| The urban heat island effect(UHI effect)refers to the fact that urban areas are warmer than their suburban/rural counterparts,and is one of the most intuitive manifestations of environmental changes in the process of urbanization.Land surface temperature(LST)derived from satellite thermal infrared(TIR)data is an important variable for understanding surface energy fluxes,global environmental changes,urban climate,land-atmosphere interactions,urban thermal environments and thermal anomalies.In recent years,it has been widely used in various disciplines such as hydrology,meteorology,climate change,ecology,and environmental monitoring.Compared to spatially sparse temperature observations from permanent meteorological stations,remotely sensed TIR data provide spatially consistent and temporally regular LSTs over large-scale geographic areas.It is the only means to monitor land surfaces in a large range and a long time series,and it is of great significance to advance the research on the spatiotemporal dynamic change of urban thermal environments.However,more than one-half of the satellite-derived LST data are missing due to the weather conditions(e.g.clouds,shadows,and other atmospheric conditions)and sensor failure,which has greatly limited the acquisition of spatially consistent and temporally regular LSTs data,reduced the utilization and accuracy of the data,hindered the understanding of the evolution of the surface thermal environment in the spatialtemporal pattern.Exploring modeling inconsistency of time-series LSTs at different spatial scales can be helpful to find the optimal scale(s)at which the environmental processes operate and to minimize discrepancies in time-series modeling of LSTs across scales.Many driving forces could affect the SUHI and surface thermal environment.These factors include land cover type,landscape,urban space morphology,climatic conditions,and anthropogenic heat release.The influence of different dynamic driving forces on the seasonality of urban thermal environment at different spatial scales play an important role in urban planning decisions.Therefore,this paper will study the spatial and temporal inconsistency of LSTs,the consistency of LSTs time series modeling at different spatial scales,and the seasonal influence of dynamic driving factors on urban thermal environment at different spatial scales.The main contents of this thesis could be divided into the following three aspects:(1)An integrated method combing ATC model and 3D-CNN model for reconstruction high spatial-temperal LSTs considering SATs as auxiliary data.LSTs often have missing data and outliers due to cloud cover,sensor failure,and aerosol,which has greatly limited the applications of LSTs.Several algorithms have been developed to reconstruct the missing LSTs data.It should be noted that the aforementioned methods can only provide hypothetical clear-sky and low wind speed LST values,but not the real LSTs under cloudy conditions.In general,LSTs are sensitive to atmospheric conditions changes.In addition,the amount of clouds,surface wind,air humidity and land cover types are also incorporated.However,these methods are not effective for reconstructing daily LSTs for large spatial scales and high temporal dynamics.Therefore,we aimed to develop a hybrid reconstcting method based on the ATC and 3D-CNN model,which is more efficient for gapfilling urban scale and high time series daily LSTs;Firstly,SATs data viewed as auxiliary data due to LSTs is affected by weather conditions such as cloud cover,wind speed,air humidity and land cover types.Secondly,the missing LSTs data are roughly reconstructed using ATC models.Finally,a 3D-CNN model is constructed to fine reconstruct missing LSTs based on SATs data,and then the reconstruction results are evaluated.The results show that this method is simple,efficient,and highly accurate to reconstruct urban-scales and long-time series of LSTs compared with the existing reconstruction methods,especially for Landsat and MODIS data.(2)Reconciling inconsistency of annual temperature cycles modeled from Landsat and MODIS LSTs through a percentile approach.Time-series analysis of LSTs through semi-physical models such as the annual temperature cycle(ATC)model has become critical for these understandings.However,few studies have so far been conducted to examine/reconcile inconsistency of timeseries LST modeling results across spatial scales,weakening the reliability of these semi-physical models to characterize landscape thermal patterns.In this study,a percentile approach was proposed to reveal and reconcile discrepancies of ATC parameters estimated from Landsat(100 m)and MODIS(1000 m)LSTs.Results revealed substantial differences across spatial scales for each of the ATC parameters,i.e.,mean annual surface temperature(MAST),yearly amplitude of surface temperature(YAST),and revised phase shift(RPS),within the same land cover.The spatial distribution of ATC parameters estimated from MODIS LSTs across land cover types was quite different from that from Landsat LSTs.The percentile aggregation analysis suggested that the difference between MAST/YAST(and RPS)derived from MODIS LSTs and Landsat aggregated values at the 25th(and 40th)percentile within a MODIS block was close to zero.Further regression analysis showed that differences in ATC parameters,particularly MAST and YAST,derived from different datasets could be reconciled.Our study offers new insights into understanding inconsistencies in and reconciliations of ATC parameters modeled at different spatial scales for quantifying landscape thermal patterns over time.(3)The relationship between comprehensive/single dynamic driving forces and seasonal LSTs at different spatial scales.Many studies used only a single driving factor to establish the regression model with LSTs,and compared the individual effects of different factors on LSTs.However,the spatiotemporal pattern of LSTs is usually affected by a number of driving factors.We developed a new method to study the comprehensive/single dynamic driving forces and seasonal effects of LSTs at different scales.Firstly,the dynamic driving factors at different scales(100*100 m,250*250 m,500*500 m grid)are extracted.Secondly,the spatial pattern changes of LSTs in different seasons and the correlation between different dynamic driving forces and LSTs are analyzed.The random forest algorithm is used to simulate the seasonal impact of dynamic driving factors on LSTs at different scales,the importance of a single driving factor relative to LSTs is calculated,and the optimal scale is obtained.Then,the integrated indicators were defined based on the Fuzzy-AHP method by evaluating the contribution of a single driving factor to LSTs.Finally,to evaluate the correlation between dynamic driving factors and LSTs using spatial statistical models.This method provides a new exploration idea for quantifying various types of comprehensive driving factors and the relationship with LSTs.It also has important significance for urban thermal environment planning and urban design.The thesis resolves spatially consistent and temporally continuous LSTs,inconsistency of time-series LST modeling at different spatial scales,and quantifying various dynamic driving factors and their seasonal impacts on urban thermal environments.It provides a new and effective way to solve the problem of missing LSTs data,reliability at different scales and the comprehensive influence of dynamic driving factors in urban thermal environment. |
PDF Full Text Request