Space-time analysis method and device for urban thermal environment

Abstract

The invention discloses a space-time analysis method and device for an urban thermal environment, relates to the field of urban thermal environment space-time change and driving mechanism research, and aims to solve the problem of poor accuracy of deducing a dynamic evolution rule of the urban thermal environment in the prior art. The method comprises the following steps: acquiring target surface temperature data and human activity data of a target city, and based on the target surface temperature data, performing regional division on the target city according to a preset thermal environment division rule to obtain a plurality of target islands for representing temporal and spatial change conditions of the target city in daytime and night thermal environments; the surface temperature grades of the target islands in the plurality of target islands are different; further determining human activity intensity index features according to the human activity data; and finally, based on the target surface temperature data, the human activity intensity index characteristics and the plurality of target islands, determining a space-time relationship between the human activity and the target surface temperature data. And the accuracy of deducing the dynamic evolution rule of the urban thermal environment is improved.

Description

Technical Field

[0001] The present invention relates to the research field of spatiotemporal changes and driving mechanisms of urban thermal environment, and in particular to a spatiotemporal analysis method and device for urban thermal environment. Background Art

[0002] Land Surface Temperature (LST) is a key indicator for describing the surface thermal environment and plays an important role in the study of urban heat island effect, ecological environment monitoring, climate change analysis, etc. Therefore, accurately inverting the surface temperature and analyzing its spatiotemporal dynamic changes are of great significance to the study and management of the urban thermal environment.

[0003] Existing studies mostly focus on the analysis of surface temperature changes in a single time period, and there are relatively few studies on the dynamic characteristics of day and night. The temperature difference between day and night is an important indicator for evaluating the urban thermal environment, which has a direct impact on energy consumption, urban planning and ecological management. However, due to the lack of nighttime data or insufficient resolution, the dynamic analysis of daytime and nighttime surface temperature faces many challenges. In addition, the formation and change of the urban thermal environment are jointly affected by natural factors (such as temperature, precipitation, topography, etc.) and human factors (such as economic activities, land use changes, population density, etc.). It can be seen that the traditional urban thermal environment research program ignores the relationship between urban spatial heterogeneity and multiple factors, and is also limited to the analysis of surface temperature changes in a single time period, which cannot effectively reflect the differentiated impact of human activities on the thermal environment during the day and at night, resulting in poor accuracy in the deduced dynamic evolution law of the urban thermal environment, which limits the research and governance of the urban thermal environment.

[0004] In view of this, there is an urgent need to design a more advanced spatiotemporal analysis method for urban thermal environment, which can study the dynamic characteristics of the city during the day and night, so as to solve the problem of poor accuracy in deducing the dynamic evolution law of the urban thermal environment in the existing technology. Summary of the invention

[0005] The present invention aims to provide a method and device for spatiotemporal analysis of urban thermal environment. By acquiring target surface temperature data of a target city including nighttime surface temperature data and daytime surface temperature data and human activity data, the target surface temperature data is firstly divided into regions to obtain a plurality of target islands for expressing the spatiotemporal characteristics of the thermal environment of the target city during the day and at night. The spatiotemporal relationship between human activities and target surface temperature data is deduced in combination with a human activity intensity index constructed based on the human activity data, so as to evaluate the impact of human activities on the urban thermal environment. This improves the accuracy of deducing the dynamic evolution law of the urban thermal environment.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a spatiotemporal analysis method for urban thermal environment, which may include: Obtain target surface temperature data and human activity data of the target city, wherein the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data; the human activity data includes at least nighttime lighting, land use, population density, and GDP data; Based on the target surface temperature data, the target city is divided into regions according to a preset thermal environment division rule to obtain a plurality of target islands; each of the plurality of target islands has a different surface temperature level, and the plurality of target islands are used to represent the spatiotemporal changes of the thermal environment of the target city during the day and at night; Determining a human activity intensity index characteristic based on the human activity data; Based on the target surface temperature data, the human activity intensity index characteristics and the multiple target islands, the spatiotemporal relationship between human activities and the target surface temperature data is determined, and the spatiotemporal relationship is used to characterize the degree of influence of human activities on the thermal environment of the target city.

[0007] Preferably, the target city is divided into regions based on the target surface temperature data according to a preset thermal environment division rule to obtain a plurality of target islands, which may include: Counting the target mean and target standard deviation of the target surface temperature data corresponding to the target area image; the target mean represents the average value of the surface temperature pixels of the target area image, and the target standard deviation represents the degree of dispersion of the surface temperature data of the target area image; Based on the target mean and the target standard deviation, the target city is graded according to a preset thermal environment classification rule to obtain a plurality of target islands that meet preset surface temperature categories; the preset surface temperature categories include strong heat island, hot island, normal island, green island and cold island.

[0008] Preferably, determining the human activity intensity index characteristics according to the human activity data may include: Using the formula: ; Construct the characteristic value of human activity intensity index; among them, Grid The cumulative human activity intensity index characteristic value, Standardized grid Land cover human activity score, Standardized grid Night light brightness value, Standardized grid GDP value, Standardized grid population density value.

[0009] Preferably, before determining the human activity intensity index characteristics according to the human activity data, the following steps may be included: Using the formula: ; Calculate the standardized grid The night light brightness value; Grid The total nighttime light radiance value, is the maximum value of night light radiance, It is the minimum value of night light radiance.

[0010] Preferably, before determining the human activity intensity index characteristics according to the human activity data, the following steps may be included: Using the formula: ; Compute Grid The land cover human activity score value; among them, Grid The land cover human activity score value, Grid The number of land use types within Grid The area of ​​land use type within is the weight of each land use type.

[0011] Preferably, determining the spatiotemporal relationship between human activities and target surface temperature data based on the target surface temperature data, the human activity intensity index characteristics and the plurality of target islands may include: Based on the target surface temperature data, the human activity intensity index characteristics and the multiple target islands, a local regression equation of the target surface temperature data corresponding to any grid in the target area is established using a GWR model to determine the spatiotemporal relationship between human activities and the target surface temperature data; This includes using the formula: ; Establish the local regression equation; wherein, Grid The surface temperature, It is a grid The constant term, It is a grid The random error, is a variable In the grid The regression coefficient at is the number of explanatory variables.

[0012] Preferably, before acquiring the target surface temperature data of the target city, the following steps may be included: For the target city, obtain Landsat remote sensing data collected by the target satellite and MODIS data collected by the medium-resolution imaging spectrum; Performing remote sensing data preprocessing on the Landsat remote sensing data to obtain first target data; the remote sensing data preprocessing at least includes radiation correction and atmospheric correction; Calculating the first target data using a radiation transfer equation method to obtain the daytime surface temperature data; Performing ground temperature data preprocessing on the MODIS data to obtain second target data; the ground temperature data preprocessing at least includes data extraction, data format conversion and data projection; the second target data includes MODIS daytime surface temperature data and MODIS nighttime surface temperature data; The nighttime surface temperature data is calculated based on the daytime surface temperature data and the second target data.

[0013] Preferably, calculating the first target data using the radiation transfer equation method to obtain the daytime surface temperature may include: Using the formula: ; The daytime surface temperature is calculated; wherein, is the radiation intensity received by the remote sensor, is the atmospheric transmittance, is the surface emissivity, is the daytime surface temperature, The surface temperature is The black body radiation intensity at is the upward radiation intensity of the atmosphere, is the downward radiation intensity of the atmosphere.

[0014] Preferably, the calculating the nighttime ground surface temperature data based on the daytime ground surface temperature data and the second target data may include: Using the formula: ; ; The nighttime surface temperature is calculated; wherein, is the nighttime surface temperature, is the MODIS nighttime surface temperature, is the MODIS daytime surface temperature, is the daytime surface temperature, is the weight.

[0015] In a second aspect, the present invention provides a spatiotemporal analysis device for urban thermal environment, which may include: A data acquisition module, the data acquisition module is used to acquire target surface temperature data and human activity data of a target city, the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data; the human activity data includes at least nighttime lighting, land use, population density and GDP data; A region division module, the region division module is used to divide the target city into regions based on the target surface temperature data according to a preset thermal environment division rule to obtain a plurality of target islands; each of the plurality of target islands has a different surface temperature level, and the plurality of target islands are used to represent the spatiotemporal changes of the thermal environment of the target city during the day and at night; an intensity determination module, the intensity determination module being used to determine a human activity intensity index feature based on the human activity data; A spatiotemporal relationship determination module, the spatiotemporal relationship determination module is used to determine the spatiotemporal relationship between human activities and target surface temperature data based on the target surface temperature data, the human activity intensity index characteristic data and the multiple target islands, and the spatiotemporal relationship is used to characterize the degree of influence of human activities on the thermal environment of the target city.

[0016] Compared with the prior art, the present invention provides a spatiotemporal analysis method for urban thermal environment, which obtains target surface temperature data and human activity data of a target city; wherein the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data, and the human activity data includes at least nighttime lighting, land use, population density and GDP data; firstly, based on the target surface temperature data, the target city is divided into regions according to a preset thermal environment division rule to obtain a plurality of target islands; each of the plurality of target islands has a different surface temperature level, and the plurality of target islands are used to represent the spatiotemporal changes of the thermal environment of the target city during the day and at night; further, based on the human activity data, the human activity intensity index characteristics are determined; finally, based on the target surface temperature data, the human activity intensity index characteristics and the plurality of target islands, the spatiotemporal relationship between human activities and the target surface temperature data is determined, and the spatiotemporal relationship is used to characterize the impact of human activities on the target city The degree of influence of the thermal environment; based on this, the multi-source data of the nighttime surface temperature data, the daytime surface temperature data and the human activity data of the target city of the present invention enrich the data source; further based on the human activity data, the characteristics of the human activity intensity index are determined, which effectively reflects the key indicators of human activities in the target city; finally, the characteristics of the human activity intensity index are determined according to the human activity data, and the spatiotemporal relationship between human activities and the target surface temperature data is established in combination with the target surface temperature data and the multiple target islands, that is, the correlation between the spatial heterogeneity of the target city and other factors is established, so that the degree of influence of human activities on the thermal environment of the target city can be deduced, which effectively reflects the differentiated influence of human activities on the thermal environment of the city during the day and at night, and improves the accuracy of deducing the dynamic evolution law of the urban thermal environment; further, it can provide scientific basis and technical support for the fields of urban planning, ecological environment protection and energy optimization management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 A main flow chart of a spatiotemporal analysis method of urban thermal environment provided by the present invention; Figure 2 A schematic diagram of the daytime thermal environment distribution results of a target island of a spatiotemporal analysis method of an urban thermal environment provided by the present invention; Figure 3 A schematic diagram of the nighttime thermal environment distribution results of a target island of a spatiotemporal analysis method of an urban thermal environment provided by the present invention; Figure 4A schematic diagram of the daytime thermal environment distribution results of the target island deduction trend of the spatiotemporal analysis method of the urban thermal environment provided by the present invention; Figure 5 A schematic diagram of nighttime thermal environment distribution results of a target island deduction trend of a spatiotemporal analysis method of an urban thermal environment provided by the present invention; Figure 6 A schematic diagram of the spatiotemporal relationship between human activities and target surface temperature data of a spatiotemporal analysis method of urban thermal environment provided by the present invention; Figure 7 A schematic structural diagram of a spatiotemporal analysis device for urban thermal environment provided by the present invention. DETAILED DESCRIPTION

[0018] In order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second" and the like are used to distinguish the same items or similar items with substantially the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and do not limit their order of precedence. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit them to be different.

[0019] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0020] In the present invention, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects in the previous time are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0021] The formation and changes of urban thermal environment are jointly affected by natural factors (such as temperature, precipitation, topography, etc.) and human factors (such as economic activities, land use changes, population density, etc.). In recent years, nighttime light data (NTL) has been widely used in the field of environmental monitoring as an important indicator to measure the intensity of human activities. Combined with data such as GDP, land use (LU), and population density (PD), it can effectively reflect the differentiated impact of human activities on the thermal environment during the day and at night. However, the analysis methods in the existing technology mostly use simple linear regression models, ignoring the complex relationship between spatial heterogeneity and multiple factors, resulting in the problem of poor accuracy in the deduced dynamic evolution law of the urban thermal environment.

[0022] In view of this, the present invention proposes a spatiotemporal analysis method and device for urban thermal environment. Aiming at the problem that Landsat satellite data lacks nighttime images, the present invention combines the daytime and nighttime surface temperature data of MODIS (Moderate Resolution Imaging Spectroradiometer) to generate Landsat satellite nighttime surface temperature, which makes up for the deficiency of single data source in temporal resolution; and classifies the daytime and nighttime surface temperatures, analyzes the distribution of different levels of urban heat island areas and their dynamic change characteristics; finally, the cumulative human activity intensity index (CHAI) is constructed by integrating human factors such as GDP, nighttime light (NTL), land use (LU), and population density (PD), and the spatial heterogeneity relationship between the CHAI index and the daytime and nighttime surface temperature is analyzed by using a geographically weighted regression model (GWR), thereby improving the accuracy of deducing the dynamic evolution law of the urban thermal environment and solving the problem of poor accuracy of deducing the dynamic evolution law of the urban thermal environment in the prior art.

[0023] Next, the technical solution of the present invention is described in detail with reference to the accompanying drawings: See also Figure 1 , Figure 1 This is a main flow chart of a spatiotemporal analysis method of urban thermal environment provided by the present invention. Its execution subject is a server or terminal equipped with the technical solution disclosed in the embodiment of the present invention, such as a thermal environment analysis service platform or a handheld thermal environment analysis device, etc.

[0024] exist Figure 1 In, the method may include: Step 110: Acquire target surface temperature data and human activity data of the target city, wherein the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data; and the human activity data includes at least nighttime lighting, land use, population density, and GDP data.

[0025] In step 110, because existing studies mostly focus on the analysis of surface temperature changes in a single time period, there are relatively few studies on diurnal dynamic characteristics. The temperature difference between day and night is an important indicator for evaluating the urban thermal environment, which has a direct impact on energy consumption, urban planning and ecological management; however, the lack of nighttime data or insufficient resolution will lead to research failure or insufficient accuracy; current remote sensing technology, with its large-scale, long-time series and high-frequency data acquisition capabilities, provides reliable data support for the study of surface temperature. Among them, the Landsat series of satellites are widely used in the detailed study of urban thermal environment due to their high spatial resolution. However, the temporal resolution of Landsat data is low and there is a lack of nighttime image data, which limits its application in day and night dynamic analysis. The MODIS spectrometer data has a high temporal resolution and can provide both daytime and nighttime surface temperature products, but its spatial resolution is low and is subject to certain limitations in the study of fine-scale urban thermal environment. Therefore, the Landsat and MODIS data can be fused to make up for the shortcomings of a single data source, thereby obtaining the nighttime surface temperature data in step 110. That is, the daytime surface temperature data in step 110 is data collected by Landsat satellite data, and the nighttime surface temperature data is based on data collected by Landsat and MODIS respectively, and is obtained after data fusion. This can make up for the shortcomings of a single data source to a certain extent and improve the comprehensiveness of data coverage.

[0026] Furthermore, the formation and changes of the urban thermal environment are affected by both natural and human factors. Night light data, as an important indicator for measuring the intensity of human activities, has been widely used in the field of environmental monitoring. Therefore, the present invention also obtains human activity data, so that the differentiated impact of human activities on the thermal environment during the day and at night can be reflected based on these data, further improving the comprehensiveness of data coverage.

[0027] It should be noted that the target surface temperature data and human activity data of the target city are data from the same period.

[0028] Step 120: Based on the target surface temperature data, the target city is divided into regions according to a preset thermal environment division rule to obtain a plurality of target islands; each of the plurality of target islands has a different surface temperature level, and the plurality of target islands are used to represent the spatiotemporal changes of the thermal environment of the target city during the day and at night.

[0029] Step 130: Determine the human activity intensity index characteristics based on the human activity data.

[0030] In step 120 to step 130, firstly, based on the target surface temperature data including the nighttime surface temperature data and the daytime surface temperature data, the daytime and nighttime surface temperatures are graded, and a plurality of target islands with different geothermal heat values ​​for the target city are obtained; for example, the surface temperature can be divided into a plurality of levels such as a cold island, a green island, a normal island, a hot island, and a strong hot island, so that the target city can be regionally divided based on these surface temperature levels to obtain a plurality of target islands. Because each target island has a different surface temperature level and will show different temperature characteristics over time, a plurality of target islands can be used to represent the spatiotemporal changes in the thermal environment of the target city during the day and at night.

[0031] Furthermore, the acquired human activity data, including at least nighttime lights, land use, population density and GDP data, can be used to accumulate the human activity intensity index (CHAI) to obtain the human activity intensity index characteristics of each area in the target city; CHAI combines multiple key indicators reflecting human activities to more comprehensively reflect the level of human activity in a region.

[0032] Step 140: Based on the target surface temperature data, the human activity intensity index characteristics and the multiple target islands, determine the spatiotemporal relationship between human activities and the target surface temperature data, wherein the spatiotemporal relationship is used to characterize the degree of impact of human activities on the thermal environment of the target city.

[0033] In step 140, based on the target surface temperature data of the target city, the characteristics of the human activity intensity index, and multiple target islands, a geographically weighted regression (GWR) model can be used to analyze the relationship between the cumulative human activity intensity index and the day and night surface temperature, evaluate the impact of human activities on the urban thermal environment, and identify areas that are greatly affected by human activities; thereby obtaining a spatiotemporal relationship for characterizing the degree of impact of human activities on the thermal environment of the target city; and improving the accuracy of deducing the dynamic evolution law of the urban thermal environment.

[0034] Based on this, the present invention provides a spatiotemporal analysis method for urban thermal environment. By acquiring target surface temperature data of a target city including nighttime surface temperature data and daytime surface temperature data and multi-source data of human activity data, the target surface temperature data is firstly divided into regions to obtain multiple target islands for expressing the spatiotemporal characteristics of the thermal environment of the target city during the day and at night. The spatiotemporal relationship between human activities and target surface temperature data is further deduced in combination with the human activity intensity index constructed based on the human activity data, which is used to characterize the degree of influence of human activities on the thermal environment of the target city; and the accuracy of deducing the dynamic evolution law of the urban thermal environment is improved.

[0035] It should be noted that the spatiotemporal analysis method of the urban thermal environment provided by the present invention is applicable to cities with different terrain characteristics, such as plateaus, hills, mountains or plains, and can be deduced using the spatiotemporal analysis method of the urban thermal environment provided by the present invention. In practical applications, the spatiotemporal analysis method of the urban thermal environment provided by the present invention is applied to cities with different terrains. When performing spatiotemporal analysis, some parameter adjustments can be made according to different terrain needs, such as parameter adjustments of the atmospheric correction model elevation when remote sensing inverts the surface temperature, and weight adjustments of various indicators when constructing the cumulative human activity index. However, due to the complexity of different terrains, the existing method for analyzing the urban thermal environment in plain areas may not be applicable to cities in plateau areas after adjusting the parameters.

[0036] The specific analysis is as follows: The main features of plateau terrain include: 1. High altitude: The altitude of the plateau is usually above 500 meters. Some large plateaus, such as the Qinghai-Tibet Plateau, have an average altitude of more than 4,000 meters.

[0037] 2. Relatively flat terrain: Although the plateau is at a high altitude, the terrain at the top is relatively flat. However, there are also some plateaus that have become rugged and uneven due to long-term external erosion, such as the Loess Plateau and the Yunnan-Guizhou Plateau.

[0038] 3. Climate and vegetation: Due to the high altitude, the plateau is usually colder and the vegetation is sparser. For example, the vegetation on the Qinghai-Tibet Plateau is mainly alpine meadows and deserts.

[0039] 4. Hydrological characteristics: The hydrological characteristics of rivers and lakes on the plateau are also very obvious. For example, the Qinghai-Tibet Plateau is the source of many large rivers, such as the Yangtze River, the Yellow River, and the Lancang River.

[0040] 5. Diversity of terrain: There may be many types of terrain on the plateau, such as mountains, hills, basins, etc. For example, there are many types of terrain on the Loess Plateau, such as loess plateau, loess ridge and loess hill.

[0041] 6. Special geomorphic phenomena: Some plateaus also have some special geomorphic phenomena, such as karst landforms, Danxia landforms, etc. For example, there are extensive karst landforms on the Yunnan-Guizhou Plateau, and unique loess landforms on the Loess Plateau.

[0042] The main characteristics of plain terrain include: 1. The altitude is generally between 0 and 50 meters, the ground is flat or has small undulations, and it is mainly distributed on both sides of large rivers and in areas bordering the ocean.

[0043] 2. Plains are places where the population is concentrated.

[0044] 3. A plain is a larger area with flat ground or small undulations, mainly distributed on both sides of large rivers and in areas bordering the ocean.

[0045] In summary, we can see the difference between plateau terrain and plain terrain. There are relatively fewer environmental influencing factors in plain terrain. The thermal environment analysis of cities on plain terrain is relatively simpler than that of cities on plateau terrain. Therefore, the spatiotemporal analysis method of urban thermal environment suitable for plateau terrain can be used to conduct spatiotemporal analysis of urban thermal environment on plain terrain through some adaptive parameter adjustments; while the spatiotemporal analysis method of urban thermal environment suitable for plain terrain is not suitable for thermal environment analysis of cities on plateau terrain.

[0046] As a specific embodiment, the spatiotemporal analysis method of urban thermal environment provided by the present invention is introduced in detail by taking the plateau city of Kunming as an example, and the spatiotemporal characteristics of the thermal environment of the plateau city of Kunming and its response mechanism to human activities are deduced.

[0047] Preferably, before step 110, that is, before obtaining the target surface temperature data of the target city, it can include: for the target city, obtaining Landsat remote sensing data collected by the target satellite and MODIS data collected by the medium resolution imaging spectrum; performing remote sensing data preprocessing on the Landsat remote sensing data to obtain first target data; the remote sensing data preprocessing at least includes radiation correction and atmospheric correction; using the radiation transfer equation method to calculate the first target data to obtain the daytime surface temperature data; performing ground temperature data preprocessing on the MODIS data to obtain second target data; the ground temperature data preprocessing at least includes data extraction, data format conversion and data projection; the second target data includes MODIS daytime surface temperature data and MODIS nighttime surface temperature data; based on the daytime surface temperature data and the second target data, the nighttime surface temperature data is calculated.

[0048] Specifically, before step 110, data preparation needs to be done for Kunming City, that is, Landsat satellite data, MODIS spectrometer data, night light data, land use data, population density data, and GDP data are collected and processed; for example, the following method can be used for data collection.

[0049] (1) Landsat satellite data collection: Landsat satellite data can come from USGS. Landsat data is a collection 2 level 1 product and needs to be processed in ENVI software such as radiometric calibration and atmospheric correction. At the same time, in order to improve processing efficiency, Landsat remote sensing image data is clipped according to the vector boundary of the study area, and finally the four remote sensing images of the same period covering the Kunming area are mosaicked.

[0050] (2) MODIS spectrometer data collection: MODIS spectrometer data can come from LAADS DAAC. Use the MRT (Modis Reprojection Tool) tool to process the LST_Day_1km and LST_Night_1km bands, project and convert them into TIFF format, and crop the Kunming city data. For data with too much cloud cover, use data with a similar time as a substitute.

[0051] (3) Regarding night light data collection: Night light data can come from the Earth Observation Group. In the original night light data, there are pixels with negative DN values. Since there is no description of these pixels in the metadata of the original data, we assume that the negative DN values ​​of these pixels are caused by background noise and outliers in data processing, and assign negative values ​​to 0. This study does not involve the calculation of specific physical parameters, so the original DN values ​​of the NTL data are normalized and directly used to calculate the human activity index.

[0052] (4) GDP data collection: GDP data can be obtained from the research results of Zhao et al. (Forecasting China's GDP at the pixel level using nighttime lights time series and population images). After projection transformation, the GDP of Kunming City is extracted and normalized.

[0053] (5) Land use data collection: Land use data can be obtained from the China LandCover Dataset of Wuhan University. After projection transformation, the land use of Kunming City is extracted and normalized.

[0054] (6) Population density data collection: Population density data can be obtained from GlobPOP, projection transformation, extraction of Kunming’s population density and then normalization.

[0055] Furthermore, it is necessary to perform radiation correction, atmospheric correction and other preprocessing on the Landsat remote sensing data collected over the years, and use the radiation transfer equation method to calculate the surface temperature. It should be noted that the radiation flux received by the thermal infrared sensor is mainly composed of the part of the surface thermal radiation that directly reaches the sensor after being acted on by the atmosphere, and the part that reaches the sensor after being acted on by the atmosphere after being acted on by the atmosphere.

[0056] Preferably, the first target data is calculated using the radiation transfer equation method to obtain the daytime surface temperature, which may include using the formula: (1) The daytime surface temperature is calculated; where is the radiation intensity received by the remote sensor, is the atmospheric transmittance, is the surface emissivity, is the daytime surface temperature, The surface temperature is The black body radiation intensity at is the upward radiation intensity of the atmosphere, is the downward radiation intensity of the atmosphere.

[0057] Specifically, the atmospheric transmittance , upward radiation intensity of the atmosphere and the downward radiation intensity of the atmosphere The three atmospheric profile parameters were obtained from the website (https: / / atmcorr.gsfc.nasa.gov / ).

[0058] Furthermore, the surface emissivity can be calculated using the Normalized Difference Vegetation Index (NDVI) threshold method: (2) (3) in, is the minimum value of NDVI, is the maximum NDVI value, is the surface emissivity, is the vegetation coverage.

[0059] Furthermore, the blackbody radiation intensity can be calculated by Planck's formula: (4) Therefore, the daytime surface temperature can be obtained by Planck's inverse function formula, that is, the daytime surface temperature can be obtained by inverting the Landsat data. : (5) in, , is a constant. For Landsat 8 satellite data, , ; For Landsat 9 satellite data, , .

[0060] Furthermore, the original MODIS daytime and nighttime surface temperature data can be extracted from the MODIS surface temperature product, and the data format conversion and projection processing can be performed to ensure the consistency and availability of the data. After reprojecting and converting the format of the MYD11A2 data using the MODIS Reprojection Tool (MRT), the following calculations are performed to convert its unit to degrees Celsius: (6) in, is the temperature value after conversion, is the temperature value before conversion, SF is the proportional coefficient, which is 0.02 in this example; thus, the second target data including the MODIS nighttime surface temperature and the MODIS daytime surface temperature after conversion can be obtained.

[0061] Preferably, calculating the nighttime surface temperature data based on the daytime surface temperature data and the second target data may include using the formula: (7) (8) The nighttime surface temperature is calculated; wherein, is the nighttime surface temperature, is the MODIS nighttime surface temperature, is the MODIS daytime surface temperature, is the daytime surface temperature, is the weight.

[0062] Based on this, the daytime surface temperature inverted from Landsat is , MODIS daytime surface temperature Nighttime surface temperature , get the nighttime surface temperature from Landsat ; It realizes the fusion of Landsat and MODIS data to make up for the shortcomings of a single data source, solves the problem of low temporal resolution of Landsat data and lack of night image data, and promotes the application of its data in day and night dynamic analysis.

[0063] Preferably, in step 120, based on the target surface temperature data, the target city is divided into regions according to a preset thermal environment division rule to obtain multiple target islands, which may include: Firstly, the target mean and target standard deviation of the target surface temperature data corresponding to the target area image are statistically analyzed; the target mean represents the average value of the surface temperature pixels of the target area image, and the target standard deviation represents the degree of dispersion of the surface temperature data of the target area image.

[0064] Then, based on the target mean and target standard deviation, the target cities are graded according to the preset thermal environment classification rules to obtain multiple target islands that meet the preset surface temperature categories; the preset surface temperature categories include strong heat island, hot island, normal island, green island and cold island.

[0065] Specifically, the surface temperature data is raster data. To calculate the overall mean μ and standard deviation std of the surface temperature, ArcMap software can be used to count the mean and standard deviation of the entire image, and then the mean-standard deviation method is used to calculate the nighttime surface temperature ( ) and daytime surface temperature ( ) for classification; specifically, the target cities can be classified according to the thermal environment classification rules shown in Table 1.

[0066] Table 1 shows the thermal environment division rules Serial number Heat island level Classification criteria 1 Severe heat island T>μ+std 2 Heat Island m+0.5std <T≤μ+std 3 Normal Island m-0.5std <T≤μ+0.5std 4 Green Island μ-std <T≤μ-0.5std 5 Cold Island T≤μ-std Furthermore, the classified surface temperature data can be mapped to geographic space to generate a spatiotemporal distribution map of the day and night surface temperature, and visually display the spatial distribution of the surface temperature and its changes over time through visualization. Figures 2 to 3 , Figure 2 A schematic diagram of the daytime thermal environment distribution results of a target island of a spatiotemporal analysis method of an urban thermal environment provided by the present invention; Figure 3 A schematic diagram of the night-time thermal environment distribution results of a target island of a spatiotemporal analysis method of an urban thermal environment provided in the present invention; it should be noted that the present invention obtains the geothermal data and human activity data of the plateau city of Kunming from 2013 to 2023 as original data. In this embodiment, only the day and night geothermal change trend chart of Kunming in 2013, 2018 and 2023 is used for display and introduction.

[0067] exist Figure 2 The three pictures shown in the figure respectively represent the schematic diagrams of the daytime heat island classification results of Kunming in 2013, 2018 and 2023; Figure 3 The three pictures shown in the figure respectively represent the schematic diagrams of the nighttime heat island classification results of Kunming in 2013, 2018 and 2023; Figure 2 and Figure 3The figure shown is a daytime and nighttime heat island grade distribution map of multiple target islands obtained by using the mean-standard deviation method disclosed in the present invention to classify the daytime and nighttime surface temperature data, and visually expresses it in different colors to intuitively show the spatial distribution characteristics of the thermal environment in Kunming during the day and night; thus, the nighttime and daytime heat island distribution can be compared to analyze the spatial differences in the daytime and nighttime thermal environments. Based on this, from Figure 2 and Figure 3 It can be concluded without a doubt that during the day, most areas of Kunming are normal heat islands, and strong heat islands are mainly distributed in Dongchuan District, Xundian Hui and Yi Autonomous County in the northeast, and Yiliang County and Shilin Yi Autonomous County in the southeast. Heat islands are scattered in Kunming, covering the entire city, but the roughly concentrated distribution range is roughly consistent with the strong heat island. Although the thermal environment of Kunming has changed slightly in the decade from 2013 to 2023, it still shows an overall warming trend. The heat island level in Kunming has evolved from normal islands accounting for the majority in 2013 and the rest of the levels are evenly distributed to the current situation in 2023 where normal islands and heat islands account for the majority, and the thermal environment in Kunming has gradually strengthened. The coefficient of variation of daytime surface temperature in Kunming in 2013, 2018 and 2023 (coefficient of variation = standard deviation / average temperature) are 0.147, 0.149 and 0.186, respectively, indicating that the overall temperature difference within Kunming has shown a trend of increasing under the background of a slight increase in overall temperature. At the same time, the extreme difference of surface temperature from 2013 to 2023 has increased, from 57.48℃ in 2013 to 63.33℃ in 2023. The highest temperature is rising, while the lowest temperature is falling. Therefore, the overall temperature difference in Kunming has increased. Although the surface temperature in Kunming has decreased at night, the distribution of heat islands is more concentrated at night. Among them, normal islands still occupy a dominant position, and heat islands and strong heat islands are concentrated in three main areas: Dongchuan District and Luquan Yi and Miao Autonomous County in the north, Yiling County and Dianchi Lake and its surrounding urban areas in the southeast, and cold islands and green islands are mainly distributed in Dongchuan District and Luquan Yi and Miao Autonomous County in the north, Xundian Hui and Yi Autonomous County and Panlong District in the middle. Similar to the daytime, the thermal environment in Kunming at night also shows a warming trend. Similarly, the coefficients of variation of nLST in Kunming in 2013, 2018, and 2023 (coefficient of variation = standard deviation / mean temperature) are 0.236, 0.162, and 0.213, respectively. Against the background of the overall increase in LST at night, the overall difference in LST within Kunming shows a trend of first decreasing and then increasing, but the overall difference is decreasing.

[0068] Furthermore, the standard deviation ellipse method can be used to analyze the temporal and spatial distribution of heat islands of different levels and extract the evolution characteristics of the thermal environment, so as to better deduce the changing trend of the daytime and nighttime surface temperature of the target city; the evolution characteristics of the thermal environment can at least include the concentration, directionality, migration, and morphology of the regional distribution of different levels; among them, the standard deviation ellipse is used to analyze the temporal and spatial variation characteristics of the distribution of each heat island level, and the main parameters are the center point, major axis, minor axis and azimuth; the movement of the center point reflects the spatial trajectory of the evolution of the thermal environment, the major axis and minor axis are used to reflect the discreteness and aggregation degree of the spatial distribution of each heat island level, and the azimuth reveals the main trend of the spatial distribution of the thermal environment. Please refer to Figures 4 to 5 , Figure 4 A schematic diagram of the daytime thermal environment distribution results of the target island deduction trend of the spatiotemporal analysis method of the urban thermal environment provided by the present invention; Figure 5 A schematic diagram of the nighttime thermal environment distribution results of the target island deduction trend of the spatiotemporal analysis method of the urban thermal environment provided by the present invention.

[0069] It should be noted that Figure 4 This is the result of the heat island evolution trend analysis (daytime). Based on the standard deviation ellipse method, the spatial distribution characteristics of target islands of different levels during the day were analyzed, and the ellipse center position, major and minor axis directions and their changes were calculated to quantify the spatial evolution trend of the daytime thermal environment in Kunming. Figure 5 This is the result of the target island evolution trend analysis (nighttime). The standard deviation ellipse method is used to analyze the spatial variation of the nighttime heat island area, extract the dynamic variation characteristics of the nighttime thermal environment, and compare them with the daytime variation trend.

[0070] Specifically, ArcGIS software can be used to calculate different levels of standard deviation ellipses, and the results are as follows: Figure 4 and Figure 5 The thermal environment distribution results of the deduction trend are shown in the figure. Based on this, Figure 4 and Figure 5It is undoubtedly obtained that from 2013 to 2023, during the daytime, the distribution center of cold islands, green islands and normal islands migrated to the southeast, the distribution center of heat islands generally migrated to the northwest, and the strong heat island showed a clear trend of migrating to the northwest. Except for the cold island, the rest of the levels expanded on the northwest-southeast axis. The elliptical shapes of heat islands at all levels are slightly different, but they roughly maintain similar directionality, which shows that although the coverage of heat islands has changed in different years, the distribution direction of heat islands remains relatively consistent overall. At night, from the distribution center, cold islands, heat islands, and strong heat islands show a trend of moving to the northwest, and green islands and normal islands move to the southeast as a whole. From the distribution direction, except for the strong heat island, the distribution direction of the remaining levels is increasing, among which the distribution direction of the strong heat island is the most obvious; from the distribution range, the distribution of cold islands and green islands tends to be discrete, and normal islands, heat islands and strong heat islands tend to be concentrated.

[0071] Furthermore, in step 130, the human factors such as GDP, night lights, land use, and population density can be integrated to construct a cumulative human activity intensity index (CHAI); CHAI combines multiple key indicators reflecting human activities to more comprehensively reflect the level of human activity in a region. Therefore, the applicant believes that the four human activity factors selected in the present invention, namely GDP, night lights, land use, and population density, are all factors that have an important impact on the urban surface temperature, and the research on them does not involve specific weights, and the four influencing factors all maintain the default weights. Therefore, the characteristics of the human activity intensity index can be determined based on human activity data. Preferably, the formula can be used: (9) Construct the characteristic value of human activity intensity index; among them, Grid The cumulative human activity intensity index characteristic value, Standardized grid Land cover human activity score, Standardized grid Night light brightness value, Standardized grid GDP value, Standardized grid It should be noted that the characteristic value of the human activity intensity index in the present invention is the grid The cumulative human activity intensity index characteristic value.

[0072] Specifically, for the grid The data in the data can be standardized in the same way, that is, , , and The standardization calculation method is the same.

[0073] by For example, before determining the characteristics of the human activity intensity index based on human activity data, the formula may be used: (10) Calculate the standardized grid The night light brightness value; Grid The total nighttime light radiance value, is the maximum value of night light radiance, It is the minimum value of night light radiance.

[0074] further, Grid The total GDP in It is a grid The total population density can be obtained from the GDP and population density data of the target city; and the intensity of human activities varies with different land use types.

[0075] Preferably, before determining the human activity intensity index characteristics based on the human activity data, the formula may be used: (11) Compute Grid The land cover human activity score value; among them, Grid The land cover human activity score value, Grid The number of land use types within Grid The area of ​​land use type within is the weight of each land use type.

[0076] It should be noted that the weight of each land use type can be obtained using the analytic hierarchy process (AHP), and the specific weight values ​​are shown in Table 2.

[0077] Table 2 shows the land use weights Land use type Bare Land Water grassland woodland shrub arable land Impervious surface Weight 0.03 0.05 0.06 0.06 0.06 0.24 0.50 Based on this, the cumulative human activity intensity index (CHAI) or the characteristics of the human activity intensity index are constructed, so that the geographically weighted regression model (GWR) can be used to analyze the spatial heterogeneity of the relationship between the CHAI index and the day and night surface temperature; thereby improving the accuracy of deducing the dynamic evolution of the urban thermal environment.

[0078] Preferably, in step 140, determining the spatiotemporal relationship between human activities and target surface temperature data based on the target surface temperature data, the human activity intensity index characteristics, and the plurality of target islands may include: Based on the target surface temperature data, the characteristics of the human activity intensity index and multiple target islands, the GWR model is used to establish a local regression equation for the target surface temperature data corresponding to any grid in the target area to determine the spatiotemporal relationship between human activities and the target surface temperature data.

[0079] This may include using the formula: (12) Establish the local regression equation; wherein, Grid The surface temperature, It is a grid The constant term, It is a grid The random error, is a variable In the grid The regression coefficient at is the number of explanatory variables.

[0080] For details, please refer to Figure 6 A schematic diagram of the spatiotemporal relationship between human activities and target surface temperature data of a spatiotemporal analysis method for urban thermal environment provided by the present invention; Figure 6 The results shown in the figure are the results of using the GWR model to analyze the spatial relationship between the cumulative human activity intensity index and the daytime and nighttime surface temperatures, assess the different impacts of human activities on the thermal environment during the day and at night, and identify areas that are most affected by human activities, thereby showing the cumulative human activity intensity index and the daytime and nighttime surface temperature relationship analysis results. Figure 6 It can be concluded without a doubt that: at night, the main urban area of ​​Kunming and the southern part of Dongchuan District are the areas where human activities have the most significant impact on the nighttime surface temperature; while the natural areas on the edge of Kunming, such as Xundian and Luquan, are less affected by human activities, and the surface temperature is more determined by natural factors, showing a lower R² value. During the day, there is a different local R² distribution from that at night, with more high-value areas than at night. The figure shows several high-value centers, and the influence of these centers spreads from the inside to the outside. As the distance from the central area increases, the R² value gradually decreases, and the fitting effect of the model also weakens. Although the city center also shows a high R² value, compared with Dianchi Lake, the R² value of the main urban area is relatively low, and the surface temperature of the main urban area may be affected by many factors.

[0081] In summary, the present invention provides a spatiotemporal analysis method for urban thermal environment, which uses Landsat, MODIS data and human activity data including at least nighttime lighting, land use, population density and GDP data to conduct a day and night spatiotemporal dynamic analysis of Kunming's thermal environment, revealing the diurnal evolution process of the urban thermal environment and its human activity driving mechanism, with an accuracy of more than 97%. Compared with the existing technology, it greatly improves the accuracy of deducing the dynamic evolution law of the urban thermal environment; it can provide data support and scientific guidance for the scientific management of the urban heat island effect.

[0082] In a second aspect, the present invention provides a device for analyzing the spatiotemporal nature of urban thermal environments. Figure 7 , Figure 7 A schematic structural diagram of a spatiotemporal analysis device for urban thermal environment provided by the present invention.

[0083] exist Figure 7 In the embodiment, the device may include: The data acquisition module 710 is used to acquire target surface temperature data and human activity data of a target city; the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data, and the human activity data includes at least nighttime lighting, land use, population density and GDP data.

[0084] The regional division module 720 is used to divide the target city into regions based on the target surface temperature data and according to a preset thermal environment division rule to obtain multiple target islands; each of the multiple target islands has a different surface temperature level, and the multiple target islands are used to represent the spatiotemporal changes of the thermal environment of the target city during the day and night.

[0085] The intensity determination module 730 is used to determine the human activity intensity index characteristics according to the human activity data.

[0086] The spatiotemporal relationship determination module 740 is used to determine the spatiotemporal relationship between human activities and the target surface temperature data based on the target surface temperature data, the human activity intensity index characteristic data and the multiple target islands. The spatiotemporal relationship is used to characterize the degree of influence of human activities on the thermal environment of the target city.

[0087] Based on this, the present invention provides a spatiotemporal analysis device for urban thermal environment, which first acquires target surface temperature data and human activity data of a target city through a data acquisition module 710; wherein the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data, and the human activity data at least includes nighttime lighting, land use, population density and GDP data; further, the target city is divided into regions according to a preset thermal environment division rule based on the target surface temperature data by using a regional division module 720 to obtain a plurality of target islands; each of the plurality of target islands has a different surface temperature level, and the plurality of target islands have different surface temperature levels. The island is used to represent the spatiotemporal changes in the thermal environment of the target city during the day and at night; the intensity determination module 730 is further used to determine the characteristics of the human activity intensity index based on the human activity data; finally, the spatiotemporal relationship between human activities and the target surface temperature data is determined by the spatiotemporal relationship determination module 740 based on the target surface temperature data, the characteristic data of the human activity intensity index and multiple target islands. The spatiotemporal relationship is used to characterize the degree of influence of human activities on the thermal environment of the target city; the accuracy of the dynamic evolution law of the urban thermal environment is improved, and the problem of poor accuracy in the dynamic evolution law of the urban thermal environment in the prior art is solved.

[0088] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0089] Although the present invention has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present invention. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present invention as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present invention. Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such modifications and variations if they fall within the scope of the claims of the present invention and their equivalents.

Claims

1. A spatiotemporal analysis method for urban thermal environment, characterized in that: include: Obtain target surface temperature data and human activity data of the target city; the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data, and the human activity data includes at least nighttime lighting, land use, population density, and GDP data; Based on the target surface temperature data, the target city is divided into regions according to a preset thermal environment division rule to obtain a plurality of target islands; each of the plurality of target islands has a different surface temperature level, and the plurality of target islands are used to represent the spatiotemporal changes of the thermal environment of the target city during the day and at night; Determining a human activity intensity index characteristic based on the human activity data; Based on the target surface temperature data, the human activity intensity index characteristics and the multiple target islands, the spatiotemporal relationship between human activities and the target surface temperature data is determined; the spatiotemporal relationship is used to characterize the degree of influence of human activities on the thermal environment of the target city.

2. The spatiotemporal analysis method of urban thermal environment according to claim 1, characterized in that: Based on the target surface temperature data, the target city is divided into regions according to a preset thermal environment division rule to obtain a plurality of target islands, including: Counting the target mean and target standard deviation of the target surface temperature data corresponding to the target area image; the target mean represents the average value of the surface temperature pixels of the target area image, and the target standard deviation represents the degree of dispersion of the surface temperature data of the target area image; Based on the target mean and the target standard deviation, the target city is graded according to a preset thermal environment classification rule to obtain a plurality of target islands that meet preset surface temperature categories; the preset surface temperature categories include strong heat island, hot island, normal island, green island and cold island.

3. The spatiotemporal analysis method of urban thermal environment according to claim 1, characterized in that: Determining the human activity intensity index characteristics according to the human activity data includes: Using the formula: ； Construct the characteristic value of human activity intensity index; among them, Grid The cumulative human activity intensity index characteristic value, Standardized grid Land cover human activity score, Standardized grid Night light brightness value, Standardized grid GDP value, Standardized grid population density value.

4. The spatiotemporal analysis method of urban thermal environment according to claim 1, characterized in that: Before determining the human activity intensity index characteristics according to the human activity data, the method includes: Using the formula: ； Calculate the standardized grid The night light brightness value; Grid The total nighttime light radiance value, is the maximum value of the night light radiance, It is the minimum value of night light radiance.

5. The spatiotemporal analysis method of urban thermal environment according to claim 1, characterized in that: Before determining the human activity intensity index characteristics according to the human activity data, the method includes: Using the formula: ； Compute Grid The land cover human activity score value; among them, Grid The land cover human activity score value, Grid The number of land use types within Grid The area of ​​land use type within is the weight of each land use type.

6. The spatiotemporal analysis method of urban thermal environment according to claim 1, characterized in that: The determining the spatiotemporal relationship between human activities and the target surface temperature data based on the target surface temperature data, the human activity intensity index characteristics and the plurality of target islands includes: Based on the target surface temperature data, the human activity intensity index characteristics and the multiple target islands, a local regression equation of the target surface temperature data corresponding to any grid in the target area is established using a GWR model to determine the spatiotemporal relationship between human activities and the target surface temperature data; Using the formula: ； Establish the local regression equation; wherein, Grid The surface temperature, It is a grid The constant term, It is a grid The random error, is a variable In the grid The regression coefficient at is the number of explanatory variables.

7. The spatiotemporal analysis method of urban thermal environment according to claim 1, characterized in that: Before obtaining the target surface temperature data of the target city, the method includes: For the target city, obtain Landsat remote sensing data collected by the target satellite and MODIS data collected by the medium-resolution imaging spectrum; Performing remote sensing data preprocessing on the Landsat remote sensing data to obtain first target data; the remote sensing data preprocessing at least includes radiation correction and atmospheric correction; Calculating the first target data using a radiation transfer equation method to obtain the daytime surface temperature data; Performing ground temperature data preprocessing on the MODIS data to obtain second target data; the ground temperature data preprocessing at least includes data extraction, data format conversion and data projection; the second target data includes MODIS daytime surface temperature data and MODIS nighttime surface temperature data; The nighttime surface temperature data is calculated based on the daytime surface temperature data and the second target data.

8. The spatiotemporal analysis method of urban thermal environment according to claim 7, characterized in that: The first target data is calculated using a radiation transfer equation method to obtain the daytime surface temperature, including: Using the formula: ； The daytime surface temperature is calculated; wherein, is the radiation intensity received by the remote sensor, is the atmospheric transmittance, is the surface emissivity, is the daytime surface temperature, The surface temperature is The black body radiation intensity at is the upward radiation intensity of the atmosphere, is the downward radiation intensity of the atmosphere.

9. The spatiotemporal analysis method of urban thermal environment according to claim 7, characterized in that: The calculating the nighttime ground surface temperature data based on the daytime ground surface temperature data and the second target data includes: Using the formula: ； ； The nighttime surface temperature is calculated; wherein, is the nighttime surface temperature, is the MODIS nighttime surface temperature, is the MODIS daytime surface temperature, is the daytime surface temperature, is the weight.

10. A device for spatiotemporal analysis of urban thermal environment, characterized in that: include: A data acquisition module, the data acquisition module is used to acquire target surface temperature data and human activity data of a target city; the target surface temperature data includes nighttime surface temperature data and daytime surface temperature data, and the human activity data includes at least nighttime lighting, land use, population density, and GDP data; A region division module, the region division module is used to divide the target city into regions based on the target surface temperature data according to a preset thermal environment division rule to obtain a plurality of target islands; each of the plurality of target islands has a different surface temperature level, and the plurality of target islands are used to represent the spatiotemporal changes of the thermal environment of the target city during the day and at night; an intensity determination module, the intensity determination module being used to determine a human activity intensity index feature based on the human activity data; A spatiotemporal relationship determination module, the spatiotemporal relationship determination module is used to determine the spatiotemporal relationship between human activities and target surface temperature data based on the target surface temperature data, the human activity intensity index characteristic data and the multiple target islands; the spatiotemporal relationship is used to characterize the degree of influence of human activities on the thermal environment of the target city.

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