Comprehensive analysis method and system for long-term coupling relationship between urban development and urban thermal environment

Abstract

The invention provides a comprehensive analysis method and system for a long-term coupling relationship between urban development and an urban thermal environment. The method comprises the following steps: S1, acquiring and preprocessing surface temperature data and night light intensity data; s2, based on the preprocessed data, interaction relation analysis of the surface temperature and the night light intensity is carried out, wherein the interaction relation analysis comprises the steps that the long-term change trend of the surface temperature and the night light intensity is calculated; evaluating the correlation between the surface temperature and the dynamic change trend of the noctilucent intensity; calculating the double-variant spatial autocorrelation of the surface temperature and the luminous intensity; an LST-NTL change trend coupling coordination degree model is constructed, and the coordination degree of the thermal environment and the urbanization level is measured; and S3, comprehensively analyzing a result, and revealing a spatial law and an internal mechanism of co-evolution of the surface temperature and the luminous intensity in the urban development process. Theoretical support and scientific basis are provided for urban thermal environment optimization and toughness improvement, and policy making of low-carbon urban construction and climate adaptation type space governance is assisted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban environment and urban sustainable development, and particularly relates to a comprehensive analysis method and system for long-term coupling relationship between urban development and urban thermal environment. BACKGROUND

[0002] With the accelerating process of global urbanization, the change of urban landscape structure significantly affects the process of land surface energy balance. The continuous rise of land surface temperature (LST) in urban areas, i.e. urban heat island effect (UHI), has become an important issue in contemporary urban environmental research. A large number of studies have shown that the rapid increase of impervious surface and the continuous reduction of natural cover such as green land and water body during the process of urban expansion are the main driving factors leading to the rise of land surface temperature. This process not only poses potential threats to air quality, energy consumption and residents' health, but also increases the frequency of extreme weather events and affects the stability and sustainable development capacity of urban ecosystems.

[0003] However, existing methods mainly focus on modeling nighttime light intensity (NTL) as one of the influencing factors of thermal environment, lack of systematic characterization of the interaction mechanism between the two, and are difficult to support the fine identification and dynamic monitoring of the evolution process of urban thermal environment. Therefore, it is urgent to build a comprehensive analysis method integrating NTL and LST spatio-temporal data, to deeply characterize the coupling characteristics between urban development and thermal environment, and to serve the urban climate adaptation and sustainable development goals. SUMMARY

[0004] The purpose of the present application is to provide a comprehensive analysis method and system for long-term coupling relationship between urban development and urban thermal environment, to provide theoretical support and scientific basis for urban thermal environment optimization and resilience improvement, and to help policy making for low-carbon city construction and climate adaptation spatial governance.

[0005] To achieve the above purpose, the technical solutions of the present application are as follows:

[0006] A comprehensive analysis method for long-term coupling relationship between urban development and urban thermal environment, comprising the following steps:

[0007] S1, obtaining land surface temperature data and nighttime light intensity data, and performing data preprocessing;

[0008] S2, based on the preprocessed land surface temperature data and nighttime light intensity data, analyzing the interaction relationship between land surface temperature and nighttime light intensity, comprising:

[0009] calculating the long-term change trend of land surface temperature and nighttime light intensity;

[0010] Based on the change trend of land surface temperature and night light intensity, the correlation of the dynamic change trend of the two is evaluated;

[0011] The bivariate spatial autocorrelation of land surface temperature and night light intensity is calculated;

[0012] A coupling coordination degree model of LST-NTL change trend is constructed to measure the synergistic degree of the thermal environment and the urbanization level;

[0013] S3, comprehensive analysis results, reveal the spatial law and internal mechanism of the synergistic evolution of land surface temperature and night light intensity in the process of urban development.

[0014] Preferably, the land surface temperature data and the night light intensity data are specifically obtained by:

[0015] Selecting a region to be analyzed and a time range and obtaining corresponding multi-source remote sensing data, obtaining land surface temperature observation data and night light intensity observation data about different spatial units and different sampling time points.

[0016] Preferably, the data preprocessing includes projection conversion, removal of outliers, resampling, normalization and image cropping.

[0017] Preferably, the calculation formula of the normalized data processing is:

[0018]

[0019] Wherein: X' is the standardization result; X is the land surface temperature observation value or the night light intensity observation value; X min , X max are the minimum value and the maximum value of the land surface temperature or the night light intensity of the spatial unit and the year in which X is located, and the value range after standardization is 0≤X'≤1.

[0020] Preferably, for each spatial unit, the long-term change trend of land surface temperature LST and night light intensity NTL is obtained based on the Mann-Kendall trend test method respectively:

[0021] The Theil-Sen Median trend slope estimation is performed on the land surface temperature time series and the night light intensity time series of each spatial unit, and the Sen slope β is defined as the median of the slope of all data points:

[0022]

[0023] Wherein: x t is used to refer to the land surface temperature time series or the night light intensity time series, t=1,2,……,t n , x j' and x i' are the time series xt If β > 0, it represents that the time series has an upward trend; if β < 0, it represents that the time series has a downward trend;

[0024] The Mann-Kendall test is used for statistical verification to determine the significance of the trend, and the calculation formula is as follows:

[0025]

[0026] Where S represents the test statistic; the function sgn(·) is calculated as follows:

[0027]

[0028] Δx represents the input variable of the function sgn;

[0029] According to the value of S and its variance, the standardized statistic Z is calculated:

[0030]

[0031] Where: the value of Z is used to measure the significance of the trend, and |Z| > 1.96 indicates that the trend is significant at the 95% confidence level, otherwise the trend is not significant; where Z > 1.96 is a significant increase, and Z < -1.96 is a significant decrease.

[0032] Preferably, the correlation between the dynamic change trend of the surface temperature and the luminescence intensity is evaluated by calculating the correlation coefficient:

[0033]

[0034] Where: the correlation coefficient r xy represents the correlation between the dynamic change trend of the surface temperature and the luminescence intensity; x i and y i respectively represent the luminescence intensity change trend level and the surface temperature change trend level of the i-th spatial unit, and the change trend level refers to the Sen slope β of the corresponding time series; n is the total number of spatial units; represents the sample mean of all luminescence intensity change trend levels, represents the sample mean of all surface temperature change trend levels;

[0035] The value range of the correlation coefficient r xy is [-1, 1], when r xy > 0, it indicates that the two are positively correlated, when r xy < 0, it indicates that the two are negatively correlated, and the absolute value of r xy is closer to 1, the stronger the linear correlation between the variables.

[0036] Preferably, based on the bivariate global spatial autocorrelation and the bivariate local spatial autocorrelation, the bivariate spatial autocorrelation between the land surface temperature and the luminescence intensity is calculated;

[0037] The bivariate global spatial autocorrelation calculation formula is:

[0038]

[0039] Wherein, I is the bivariate global spatial autocorrelation index, the luminescence intensity trend level is the independent variable, and the land surface temperature trend level is the dependent variable; n is the total number of spatial units; W ij is the spatial weight matrix element based on the K nearest neighbor criterion, each spatial unit is only connected with the nearest K neighbors, and the elements in the weight matrix corresponding to the position are valued according to the adjacency relationship, and the rest positions are 0; x i represents the luminescence intensity trend level of the spatial unit i, y j represents the land surface temperature trend level of the spatial unit j, and the trend level refers to the Sen slope β of the corresponding time series;

[0040] represents the sample mean of the luminescence intensity trend level of all spatial units, represents the sample mean of the land surface temperature trend level of all spatial units; S is the bivariate standardized covariance term;

[0041] The bivariate local spatial autocorrelation calculation formula is:

[0042]

[0043] Wherein, I i is the local spatial relationship between the independent variable and the dependent variable of the spatial unit i; z i is the variance standardized value of the luminescence intensity trend level of the spatial unit i; z j is the variance standardized value of the land surface temperature trend level of the spatial unit j.

[0044] Preferably, based on I i , four clustering modes of HH aggregation, LL aggregation, LH aggregation and HL aggregation are formed, and the area of the spatial unit occupied by each clustering mode is obtained; when z i >0, , it is HH aggregation; when z i <0, , it is LL aggregation; when z i <0, , it is LH aggregation; when z i >0, , it is HL aggregation.

[0045] Preferably, the calculation formula of the LST-NTL change trend coupling coordination degree model is:

[0046]

[0047] T = αU1 + βU2

[0048] Wherein: D represents the coupling coordination degree of the night light intensity change trend level and the ground temperature change trend level,

[0049] U1 and U2 respectively represent the night light intensity change trend level and the ground temperature change trend level, the change trend level refers to the Sen slope β of the corresponding time sequence; the coupling degree C value reflects the interaction strength between the night light intensity and the ground temperature, the greater the value is, the stronger the synergy between the two is; the coordination index T value represents the comprehensive development level between the night light intensity and the ground temperature, the higher the value is, the closer the interaction between the two is and the better the balance is; α and β are preset coefficients, and α

[0050] β = 1.

[0051] A comprehensive analysis system of long-term coupling relationship between urban development and urban thermal environment, the system is realized by using any one of the above comprehensive analysis methods of long-term coupling relationship between urban development and urban thermal environment, comprising a data collection and preprocessing unit and an LST and NTL interaction relationship analysis unit;

[0052] The data collection and preprocessing unit is used for acquiring ground temperature data and night light intensity data and performing data preprocessing;

[0053] The LST and NTL interaction relationship analysis unit is used for performing interaction relationship analysis of the ground temperature and the night light intensity based on the preprocessed ground temperature data and night light intensity data.

[0054] Compared with the prior art, the present application has the following beneficial effects:

[0055] This invention provides a comprehensive analysis method and system for the long-term coupling relationship between urban development and the urban thermal environment. The method includes: acquiring surface temperature (LST) and nighttime light remote sensing data, and performing data preprocessing to obtain preprocessed data; calculating the long-term trends of LST and NTL to reveal their spatial structure and dynamic evolution; assessing the correlation between the dynamic trends of LST and NTL based on their trends; further calculating the bivariate spatial autocorrelation of LST and NTL to assess their spatial heterogeneity; constructing an LST-NTL trend coupling coordination degree model to measure the degree of synergy between the thermal environment and urbanization level; and comprehensively analyzing the results to reveal the spatial patterns and underlying mechanisms of the co-evolution of LST and NTL during urban development. This method integrates surface temperature and nighttime light remote sensing data, constructs a coupling coordination degree model, and systematically assesses the synergistic relationship between the urban thermal environment and urbanization level, enhancing the understanding of their spatiotemporal evolution mechanisms. Its beneficial effect lies in revealing the spatial heterogeneity of thermal environment changes in urban development, providing a scientific basis for thermal risk identification and urban planning. Attached Figure Description

[0056] Figure 1 A flowchart illustrating the comprehensive analysis method for the long-term coupling relationship between urban development and urban thermal environment according to an embodiment of the present invention;

[0057] Figure 2 This is a graph showing the correlation analysis results of LST and NTL trend changes in typical cities in Chinese climate zones according to an embodiment of the present invention.

[0058] Figure 3 This is a comparison chart of the bivariate spatial autocorrelation distribution and clustering pattern proportion of LST and NTL variation trends in typical cities in Chinese climate zones according to an embodiment of the present invention.

[0059] Figure 4 This invention presents a spatial distribution of the coupling coordination degree between LST and NTL change trends in typical cities in Chinese climate zones and a comparison of the mean values ​​between urban, suburban, and rural areas.

[0060] Figure 5 This is a statistical distribution of the coupling coordination degree of LST and NTL change trends in typical cities in Chinese climate zones according to an embodiment of the present invention. Detailed Implementation

[0061] The following is in conjunction with the appendix Figures 1-5 The technical solution of the present invention will be described in detail below.

[0062] like Figure 1 As shown, this invention proposes a comprehensive analysis method for the long-term coupling relationship between urban development and the urban thermal environment, comprising the following steps:

[0063] Step 100: Obtain land surface temperature data and night light intensity data, and perform data preprocessing to obtain preprocessed data;

[0064] Specifically, a selected analysis area and time range are selected, and corresponding multi-source remote sensing data are obtained to obtain land surface temperature observation data and night light intensity observation data about different spatial units and different sampling time points.

[0065] The selected land surface temperature data are determined based on correlation fitting results with night light intensity, and data of July and August with higher correlation are preferentially selected to synthesize average annual data, and years are used as sampling time points.

[0066] Data preprocessing includes projection conversion, removal of outliers, resampling, normalization, and image cropping.

[0067] Step 200: Calculate the long-term change trend of land surface temperature LST and night light intensity NTL, and reveal the spatial structure and dynamic evolution law of the two;

[0068] Specifically, the long-term change trend analysis method is based on the Mann-Kendall trend test method.

[0069] Theil-Sen Median trend slope estimation is performed on the land surface temperature time series and night light intensity time series of each spatial unit, and the Sen slope β is defined as the median of the slopes of all data points:

[0070]

[0071] wherein x t is used to refer to the land surface temperature time series or the night light intensity time series, t = 1, 2, …, t n , x j' and x i' are the i'th and j'th sampling time point corresponding data in the time series x t ; if β > 0, it indicates that the time series has an upward trend; if β < 0, it indicates that the time series has a downward trend;

[0072] Mann-Kendall test is used for statistical verification to determine the significance of the trend, and the calculation formula is as follows:

[0073]

[0074] wherein S represents the test statistic; the function sgn(·) is calculated as follows:

[0075]

[0076] Δx represents the input variable of the function sgn.

[0077] Calculate the standardized statistic Z based on the S-value and its variance:

[0078]

[0079] Wherein: The Z-value is used to measure the significance of the trend. |Z|>1.96 indicates that the trend is significant at the 95% confidence level, otherwise the trend is not significant; Z>1.96 indicates a significant increase, and Z<-1.96 indicates a significant decrease.

[0080] Step 300: Based on the changing trends of LST and NTL, assess the correlation between their dynamic changing trends;

[0081] like Figure 2 As shown, correlation analysis was conducted based on the long-term trends of LST and NTL in the long-term series to obtain the correlation coefficient results for different cities.

[0082] Specifically, the formula for calculating the correlation coefficient is:

[0083]

[0084] Where: correlation coefficient r xy This indicates the correlation between the dynamic trends of surface temperature and nighttime light intensity; x i With y i These represent the trend levels of nighttime light intensity and surface temperature change in the i-th spatial unit, respectively. The trend level refers to the Sen slope β of the corresponding time series; n is the total number of spatial units. This represents the sample mean of all trends in night light intensity. The sample mean representing the trend level of all surface temperature changes;

[0085] Correlation coefficient r xy The value range of r is [-1, 1]. xy >0 indicates a positive correlation between the two; when r xy <0 indicates a negative correlation between the two, and r xy The closer the absolute value is to 1, the stronger the linear correlation between the variables.

[0086] Step 400: Use the bivariate spatial autocorrelation method to reveal the heterogeneity of the spatial correlation between the NTL and LST trends in each city, and analyze the spatial response pattern of LST to NTL.

[0087] like Figure 3 Moran's index and spatial clustering type of LST and NTL are obtained by bivariate autocorrelation method.

[0088] Specifically, the double variable global spatial autocorrelation calculation formula is:

[0089]

[0090] Wherein: I is the double variable global spatial autocorrelation index, the night light intensity trend level is the independent variable, and the land surface temperature trend level is the dependent variable; n is the total number of spatial units; W ij is the spatial weight matrix element based on K nearest neighbor criterion, each spatial unit only establishes connection with the nearest K neighbors, and the elements in the weight matrix corresponding position are valued according to the adjacency relationship, and the rest positions are 0; x i represents the night light intensity trend level of spatial unit i, y j represents the land surface temperature trend level of spatial unit j, and the trend level refers to the Sen slope β of the corresponding time series; represents the sample mean of the night light intensity trend level of all spatial units, represents the sample mean of the land surface temperature trend level of all spatial units; S is the double variable standardized covariance term;

[0091] Specifically, the double variable local spatial autocorrelation calculation formula is:

[0092]

[0093] Wherein: I i is the local spatial relationship between the independent variable and the dependent variable of spatial unit i; z i is the variance standardized value of the night light intensity trend level of spatial unit i; z j is the variance standardized value of the land surface temperature trend level of spatial unit j.

[0094] Based on I i , four clustering modes of HH aggregation, LL aggregation, LH aggregation and HL aggregation are formed, and the area of spatial units occupied by each clustering mode is obtained; when z i >0, , it is HH aggregation; when z i <0, , it is LL aggregation; when z i <0, , it is LH aggregation; when z i >0, , it is HL aggregation.

[0095] Step 500: constructing LST-NTL trend coupling coordination degree model to measure the synergistic degree of urban thermal environment and urbanization level;

[0096] Quantify the coordination between LST and NTL trends, and further systematically characterize the degree of their coordinated development.

[0097] As shown in Figure 4 , the coupling coordination degree model is used to quantitatively evaluate the coordination level of urban development and land surface temperature;

[0098] As shown in Figure 5 , according to the coupling coordination degree results, the comparative results of the numerical distribution of the coupling coordination degree of different cities are obtained; specifically, the calculation formula of the LST-NTL trend coupling coordination degree model is:

[0099]

[0100] T = αU1 + βU2

[0101] Where: D represents the coupling coordination degree of the nighttime light intensity trend level and the land surface temperature trend level, U1 and U2 represent the nighttime light intensity trend level and the land surface temperature trend level, respectively, and the trend level refers to the Sen slope β of the corresponding time series; the coupling degree C value reflects the interaction strength between nighttime light intensity and land surface temperature, and the larger the value, the stronger the synergy between the two; the coordination index T value represents the comprehensive development level between nighttime light intensity and land surface temperature, and the higher the value, the closer the interaction between the two and the better the balance; α and β are preset coefficients, and α + β = 1.

[0102] Step 600: Comprehensive analysis results to get the correlation coefficient (r xy ), Moran's index (bivariate global spatial autocorrelation index I), the area of each cluster pattern, and the analysis of the coordination degree distribution and the mean value, to reveal the spatial law and internal mechanism of the coordinated evolution of LST and NTL in the process of urban development.

[0103] Corresponding to the above method, the embodiment also provides a comprehensive analysis system for the long-term coupling relationship between urban development and urban thermal environment, which comprises a data collection and preprocessing unit and an LST and NTL interaction analysis unit.

[0104] The data collection and preprocessing unit is used to obtain the LST and NTL remote sensing data sets of 21 years (2000-2020) of typical cities in China climate zones, and to perform preprocessing operations on the image data;

[0105] The LST and NTL interaction analysis unit is used to extract the long-term change trend of LST and NTL, calculate the Pearson correlation coefficient and bivariate Moran index of the two, so as to evaluate the correlation and spatial heterogeneity characteristics; also used to build a LST-NTL change trend coupling coordination degree model, to quantitatively measure the synergistic relationship between urban heat environment and urbanization level, and reveal the spatial differentiation law and internal driving mechanism of the synergistic evolution, to provide data support and decision basis for urban heat risk identification and spatial planning regulation.

[0106] The beneficial effects of the present application are as follows:

[0107] 1) The present application comprehensively utilizes multi-source remote sensing data of land surface temperature (LST) and nighttime light intensity (NTL), systematically reveals the long-term coupling mechanism of urban heat environment and urbanization level, and can dynamically depict the heat environment response characteristics in the process of urban development;

[0108] 2) The present application builds a LST-NTL change trend coupling coordination degree model, quantitatively evaluates the synergistic degree between urban heat environment and urbanization process, realizes the change from single variable description to comprehensive synergistic mechanism evaluation, and enhances the scientificity and systematicness in urban spatial planning;

[0109] 3) The present application introduces the joint analysis method of Pearson correlation coefficient and bivariate Moran index, reveals the relationship strength of LST and NTL, identifies the spatial heterogeneity characteristics, and improves the spatial explanation of urban heat environment research, and provides technical support for regional differentiated governance;

[0110] 4) The present application has good universality and generalizability, the data source used is publicly available, the analysis method is standard and reproducible, and is suitable for heat environment evolution and urban development coupling analysis of different climate zones and different city types, and provides a replicable research framework for urban heat risk identification and climate adaptation city construction;

[0111] 5) The present application can serve the practical needs of urban heat island governance, land use optimization and infrastructure planning, and is helpful to improve the urban climate resilience and the quality of human settlement environment, and has important theoretical value and application prospect.

[0112] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the various embodiments can be mutually referred to.

[0113] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used for helping to understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, according to the idea of the present application, the specific implementation manners and application ranges will be changed. In conclusion, the content of the present specification should not be understood as the limitation of the present application.

Claims

1. A method for comprehensive analysis of long-term coupling relationship between urban development and urban heat environment, characterized in that, The method comprises the following steps: S1, obtaining surface temperature data and night light intensity data, and performing data preprocessing; S2, based on the preprocessed surface temperature data and night light intensity data, analyzing the interaction between surface temperature and night light intensity, including: calculating the long-term change trend of surface temperature and night light intensity; based on the change trend of surface temperature and night light intensity, evaluating the correlation of the dynamic change trend of the two; calculating the bivariate spatial autocorrelation of surface temperature and night light intensity; constructing a LST-NTL change trend coupling coordination degree model to measure the synergy degree of thermal environment and urbanization level; S3, comprehensively analyzing the results to reveal the spatial law and internal mechanism of the synergistic evolution of surface temperature and night light intensity in the process of urban development.

2. A comprehensive analysis method of long-term coupling relationship between urban development and urban heat environment, characterized in that, The surface temperature data and night light intensity data are obtained by: selecting the region to be analyzed and the time range and obtaining the corresponding multi-source remote sensing data to obtain the surface temperature observation data and night light intensity observation data of different spatial units and different sampling time points.

3. The method of claim 1, wherein, The data preprocessing includes projection conversion, removal of outliers, resampling, normalization and image cropping.

4. The method of claim 3, wherein, The calculation formula of the normalized data processing is: Wherein: X' is the standardized result; X is the observed value of land surface temperature or night light intensity; X min , X max is the minimum and maximum values of land surface temperature or night light intensity in the space unit and year in which X is located, and the standardized value range is 0≤X'≤1.

5. The method of claim 1, wherein, For each spatial unit, the long-term change trend of surface temperature LST and night light intensity NTL is obtained based on the Mann-Kendall trend test method: The Theil-Sen Median trend slope of the surface temperature time series and the night light intensity time series of each spatial unit is estimated, and the Sen slope β is defined as the median of all data point slopes: wherein: x t for referring to the ground surface temperature time series or the night light intensity time series, t = 1, 2, …, t n , x j' and x i' are the i'th and j'th sampling time point corresponding data in the time series x t , respectively; if β > 0, it represents an upward trend of the time series; if β < 0, it represents a downward trend of the time series; Mann-Kendall test is used for statistical verification to judge the significance of the trend, and the calculation formula is as follows: Wherein, S represents the test statistic; The calculation of function sgn(·) is as follows: Δx represents the input variable of function sgn; According to the value of S and its variance, the normalized statistic Z is calculated: Wherein: the value of Z is used to measure the significance of the trend, |Z|>1.96 indicates that the trend is significant at the 95% confidence level, otherwise the trend is not significant; Where Z>1.96 is a significant increase, and Z<-1.96 is a significant decrease.

6. The method of claim 5, wherein, By calculating the correlation coefficient, the correlation of the dynamic change trend of surface temperature and night light intensity is evaluated: wherein: the correlation coefficient r xy represents the correlation between the dynamic change trend of the surface temperature and the luminescence intensity; x i and y i respectively represent the luminescence intensity change trend level and the surface temperature change trend level of the i-th spatial unit, and the change trend level refers to the Sen slope β of the corresponding time series; n is the total number of spatial units; represents the sample mean of all luminescence intensity change trend levels, represents the sample mean of all surface temperature change trend levels; Correlation coefficient r xy r∈[-1,1], when r xy >0, it indicates that the two are positively correlated, when r xy <0, it indicates that the two are negatively correlated, and the absolute value of r xy is closer to 1, the stronger the linear correlation between variables.

7. The method of claim 5, wherein the method further comprises: Based on the bivariate global spatial autocorrelation and bivariate local spatial autocorrelation, the bivariate spatial autocorrelation of surface temperature and night light intensity is calculated; The calculation formula of the bivariate global spatial autocorrelation is: Where: I is the bivariate global spatial autocorrelation index, the trend level of the intensity of the night light is the independent variable, and the trend level of the land surface temperature is the dependent variable; n is the total number of spatial units; W ij is the spatial weight matrix element constructed based on the K nearest neighbor criterion, each spatial unit is connected only with the nearest K neighbors, and the elements in the corresponding position of the weight matrix are valued according to the adjacency relationship, and the rest are 0; x i represents the trend level of the intensity of the night light of the spatial unit i, y j represents the trend level of the land surface temperature of the spatial unit j, and the trend level refers to the Sen slope β of the corresponding time series; represents the sample mean of the trend level of the intensity of the night light of all spatial units, represents the sample mean of the trend level of the land surface temperature of all spatial units; S is the bivariate standardized covariance term; The calculation formula of the bivariate local spatial autocorrelation is: where: I i is the local spatial relationship of the independent variable to the dependent variable for spatial unit i; z i is the variance standardized value of the noctilucent intensity trend level for spatial unit i; z j is the variance standardized value of the surface temperature trend level for spatial unit j.

8. The method of claim 7, wherein, Based on I i Forming HH aggregation, LL aggregation, LH aggregation and HL aggregation four clustering modes, obtaining the area of the space unit occupied by each clustering mode; when z i > 0, is HH aggregation; when z i < 0, is LL aggregation; when z i < 0, is LH aggregation; when z i > 0, is HL aggregation.

9. The method of claim 5, wherein, The calculation formula of the LST-NTL change trend coupling coordination degree model is: T=αU1+βU2 Wherein: D represents the coupling coordination degree of the night light intensity change trend level and the ground temperature change trend level, U1 and U2 represent the night light intensity change trend level and the ground temperature change trend level respectively, the change trend level refers to the Sen slope β of the corresponding time sequence; the coupling degree C value reflects the interaction strength between the night light intensity and the ground temperature, the larger the value is, the stronger the synergy between the two is; the coordination index T value represents the comprehensive development level between the night light intensity and the ground temperature, the higher the value is, the closer the interaction between the two is and the better the balance is; α and β are preset coefficients, and α+β=1.

10. A comprehensive analysis system of long-term coupling relationship between urban development and urban heat environment, characterized in that, The system is realized by using the comprehensive analysis method of long-term coupling relationship between urban development and urban thermal environment according to any one of claims 1-9, comprising a data collection and preprocessing unit and an interactive relationship analysis unit of LST and NTL; The data collection and preprocessing unit is used for acquiring ground temperature data and night light intensity data, and performing data preprocessing; The interactive relationship analysis unit of LST and NTL is used for analyzing the interactive relationship between ground temperature and night light intensity based on the preprocessed ground temperature data and night light intensity data.

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