Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.
Because of the distinctive vertical climate and topography gradients in the alpine region, the snow cover of the Tienshan Mountains possesses complex spatiotemporal heterogeneity, particularly during the melting process. Quantifying the environmental factors is therefore crucial to understanding the melting process and for predicting and managing snowmelt runoff. Herein, the snow cover area, grain size, and contamination extent were determined to characterize the detailed melting status based on surface reflectance data of MOD09A1 in the central Tienshan Mountains from 2013 to 2017. The environmental factors collected include relief (elevation, slope, and aspect); meteorology (surface air temperature, land surface temperature, solar radiation, and wind speed); and land surface vegetation. Analysis of the geodetector results indicated the following. (1) Patterns of changes in the overall dominant environmental variables were consistent for the pre-, mid-, and post-melting periods defined according to the decline of snow cover area over five years. (2) The overall major environmental factors were wind speed and radiation (pre-period), land surface temperature and elevation (mid-period), and elevation and land surface types (post-period), respectively. (3) Regional distinctions were detected of the dominant environmental factors. In the pre-melting period, the effects of solar radiation and wind speed were noticeable in the north and south regions, respectively. The effects of elevation, land surface temperature, and land cover types became more prominent in all regions during the mid- and post-melting periods. (4) Interaction between the major environmental factors was significantly enhanced on both the overall and regional scales, thus affecting the snow-melting process. Finally, the energy distribution mismatch resulted in the snowmelt. Multiple environmental factors substantially affect heat redistribution at different spatiotemporal scales, resulting in the snowmelt as a complex manifestation of the factors and their interactions. The findings highlight regional differences in various environmental factors of the melting process and offer a theoretical foundation for the melting process at various scales over multiple years.
Because of the distinctive vertical climate and topography gradients in the alpine region, the snow cover of the Tienshan Mountains possesses complex spatial-temporal heterogeneity especially during the melting process. Quantifying the environmental factors is therefore crucial to understanding the melting process and for prediction and management of snowmelt runoff. In this study, the snow cover area, grain size, and contamination extent were determined to characterize the detailed melting status based on surface reflectance data of MOD09A1 in the central Tienshan Mountains from 2013 to 2017. The environmental factors collected include relief (elevation, slope, and aspect), meteorology (air temperature, land surface temperature, solar radiation, and wind speed), and land surface vegetation. Analysis of the Geo-detector results indicated the following. 1) The patterns of change in the overall dominant environmental variables were consistent for the pre-, mid-, and post-melting periods defined according to the decline of snow cover area over five years. 2) The overall major environmental factors were wind speed and radiation (pre-period), land surface temperature and elevation (mid-period), and elevation and land surface (post-period), respectively. 3) Regional distinctions were found in the spatiotemporal heterogeneity of the dominant environmental factors. In the pre-melting period, the effects of solar radiation and wind speed were noticeable in the north and south regions, respectively. The effects of elevation, land surface temperature, and land cover types became more prominent in all regions during the mid- and post-melting periods. 4) Interaction between the major environmental factors was significantly enhanced on both the overall and regional scales, thus affecting the snow melting process. Finally, the snowmelt is a response to an energy distribution mismatch. Multiple environmental factors substantially affect heat redistribution at different spatiotemporal scales, resulting in snow melt as a complex manifestation of the factors and their interactions. The findings highlighted regional differences in the various environmental factors of the melting process and offer a theoretical foundation for melting process at various scales over multiple years.
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