Method and system for analyzing urban thermal environment driving factors

Abstract

The invention relates to the field of urban thermal environment analysis, in particular to an urban thermal environment driving factor analysis method and system. A plurality of possible driving factors used for reflecting the influence of surface coverage, urban spatial forms and human activities on the surface temperature are constructed, and the visual influence of each driving factor on the surface temperature is visually observed through a driving factor spatial distribution diagram and a surface temperature spatial distribution diagram. Then, a plurality of first driving factors are obtained by calculating Pearson correlation and performing collinearity test, and a plurality of main driving factors are obtained through geographically weighted regression analysis, so that deep analysis of the driving factors on the difference of the overall and local action directions of the LST and comparative analysis of the driving factors on the marginal action of the specific elements of the local space are realized; the problems that in the prior art, deep discussion of driving factors on LST overall and local action direction differences is lacked, and comparative analysis of local space specific element marginal action is lacked are solved.

Description

Technical Field

[0001] The present invention relates to the field of urban thermal environment analysis, and in particular to an analysis method and system for urban thermal environment driving factors. Background Art

[0002] Since the driving factors of urban thermal environment are complex and diverse, existing studies mostly focus on empirical research on single urban factors, integrated artificial environment or natural climate. From the perspective of the dominant causes of the formation of the urban heat island effect, the increase in artificial heat sources such as production and transportation caused by urbanization and the transfer of population to cities is the key driving force of the urban thermal environment, while comprehensive analysis of this is rarely involved. In terms of spatial non-stationarity analysis, related studies mostly use multiple regression analysis, geographic detectors and other methods to focus on the global effects of various factor indicators, and lack in-depth discussions on the differences in the overall and local directions of the driving factors on LST, especially in different spatial locations within the study area, and comparative analysis of the marginal effects of specific local spatial factors. Summary of the invention

[0003] In view of the shortcomings of the existing technology, the present invention provides an analysis method and system for the driving factors of the urban thermal environment, which solves the problems that the existing technology lacks an in-depth discussion on the differences in the overall and local action directions of the driving factors on LST, and lacks a comparative analysis of the marginal effects of specific elements in the local space.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An analysis method for driving factors of urban thermal environment, the analysis method comprising the following steps:

[0006] S1. Obtain basic data including remote sensing image data of the city to be analyzed, building vector data of the main urban area, city map, daytime population thermal data and nighttime lighting data;

[0007] S2. Based on the basic data, calculate the normalized building index, normalized vegetation index, normalized water index, volume ratio, average building height, building density and sky openness, and construct the spatial distribution map of each driving factor;

[0008] S3. Invert the surface temperature of each location in the city to be analyzed based on the basic data, and classify the surface temperature to generate a surface temperature spatial distribution map and a surface temperature classification distribution map;

[0009] S4. Calculate the local Moran's index and the global Moran's index of each location in the city to be analyzed based on the data in the surface temperature spatial distribution map, and divide the locations in the city to be analyzed into four clustering types: high-high clustering, low-low clustering, high-low clustering, and low-high clustering, so as to generate a surface temperature spatial clustering distribution map;

[0010] S5. Calculate the Pearson correlation based on the data in the spatial distribution map of surface temperature and the spatial distribution map of driving factors, and select several driving factors with the highest goodness of fit through collinearity test, which are marked as the first driving factors;

[0011] S6. Perform geographically weighted regression analysis on the first driving factors and surface temperature, integrate the marginal effects and significance test results of each first driving factor, and mark several main driving factors.

[0012] Preferably, in step S2, the following steps are specifically included:

[0013] S21. Obtain a city map of the city to be analyzed based on the basic data, and grid the city map;

[0014] S22. Calculate the values ​​of the driving factors of the normalized building index, normalized vegetation index, normalized water index, volume ratio, average building height, building density and sky openness of each grid area in the city map based on the basic data;

[0015] S23. Construct a spatial distribution map of driving factors based on the values ​​of each driving factor and the city map.

[0016] Preferably, in step S22, the calculation formula of the normalized building index is:

[0017]

[0018] The calculation formula of normalized difference vegetation index is:

[0019]

[0020] The calculation formula of normalized water index is:

[0021]

[0022] The formula for calculating the volume ratio is:

[0023]

[0024] The formula for calculating the average building height is:

[0025]

[0026] The formula for calculating building density is:

[0027]

[0028] The calculation formula for sky openness is:

[0029]

[0030] in,

[0031]

[0032] In the above formula, I NDBI ,I NDVI ,I MNDWI , FAR, BH, BD and SVF represent the normalized building index, normalized vegetation index, normalized water index, floor area ratio, average building height, building density and sky openness, respectively, ρ GREEN , R , NIR and ρ SWIR They correspond to the reflectance of bands 3, 4, 5 and 6 in Landsat9 OLI / TIRS images, i and N are the number of buildings and the total number of buildings in a fishing net unit, respectively. base(i) is the base area of ​​the i-th building, n i is the number of floors of the i-th building, A fishnet is the area of ​​a fishing net unit, H i is the height of the i-th building in a fishing net unit, i and N are the number of buildings and the total number of buildings in a fishing net unit respectively, ΔH is the height difference from the selected base point to the roof of the building, W is the block width, that is, the width range of the GIS basic point search, there are M directions in total, and θ represents the azimuth within the search range.

[0033] Preferably, in step S3, the following steps are specifically included:

[0034] S31. Calculate the surface temperature of the city to be analyzed based on the basic data to obtain a spatial distribution map of the surface temperature;

[0035] S32, calculating the average and standard deviation of the surface temperature at each location in the city to be analyzed;

[0036] S33. Divide the surface temperature of each location in the city to be analyzed into several temperature levels and corresponding temperature ranges according to the average value and standard deviation, so as to generate a surface temperature classification distribution map.

[0037] Preferably, in step S31, the calculation formula of the ground surface temperature is:

[0038] L λ =[ε·B(T S )+(1-ε)L↓ ]·τ+L ↑

[0039]

[0040] In the above formula, L λ is the thermal infrared radiation value received by the satellite sensor, ε is the surface emissivity (dimensionless), T S is the true surface temperature, B(T S ) is the blackbody thermal radiation brightness, τ is the transmittance of the atmosphere in the thermal infrared band (dimensionless), L ↓ and L ↑ are respectively the downward and upward radiation of the atmosphere, K 1 and K 2 are the first constant and the second constant, respectively, which can be obtained from the MTL file of Landsat9 OLI / TIRS image. For the TIRS10 thermal infrared band, K 1 =799.03W / (m 2 ·um·sr), K2=1329.24K.

[0041] Preferably, in step S5, the following steps are specifically included:

[0042] S51. Randomly select a number of sample points in the city to be analyzed;

[0043] S52, obtaining the surface temperature of the sample point and the values ​​of each driving factor according to the surface temperature spatial distribution map and the driving factor spatial distribution map;

[0044] S53, calculating the Pearson coefficient of each driving factor of the sample point to the surface temperature, and arranging the driving factors in descending order according to the absolute value of the Pearson coefficient;

[0045] S54, perform collinearity test on each driving factor and surface temperature, and select driving factors with multicollinearity;

[0046] S55. After removing driving factors with multicollinearity in turn, calculate the goodness of fit between the remaining driving factors and the surface temperature, and mark the driving factors corresponding to the highest goodness of fit as the first driving factors.

[0047] Preferably, in step S6, the following steps are specifically included:

[0048] S61. Calculate the regression coefficient and P value of each first driving factor in each city to be analyzed by geographically weighted regression analysis. The calculation formula of the regression coefficient of each first driving factor in the geographically weighted regression analysis model is:

[0049]

[0050] In the above formula, Y i represents the surface temperature at sample point i in the city to be analyzed, β 0 (μ i , v i ) is the constant term at point i, (μ i , v i ) is the coordinate of point i, β k (μ i , v i ) is the regression coefficient of the kth driving factor at point i, x ik is the value of the kth driving factor at point i, f is the number of driving factors, ε i is the random error at point i;

[0051] S62. Select several first driving factors according to the regression coefficient and P value of each first driving factor and mark them as main driving factors.

[0052] The technical solution also provides an analysis system for implementing the analysis method, the system comprising: a processor and a memory, the memory being used to store a computer program, and the computer program implementing any one of the analysis methods when executed by the processor.

[0053] Compared with the prior art, the present invention provides a method and system for analyzing driving factors of urban thermal environment, which has the following beneficial effects:

[0054] 1. The present invention constructs several possible driving factors that reflect the impact of land cover, urban spatial form and human activities on land surface temperature, and constructs corresponding spatial distribution maps of driving factors and land surface temperature to visually observe the intuitive impact of each driving factor on land surface temperature. Then, several main driving factors are obtained by calculating Pearson correlation, collinearity test and geographically weighted regression analysis in sequence, thereby realizing an in-depth analysis of the differences in the overall and local action directions of driving factors on LST, and a comparative analysis of the marginal effects of specific elements in local space.

[0055] 2. The present invention establishes a spatial distribution map of surface temperature and a spatial distribution map of driving factors, selects the first driving factor by calculating the Pearson coefficient and the collinearity test, and then screens out several main driving factors most relevant to the surface temperature through the geographically weighted regression model, so as to facilitate the analysis of the impact of the regression coefficient of the main driving factor on the surface temperature.

[0056] 3. The present invention constructs a spatial cluster distribution map of surface temperature. The global Moran's index can reflect the correlation of attribute values ​​of all spatial units in the entire study area. The local Moran's index is used to measure the spatial dependence of surface temperature within the local geographical range. Based on the results of the local Moran's index, four clustering types are determined: high-high clustering, low-low clustering, high-low clustering, and low-high clustering. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0058] Figure 1 It is a flow chart of the method for analyzing the driving factors of urban thermal environment of the present invention;

[0059] Figure 2 A zoning map of the urban area to be analyzed in the present invention;

[0060] Figure 3 It is the spatial distribution characteristic diagram of the normalized vegetation index NDVI of the present invention;

[0061] Figure 4 It is the spatial distribution characteristic diagram of the normalized water index MNDWI of the present invention;

[0062] Figure 5 It is a spatial distribution characteristic diagram of the floor area ratio FAR of the present invention;

[0063] Figure 6 It is the spatial distribution characteristic diagram of the average building height BH of the present invention;

[0064] Figure 7 It is the spatial distribution characteristic diagram of the building density BD of the present invention;

[0065] Figure 8 It is the spatial distribution characteristic diagram of the sky openness SVF of the present invention;

[0066] Fig. 9 It is the spatial distribution characteristic diagram of the daytime population thermal DP of the present invention;

[0067] Fig.10 It is the spatial distribution characteristic diagram of nighttime population density NP of the present invention;

[0068] Fig.11 The spatial distribution map of LST in the main urban area of ​​the city to be analyzed in the present invention;

[0069] Fig.12 This is the hierarchical distribution map of LST in the main urban area of ​​the city to be analyzed in the present invention;

[0070] Fig.13It is the spatial cluster distribution map of surface temperature of the present invention;

[0071] Fig.14 This is a regional difference diagram of the impact of the regression coefficient of MNDWI on the surface temperature of the present invention;

[0072] Fig.15 It is a regional difference diagram of the impact of the regression coefficient of NDVI on the surface temperature of the present invention;

[0073] Fig.16 It is a regional difference diagram of the impact of the regression coefficient of FAR on the surface temperature of the present invention;

[0074] Fig.17 It is a regional difference diagram of the impact of the regression coefficient of NP on the surface temperature of the present invention;

[0075] Fig.18 It is a regional difference diagram of the impact of the regression coefficient of BD on the surface temperature of the present invention;

[0076] Fig.19 This is a regional difference diagram of the impact of the regression coefficient of BH on the surface temperature of the present invention;

[0077] Fig. 20 It is a regional difference diagram of the impact of the regression coefficient of DP on the surface temperature of the present invention;

[0078] Fig.21 This is a regional difference diagram of the impact of the regression coefficient of SVF on the surface temperature of the present invention. DETAILED DESCRIPTION

[0079] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0080] Those skilled in the art can understand that all or part of the steps in the following embodiments can be completed by instructing the relevant hardware through a program, so the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0081] In order to solve the problems that the prior art lacks an in-depth discussion on the differences in the overall and local directions of LST effects of driving factors, and lacks a comparative analysis of the marginal effects of specific local spatial elements, the present invention provides an analysis method for urban thermal environment driving factors. Starting from the specific indicators of the urbanization process, the study selects three types of significant elements that are constantly evolving in urban construction, namely, land cover, urban spatial form, and human activities. The GWR model is used to explore the global and local spatial action mechanisms of LST driving factors, clarify the impact of regional key driving factors and their spatial differences, thereby intuitively reflecting the spatial characteristic relationship between LST and its driving factors, in order to provide an effective idea for improving the urban heat island effect. Taking the urban area of ​​Hefei as an example, this analysis method is described in detail below. Figure 1 As shown, the analysis method includes the following steps:

[0082] S1. Obtain basic data including remote sensing image data of the city to be analyzed, building vector data of the main urban area, city map, daytime population thermal data and nighttime light data; the remote sensing image data can be Landsat9 OLI / TIRS image and MOD11A1 data, the building vector data of the main urban area can be obtained through websites such as Baidu Street View and Baidu Architecture, and the city map can be obtained through relevant websites of the city, such as the website of Hefei Natural Resources and Planning Bureau, such as Figure 2 Shown is the district map of Hefei city;

[0083] S2. Based on the basic data, calculate the normalized building index, normalized vegetation index, normalized water index, volume ratio, average building height, building density and sky openness, and construct the spatial distribution map of each driving factor;

[0084] The surface cover properties are closely related to the spatial distribution pattern of urban thermal environment. The surface of urban space mainly includes buildings, vegetation and water bodies. The spatial distribution of specific cover types can be clarified by extracting the corresponding index information from remote sensing images. Among them, the building index NDBI can clearly show the density of urban buildings and the impermeability of the surface, and is widely used to extract urban building land. The vegetation index NDVI can directly characterize the growth state of plants and their distribution in different areas; the water body index MNDWI is not only a general reflection of the spatial morphology of water bodies, but also plays an important role in water resources management and water quality monitoring. Therefore, this study selected the building index NDBI, vegetation index NDVI and water body index MNDWI that were normalized or improved normalized as the driving factors of this type. By calculating the corresponding bands of the pre-processed remote sensing image data, the quantified surface cover factors were obtained. After detecting and removing the outliers of each factor, the spatial distribution grids of the three types of factors were finally obtained.

[0085] Urban spatial form is the physical reflection of urban development. It covers the representation of functional regional pattern, three-dimensional orientation of land development and combination form of architectural space. Different urban spatial forms must be differentiated combinations of material spatial elements. High-intensity and high-density development brings not only changes to the natural environment, but also aggravates the high-temperature thermal environment. The floor area ratio FAR is an important indicator to measure the intensity of construction land use. There are two main views on its spatial characteristic relationship with LST. One view is that FAR is positively correlated with surface temperature, and the other view is the opposite. These previous studies on FAR and heat island effect provide valuable experience. Regarding the relationship between building height BH and thermal environment, generally speaking, the higher the BH of the planned plot, the higher the proportion of building shadows projected onto the surface, which in turn affects the thermal comfort in the area. The building density BD is related to Sky openness SVF can reflect the building density or vacancy rate within a certain land use range, and has an impact on the daylighting, ventilation efficiency, and thermal radiation in the area. It is an important factor affecting the urban climate environment. Therefore, this study selects driving factors such as floor area ratio FAR, building height BH, building density BD, and sky openness SVF to quantify the urban spatial form and show the morphological characteristics of the city. The location, shape, and height of existing buildings in the main urban area of ​​Hefei are obtained through Baidu Architecture. Combined with Baidu Street View Map and on-site surveys, the demolished buildings and newly added buildings are deleted, rectified, and supplemented to accurately reflect the building space form in the main urban area. Taking into account the differences in building heights in different types of land, the building height value follows the principle of uniformly using 3m floor height for residential buildings and 4m floor height for industrial buildings, commercial and office buildings, etc.

[0086] Artificial heat generated by human activities is one of the main driving forces of urban thermal environment, mainly involving energy consumption, social production, transportation and building systems. However, no matter which level it is, it is closely related to the distribution of urban population. Although the spatial distribution of urban population cannot accurately reflect the actual generation and emission of artificial heat sources, it has a macroscopic reflection of the distribution characteristics of human activities and the general law of the correspondence between space and activities. It is suitable to use it as a proxy variable to explore the reasons for the spatial differentiation of thermal environment from an overall or local spatial perspective. The study uses population thermal data and night light data to quantify population density, and characterizes the spatial distribution status of the two driving factors, daytime population DP and nighttime population NP, respectively.

[0087] In order to digitize and visualize the above driving factors, step S2 specifically includes the following steps:

[0088] S21. Obtain a city map of the city to be analyzed based on the basic data, and grid the city map;

[0089] S22. Calculate the values ​​of the driving factors of the normalized building index, normalized vegetation index, normalized water index, volume ratio, average building height, building density and sky openness of each grid area in the city map based on the basic data;

[0090] The calculation formula of the normalized building index is:

[0091]

[0092] The calculation formula of normalized difference vegetation index is:

[0093]

[0094] The calculation formula of normalized water index is:

[0095]

[0096] The formula for calculating the volume ratio is:

[0097]

[0098] The formula for calculating the average building height is:

[0099]

[0100] The formula for calculating building density is:

[0101]

[0102] The calculation formula for sky openness is:

[0103]

[0104] in,

[0105]

[0106] In the above formula, I NDBI ,I NDVI ,I MNDWI , FAR, BH, BD and SVF represent the normalized building index, normalized vegetation index, normalized water index, floor area ratio, average building height, building density and sky openness, respectively, ρ GREEN , R , NIR and ρ SWIR They correspond to the reflectance of bands 3, 4, 5 and 6 in Landsat9 OLI / TIRS images, i and N are the number of buildings and the total number of buildings in a fishing net unit, respectively. base(i) is the base area of ​​the i-th building, n i is the number of floors of the i-th building, Afishnet is the area of ​​a fishing net unit, H i is the height of the i-th building in a fishing net unit, i and N are the number of buildings and the total number of buildings in a fishing net unit respectively, ΔH is the height difference from the selected base point to the roof of the building, W is the block width, that is, the width range of the GIS basic point search, there are M directions in total, θ represents the azimuth within the search range, the daytime population heat is obtained through the population thermal data, and the nighttime population density is obtained through the nighttime data.

[0107] S23. Construct a spatial distribution map of driving factors based on the values ​​of each driving factor and the city map, such as Figure 3-Figure 10 As shown in the figure, the spatial distribution characteristic maps of these driving factors, namely, normalized vegetation index NDVI, normalized water index MNDWI, floor area ratio FAR, average building height BH, building density BD, sky openness SVF, daytime population thermal power DP and nighttime population density NP, are represented in turn.

[0108] By visualizing and simulating each driving factor, its spatial distribution pattern can be clarified. The spatial distribution differences of NDVI and MNDWI are quite obvious. The average NDVI value is 0.18. The high-value areas (>0.40) are mainly distributed in the southern Chaohu coast, the northern and western high vegetation coverage areas. The average MNDWI value is -0.31. The values ​​are high in the water distribution areas, especially the Dongpu Reservoir and Dafangying Reservoir in the north. The values ​​in other areas are relatively low. The high-value areas of FAR (>3.35) are relatively concentrated, mainly distributed in the Binhu New District in the southeast of the main urban area. The development and construction in this area are relatively sufficient. The high-value areas of BH (>57.60) are mainly around the built-up areas within the Second Ring Road of the city, the newly built residential buildings and commercial buildings in the Binhu New District, etc. The low-value areas are concentrated in the old urban area and the industrial plants with large land area and low building height in various development zones. , BD high value areas (>0.55) are mainly concentrated in industrial parks such as the Economic Development Zone, Baohe District, and Xinzhan District, the old city area with many old buildings and dense construction, and the area north of Changjiang West Road on the periphery of the built environment; SVF high value areas (>0.98) are mainly concentrated in the western, southeastern and northeastern areas of the urban area, reflecting the peripheral areas of the urban built environment, with low construction intensity and population distribution; SVF low values ​​are mostly distributed in the old city area, Shushan government affairs area, Economic Development Zone and the densely distributed areas in the northern part of Baohe District. The daytime and nighttime population distribution generally presents a spatial pattern of "high in the main city center and low in the periphery". Among them, the population distribution differences shown by the nighttime light data are more obvious, with more high-value areas and a large radiation area, while the daytime population distribution differences in the main city center are not obvious, with fewer high-value areas and more scattered.

[0109] S3. Invert the surface temperature of each location in the city to be analyzed based on the basic data, and classify the surface temperature to generate a surface temperature spatial distribution map and a surface temperature classification distribution map; in order to better observe the surface temperature spatial distribution results, the surface temperature is classified using the mean-standard deviation method. For example, the surface temperature can be divided into 6 levels from low to high, among which the thermal risk area is composed of a sub-high temperature area, a high temperature area, and an extremely high temperature area. The spatial distribution and classification results of LST in the main urban area of ​​Hefei are as follows: Fig.11 and Fig.12 As shown, the overall spatial pattern characteristics of "high in the eastern region, low in the western region, low in the northwest and southeast regions, and high in other regions" are presented. The distribution range of urban heat risk areas is wide and dense, mostly concentrated in the densely built-up areas in the central part of the city, extending outward along the main roads, presenting a continuous aggregation trend. The temperature difference between the low temperature area and the surrounding area is relatively obvious. It is distributed in the spatial areas with natural ecology and ecological agriculture as the dominant functions in the northwest and southeast of the city. According to the classification results, a quantitative study is conducted on the heat risk status in different administrative regions. Comprehensively considering the heat risk area in each administrative region, the heat risk area accounts for 53.46% of the total in the study area, which is a large proportion. Among them, the heat risk area in the Economic Development Zone accounts for 90.75% in the Economic Development Zone, which accounts for the largest proportion. The heat risk area in Yaohai District accounts for 87.60% in Yaohai District, ranking second. The heat risk area in Luyang District accounts for the smallest proportion, but it also reaches 41.08%. In step S3, the specific steps include:

[0110] S31. Calculate the surface temperature of the city to be analyzed based on the basic data to obtain a spatial distribution map of the surface temperature. The calculation formula for the surface temperature is:

[0111] L λ =[ε·B(T S )+(1-ε)L ↓ ]·τ+L ↑

[0112]

[0113] In the above formula, Lλ is the thermal infrared radiation value received by the satellite sensor, ε is the surface emissivity (dimensionless), T S is the true surface temperature, B(T S ) is the blackbody thermal radiation brightness, τ is the transmittance of the atmosphere in the thermal infrared band (dimensionless), L ↓ and L↑ are the downward and upward radiation of the atmosphere, respectively, K 1 and K 2 are the first constant and the second constant, respectively, which can be obtained from the MTL file of Landsat9 OLI / TIRS image. For the TIRS10 thermal infrared band, K 1 =799.03W / (m2 ·um·sr), K2=1329.24K.

[0114] S32, calculating the average and standard deviation of the surface temperature at each location in the city to be analyzed;

[0115] S33. According to the average value and standard deviation of the surface temperature of each location in the city to be analyzed, several temperature levels and corresponding temperature intervals are divided to generate a surface temperature classification distribution map; the expression of the temperature interval is: In the above formula, and SD represent the mean and standard deviation of the surface temperature of the city to be analyzed, respectively. i represents the i-th interval coefficient.

[0116] S4. Calculate the local Moran's index and the global Moran's index of each location in the city to be analyzed based on the data in the surface temperature spatial distribution map, and divide the city to be analyzed into four clustering types: high-high clustering, low-low clustering, high-low clustering, and low-high clustering, so as to generate a surface temperature spatial clustering distribution map, such as Fig.13 As shown in the figure, the cluster diagram shows that there are certain high-high clustering and low-low clustering areas in LST. The high-high clustering areas are mainly distributed in: (1) industrial concentration areas, including industrial park concentration areas such as Jingkai Industrial Park, Yaohai Industrial Park, Baohe Industrial Park, and Luyang Industrial Park; (2) high-density commercial housing and mixed residential areas, such as commercial housing and residential areas within the second ring road of the city; (3) large hard squares, such as Xinqiao International Airport; In general, among the high-clustering areas, the proportion of industrial concentration areas is the highest. In contrast, the low-low clustering areas are mainly located in large parks, water bodies and their surrounding areas, such as the two large water sources in the north, Dashushan Park, and the lakeside area on the north bank of Chaohu Lake, Daxu Ecological Agriculture Area, etc.

[0117] S5. Pearson correlation is calculated based on the data in the spatial distribution map of land surface temperature and the spatial distribution map of driving factors, and several driving factors with the highest goodness of fit are selected through collinearity test and marked as the first driving factor; 2000 sample points are randomly selected in the city to be analyzed by using ArcGIS random point tool to extract LST and grid average values ​​of each driving factor, and correlation analysis is performed on the relevant data using SPSS software. It can be found that the selected 9 driving factors are significantly correlated with LST, NDVI, MNDWI, and SVF are negatively correlated with land surface temperature, and NDBI, FAR, BD, BH, DP, and NP are positively correlated with land surface temperature. In step S5, the following steps are specifically included:

[0118] S51. Randomly select a number of sample points in the city to be analyzed; for example, select 2000 sample points;

[0119] S52, obtaining the surface temperature of the sample point and the values ​​of each driving factor according to the surface temperature spatial distribution map and the driving factor spatial distribution map;

[0120] S53. Calculate the Pearson coefficient of each driving factor of the sample point on the surface temperature, and arrange each driving factor in descending order according to the absolute value of the Pearson coefficient; Taking Hefei city as an example, the Pearson correlation coefficient is ranked from large to small as follows: NDBI (0.805) > BD (0.496) > NP (0.489) > |SVF| (-0.443) > FAR (0.343) > |NDVI| (-0.340) > BH (0.272) > |MNDWI| (-0.265) > DP (0.179). Among the surface cover factors, NDBI has the most obvious impact on LST, and among all factors, NDBI has the greatest correlation with LST. Among the urban spatial morphology factors, BD has the greatest impact on LST. Among the human activity factors, NP has a more significant warming effect on LST than DP.

[0121] S54, perform collinearity test on each driving factor and surface temperature, and select driving factors with multicollinearity;

[0122] S55. After removing driving factors with multicollinearity in turn, calculate the goodness of fit between the remaining driving factors and the surface temperature, and mark the driving factors corresponding to the highest goodness of fit as the first driving factors.

[0123] S6. Geographically weighted regression analysis was performed on the first driving factor and land surface temperature. The marginal effects and significance test results of each first driving factor were integrated, and several main driving factors were marked. LST had spatial autocorrelation (Moran's I = 0.871), and the spatial distribution of the research data showed obvious non-uniform distribution. Therefore, GWR analysis was carried out on this basis. From the GWR regression results, it can be seen that the R 2 The value is 0.864, which is higher than that of the ordinary linear regression analysis model (R 2 =0.806), the model has better goodness of fit and higher confidence. In step S6, the following steps are specifically included:

[0124] S61. Calculate the regression coefficient and P value of each first driving factor in each city to be analyzed by geographically weighted regression analysis. The calculation formula of the regression coefficient of each first driving factor in the geographically weighted regression analysis model is:

[0125]

[0126] In the above formula, Y i represents the surface temperature at sample point i in the city to be analyzed, β 0 (μi , v i ) is the constant term at point i, (μ i , v i ) is the coordinate of point i, β k (μ i , v i ) is the regression coefficient of the kth driving factor at point i, x ik is the value of the kth driving factor at point i, f is the number of driving factors, ε i is the random error at point i;

[0127] S62. Select several first driving factors according to the regression coefficient and P value of each first driving factor and mark them as main driving factors.

[0128] Table 1 below shows the results calculated by the GWR model, where the average value represents the average value of the regression coefficient, which is used to reflect the marginal contribution of each driving factor. It can be found that the P values ​​of each variable are ranked from small to large as 0.00 = NDVI = MNDWI = NP < FAR < BH = BD < SVF < DP ≤ 0.05, and the marginal contribution of each driving factor is ranked as MNDWI > NDVI > BD > SVF > FAR > DP > NP > BH. Considering the marginal effects of various factors and the results of significance tests, when selecting the main driving factors, first select the driving factors with the smallest P value. For example, if four main driving factors are selected, first select the driving factors with a P value of 0.00, and then select the driving factors with large marginal contributions. If there are 3, then select the driving factors NDVI, MNDWI and NP as the main driving factors, and then select the driving factor FAR with a P value of 0.01, so as to complete the selection of 4 main driving factors. If 2 driving factors are selected, the selected driving factors are NDVI and MNDWI. Figure 14-Figure 21 Shown are the regional differences of the effects of the regression coefficients of MNDWI, NDVI, FAR, NP, BD, BH, DP and SVF on the surface temperature;

[0129] Table 1

[0130]

[0131] Through the present invention, several main driving factors and regional difference maps of the main driving factors on the surface temperature are analyzed and obtained, and the following conclusions can be drawn from the analysis: ① Spatial characteristic relationship: Except for NP, each factor shows a negative correlation with the surface temperature, MNDWI and NDVI have a significant thermal environment mitigation effect, and the regional difference maps of MNDWI and NDVI on LST are similar. ② Global contribution strength: There are significant spatial differences in the force of each factor on LST, among which MNDWI has the greatest influence on LST, followed by NDVI, and NP has the least influence. The marginal efficiency of surface cover driving factors on LST is significantly better than that of urban space morphology and human activities. ③ Local impact efficiency: Compared with areas with concentrated thermal environments, the efficiency of MNDWI and NDVI on LST in areas dominated by medium and low temperatures will be weakened, while the local efficiency of FAR and NP on LST is just the opposite.

[0132] Corresponding to the analysis method provided in the above embodiment, this embodiment also provides a system for implementing the analysis method. Since the analysis system provided in this embodiment corresponds to the analysis method provided in the above embodiment, the implementation method of the aforementioned analysis method is also applicable to the analysis system provided in this embodiment and will not be described in detail in this embodiment.

[0133] The system includes a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, an analysis method for driving factors of an urban thermal environment is implemented.

[0134] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for analyzing driving factors of urban thermal environment, characterized in that: The analysis method comprises the following steps: S1. Obtain basic data including remote sensing image data of the city to be analyzed, building vector data of the main urban area, city map, daytime population thermal data and nighttime lighting data; S2. Based on the basic data, calculate the normalized building index, normalized vegetation index, normalized water index, volume ratio, average building height, building density and sky openness, and construct the spatial distribution map of each driving factor; S3. Invert the surface temperature of each location in the city to be analyzed based on the basic data, and classify the surface temperature to generate a surface temperature spatial distribution map and a surface temperature classification distribution map; S4. Calculate the local Moran's index and the global Moran's index of each location in the city to be analyzed based on the data in the surface temperature spatial distribution map, and divide the locations in the city to be analyzed into four clustering types: high-high clustering, low-low clustering, high-low clustering, and low-high clustering, so as to generate a surface temperature spatial clustering distribution map; S5. Calculate the Pearson correlation based on the data in the spatial distribution map of surface temperature and the spatial distribution map of driving factors, and select several driving factors with the highest goodness of fit through collinearity test, which are marked as the first driving factors; S6. Perform geographically weighted regression analysis on the first driving factors and surface temperature, integrate the marginal effects and significance test results of each first driving factor, and mark several main driving factors.

2. The analysis method according to claim 1, characterized in that In step S2, the following steps are specifically included: S21. Obtain a city map of the city to be analyzed based on the basic data, and grid the city map; S22. Calculate the values ​​of the driving factors of the normalized building index, normalized vegetation index, normalized water index, volume ratio, average building height, building density and sky openness of each grid area in the city map based on the basic data; S23. Construct a spatial distribution map of driving factors based on the values ​​of each driving factor and the city map.

3. The analysis method according to claim 2, characterized in that In step S22, the calculation formula of the normalized building index is: The calculation formula of normalized difference vegetation index is: The calculation formula of normalized water index is: The formula for calculating the volume ratio is: The formula for calculating the average building height is: The formula for calculating building density is: The calculation formula for sky openness is: in, In the above formula, I NDBI ,I NDVI ,I MNDWI , FAR, BH, BD and SVF represent the normalized building index, normalized vegetation index, normalized water index, floor area ratio, average building height, building density and sky openness, respectively, ρ GREEN , R , NIR and ρ SWIR They correspond to the reflectance of bands 3, 4, 5 and 6 in the Landsat9OLI / TIRS image, i and N are the number of buildings and the total number of buildings in a fishing net unit, respectively. basei is the base area of ​​the i-th building, n i is the number of floors of the i-th building, A fishnet is the area of ​​a fishing net unit, H i is the height of the i-th building in a fishing net unit, i and N are the number of buildings and the total number of buildings in a fishing net unit respectively, ΔH is the height difference from the selected base point to the roof of the building, W is the block width, that is, the width range of the GIS basic point search, there are M directions in total, and θ represents the azimuth within the search range.

4. The analysis method according to claim 1, characterized in that In step S3, the following steps are specifically included: S31. Calculate the surface temperature of the city to be analyzed based on the basic data to obtain a spatial distribution map of the surface temperature; S32, calculating the average and standard deviation of the surface temperature at each location in the city to be analyzed; S33. Divide the surface temperature of each location in the city to be analyzed into several temperature levels and corresponding temperature ranges according to the average value and standard deviation, so as to generate a surface temperature classification distribution map.

5. The analysis method according to claim 4, characterized in that In step S31, the calculation formula of the ground surface temperature is: L λ =[ε·B(T S )+(1-e)L ↓ ]·τ+L ↑ In the above formula, L λ is the thermal infrared radiation value received by the satellite sensor, ε is the surface emissivity (dimensionless), T S is the true surface temperature, B(T S ) is the blackbody thermal radiation brightness, τ is the transmittance of the atmosphere in the thermal infrared band (dimensionless), L ↓ and L ↑ are the downward and upward radiation of the atmosphere, respectively. K1 and K2 are the first constant and the second constant, respectively, which can be obtained from the MTL file of Landsat9OLI / TIRS image. For the TIRS10 thermal infrared band, K1=799.03W / (m 2 ·um·sr), K2=1329.24K.

6. The analysis method according to claim 1, characterized in that In step S5, the following steps are specifically included: S51. Randomly select a number of sample points in the city to be analyzed; S52, obtaining the surface temperature of the sample point and the values ​​of each driving factor according to the surface temperature spatial distribution map and the driving factor spatial distribution map; S53, calculating the Pearson coefficient of each driving factor of the sample point to the surface temperature, and arranging the driving factors in descending order according to the absolute value of the Pearson coefficient; S54, perform collinearity test on each driving factor and surface temperature, and select driving factors with multicollinearity; S55. After removing driving factors with multicollinearity in turn, calculate the goodness of fit between the remaining driving factors and the surface temperature, and mark the driving factors corresponding to the highest goodness of fit as the first driving factors.

7. The analysis method according to claim 1, characterized in that In step S6, the following steps are specifically included: S61. Calculate the regression coefficient and P value of each first driving factor in each city to be analyzed by geographically weighted regression analysis. The calculation formula of the regression coefficient of each first driving factor in the geographically weighted regression analysis model is: In the above formula, Y i represents the surface temperature at sample point i in the city to be analyzed, β0(μ i , v i ) is the constant term at point i, (μ i , v i ) is the coordinate of point i, β k (μ i , v i ) is the regression coefficient of the kth driving factor at point i, x ik is the value of the kth driving factor at point i, f is the number of driving factors, ε i is the random error at point i; S62. Select several first driving factors according to the regression coefficient and P value of each first driving factor and mark them as main driving factors.

8. An analysis system for implementing the analysis method according to any one of claims 1 to 7, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the analysis method according to any one of claims 1 to 7 is implemented.

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