Influence Of Human Activities On Thermal Environment Based On Remote Sensing

At present,human beings are faced with the huge challenge of global climate change,and thermal environment change is an important research object in climate change studies.Thermal environment changes can affect the process of water vapor cycle,haze dissipation,and and extreme weather formation.Human activities are an important factor affecting the change of thermal environment.Human activities such as changing the underlying surface and anthropogenic heat emission have an important impact on regional thermal environment.How to quantify the impact of human activities and understand the origin of thermal environmental changes is of great significance for improving the ability to cope with thermal environmental changes.This is an urgent scientific problem to be solved.The impact of human activities on the thermal environment is studied from two aspects.On the one hand,the researches on the impact of changes in the land use/cover of the underlying surface are not precise enough,and the researchers ignore the impact of changes in different species within the same category.This study takes crop rotation as an example to explore the correlation between the fine changes of the underlying surface and the temporal and spatial changes in regional temperature.On the other hand,quantitative assessments of process impacts are lacking in studies on the impact of anthropogenic heat emissions using central heating as an example.There is a problem of overestimating the heat of central heating in the research based on the numerical model method.Therefore,combined with actual observation and statistical data,an indepth study on the thermal impact of central heating was performed.The advantages of remote sensing image time series data are fully exploited,and a quantitative evaluation method for the cumulative effect of the thermal environment is proposed,which breaks through the current technical problem of lack of thermal environment process evaluation.The main research results and innovations are summarized as follows.(1)The cumulative heat variation index was constructed based on multi-temporal remote sensing images,which solved the problem that the process impact of crop growth changes could not be evaluated based on single-phase images.Based on the proposed index,the effects of fine crop change,ie crop rotation,on the thermal environment were investigated for the first time.The study found that the rotation of some crops had an impact on the regional thermal environment,and changes in crop area is closely related to changes in the thermal environment.Area ratio changes of cotton/corn and cotton/winter wheat have a significant positive correlation with the annual cumulative heat variation index.The coefficients are 0.85 and 0.75,respectively.The cropland changes from winter wheat and corn to cotton will increase the area’s annual cumulative land surface temperature.And the effect of cotton/wheat rotation on thermal environment(35%)was significantly higher than that of temperature change(26%).Compared to traditional single-phase based method,the cumulative heat variation index more comprehensively reflects the impact of complete changes in crop growth,realizes the transition from instantaneous evaluation to process evaluation,and improves the comprehensiveness of evaluation.(2)Based on remote sensing time series images,an equivalent heat island index is proposed,which realizes a comprehensive evaluation of the effect of central heating on the central heating season.Aiming at the problem that single-phase temperature information cannot reflect the impact of anthropogenic heat changes on the thermal environment in the entire central heating season,this paper constructs an equivalent heat island index based on the time series of remote sensing images to quantitatively evaluate the urban heat island changes in the central heating season.The study found that the urban heat island phenomenon is evident at night,and the nighttime equivalent heat island area shows an increasing trend.The direction of the spatial change of the inter-annual equivalent heat island is the same as the direction of the center shift in urban expansion.The urban nighttime equivalent heat island area has a significant positive correlation with the central heating heat,of which the correlation coefficient of Shenyang is 0.89,the correlation coefficient of Changchun is 0.93,and the correlation coefficient of Harbin is 0.86,indicating that with the increase of central heating heat,the equivalent heat island area at night increases.And the contribution of central heating to the heat island is similar to that of the impervious surface,far greater than the contribution of air temperature.The proposed equivalent heat island index based on time accumulation can show the differences in different areas within the city,reflect the cumulative effect of the entire heating season,and help to comprehensively evaluate the long-term thermal environment.(3)The weather forecast model WRF was optimized based on remote sensing data and population data,and the accuracy of urban thermal environment simulation was improved.There is a gap between the old land use data of WRF and the situation in the study area,and there is a problem of overestimating building dense areas when dividing urban areas based on impervious surfaces.Therefore,an improved method for separating urban building dense areas based on the combination of impervious surface and population data is proposed,and the input of a new land use map is made.At the same time,the anthropogenic heat flux in the study area was calculated according to the anthropogenic heat model,and the default anthropogenic heat value of the model was updated.The study found that the correlation coefficient between the model simulation results and the monitoring data of the actual station in Shenyang is 0.93,and the root mean square error is 1.74;in Changchun,the correlation coefficient is 0.96,and the root mean square error is 2.52;in Harbin,the correlation coefficient is 0.94,the root mean square error is 1.56.It shows that the accuracy of the optimized WRF model is improved,and it can be used for fine simulation of urban thermal environment.(4)Based on the anthropogenic heat model and central heating data,the problem of overestimation of central heating heat was solved.The response of urban air temperature to different central heating heats was simulated and the results show that the increase in central heating heat leads to an increase in the air temperature of the city.The increase of urban temperature in high-density urban areas is about 0.26℃,and the increase in medium-density areas is about 0.11℃.In contrast,the impact on lowdensity urban areas is weak.When the central heating is doubled,the increase in the air temperature increases accordingly.Compared with no central heating,the cumulative heat island frequency and the equivalent heat island intensity increase in the city’s central area after adding the central heating,indicating that the central heating has an influence on the changes of the urban heat island.The study results show the influence of central heating on the urban thermal environment varies with urban heating and urban density.The research results can provide theoretical reference for the prediction of urban thermal environment change.