 | 2.725 | 0.00696 | 7 |
SR | 8.181 | 8.809 | 17.772 | <2×10-16 | 3 |
RH | 3.129 | 4.000 | 6.415 | 0.00004 | 5 |
DP | 7.023 | 8.100 | 12.481 | <2×10-16 | 4 |
NL | 6.029 | 7.213 | 129.351 | <2×10-16 | 1 |
DEM | 6.67 | 7.664 | 4.700 | 0.00002 | 6 |
NDVI | 5.691 | 6.902 | 55.814 | <2×10-16 | 2 |
交互项 | 估计自由度 | 参考自由度 | F值 | P值 |
---|---|---|---|---|
AP×DP | 8.007 | 9.146 | 19.13 | <2×10-16 |
AP×SR | 10.441 | 11.923 | 13.39 | <2×10-16 |
AP×NL | 10.844 | 12.114 | 8.332 | <2×10-16 |
AP×RH | 12.333 | 13.342 | 40.6 | <2×10-16 |
AP×DEM | 10.505 | 11.654 | 16.97 | <2×10-16 |
AP×NDVI | 9.788 | 11.417 | 11.42 | <2×10-16 |
SR×DP | 7.795 | 9.116 | 27.66 | <2×10-16 |
SR×NL | 10.183 | 11.929 | 7.047 | <2×10-16 |
SR×RH | 6.865 | 8.346 | 5.536 | <0.00001 |
SR×DEM | 9.610 | 11.07 | 17.22 | <2×10-16 |
SR×NDVI | 7.459 | 9.117 | 25.1 | <2×10-16 |
DP×NL | 9.313 | 10.723 | 14.9 | <2×10-16 |
DP×RH | 5.958 | 7.254 | 11.05 | <2×10-16 |
DP×DEM | 7.052 | 15 | 11.4 | <2×10-16 |
DP×NDVI | 6.364 | 7.756 | 16.78 | <2×10-16 |
NL×RH | 8.992 | 11.045 | 4.528 | <0.00001 |
NL×DEM | 8.564 | 10.221 | 7.949 | <2×10-16 |
NL×NDVI | 8.707 | 10.852 | 8.717 | <2×10-16 |
RH×DEM | 8.438 | 10.03 | 6.041 | <2×10-16 |
RH×NDVI | 9.634 | 11.406 | 7.525 | <2×10-16 |
DEM×NDVI | 7.715 | 9.19 | 16.3 | <2×10-16 |
Table 5 GAM fitting results of interaction terms between LST and influencing factors in the high temperature region
交互项 | 估计自由度 | 参考自由度 | F值 | P值 |
---|---|---|---|---|
AP×DP | 8.007 | 9.146 | 19.13 | <2×10-16 |
AP×SR | 10.441 | 11.923 | 13.39 | <2×10-16 |
AP×NL | 10.844 | 12.114 | 8.332 | <2×10-16 |
AP×RH | 12.333 | 13.342 | 40.6 | <2×10-16 |
AP×DEM | 10.505 | 11.654 | 16.97 | <2×10-16 |
AP×NDVI | 9.788 | 11.417 | 11.42 | <2×10-16 |
SR×DP | 7.795 | 9.116 | 27.66 | <2×10-16 |
SR×NL | 10.183 | 11.929 | 7.047 | <2×10-16 |
SR×RH | 6.865 | 8.346 | 5.536 | <0.00001 |
SR×DEM | 9.610 | 11.07 | 17.22 | <2×10-16 |
SR×NDVI | 7.459 | 9.117 | 25.1 | <2×10-16 |
DP×NL | 9.313 | 10.723 | 14.9 | <2×10-16 |
DP×RH | 5.958 | 7.254 | 11.05 | <2×10-16 |
DP×DEM | 7.052 | 15 | 11.4 | <2×10-16 |
DP×NDVI | 6.364 | 7.756 | 16.78 | <2×10-16 |
NL×RH | 8.992 | 11.045 | 4.528 | <0.00001 |
NL×DEM | 8.564 | 10.221 | 7.949 | <2×10-16 |
NL×NDVI | 8.707 | 10.852 | 8.717 | <2×10-16 |
RH×DEM | 8.438 | 10.03 | 6.041 | <2×10-16 |
RH×NDVI | 9.634 | 11.406 | 7.525 | <2×10-16 |
DEM×NDVI | 7.715 | 9.19 | 16.3 | <2×10-16 |
平滑效应项 | 估计自由度 | 参考自由度 | F值 | P值 | 显著性排序 |
---|---|---|---|---|---|
AP | 8.498 | 8.920 | 22.069 | <2×10-16 | 5 |
SR | 8.418 | 8.902 | 48.090 | <2×10-16 | 3 |
RH | 4.812 | 6.001 | 16.972 | <2×10-16 | 6 |
DP | 8.728 | 8.976 | 34.661 | <2×10-16 | 4 |
NL | 6.954 | 7.972 | 67.440 | <2×10-16 | 2 |
DEM | 6.787 | 7.816 | 4.903 | <0.0001 | 7 |
NDVI | 7.781 | 8.617 | 166.857 | <2×10-16 | 1 |
Table 6 GAM fitting results of LST and 7 factors the low temperature region
平滑效应项 | 估计自由度 | 参考自由度 | F值 | P值 | 显著性排序 |
---|---|---|---|---|---|
AP | 8.498 | 8.920 | 22.069 | <2×10-16 | 5 |
SR | 8.418 | 8.902 | 48.090 | <2×10-16 | 3 |
RH | 4.812 | 6.001 | 16.972 | <2×10-16 | 6 |
DP | 8.728 | 8.976 | 34.661 | <2×10-16 | 4 |
NL | 6.954 | 7.972 | 67.440 | <2×10-16 | 2 |
DEM | 6.787 | 7.816 | 4.903 | <0.0001 | 7 |
NDVI | 7.781 | 8.617 | 166.857 | <2×10-16 | 1 |
交互项 | 估计自由度 | 参考自由度 | F值 | P值 |
---|---|---|---|---|
AP×DP | 11.829 | 12.766 | 44.76 | <2×10-16 |
AP×SR | 15.328 | 15.615 | 30.88 | <2×10-16 |
AP×NL | 15.873 | 15.992 | 18.91 | <2×10-16 |
AP×RH | 15.873 | 15.992 | 18.91 | <2×10-16 |
AP×DEM | 13.997 | 14.273 | 3.251 | 0.0000836 |
AP×NDVI | 14.689 | 15.436 | 7.433 | <2×10-16 |
SR×DP | 12.726 | 13.683 | 15.95 | <2×10-16 |
SR×NL | 13.134 | 13.724 | 41.44 | <2×10-16 |
SR×RH | 14.677 | 15.632 | 34.17 | <2×10-16 |
SR×DEM | 11.411 | 12.27 | 28 | <2×10-16 |
SR×NDVI | 15.948 | 15.998 | 12.57 | <2×10-16 |
DP×NL | 14.949 | 15.52 | 15.45 | <2×10-16 |
DP×RH | 9.632 | 10.709 | 18.84 | <2×10-16 |
DP×DEM | 7.966 | 15.000 | 56.14 | <2×10-16 |
DP×NDVI | 6.973 | 8.653 | 9.933 | <2×10-16 |
NL×RH | 13.927 | 14.825 | 16.02 | <2×10-16 |
NL×DEM | 11.08 | 12.148 | 43.48 | <2×10-16 |
NL×NDVI | 11.938 | 13.475 | 5.941 | <2×10-16 |
RH×DEM | 12.245 | 13.153 | 22.559 | <2×10-16 |
RH×NDVI | 12.988 | 14.064 | 6.376 | <2×10-16 |
DEM×NDVI | 6.062 | 7.44 | 21.5 | <2×10-16 |
Table 7 GAM fitting results of interaction terms between LST and influencing factors in the low temperature region
交互项 | 估计自由度 | 参考自由度 | F值 | P值 |
---|---|---|---|---|
AP×DP | 11.829 | 12.766 | 44.76 | <2×10-16 |
AP×SR | 15.328 | 15.615 | 30.88 | <2×10-16 |
AP×NL | 15.873 | 15.992 | 18.91 | <2×10-16 |
AP×RH | 15.873 | 15.992 | 18.91 | <2×10-16 |
AP×DEM | 13.997 | 14.273 | 3.251 | 0.0000836 |
AP×NDVI | 14.689 | 15.436 | 7.433 | <2×10-16 |
SR×DP | 12.726 | 13.683 | 15.95 | <2×10-16 |
SR×NL | 13.134 | 13.724 | 41.44 | <2×10-16 |
SR×RH | 14.677 | 15.632 | 34.17 | <2×10-16 |
SR×DEM | 11.411 | 12.27 | 28 | <2×10-16 |
SR×NDVI | 15.948 | 15.998 | 12.57 | <2×10-16 |
DP×NL | 14.949 | 15.52 | 15.45 | <2×10-16 |
DP×RH | 9.632 | 10.709 | 18.84 | <2×10-16 |
DP×DEM | 7.966 | 15.000 | 56.14 | <2×10-16 |
DP×NDVI | 6.973 | 8.653 | 9.933 | <2×10-16 |
NL×RH | 13.927 | 14.825 | 16.02 | <2×10-16 |
NL×DEM | 11.08 | 12.148 | 43.48 | <2×10-16 |
NL×NDVI | 11.938 | 13.475 | 5.941 | <2×10-16 |
RH×DEM | 12.245 | 13.153 | 22.559 | <2×10-16 |
RH×NDVI | 12.988 | 14.064 | 6.376 | <2×10-16 |
DEM×NDVI | 6.062 | 7.44 | 21.5 | <2×10-16 |
[1] |
BARAT A, KUMAR S, KUMAR P, et al., 2018. Characteristics of surface urban heat island (SUHI) over the Gangetic Plain of Bihar, India[J]. Asia-Pacific Journal of Atmospheric Sciences, 54(2): 205-214.
DOI |
[2] |
CHAKRABORTI S, BANERJEE A, SANNIGRAHI S, et al., 2019. Assessing the dynamic relationship among land use pattern and land surface temperature: A spatial regression approach[J]. Asian Geographer, 36(2): 93-116.
DOI URL |
[3] |
CHEN L, WANG X L, CAI X B, et al., 2021. Seasonal variations of daytime land surface temperature and their underlying drivers over Wuhan, China[J]. Remote Sensing, 13(2): 323.
DOI URL |
[4] |
CHEN W, ZHANG Y, PENGWANG C Y, et al., 2017. Evaluation of urbanization dynamics and its impacts on surface heat islands: A case study of Beijing, China[J]. Remote Sensing, 9(5): 453.
DOI URL |
[5] |
CHEN X L, ZHAO H M, LI P X, et al., 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes[J]. Remote Sensing of Environment, 104(2): 133-146.
DOI URL |
[6] |
DEILAMI K, KAMRUZZAMAN M, 2017. Modelling the urban heat island effect of smart growth policy scenarios in Brisbane[J]. Land Use Policy, 64: 38-55.
DOI URL |
[7] |
FARIDEH T, ABOLFAZL R, 2021. Quantitative analysis of spatial distribution of land surface temperature (LST) in relation Ecohydrological, terrain and socio-economic factors based on Landsat data in mountainous area[J]. Advances in Space Research, 68(9): 3622-3640.
DOI URL |
[8] |
FATTAH M A, MORSHED S R, MORSHED S Y, 2021. Impacts of land use-based carbon emission pattern on surface temperature dynamics: Experience from the urban and suburban areas of Khulna, Bangladesh[J]. Remote Sensing Applications: Society and Environment, 22: 100508.
DOI URL |
[9] |
HAN D L, ZHANG T T, ZHANG X D, et al., 2022. Study on spatiotemporal characteristics and influencing factors of pedestrian- level PM2.5 concentrations in outdoor open spaces of Harbin in winter, using a generalized additive model (GAM)[J]. Urban Climate, 46: 101313.
DOI URL |
[10] | KABACOFF R I, 2015. R in Action: Data Analysis and Graphics with R[M]. 2nd ed. Shelter Island: Manning Publications Co.: 171-181. |
[11] | LI X M, ZHOU W Q, OUYANG Z Y, 2013. Relationship between land surface temperature and spatial pattern of greenspace: What are the effects of spatial resolution?[J]. Landscape & Urban Planning, 114: 1-8. |
[12] |
LIAO J J, YU C Y, FENG Z, et al., 2020. Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services[J]. Journal of Cleaner Production, 288: 125466.
DOI URL |
[13] |
LIU L L, DONG Y C, KONG M, et al., 2020. Towards the comprehensive water quality control in Lake Taihu: correlating chlorophyll and water quality parameters with generalized additive model[J]. Science of the Total Environment, 705: 135993.
DOI URL |
[14] |
MORABITO M, CRISCI A, GUERRI G, et al., 2021. Surface urban heat islands in Italian metropolitan cities: Tree cover and impervious surface influences[J]. Science of the Total Environment, 751: 142334.
DOI URL |
[15] |
PAL S, ZIAUL S K, 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre[J]. The Egyptian Journal of Remote Sensing and Space Science, 20(1): 125-145.
DOI URL |
[16] |
RA A, CSTK A, EBD A, et al., 2021. Exploring the relationship between urban form and land surface temperature (LST) in a semi-arid region case study of Ben Guerir City-Morocco[J]. Environmental Challenges, 5: 100229.
DOI URL |
[17] | SIDDIQUI A, KUSHWAHA G, NIKAM B, et al., 2021. Analysing the day/night seasonal and annual changes and trends in land surface temperature and surface urban heat island intensity (SUHII) for Indian cities[J]. Sustainable Cities and Society, 75: 2210-6707. |
[18] |
WANG J F, ZHANG T L, FU B J, 2016. A measure of spatial stratified heterogeneity[J]. Ecological Indicators, 67: 250-256.
DOI URL |
[19] |
WANG X, ZHOU T, TAO F, et al., 2019. Correlation analysis between UBD and LST in Hefei, China, using Luojia1-01 night-time light imagery[J]. Applied Sciences, 9(23): 5224.
DOI URL |
[20] |
陈颖彪, 郑子豪, 吴志峰, 等, 2019. 夜间灯光遥感数据应用综述和展望[J]. 地理科学进展, 38(2): 205-223.
DOI |
CHEN Y B, ZHENG Z H, WU Z F, et al., 2019. Review and prospect of application of nighttime light remote sensing data[J]. Progress in Geography, 38(2): 205-223.
DOI |
|
[21] | 邓玉娇, 杜尧东, 王捷纯, 等, 2020. 粤港澳大湾区城市热岛时空特征及驱动因素[J]. 生态学杂志, 39(8): 2671-2677. |
DENG Y J, DU Y D, WANG J C, et al., 2020. Spatiotemporal characteristics and driving factors of urban heat islands in Guangdong Hong Kong-Marco Greater Bay Area[J]. Chinese Journal of Ecology, 39(8): 2671-2677. | |
[22] |
杜正静, 夏晓玲, 裴兴云, 等, 2015. 基于GAM模型的三种降雨因子对贵州地质灾害危险性预测的对比分析[J]. 中国农学通报, 31(6): 187-193.
DOI |
DU Z J, XIA X L, PEI X Y, et al., 2015. Comparative analysis of Three kinds of rainfall factor for geological hazard risk prediction based on GAM in Guizhou province[J]. Chinese Agricultural Science Bulletin, 31(6): 187-193.
DOI |
|
[23] |
樊智宇, 詹庆明, 刘慧民, 等, 2019. 武汉市夏季城市热岛与不透水面增温强度时空分布[J]. 地球信息科学学报, 21(2): 226-235.
DOI |
FAN Z Y, ZHAN Q M, LIU H M, et al., 2019. Spatial-temporal distribution of urban heat island and the heating effect of impervious surface in summer in Wuhan[J]. Journal of Geo-information Science, 21(2): 226-235. | |
[24] |
方颖, 李连发, 2019. 基于机器学习的高精度高分辨率气象因子时空估计[J]. 地球信息科学学报, 21(6): 799-813.
DOI |
FANG Y, LI L F, 2019. Spatiotemporal estimation of high-accuracy and high-resolution meteorological parameters based on machine learning[J]. Journal of Geo-information Science, 21(6): 799-813. | |
[25] | 葛静茹, 王海军, 贺三维, 等, 2021. 武汉市都市发展区地表温度季节性空间分布与驱动力分析[J]. 长江流域资源与环境, 30(2): 351-360. |
GE J R, WANG H J, HE S W, et al., 2021. Seasonal-spatial distribution and driving forces of land surface temperature in the urban development area of Wuhan[J]. Resources and Environment in the Yangtze Basin, 30(2): 351-360. | |
[26] |
顾西辉, 张强, 孔冬冬, 2016. 中国极端降水事件时空特征及其对夏季温度响应[J]. 地理学报, 71(5): 718-730.
DOI |
GU X H, ZHANG Q, KONG D D, et al., 2016. Spatiotemporal patterns of extreme precipitation with their responses to summer temperature[J]. Acta Geographica Sinica, 71(5): 718-730.
DOI |
|
[27] |
郭宇, 王宏伟, 张喆, 等, 2020. 南京市热环境与地表覆被的时空尺度效应及驱动机制研究[J]. 生态环境学报, 29(7): 1403-1411.
DOI URL |
GUO Y, WANG H W, ZHANG Z, et al., 2020. Spatio-temporal scale effect and driving mechanism of thermal environment and land surface cover in Nanjing[J]. Ecology and Environmental Sciences, 29(7): 1403-1411. | |
[28] | 贾明秀, 黄六一, 褚建伟, 等, 2019. 基于GAM和GWR模型分析环境因子对南极磷虾资源分布的非线性和非静态性影响[J]. 中国海洋大学学报 (自然科学版), 49(8): 19-26. |
JIA M X, HUANG L Y, CHU J W, et al., 2019. Studies on the nonlinear and spatial nonstationary effects of environmental factors on the distribution of Antarctic krill (Euphausia superba)[J]. Periodical of Ocean University of China, 49(8): 19-26. | |
[29] | 乐柯君, 方陆明, 何小兵, 等, 2019. 森林城市景观格局与热环境的关系——以龙泉市为例[J]. 应用生态学报, 30(9): 3066-3074. |
LE K J, FANG L M, HE X B, et al., 2019. Relationship between forest city landscape pattern and thermal environment: A case study of Longquan city, China[J]. Chinese Journal of Applied Ecology, 30(9): 3066-3074. | |
[30] |
李喆, 陈圣宾, 陈芝阳, 2022. 地表温度与土地利用类型间的空间尺度依赖性——以成都为例[J]. 生态环境学报, 31(5): 999-1007.
DOI URL |
LI Z, CHEN S B, CHEN Z Y, 2022. Spatial scale dependence between land surface temperature and land use types: A case study of Chengdu city[J]. Ecology and Environmental Sciences, 31(5): 999-1007. | |
[31] | 李壮, 季民, 张镯漫, 2022. 基于地理探测器的合肥市热岛效应影响因素分析[J]. 测绘与空间地理信息, 45(3): 56-59. |
LI Z, JI M, ZHANG Z M, 2022. Analysis of influencing factors of heat island effect in Hefei city based on geographicaldetectors[J]. Geomatics & Spatial Information Technology, 45(3): 56-59. | |
[32] | 刘大龙, 马岚, 赵辉辉, 2021. 城市复杂辐射场形成机理[J]. 太阳能学报, 42(10): 458-463. |
LIU D L, MA L, ZHAO H H, 2021. Formation mechanism of urban complex radiation field[J]. Acta Energiae Solaris Sinica, 42(10): 458-463. | |
[33] | 刘晓彤, 黄金金, 张逸如, 等, 2022. 基于广义可加模型的广东省森林土壤有机质影响因子[J]. 生态学杂志, 41(11): 2278-2288. |
LIU X T, HUANG J J, ZHANG Y R, et al., 2022. Analysis of influencing factors on forest soil organic matter in Guangdong Province based on GAM model[J]. Chinese Journal of Ecology, 41(11): 2278-2288. | |
[34] |
聂桐, 董国涛, 蒋晓辉, 等, 2022. 榆林地区植被时空分异特征及其影响因素研究[J]. 生态环境学报, 31(1): 26-36.
DOI URL |
NIE T, DONG G T, JIANG X H, et al., 2022. Spatio-temporal variations and influencing factors of vegetation in Yulin[J]. Ecology and Environmental Sciences, 31(1): 26-36. | |
[35] | 任至涵, 倪长健, 花瑞阳, 等, 2021. 成都O3逐日污染潜势关键时段优选的GAM模型[J]. 中国环境科学, 41(11): 5079-5085. |
REN Z H, NI C J, HUA R Y, et al., 2021. Optimization of the key period of daily ozone pollution potential in Chengdu based on Generalized Additive Model[J]. China Environmental Science, 41(11): 5079-5085. | |
[36] |
阮惠华, 许剑辉, 张菲菲, 2022. 2001—2020年粤港澳大湾区植被和地表温度时空变化研究[J]. 生态环境学报, 31(8): 1510-1520.
DOI URL |
RUAN H H, XU J H, ZHANG F F, 2022. Spatiotemporal changes of vegetation and land surface temperature during 2001 and 2020 in the Guangdong-Hong Kong-Macao greater bay area of China[J]. Ecology and Environmental Sciences, 31(8): 1510-1520. | |
[37] |
单宝艳, 张巧, 任启新, 等, 2022. 基于局地气候分区的济南市热环境空间分异及其影响因素[J]. 地球信息科学学报, 24(4): 711-722.
DOI |
SHAN B Y, ZHANG Q, REN Q X, et al., 2022. Spatial differentiation of urban thermal environment and its influencing factors based on local climate zones in Ji’nan[J]. Journal of Geo-information Science, 24(4): 711-722. | |
[38] |
王劲峰, 徐成东, 2017. 地理探测器: 原理与展望[J]. 地理学报, 72(1): 116-134.
DOI |
WANG J F, XU C D, 2017. Geodetector: Principle and prospective[J]. Acta Geographica Sinica, 72(1): 116-134.
DOI |
|
[39] | 王念, 卢致宇, 徐建红, 等, 2021. 遥感反演和站点观测的地气温度分布特征差异[J]. 遥感学报, 25(8): 1848-1861. |
WANG N, LU Z Y, XU J H, et al., 2021. Difference of temperature distribution characteristics based on remote sensing and meteorological station temperature data[J]. National Remote Sensing Bulletin, 25(8): 1848-1861. | |
[40] | 王修信, 朱启疆, 梁宗经, 等, 2011. 喀斯特城市植被覆盖度时空变化对地表温度的影响[J]. 地理与地理信息科学, 27(4): 107-110. |
WANG X X, ZHU Q J, LIANG Z J, et al., 2011. Influence of spatio-temporal change of vegetation coverage on land surface temperature in Karst city[J]. Geography and Geo-Information Science, 27(4): 107-110. | |
[41] | 吴昌广, 夏丽丽, 林姚宇, 等, 2015. 深圳市典型住区热环境特征及其影响因子分析[J]. 哈尔滨工业大学学报, 47(6): 59-62. |
WU C G, XIA L L, LIN Y Y, et al., 2015. Analysis on characteristics of thermal environments in typical residential districts and its influencing factors in Shenzhen[J]. Journal of Harbin Institute of Technology, 47(6): 59-62. | |
[42] | 向炀, 周志翔, 2020. 基于地理探测器的城市热岛驱动因素分析——以武汉市为例[J]. 长江流域资源与环境, 29(8): 1768-1779. |
XIANG Y, ZHOU Z X, 2020. Analysis of driving factors of urban heat island based on geographical detector: Taking Wuhan city as an example[J]. Resources and Environment in the Yangtze Basin, 29(8): 1768-1779. | |
[43] | 谢苗苗, 王仰麟, 付梅臣, 2011. 城市地表温度热岛影响因素研究进展[J]. 地理科学进展, 30(1): 35-41. |
XIE M M, WANG Y L, FU M C, 2011. An overview and perspective about causative factors of surface urban heat island effects[J]. Progress in Geography, 30(1): 35-41.
DOI |
|
[44] |
熊鹰, 章芳, 2020. 基于多源数据的长沙市人居热环境效应及其影响因素分析[J]. 地理学报, 75(11): 2443-2458.
DOI |
XIONG Y, ZHANG F, 2020. Thermal environment effects of urban human settlements and influencing factors based on multi-source data: A case study of Changsha city[J]. Acta Geographica Sinica, 75(11): 2443-2458.
DOI |
|
[45] | 徐涵秋, 2009. 城市不透水面与相关城市生态要素关系的定量分析[J]. 生态学报, 29(5): 2456-2462. |
XU H Q, 2009. Quantitative analysis on the relationship of urban impervious surface with other components of the urban ecosystem[J]. Acta Ecologica Sinica, 29(5): 2456-2462. | |
[46] |
杨艳艳, 朱明明, 徐炳庆, 等, 2021. 山东半岛南部近岸海域鱼卵、仔稚鱼群落结构与环境因子相关性[J]. 生态环境学报, 30(5): 995-1004.
DOI URL |
YANG Y Y, ZHU M M, XU B Q, et al., 2021. Community structure of ichthyoplankton and its relationship with environmental factors in coastal waters of southern Shandong peninsula[J]. Ecology and Environmental Sciences, 30(5): 995-1004. | |
[47] |
于琛, 胡德勇, 曹诗颂, 等, 2017. 2005—2016年北京中心城区热岛时空格局及影响因子多元建模[J]. 地球信息科学学报, 19(11): 1485-1494.
DOI |
YU C, HU D Y, CAO S S, et al., 2017. Spatio-temporal pattern of heat island and multivariate modeling of impact factors of Beijing downtown from 2005 to 2016[J]. Journal of Geo-information Science, 19(11): 1485-1494. | |
[48] |
朱梓弘, 朱同彬, 杨霖, 等, 2019. 中国土壤碱解氮含量与影响因子的空间关系研究[J]. 生态环境学报, 28(11): 2199-2207.
DOI URL |
ZHU Z H, ZHU T B, YANG L, et al., 2019. The spatial relationship between soil alkeline-nitrogen content and environmental factors in China[J]. Ecology and Environmental Sciences, 28(11): 2199-2207. | |
[49] | 祝新明, 宋小宁, 冷佩, 等, 2021. 多尺度地理加权回归的地表温度降尺度研究[J]. 遥感学报, 25(8): 1749-1766. |
ZHU X M, SONG X N, LENG P, et al., 2021. Spatial downscaling of land surface temperature with the multi-scale geographically weighted regression[J]. National Remote Sensing Bulletin, 25(8): 1749-1766. |
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