WO2022161105A1 - Multi-temporal and multi-level urban temperature remote sensing data analysis method - Google Patents

Multi-temporal and multi-level urban temperature remote sensing data analysis method Download PDF

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WO2022161105A1
WO2022161105A1 PCT/CN2021/143579 CN2021143579W WO2022161105A1 WO 2022161105 A1 WO2022161105 A1 WO 2022161105A1 CN 2021143579 W CN2021143579 W CN 2021143579W WO 2022161105 A1 WO2022161105 A1 WO 2022161105A1
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temperature
average
analysis
level
area
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赵宗泽
王成
程钢
王双亭
王宏涛
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河南理工大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K3/00—Thermometers giving results other than momentary value of temperature
    • G01K3/02—Thermometers giving results other than momentary value of temperature giving means values; giving integrated values
    • G01K3/06—Thermometers giving results other than momentary value of temperature giving means values; giving integrated values in respect of space
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/007—Radiation pyrometry, e.g. infrared or optical thermometry for earth observation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/48—Thermography; Techniques using wholly visual means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K7/00—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements
    • G01K7/42—Circuits effecting compensation of thermal inertia; Circuits for predicting the stationary value of a temperature
    • G01K7/427—Temperature calculation based on spatial modeling, e.g. spatial inter- or extrapolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J2005/0092—Temperature by averaging, e.g. by scan
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure

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  • the invention belongs to the technical field of surveying, mapping and urban planning, and particularly relates to a multi-temporal and multi-level urban temperature remote sensing data analysis method.
  • temperature plays a crucial role in the change of urban environment.
  • thermal infrared band data of remote sensing images to invert the surface temperature, analyze the change of the surface temperature, and explore the relationship between the surface temperature and the surface type.
  • many researchers have conducted multi-temporal analysis and discussed the relationship between surface temperature and surface types.
  • scholars have not performed multi-level analysis of temperature data, and cannot perform a single analysis of high temperature regions.
  • the present invention provides a multi-temporal and multi-level urban temperature remote sensing data analysis method to solve the problems existing in the background technology. Finally, a multi-level and multi-temporal remote sensing temperature data analysis method is proposed.
  • the present invention provides the following technical solutions:
  • a multi-temporal multi-level urban temperature remote sensing data analysis method comprising the following steps:
  • S1 collect temperature remote sensing data, first obtain the temperature satellite remote sensing data of the area to be analyzed and evaluated from the relevant database;
  • S2 Preliminary analysis of the data. First, use the single-channel and split-window algorithm to analyze and calculate S1, invert the temperature data of the area to be analyzed and evaluated, and generate attribute information corresponding to the surface buildings and vegetation in the area to be analyzed and evaluated in combination with the relevant bands;
  • multi-level connectivity analysis uses the threshold superposition processing method to perform connectivity analysis on the temperature data obtained in step S2.
  • the binary images on multiple temperature levels are generated with 1°C as the level interval, and then the generated binary images are analyzed.
  • Connectivity analysis is carried out on the temperature image, and the temperature connected areas at different levels are obtained;
  • step S4 attribute statistical analysis, perform statistical analysis on the attribute information of each connected area obtained in step S3, and set the is the i-th connected region of the j-th layer, is the number of pixels in this area; Num j is the number of connected areas at the jth temperature level; at the same time, the average temperature, NDVI average and NDBI average value of all connected areas under each temperature level are calculated, so as to obtain the analysis difference. Exponential changes of temperature and ground object types under temperature grades to complete urban temperature remote sensing data analysis.
  • the database is a geospatial database
  • the acquired data is Landsat land satellite data.
  • the attribute information corresponding to the surface features in the area to be analyzed and evaluated includes a vegetation index and a building index, wherein:
  • the vegetation index needs to use the red and near-infrared bands, and is obtained through the following band operations:
  • D NIR is the spectral value in the near-infrared band, and DR is the spectral value in the red band;
  • the building index needs to use the short-wave infrared band and the near-infrared band to obtain through the band operation:
  • D NIR is the spectral value in the near-infrared band
  • D SWIR1 is the spectral value in the first short-wave infrared band.
  • the analyzed attribute information includes regional area, regional average temperature, regional average vegetation index, regional average vegetation index, grade average temperature, grade average vegetation index and grade average building index.
  • the specific analysis function is:
  • Area area (number of pixels in the area):
  • Class average temperature (average temperature of all connected areas on the jth class):
  • Grade-averaged vegetation index average vegetation index of all connected areas on the jth level
  • Class average building index (average building index of all connected areas on the jth class):
  • the present invention effectively realizes the comprehensive analysis of the corresponding relationship between the main object indices, improves the comprehensiveness and accuracy of the analysis operation, and on the other hand overcomes the large amount of data calculation in the traditional similar work.
  • the shortcomings of poor standardization of the calculation process and poor comprehensive calculation data greatly improve the work efficiency, quality and accuracy of urban temperature remote sensing observation data analysis operations, which can be widely used in various urban planning operations and provide accurate information for urban planning work.
  • the work reference data and basis are improved, the work efficiency and rationality of urban planning work are improved, and the labor intensity and cost of urban planning work are reduced.
  • Fig. 1 is the schematic flow chart of the method of the present invention
  • Figure 2 shows the connected regions generated by different temperature data inversion from Landsat data
  • the temperature data obtained from Landsat data (a) the temperature data obtained from Landsat data; (b) the connected area generated by the temperature level of 25°C, (c) the connected area generated by the temperature level of 28°C, and (d) the connected area generated by the temperature level of 31°C , (e) a connected area generated at a temperature class of 34°C, (f) a connected area generated at a temperature class of 37°C;
  • Figure 3 is a schematic diagram of the three-year temperature data distribution in Zhengzhou
  • Figure 4 is a three-year temperature and attribute data statistical table in Zhengzhou
  • Figure 5 is a three-year attribute information curve distribution diagram of the connected area
  • Figure 6 is a list of attribute information statistics of connected areas with different temperature levels in Zhengzhou in the past three years.
  • a multi-temporal multi-level urban temperature remote sensing data analysis method includes the following steps:
  • S1 collect temperature remote sensing data, first obtain the temperature satellite remote sensing data of the area to be analyzed and evaluated from the relevant database;
  • S2 preliminary analysis of data, firstly use single channel and split window algorithm to analyze and calculate S1, invert the temperature data of the area to be analyzed and evaluated, and generate attribute information corresponding to the surface features of the area to be analyzed and evaluated in combination with the relevant bands;
  • multi-level connectivity analysis uses the threshold superposition processing method to perform connectivity analysis on the temperature data obtained in step S2.
  • the binary images on multiple temperature levels are generated with 1°C as the level interval, and then the generated binary images are analyzed.
  • Connectivity analysis is carried out on the temperature image, and the temperature connected areas at different levels are obtained;
  • step S4 attribute statistical analysis, perform statistical analysis on the attribute information of each connected area obtained in step S3, and set the is the i-th connected region of the j-th layer, is the number of pixels in this area; Num j is the number of connected areas at the jth temperature level; at the same time, the average temperature, NDVI average and NDBI average value of all connected areas under each temperature level are calculated, so as to obtain the analysis difference. Exponential changes of temperature and ground object types under temperature grades to complete urban temperature remote sensing data analysis.
  • the database is a geospatial database
  • the acquired data is Landsat Landsat data.
  • the attribute information corresponding to the surface features in the area to be analyzed and evaluated includes the vegetation index and the building index, wherein:
  • the vegetation index needs to use the red and near-infrared bands, and is obtained through the following band operations:
  • D NIR is the spectral value in the near-infrared band, and DR is the spectral value in the red band;
  • the building index needs to use the short-wave infrared band and the near-infrared band to obtain through the band operation:
  • D NIR is the spectral value in the near-infrared band
  • D SWIR1 is the spectral value in the first short-wave infrared band.
  • the analyzed attribute information includes regional area, regional average temperature, regional average vegetation index, regional average vegetation index, grade average temperature, grade average vegetation index and grade average building index,
  • the specific analysis function is:
  • Area area (number of pixels in the area):
  • Class average temperature (average temperature of all connected areas on the jth class):
  • Grade-averaged vegetation index average vegetation index of all connected areas on the jth level
  • Class average building index (average building index of all connected areas on the jth class):
  • the Landsat temperature inversion data in 2010, 2014 and 2017 are now analyzed by combining Zhengzhou City, Henan province, China as the research area.
  • the connectivity of temperature data is analyzed to generate temperature-connected area data at different levels; then, the attribute information of different areas at different levels is calculated, and the attribute changes at different stages and levels are analyzed; The results of the analysis are discussed and analyzed:
  • the light and near-infrared bands are obtained through the following band operations:
  • D NIR is the spectral value in the near-infrared band
  • DR is the spectral value in the red band.
  • the building index needs to use the short-wave infrared band and the near-infrared band to obtain through the band operation:
  • D NIR is the spectral value of the near-infrared band
  • D SWIR1 is the spectral value of the first short-wave infrared band
  • S2 Preliminary analysis of the data, processing the temperature data by using the threshold superposition processing method to generate binary images at different temperature levels, so as to perform connectivity analysis.
  • the connectivity analysis method based on erosion is used to analyze the connectivity of binary image data at different temperature levels; therefore, temperature connectivity regions at different levels can be generated; for example: Landsat Temperature data from data inversion, (b)-(e) are the connected regions generated by temperature data at 25°C, 28°C, 31°C, 34°C, and 37°C, respectively;
  • S4 attribute statistical analysis, set is the i-th connected region of the j-th layer, is the number of pixels in the area; Num j is the number of connected areas at the jth temperature level; according to the attribute information of the connected areas in Zhengzhou area, the area, average temperature, and average NDVI of the ith connected area at the jth level can be calculated and the average NDBI to analyze the relationship between the temperature of the connected area and the feature type index at different temperature levels. At the same time, calculate the average temperature, NDVI average and NDBI average value of all connected areas under each temperature level, and analyze the exponential changes of temperature and ground object types under different temperature levels;
  • Landsat temperature inversion data is used to conduct multi-level and multi-phase temperature analysis in Zhengzhou.
  • Landsat surface temperature data in 2010, 2014 and 2017 were obtained using single-channel and split-window algorithms, respectively.
  • the weather website reported similar temperatures on May 11, 2010, May 6, 2014 and April 28, 2017.
  • the temperature data of the three time periods can be inverted and displayed hierarchically through Landsat data.
  • the land surface temperature data in 2010 ranged from 10.68°C to 56.10°C, with an average temperature of 32.23°C and a standard deviation of 5.61; in 2014, the land surface temperature data ranged from 14.07°C to 56.03°C, with an average temperature of 33.41°C and a standard deviation of 4.38; 2017
  • the annual land surface temperature data ranges from 9.32 to 56.34°C, with an average temperature of 31.09°C and a standard deviation of 4.28. Statistics show that the average temperature in 2014 was the highest, the average temperature in 2017 was the lowest, and the standard deviation of the temperature distribution in 2010 was greater than that in 2014 and 2017.
  • the experimental results show that the temperature is negatively correlated with the vegetation index and positively correlated with the building index. As the temperature level increases, the correlation between temperature and surface feature type index decreases.
  • the present invention effectively realizes the comprehensive analysis of the corresponding relationship between the main object indices, improves the comprehensiveness and accuracy of the analysis operation, and on the other hand overcomes the large amount of data calculation in the traditional similar work.
  • the shortcomings of poor standardization of the calculation process and poor comprehensive calculation data greatly improve the work efficiency, quality and accuracy of urban temperature remote sensing observation data analysis operations, which can be widely used in various urban planning operations and provide accurate information for urban planning work.
  • the work reference data and basis are improved, the work efficiency and rationality of urban planning work are improved, and the labor intensity and cost of urban planning work are reduced.

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Abstract

Disclosed is a multi-temporal and multi-level urban temperature remote sensing data analysis method, comprising four steps of acquisition of temperature remote sensing data, preliminary analysis of the data, analysis of multi-level connectivity, and analysis of attribute statistics. Compared with traditional urban temperature measurement and analysis method, the present invention, on one hand, effectively achieves comprehensive analysis of the correspondence between main ground feature indexes and improves the comprehensiveness and precision of analysis work, and on the other hand, overcomes the defects of large data calculation amount, poor calculation process normalization, and poor calculation data comprehensiveness in traditional similar work, thereby greatly improving the working efficiency, quality, and precision of urban temperature remote sensing observation data analysis work. The present invention can be widely applied in various urban planning work, so as to provide accurate work reference data and basis for urban planning work, thereby improving the work efficiency and rationality of urban planning work, and reducing the labor intensity and cost of urban planning work.

Description

一种多时相多等级的城市温度遥感数据分析方法A multi-temporal and multi-level urban temperature remote sensing data analysis method 技术领域technical field
本发明属于测绘及城市规划技术领域，具体涉及一种多时相多等级的城市温度遥感数据分析方法。The invention belongs to the technical field of surveying, mapping and urban planning, and particularly relates to a multi-temporal and multi-level urban temperature remote sensing data analysis method.
背景技术Background technique
温度作为城市环境的重要指标之一，对城市环境变化起着至关重要的作用。通过遥感影像的热红外波段数据反演地表温度，分析地表温度的变化，探讨地表温度与地表类型的关系。当前在地表温度分析中，许多学者进行了多时相分析，讨论了地表温度与地表类型的关系。然而，学者们还没有对温度数据进行多等级的分析，无法对高温区域进行单一的分析。As one of the important indicators of urban environment, temperature plays a crucial role in the change of urban environment. Through the thermal infrared band data of remote sensing images to invert the surface temperature, analyze the change of the surface temperature, and explore the relationship between the surface temperature and the surface type. In the current analysis of surface temperature, many scholars have conducted multi-temporal analysis and discussed the relationship between surface temperature and surface types. However, scholars have not performed multi-level analysis of temperature data, and cannot perform a single analysis of high temperature regions.
同时当前在进行城市温度分析作业时，往往忽视了对观测区域中地表物的特性、温度区域属性的分析，从而导致分析数据全面全面性差，从而导致对城市温度分析作业的精度差，数据分析运算难度大且分析作业工作效率低下，难以有效满足实际工作的需要。At the same time, in the current urban temperature analysis operation, the analysis of the characteristics of the surface objects and the attributes of the temperature area in the observation area is often neglected, resulting in poor comprehensiveness and comprehensiveness of the analysis data, resulting in poor accuracy of the urban temperature analysis operation. It is difficult and the analysis work efficiency is low, and it is difficult to effectively meet the needs of the actual work.
因此，针对这一现状，迫切需要开发一种全新的城市温度遥感数据分析方法，以满足实际工作的需要。Therefore, in view of this situation, it is urgent to develop a brand-new urban temperature remote sensing data analysis method to meet the needs of practical work.
发明内容SUMMARY OF THE INVENTION
本发明提供一种多时相多等级的城市温度遥感数据分析方法，以解决背景技术存在的问题，本发明中通过采用进行多等级、多时相温度区域属性变化分析，分析高温区域属性的变化。最后，提出了一种 多等级、多时相的遥感温度数据分析方法。The present invention provides a multi-temporal and multi-level urban temperature remote sensing data analysis method to solve the problems existing in the background technology. Finally, a multi-level and multi-temporal remote sensing temperature data analysis method is proposed.
为实现以上技术目的，本发明提供以下技术方案：For realizing the above technical purpose, the present invention provides the following technical solutions:
一种多时相多等级的城市温度遥感数据分析方法，包括以下步骤：A multi-temporal multi-level urban temperature remote sensing data analysis method, comprising the following steps:
S1，采集温度遥感数据，首先从相关数据库中获取待分析评测地区温度卫星遥感数据；S1, collect temperature remote sensing data, first obtain the temperature satellite remote sensing data of the area to be analyzed and evaluated from the relevant database;
S2，数据初步分析，首先利用单通道和劈窗算法对S1进行分析计算，反演出待分析评测地区温度数据，同时结合相关波段生成待分析评测地区地表建筑和植被对应的属性信息；S2: Preliminary analysis of the data. First, use the single-channel and split-window algorithm to analyze and calculate S1, invert the temperature data of the area to be analyzed and evaluated, and generate attribute information corresponding to the surface buildings and vegetation in the area to be analyzed and evaluated in combination with the relevant bands;
S3，多等级连通性分析，利用阈值叠加处理方法对S2步骤获得的温度数据进行连通分析，分析过程中以1℃为等级间隔，生成多个温度等级上的二值影像，然后对生成的二值影像进行连通性分析，得到不同等级上的温度连通区域；S3, multi-level connectivity analysis, uses the threshold superposition processing method to perform connectivity analysis on the temperature data obtained in step S2. During the analysis process, the binary images on multiple temperature levels are generated with 1°C as the level interval, and then the generated binary images are analyzed. Connectivity analysis is carried out on the temperature image, and the temperature connected areas at different levels are obtained;
S4，属性统计分析，对S3步骤获得的各连通区域的属性信息进行统计分析，在分析过程中设
Figure PCTCN2021143579-appb-000001
为第j层第i个连通区域，
Figure PCTCN2021143579-appb-000002
为该区域的像素个数；Num j是在第j个温度等级下连通区域的数量；同时计算所有连通区域在各温度等级下的温度平均值、NDVI平均值和NDBI平均值，从而得到分析不同温度等级下温度和地物类型的指数变化，完成城市温度遥感数据分析作业。 S4, attribute statistical analysis, perform statistical analysis on the attribute information of each connected area obtained in step S3, and set the
Figure PCTCN2021143579-appb-000001
is the i-th connected region of the j-th layer,
Figure PCTCN2021143579-appb-000002
is the number of pixels in this area; Num j is the number of connected areas at the jth temperature level; at the same time, the average temperature, NDVI average and NDBI average value of all connected areas under each temperature level are calculated, so as to obtain the analysis difference. Exponential changes of temperature and ground object types under temperature grades to complete urban temperature remote sensing data analysis.
进一步的，所述S1步骤中，数据库为地理空间数据库，获取的数据为Landsat陆地卫星数据。Further, in the step S1, the database is a geospatial database, and the acquired data is Landsat land satellite data.
进一步的，所述S2步骤中，待分析评测地区地表地物对应的属性信息包括植被指数和建筑物指数，其中：Further, in the step S2, the attribute information corresponding to the surface features in the area to be analyzed and evaluated includes a vegetation index and a building index, wherein:
植被指数需要利用红光和近红外波段，通过以下波段运算获取：The vegetation index needs to use the red and near-infrared bands, and is obtained through the following band operations:
Figure PCTCN2021143579-appb-000003
Figure PCTCN2021143579-appb-000003
D NIR为近红外波段波谱值，D R为红光波段波谱值； D NIR is the spectral value in the near-infrared band, and DR is the spectral value in the red band;
建筑物指数需要利用短波红外波段和近红外波段，通过波段运算进行获取：The building index needs to use the short-wave infrared band and the near-infrared band to obtain through the band operation:
Figure PCTCN2021143579-appb-000004
Figure PCTCN2021143579-appb-000004
D NIR为近红外波段波谱值，D SWIR1为第一个短波红外波段波谱值。 D NIR is the spectral value in the near-infrared band, and D SWIR1 is the spectral value in the first short-wave infrared band.
进一步的，所述S4步骤在进行属性信息分析时，所分析属性信息包括区域面积、区域平均温度、区域平均植被指数、区域平均植被指数、等级平均温度、等级平均植被指数及等级平均建筑物指数，具体分析函数为：Further, when the attribute information analysis is performed in the step S4, the analyzed attribute information includes regional area, regional average temperature, regional average vegetation index, regional average vegetation index, grade average temperature, grade average vegetation index and grade average building index. , the specific analysis function is:
区域面积(区域的像素个数)：
Figure PCTCN2021143579-appb-000005
Area area (number of pixels in the area):
Figure PCTCN2021143579-appb-000005
区域平均温度(第j等级i连通区域的平均温度)：
Figure PCTCN2021143579-appb-000006
Area average temperature (average temperature of connected area at level j i):
Figure PCTCN2021143579-appb-000006
区域平均植被指数(第j等级i连通区域的平均植被指数)：Regional average vegetation index (average vegetation index of the j-th level i connected area):
Figure PCTCN2021143579-appb-000007
Figure PCTCN2021143579-appb-000007
区域平均植被指数(第j等级i连通区域的平均建筑物指数)：Regional average vegetation index (average building index of the j-th level i connected area):
Figure PCTCN2021143579-appb-000008
Figure PCTCN2021143579-appb-000008
等级平均温度(第j等级上所有连通区域的平均温度)：Class average temperature (average temperature of all connected areas on the jth class):
Figure PCTCN2021143579-appb-000009
Figure PCTCN2021143579-appb-000009
等级平均植被指数(第j等级上所有连通区域的平均植被指数)：Grade-averaged vegetation index (average vegetation index of all connected areas on the jth level):
Figure PCTCN2021143579-appb-000010
Figure PCTCN2021143579-appb-000010
等级平均建筑物指数(第j等级上所有连通区域的平均建筑物指数)：
Figure PCTCN2021143579-appb-000011
Class average building index (average building index of all connected areas on the jth class):
Figure PCTCN2021143579-appb-000011
本发明较传统的城市温度检测分析方法，一方面有效实现了主要地物指数之间的对应关系综合分析，提高分析作业全面性和精度，另一方面克服传统同类工作中存在的数据运算量大、计算过程规范性差及计算数据全面差的缺点，从而极大的提高了城市温度遥感观测数据分析作业的工作效率、质量和精度，可广泛应用于各类城市规划作业，为城市规划工作提供准确的工作参考数据及依据，提高城市规划工作的工作效率、合理性，并降低城市规划工作的劳动强度和成本。Compared with the traditional urban temperature detection and analysis method, on the one hand, the present invention effectively realizes the comprehensive analysis of the corresponding relationship between the main object indices, improves the comprehensiveness and accuracy of the analysis operation, and on the other hand overcomes the large amount of data calculation in the traditional similar work. The shortcomings of poor standardization of the calculation process and poor comprehensive calculation data greatly improve the work efficiency, quality and accuracy of urban temperature remote sensing observation data analysis operations, which can be widely used in various urban planning operations and provide accurate information for urban planning work. The work reference data and basis are improved, the work efficiency and rationality of urban planning work are improved, and the labor intensity and cost of urban planning work are reduced.
附图说明Description of drawings
图1为本发明方法流程示意图；Fig. 1 is the schematic flow chart of the method of the present invention;
图2为Landsat数据反演的不同温度数据生成的连通区域；Figure 2 shows the connected regions generated by different temperature data inversion from Landsat data;
其中，(a)Landsat数据获取的温度数据；(b)温度等级为25℃生成的连通区域、(c)温度等级为28℃生成的连通区域、(d)温度等级为31℃生成的连通区域、(e)温度等级为34℃生成的连通区域、(f)温度等级为37℃生成的连通区域；Among them, (a) the temperature data obtained from Landsat data; (b) the connected area generated by the temperature level of 25°C, (c) the connected area generated by the temperature level of 28°C, and (d) the connected area generated by the temperature level of 31°C , (e) a connected area generated at a temperature class of 34°C, (f) a connected area generated at a temperature class of 37°C;
图3为郑州市3年的温度数据分布情况示意图；Figure 3 is a schematic diagram of the three-year temperature data distribution in Zhengzhou;
图4为郑州市3年温度及属性数据统计表；Figure 4 is a three-year temperature and attribute data statistical table in Zhengzhou;
图5为连通区域三年属性信息曲线分布图；Figure 5 is a three-year attribute information curve distribution diagram of the connected area;
图6为郑州市不同温度等级连通区域近三年属性信息统计一览表。Figure 6 is a list of attribute information statistics of connected areas with different temperature levels in Zhengzhou in the past three years.
具体实施方式Detailed ways
为使本发明实现的技术手段、创作特征、达成目的与功效易于明白了解，下面结合具体实施方式，进一步阐述本发明。In order to make the technical means, creative features, achievement goals and effects realized by the present invention easy to understand, the present invention will be further described below with reference to the specific embodiments.
如图1所示，一种多时相多等级的城市温度遥感数据分析方法，包括以下步骤：As shown in Figure 1, a multi-temporal multi-level urban temperature remote sensing data analysis method includes the following steps:
S1，采集温度遥感数据，首先从相关数据库中获取待分析评测地区温度卫星遥感数据；S1, collect temperature remote sensing data, first obtain the temperature satellite remote sensing data of the area to be analyzed and evaluated from the relevant database;
S2，数据初步分析，首先利用单通道和劈窗算法对S1进行分析计算，反演出待分析评测地区温度数据，同时结合相关波段生成待分析评测地区地表地物对应的属性信息；S2, preliminary analysis of data, firstly use single channel and split window algorithm to analyze and calculate S1, invert the temperature data of the area to be analyzed and evaluated, and generate attribute information corresponding to the surface features of the area to be analyzed and evaluated in combination with the relevant bands;
S3，多等级连通性分析，利用阈值叠加处理方法对S2步骤获得的温度数据进行连通分析，分析过程中以1℃为等级间隔，生成多个温度等级上的二值影像，然后对生成的二值影像进行连通性分析，得到不同等级上的温度连通区域；S3, multi-level connectivity analysis, uses the threshold superposition processing method to perform connectivity analysis on the temperature data obtained in step S2. During the analysis process, the binary images on multiple temperature levels are generated with 1°C as the level interval, and then the generated binary images are analyzed. Connectivity analysis is carried out on the temperature image, and the temperature connected areas at different levels are obtained;
S4，属性统计分析，对S3步骤获得的各连通区域的属性信息进行统计分析，在分析过程中设
Figure PCTCN2021143579-appb-000012
为第j层第i个连通区域，
Figure PCTCN2021143579-appb-000013
为该区域的像素个数；Num j是在第j个温度等级下连通区域的数量；同时计算所有连通区域在各温度等级下的温度平均值、NDVI平均值和NDBI平均值，从而得到分析不同温度等级下温度和地物类型的指数 变化，完成城市温度遥感数据分析作业。 S4, attribute statistical analysis, perform statistical analysis on the attribute information of each connected area obtained in step S3, and set the
Figure PCTCN2021143579-appb-000012
is the i-th connected region of the j-th layer,
Figure PCTCN2021143579-appb-000013
is the number of pixels in this area; Num j is the number of connected areas at the jth temperature level; at the same time, the average temperature, NDVI average and NDBI average value of all connected areas under each temperature level are calculated, so as to obtain the analysis difference. Exponential changes of temperature and ground object types under temperature grades to complete urban temperature remote sensing data analysis.
本实施例中，所述S1步骤中，数据库为地理空间数据库，获取的数据为Landsat陆地卫星数据。In this embodiment, in the step S1, the database is a geospatial database, and the acquired data is Landsat Landsat data.
重点说明的，所述S2步骤中，待分析评测地区地表地物对应的属性信息包括植被指数和建筑物指数，其中：It is important to note that in the step S2, the attribute information corresponding to the surface features in the area to be analyzed and evaluated includes the vegetation index and the building index, wherein:
植被指数需要利用红光和近红外波段，通过以下波段运算获取：The vegetation index needs to use the red and near-infrared bands, and is obtained through the following band operations:
Figure PCTCN2021143579-appb-000014
Figure PCTCN2021143579-appb-000014
D NIR为近红外波段波谱值，D R为红光波段波谱值； D NIR is the spectral value in the near-infrared band, and DR is the spectral value in the red band;
建筑物指数需要利用短波红外波段和近红外波段，通过波段运算进行获取：The building index needs to use the short-wave infrared band and the near-infrared band to obtain through the band operation:
Figure PCTCN2021143579-appb-000015
Figure PCTCN2021143579-appb-000015
D NIR为近红外波段波谱值，D SWIR1为第一个短波红外波段波谱值。 D NIR is the spectral value in the near-infrared band, and D SWIR1 is the spectral value in the first short-wave infrared band.
同时，所述S4步骤在进行属性信息分析时，所分析属性信息包括区域面积、区域平均温度、区域平均植被指数、区域平均植被指数、等级平均温度、等级平均植被指数及等级平均建筑物指数，具体分析函数为：At the same time, when the attribute information is analyzed in the step S4, the analyzed attribute information includes regional area, regional average temperature, regional average vegetation index, regional average vegetation index, grade average temperature, grade average vegetation index and grade average building index, The specific analysis function is:
区域面积(区域的像素个数)：
Figure PCTCN2021143579-appb-000016
Area area (number of pixels in the area):
Figure PCTCN2021143579-appb-000016
区域平均温度(第j等级i连通区域的平均温度)：
Figure PCTCN2021143579-appb-000017
Area average temperature (average temperature of connected area at level j i):
Figure PCTCN2021143579-appb-000017
区域平均植被指数(第j等级i连通区域的平均植被指数)：Regional average vegetation index (average vegetation index of the j-th level i connected area):
Figure PCTCN2021143579-appb-000018
Figure PCTCN2021143579-appb-000018
区域平均植被指数(第j等级i连通区域的平均建筑物指数)：Regional average vegetation index (average building index of the j-th level i connected area):
Figure PCTCN2021143579-appb-000019
Figure PCTCN2021143579-appb-000019
等级平均温度(第j等级上所有连通区域的平均温度)：Class average temperature (average temperature of all connected areas on the jth class):
Figure PCTCN2021143579-appb-000020
Figure PCTCN2021143579-appb-000020
等级平均植被指数(第j等级上所有连通区域的平均植被指数)：Grade-averaged vegetation index (average vegetation index of all connected areas on the jth level):
Figure PCTCN2021143579-appb-000021
Figure PCTCN2021143579-appb-000021
等级平均建筑物指数(第j等级上所有连通区域的平均建筑物指数)：
Figure PCTCN2021143579-appb-000022
Class average building index (average building index of all connected areas on the jth class):
Figure PCTCN2021143579-appb-000022
为了更好的对本发明中所涉及的技术内容进行说明和了解，现通过结合中国河南省郑州市为研究区域，分析2010年、2014年和2017年的Landsat温度反演数据。首先，分析温度数据的连接性，生成不同层次的温度连通区域数据；然后，计算不同区域在不同层次上的属性信息，分析不同阶段和层次上的属性变化；最后，对不同区域规模和不同区域的分析结果进行了讨论和分析：In order to better explain and understand the technical content involved in the present invention, the Landsat temperature inversion data in 2010, 2014 and 2017 are now analyzed by combining Zhengzhou City, Henan Province, China as the research area. First, the connectivity of temperature data is analyzed to generate temperature-connected area data at different levels; then, the attribute information of different areas at different levels is calculated, and the attribute changes at different stages and levels are analyzed; The results of the analysis are discussed and analyzed:
S1，采集温度遥感数据，以Landsat陆地卫星数据为例，其常用的遥感数据为Landsat 5、7和8系列卫星数据。其中，Landsat 5和7的热红外波段只有1个波段(10.40--12.50μm)的数据，而Landast 8 热红外波段有2个波段(10.60--11.19和11.50--12.51μm)的数据，这些数据可以在地理空间数据云网站进行下载。然后利用单通道和劈窗算法反演出温度数据；为了对温度数据对应的其它属性信息(例如，植被指数和建筑物指数)进行分析，需要结合相关波段生成对应的属性信息；植被指数需要利用红光和近红外波段，通过以下波段运算获取：S1, collect temperature remote sensing data, taking Landsat land satellite data as an example, the commonly used remote sensing data are Landsat 5, 7 and 8 series satellite data. Among them, the thermal infrared bands of Landsat 5 and 7 only have data in one band (10.40--12.50μm), while the thermal infrared band of Landast 8 has data in two bands (10.60--11.19 and 11.50--12.51μm). Data can be downloaded from the Geospatial Data Cloud website. Then use the single-channel and split-window algorithm to invert the temperature data; in order to analyze other attribute information (for example, vegetation index and building index) corresponding to the temperature data, it is necessary to combine the relevant bands to generate the corresponding attribute information; the vegetation index needs to use red The light and near-infrared bands are obtained through the following band operations:
Figure PCTCN2021143579-appb-000023
Figure PCTCN2021143579-appb-000023
其中，D NIR为近红外波段波谱值，D R为红光波段波谱值。建筑物指数需要利用短波红外波段和近红外波段，通过波段运算进行获取： Among them, D NIR is the spectral value in the near-infrared band, and DR is the spectral value in the red band. The building index needs to use the short-wave infrared band and the near-infrared band to obtain through the band operation:
Figure PCTCN2021143579-appb-000024
Figure PCTCN2021143579-appb-000024
其中，D NIR为近红外波段波谱值，D SWIR1为第一个短波红外波段波谱值； Among them, D NIR is the spectral value of the near-infrared band, and D SWIR1 is the spectral value of the first short-wave infrared band;
S2，数据初步分析，利用阈值叠加处理方法对温度数据进行处理，生成不同温度等级上的二值影像，从而进行连通性分析。为了减小区域间的弱连接性，采用基于侵蚀的连通性分析方法，对不同温度水平下的二值影像数据进行了连通性分析；因此，可以产生不同等级上的温度连通区域；如：Landsat数据反演的温度数据，(b)-(e)为温度数据分别在25℃、28℃、31℃、34℃、37℃下生成的连通区域；S2: Preliminary analysis of the data, processing the temperature data by using the threshold superposition processing method to generate binary images at different temperature levels, so as to perform connectivity analysis. In order to reduce the weak connectivity between regions, the connectivity analysis method based on erosion is used to analyze the connectivity of binary image data at different temperature levels; therefore, temperature connectivity regions at different levels can be generated; for example: Landsat Temperature data from data inversion, (b)-(e) are the connected regions generated by temperature data at 25°C, 28°C, 31°C, 34°C, and 37°C, respectively;
S3，多等级连通性分析，为了分析不同温度等级下温度数据与地物类型指数之间的关系，以1℃为等级间隔，对温度数据进行连通 分析，生成不同温度等级下的连通区域。生成连通区域后，需要对每个连通区域的属性信息进行统计；S3, multi-level connectivity analysis, in order to analyze the relationship between the temperature data and the ground object type index under different temperature levels, with 1°C as the level interval, the temperature data is analyzed for connectivity, and the connected areas under different temperature levels are generated. After generating the connected regions, the attribute information of each connected region needs to be counted;
S4，属性统计分析，设
Figure PCTCN2021143579-appb-000025
为第j层第i个连通区域，
Figure PCTCN2021143579-appb-000026
为该区域的像素个数；Num j是在第j个温度等级下连通区域的数量；根据郑州地区连通区域的属性信息可以计算出第j等级第i个连通区域的面积、平均温度、平均NDVI和平均NDBI，分析不同温度等级下连通区域温度与地物类型指数的关系。同时计算所有连通区域在各温度等级下的温度平均值、NDVI平均值和NDBI平均值，分析不同温度等级下温度和地物类型的指数变化； S4, attribute statistical analysis, set
Figure PCTCN2021143579-appb-000025
is the i-th connected region of the j-th layer,
Figure PCTCN2021143579-appb-000026
is the number of pixels in the area; Num j is the number of connected areas at the jth temperature level; according to the attribute information of the connected areas in Zhengzhou area, the area, average temperature, and average NDVI of the ith connected area at the jth level can be calculated and the average NDBI to analyze the relationship between the temperature of the connected area and the feature type index at different temperature levels. At the same time, calculate the average temperature, NDVI average and NDBI average value of all connected areas under each temperature level, and analyze the exponential changes of temperature and ground object types under different temperature levels;
通过分析，Landsat温度反演数据对郑州市进行多等级多时相温度分析。采用单通道和劈窗算法分别获得2010年、2014年和2017年的Landsat地表温度数据。据气象网站报道，2010年5月11日、2014年5月6日和2017年4月28日的气温相似。可以通过Landsat数据对三个时段的温度数据进行反演，并分级显示。2010年的陆地表面温度数据范围为10.68℃～56.10℃，平均气温为32.23℃，标准差为5.61；2014年陆地表面温度数据范围为14.07℃～56.03℃，平均温度33.41℃，标准差4.38；2017年陆地表面温度数据范围为9.32～56.34℃，平均温度31.09℃，标准差4.28。统计数据显示，2014年平均温度最高，2017年平均温度最低，2010年温度分布标准差大于2014年和2017年。Through analysis, Landsat temperature inversion data is used to conduct multi-level and multi-phase temperature analysis in Zhengzhou. Landsat surface temperature data in 2010, 2014 and 2017 were obtained using single-channel and split-window algorithms, respectively. The weather website reported similar temperatures on May 11, 2010, May 6, 2014 and April 28, 2017. The temperature data of the three time periods can be inverted and displayed hierarchically through Landsat data. The land surface temperature data in 2010 ranged from 10.68°C to 56.10°C, with an average temperature of 32.23°C and a standard deviation of 5.61; in 2014, the land surface temperature data ranged from 14.07°C to 56.03°C, with an average temperature of 33.41°C and a standard deviation of 4.38; 2017 The annual land surface temperature data ranges from 9.32 to 56.34°C, with an average temperature of 31.09°C and a standard deviation of 4.28. Statistics show that the average temperature in 2014 was the highest, the average temperature in 2017 was the lowest, and the standard deviation of the temperature distribution in 2010 was greater than that in 2014 and 2017.
实验结果表明，温度与植被指数呈负相关，与建筑指数呈正相关。随着温度水平的升高，温度与地表特征类型指数的相关性降低。The experimental results show that the temperature is negatively correlated with the vegetation index and positively correlated with the building index. As the temperature level increases, the correlation between temperature and surface feature type index decreases.
本发明较传统的城市温度检测分析方法，一方面有效实现了主要 地物指数之间的对应关系综合分析，提高分析作业全面性和精度，另一方面克服传统同类工作中存在的数据运算量大、计算过程规范性差及计算数据全面差的缺点，从而极大的提高了城市温度遥感观测数据分析作业的工作效率、质量和精度，可广泛应用于各类城市规划作业，为城市规划工作提供准确的工作参考数据及依据，提高城市规划工作的工作效率、合理性，并降低城市规划工作的劳动强度和成本。Compared with the traditional urban temperature detection and analysis method, on the one hand, the present invention effectively realizes the comprehensive analysis of the corresponding relationship between the main object indices, improves the comprehensiveness and accuracy of the analysis operation, and on the other hand overcomes the large amount of data calculation in the traditional similar work. The shortcomings of poor standardization of the calculation process and poor comprehensive calculation data greatly improve the work efficiency, quality and accuracy of urban temperature remote sensing observation data analysis operations, which can be widely used in various urban planning operations and provide accurate information for urban planning work. The work reference data and basis are improved, the work efficiency and rationality of urban planning work are improved, and the labor intensity and cost of urban planning work are reduced.
以上内容是对本发明所作的进一步详细说明，不能认定本发明的只局限于这些说明。对于本发明所属技术领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干简单推演或替换，都应当视为属于本发明由所提交的权利要求书确定的专利保护范围。The above content is a further detailed description of the present invention, and it cannot be considered that the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the present invention, some simple deductions or substitutions can be made, all of which should be regarded as belonging to the patent of the present invention determined by the submitted claims. protected range.

Claims (4)

  1. 一种多时相多等级的城市温度遥感数据分析方法，其特征在于，所述多时相多等级的城市温度遥感数据分析方法包括以下步骤：A multi-temporal and multi-level urban temperature remote sensing data analysis method, characterized in that the multi-temporal and multi-level urban temperature remote sensing data analysis method comprises the following steps:
    S1，采集温度遥感数据，首先从相关数据库中获取待分析评测地区温度卫星遥感数据；S1, collect temperature remote sensing data, first obtain the temperature satellite remote sensing data of the area to be analyzed and evaluated from the relevant database;
    S2，数据初步分析，首先利用单通道和劈窗算法对S1进行分析计算，反演出待分析评测地区温度数据，同时结合相关波段生成待分析评测地区地表建筑和植被对应的属性信息；S2: Preliminary analysis of the data. First, use the single-channel and split-window algorithm to analyze and calculate S1, invert the temperature data of the area to be analyzed and evaluated, and generate attribute information corresponding to the surface buildings and vegetation in the area to be analyzed and evaluated in combination with the relevant bands;
    S3，多等级连通性分析，利用阈值叠加处理方法对S2步骤获得的温度数据进行连通分析，分析过程中以1℃为等级间隔，生成多个温度等级上的二值影像，然后对生成的二值影像进行连通性分析，得到不同等级上的温度连通区域；S3, multi-level connectivity analysis, uses the threshold superposition processing method to perform connectivity analysis on the temperature data obtained in step S2. During the analysis process, the binary images on multiple temperature levels are generated with 1°C as the level interval, and then the generated binary images are analyzed. Connectivity analysis is carried out on the temperature image, and the temperature connected areas at different levels are obtained;
    S4，属性统计分析，对S3步骤获得的各连通区域的属性信息进行统计分析，在分析过程中设
    Figure PCTCN2021143579-appb-100001
    为第j层第i个连通区域，
    Figure PCTCN2021143579-appb-100002
    为该区域的像素个数；Num j是在第j个温度等级下连通区域的数量；同时计算所有连通区域在各温度等级下的温度平均值、NDVI平均值和NDBI平均值，从而得到分析不同温度等级下温度和地物类型的指数变化，完成城市温度遥感数据分析作业。     S4, attribute statistical analysis, perform statistical analysis on the attribute information of each connected area obtained in step S3, and set the
    Figure PCTCN2021143579-appb-100001
    is the i-th connected region of the j-th layer,
    Figure PCTCN2021143579-appb-100002
    is the number of pixels in this area; Num j is the number of connected areas at the jth temperature level; at the same time, the average temperature, NDVI average and NDBI average value of all connected areas under each temperature level are calculated, so as to obtain the analysis difference. Exponential changes of temperature and ground object types under temperature grades to complete urban temperature remote sensing data analysis.
  2. 根据权利要求1所述的一种多时相多等级的城市温度遥感数据分析方法，其特征在于，所述S1步骤中，数据库为地理空间数据库，获取的数据为Landsat陆地卫星数据。A multi-temporal and multi-level urban temperature remote sensing data analysis method according to claim 1, characterized in that, in the step S1, the database is a geospatial database, and the acquired data is Landsat land satellite data.
  3. 根据权利要求1所述的一种多时相多等级的城市温度遥感数据分析方法，其特征在于，所述S2步骤中，待分析评测地区地表地 物对应的属性信息包括植被指数和建筑物指数，其中：A multi-temporal multi-level urban temperature remote sensing data analysis method according to claim 1, wherein, in the step S2, the attribute information corresponding to the surface objects in the area to be analyzed and evaluated includes a vegetation index and a building index, in:
    植被指数需要利用红光和近红外波段，通过以下波段运算获取：The vegetation index needs to use the red and near-infrared bands, and is obtained through the following band operations:
    Figure PCTCN2021143579-appb-100003
    Figure PCTCN2021143579-appb-100003
    D NIR为近红外波段波谱值，D R为红光波段波谱值； D NIR is the spectral value in the near-infrared band, and DR is the spectral value in the red band;
    建筑物指数需要利用短波红外波段和近红外波段，通过波段运算进行获取：The building index needs to use the short-wave infrared band and the near-infrared band to obtain through the band operation:
    Figure PCTCN2021143579-appb-100004
    Figure PCTCN2021143579-appb-100004
    D NIR为近红外波段波谱值，D SWIR1为第一个短波红外波段波谱值。 D NIR is the spectral value in the near-infrared band, and D SWIR1 is the spectral value in the first short-wave infrared band.
  4. 根据权利要求1所述的一种多时相多等级的城市温度遥感数据分析方法，其特征在于，所述S4步骤在进行属性信息分析时，所分析属性信息包括区域面积、区域平均温度、区域平均植被指数、区域平均建筑物指数、等级平均温度、等级平均植被指数及等级平均建筑物指数，具体分析函数为：A multi-temporal and multi-level urban temperature remote sensing data analysis method according to claim 1, characterized in that, when the attribute information is analyzed in the step S4, the analyzed attribute information includes regional area, regional average temperature, and regional average temperature. Vegetation index, regional average building index, grade average temperature, grade average vegetation index and grade average building index, the specific analysis function is:
    区域面积(区域的像素个数)：
    Figure PCTCN2021143579-appb-100005
    Area area (number of pixels in the area):
    Figure PCTCN2021143579-appb-100005
    区域平均温度(第j等级i连通区域的平均温度)：
    Figure PCTCN2021143579-appb-100006
    Area average temperature (average temperature of connected area at level j i):
    Figure PCTCN2021143579-appb-100006
    区域平均植被指数(第j等级i连通区域的平均植被指数)：
    Figure PCTCN2021143579-appb-100007
    Regional average vegetation index (average vegetation index of the j-th level i connected area):
    Figure PCTCN2021143579-appb-100007
    区域平均植被指数(第j等级i连通区域的平均建筑物指数)：
    Figure PCTCN2021143579-appb-100008
    Regional average vegetation index (average building index of the j-th level i connected area):
    Figure PCTCN2021143579-appb-100008
    等级平均温度(第j等级上所有连通区域的平均温度)：
    Figure PCTCN2021143579-appb-100009
    Class average temperature (average temperature of all connected areas on the jth class):
    Figure PCTCN2021143579-appb-100009
    等级平均植被指数(第j等级上所有连通区域的平均植被指数)：
    Figure PCTCN2021143579-appb-100010
    Grade-averaged vegetation index (average vegetation index of all connected areas on the jth level):
    Figure PCTCN2021143579-appb-100010
    等级平均建筑物指数(第j等级上所有连通区域的平均建筑物指数)：
    Figure PCTCN2021143579-appb-100011
    Class average building index (average building index of all connected areas on the jth class):
    Figure PCTCN2021143579-appb-100011
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