Urban land surface environment dynamic monitoring method and system fused with remote sensing technology

Abstract

The invention relates to an urban land surface environment dynamic monitoring method and system fused with a remote sensing technology. The method comprises the following steps: acquiring an original data set of a target city; performing data cleaning and standardization processing on the original data set to generate a standardized urban space data set; segmenting a city and town development boundary according to the data set, dividing evaluation units and designing a multi-dimensional evaluation index system; on the basis of the evaluation index system, measuring and calculating each index and determining the weight, and obtaining a three-level evaluation grade for evaluating the spatial quality; and finally, on the basis of the three-level evaluation level, targeted quality improvement countermeasures are formulated according to the spatial quality characteristics of different areas. By adopting the method, the urban space quality difference can be accurately identified, a quantitative basis is provided for urban planning, resource allocation optimization, facility layout improvement, heat island effect alleviation and the like are assisted, refined treatment and sustainable development of urban space are promoted, and the life welfare of residents is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of resource and environmental monitoring, and in particular relates to a method and system for dynamic monitoring of urban land surface environment integrating remote sensing technology. Background Art

[0002] With the acceleration of urbanization, the urban land surface environment faces complex challenges such as vegetation fragmentation, intensified urban heat island effect, and expansion of impervious surfaces. High-precision, dynamic monitoring technologies are urgently needed to support sustainable urban development. Traditional ground-based monitoring methods have the disadvantages of limited coverage and insufficient spatiotemporal continuity. Remote sensing technology, with its advantages of macroscopic observation, periodic revisits, and multispectral imaging, has become a core technical means for urban environmental monitoring. In existing technologies, the interpretation accuracy of a single remote sensing data source is limited, and there is a lack of a collaborative analysis framework for multiple factors of the land surface environment (such as vegetation cover, surface temperature, imperviousness, soil moisture, etc.). However, existing methods still need to be improved in terms of multi-source data fusion accuracy, dynamic change detection timeliness, and business application adaptability. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for dynamic monitoring of urban land surface environment that integrates remote sensing technology and can accurately identify differences in urban space quality and promote the refined governance and sustainable development of urban space in response to the above technical problems.

[0004] First, this application provides a method for dynamic monitoring of urban land surface environment using remote sensing technology, including:

[0005] Obtain the original data set of the target city; the original data set includes population big data, historical remote sensing data, urban traffic big data, employment and residence big data, and travel big data.

[0006] The original data set is cleaned and standardized to generate a standardized urban spatial data set that includes ecological environment characteristics, living circle characteristics, and population characteristics.

[0007] Based on the urban spatial data set, the urban and town development boundaries are segmented, the evaluation units are divided and a multi-dimensional evaluation index system is designed.

[0008] Based on the evaluation index system, each evaluation index is measured and the weight of each index is determined to obtain a three-level evaluation grade for evaluating space quality.

[0009] Based on the three-level evaluation level and the spatial quality characteristics of different regions, targeted quality improvement measures are formulated.

[0010] In one embodiment, the original data set is cleaned and standardized to generate a standardized urban spatial data set containing ecological environment characteristics, living circle characteristics, and population characteristics, including:

[0011] The original data set is formatted and missing values ​​are processed to obtain a complete data set.

[0012] The linear spectral decomposition model and VIS model were used to classify the objects in the preprocessed complete dataset and extract the ecological environment characteristic parameters; the ecological environment characteristic parameters include vegetation cover, inverted surface temperature, urban heat island, urban impermeability, soil moisture, night light index and air quality index.

[0013] Based on the complete data set and combined with industry standards, a spatial correlation analysis of living circle facilities and population distribution data was conducted, and the facility coverage rate was calculated using a formula.

[0014] The population distribution data is spatially gridded to calculate population density and flow characteristics. Combined with facility coverage and night light index, the living circle characteristics and population characteristic parameters are obtained.

[0015] The ecological environment characteristic parameters, living circle characteristics and population characteristic parameters are integrated and standardized to generate a multi-dimensional urban spatial dataset.

[0016] In one embodiment, the vegetation coverage and the inverted surface temperature are calculated using the following formula:

[0017]

[0018] NDVI = (NIR-R) / (NIR+R)

[0019] Among them, vfc represents vegetation coverage, NDVI represents normalized vegetation index, and NDVI soil Indicates the NDVI value of bare soil or no vegetation cover area, NDVI veg It represents the NDVI value of pure vegetation pixels, NIR represents the near-infrared channel reflectance, and R represents the visible light channel reflectance.

[0020] T s =[a·(1-CD)+(b·(1-CD)+C+D)·T6-D·T a ]

[0021] C=τ×ε

[0022] D = (1-τ) [1+τ(1+ε)]

[0023] Where Ts represents the inverted surface temperature, a and b represent constants, a = -67.355351, b = 0.458606, C and D represent intermediate parameters, T6 represents the brightness temperature on the star, T arepresents the average atmospheric temperature, τ represents the atmospheric transmittance, and ε represents the surface emissivity.

[0024] In one embodiment, the urban development boundary of a city is segmented based on the urban spatial dataset, evaluation units are divided, and a multi-dimensional evaluation index system is designed, including:

[0025] The urban spatial data set is measured and standardized using single-factor indicators, and indicators related to the ecological environment, living circle construction, and population characteristics are screened to obtain a three-level evaluation indicator set; the three-level evaluation indicator set includes multiple first-level indicators, each first-level indicator is associated with multiple second-level indicators, and each second-level indicator is associated with multiple third-level indicators.

[0026] Based on the three-level evaluation index set and using topological analysis tools, the urban development boundary is divided based on the urban branch road network and residential neighborhood standards to obtain spatially discretized evaluation units.

[0027] The hierarchical mapping method is used to correspond the evaluation units with the first-level indicators, second-level indicators and third-level indicators to obtain the indicator hierarchical mapping relationship.

[0028] The evaluation system framework is constructed according to the hierarchical mapping relationship of indicators; the evaluation system framework includes a hierarchical structure of primary indicators, related secondary indicators and tertiary indicators.

[0029] The minimum technical feature set is determined based on the evaluation system framework; the minimum technical feature set includes the indicator hierarchical mapping relationship and the key features of the hierarchical structure.

[0030] The minimum technical feature set is analyzed and adjusted to obtain the technical subject concept to obtain the higher-level technical concept.

[0031] If the scope of the higher-level technical concept is larger than the evaluation system framework, the indicator hierarchy mapping relationship is updated to obtain an optimized evaluation indicator system.

[0032] In one embodiment, each evaluation indicator is measured and weighted based on the evaluation indicator system to obtain a three-level evaluation grade for evaluating space quality, including:

[0033] Obtain the green space coverage rate, urban heat island intensity and pollutant concentration of the ecological environment indicators in the evaluation index system.

[0034] Obtain the service facility density and walking distance in the living circle construction indicators.

[0035] Extract the age structure and permanent population distribution from the population characteristic indicators.

[0036] The comprehensive ecological environment index is calculated based on green space coverage, urban heat island intensity and pollutant concentration; the comprehensive ecological environment index is generated through a weighted fusion algorithm.

[0037] The density of service facilities and walking distance are input into the spatial network model to calculate the comprehensive index of the living circle.

[0038] The age structure and permanent population distribution are integrated and a standardized method is used to generate the population vitality coefficient.

[0039] Based on the comprehensive index of ecological environment and comprehensive index of living circle, the hierarchical analysis method is used to determine the comprehensive carrying capacity of the region.

[0040] The population vitality coefficient is used to dynamically adjust the weight distribution of the regional comprehensive carrying capacity, and the natural discontinuity method is combined with the planning objectives to obtain a three-level evaluation grade.

[0041] In one embodiment, targeted quality improvement measures are formulated based on the three-level evaluation scale and the spatial quality characteristics of different areas, including:

[0042] The comprehensive index of spatial quality of different evaluation units is calculated using the formula according to the three-level evaluation level.

[0043] Based on the comprehensive index of spatial quality, the evaluation units are divided into superior quality areas, basic comfort areas and areas that need improvement.

[0044] Differentiated quality improvement measures are formulated based on the different spatial quality characteristics of the superior quality zone, basic comfort zone and area that needs improvement.

[0045] In one embodiment, the following formula is used to calculate the comprehensive index of space quality of different evaluation units:

[0046] Q=0.31P+0.25L 15 +0.24L 10 +0.20L5

[0047] Among them, Q represents the comprehensive index of spatial quality, P represents the comprehensive index of urban land surface environment, L 15 Indicates the comprehensive index of 15-minute living circle, L 10 L represents the comprehensive index of the 10-minute living circle, and L5 represents the comprehensive index of the 5-minute living circle.

[0048] Secondly, this application also provides a dynamic monitoring system for urban land surface environment integrating remote sensing technology, which includes:

[0049] The data acquisition and processing module is used to obtain the original data set of the target city; the original data set includes population big data, historical remote sensing data, urban traffic big data, employment and residence big data, and travel big data; it is also used to clean and standardize the original data set to generate a standardized urban spatial data set that includes ecological environment characteristics, living circle characteristics, and population characteristics.

[0050] The indicator system construction module is used to segment urban development boundaries based on urban spatial data sets, divide evaluation units and design a multi-dimensional evaluation indicator system.

[0051] The space grade classification module is used to measure various evaluation indicators based on the evaluation index system to determine the weight of each indicator and obtain a three-level evaluation grade for evaluating space quality.

[0052] The zoning strategy generation module is used to formulate targeted quality improvement strategies based on the spatial quality characteristics of different areas based on the three-level evaluation level.

[0053] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0054] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the above method when executed by a processor.

[0055] The above-mentioned method, system, computer equipment, and storage medium for dynamic monitoring of the urban land surface environment, integrating remote sensing technology, acquires raw datasets of population, historical remote sensing data, urban transportation, employment, housing, and travel data from a target city. These datasets are then cleaned and standardized to generate a standardized urban spatial dataset encompassing ecological, living, and demographic characteristics. Based on this dataset, the urban development boundary is segmented, evaluation units are divided, and a multidimensional evaluation index system is designed. Based on this evaluation index system, each indicator is calculated and weighted to produce a three-level evaluation rating for assessing spatial quality. Finally, based on the three-level evaluation ratings, targeted quality improvement strategies are formulated for each region. By integrating and standardizing multi-source data, this method constructs an urban spatial quality assessment system encompassing multiple dimensions: ecology, living, and population, achieving a complete technical closed loop from data collection to zoning policy implementation. This method can accurately identify differences in urban spatial quality, providing a quantitative basis for urban planning, helping to optimize resource allocation, improve facility layout, mitigate the urban heat island effect, and promote refined urban spatial governance and sustainable development, thereby improving residents' well-being. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 A flow chart of a method for dynamic monitoring of urban land surface environment integrating remote sensing technology provided by an embodiment of the present invention;

[0058] Figure 2 A structural block diagram of a system for dynamic monitoring of urban land surface environment integrating remote sensing technology provided by an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a solution for constructing a multi-scale evaluation system for a 15-minute life circle provided by an embodiment of the present invention;

[0060] Figure 4 A schematic diagram of a solution for constructing a multi-scale evaluation system for a 10-minute life circle provided by an embodiment of the present invention;

[0061] Figure 5 This is a schematic diagram of a solution for constructing a multi-scale evaluation system for a 5-minute life circle provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0063] In one embodiment, Figure 1 As shown, the present application provides a method for dynamic monitoring of urban land surface environment integrating remote sensing technology, which may include the following steps:

[0064] Step S101, obtaining the original data set of the target city; the original data set includes population big data, historical remote sensing data, urban traffic big data, employment and residence big data, and travel big data.

[0065] Specifically, through statistical yearbooks, remote sensing data platforms (such as USGS), traffic management department databases and Internet platforms, we collect population big data (including permanent population size, density, urbanization rate, etc.), historical remote sensing data (such as Landsat OLI images, spanning 2000-2022), urban traffic big data (road mileage, bus stop distribution), employment and residence big data (POI points, residential and corporate spatial distribution) and travel big data (commuting trajectories, shared bicycle OD data) of the target city to form a multi-source heterogeneous original data set.

[0066] Step S102 , performing data cleaning and standardization processing on the original data set to generate a standardized urban spatial data set including ecological environment characteristics, living circle characteristics, and population characteristics.

[0067] Specifically, Python data processing tools and ENVI remote sensing software were used to unify the format of the original data, fill missing values ​​(such as interpolation for population data), eliminate noise (such as MNF transformation of remote sensing images), and transform the projection (unified to Albers equal-area conic projection); the Z-score standardization formula was used to normalize the dimensions of ecological indicators (such as vegetation cover VFC and land surface temperature LST), living circle indicators (such as facility coverage), and population indicators (such as population density) to generate a structured urban spatial dataset to meet the needs of spatial analysis and modeling.

[0068] Step S103 , segmenting the urban development boundary of the city according to the urban spatial data set, dividing the evaluation units and designing a multi-dimensional evaluation index system.

[0069] Furthermore, according to the Urban Residential Area Planning and Design Standard (GB50180-2018), the ArcGIS topology analysis tool was used to divide the urban development boundary into 1000-3000 people / unit and 2-4hm2 land area based on the urban branch road. 2 evaluation units (for example, Hohhot is divided into 1,702 units); a three-level evaluation index system is designed from the three dimensions of ecological environment, living circle construction, and population and transportation. The ecological environment includes 6 first-level indicators such as vegetation coverage (weight 0.24) and urban heat island (weight 0.21); the living circle covers 17 / 12 / 11 second-level indicators such as per capita land area and facility coverage rate of 15 / 10 / 5-minute circles; population and transportation include indicators such as population density (weight 0.13) and road network density (weight 0.23); and the indicator weights are determined by constructing a pairwise comparison matrix through the analytic hierarchy process (AHP).

[0070] Step S104: Calculate each evaluation index based on the evaluation index system to determine the weight of each index, and obtain a three-level evaluation grade for evaluating the quality of the space.

[0071] Specifically, for ecological environment indicators, the vegetation coverage is calculated using the NDVI pixel dichotomy model; the surface temperature is inverted using a single window algorithm formula; for living circle indicators, the accessibility of facilities is analyzed using a 15 / 10 / 5 minute walking buffer zone; and the indicator weights are determined using principal component analysis (PCA) and analytic hierarchy process (AHP), such as the ecological environment comprehensive index P = Σ(F i ×C i )(F i represents the degree of membership, C iThe comprehensive index of living circle is calculated by weighted summation method, and finally the comprehensive index of spatial quality is integrated by weighted formula. Combined with the natural break point method, it is divided into superior quality zone (Q≥0.643726), basic comfort zone (0.5003≤Q<0.643726), and zone that needs improvement (Q<0.5003), so as to realize the quantitative evaluation of spatial quality.

[0072] Step S105 : formulating targeted quality improvement measures based on the three-level evaluation levels and the spatial quality characteristics of different areas.

[0073] Specifically, for quality-superior areas (such as the Changle Palace area), small sports venues will be added in accordance with the "Requirements for the Configuration of Multifunctional Public Sports Fields in Urban Communities", with a service radius of ≤300 meters; for basic comfort areas (such as the Yongtai City area), new primary schools will be built in accordance with the "Standards for the Construction of Primary and Secondary Schools", with a service radius of ≤500 meters, and the "small block dense road network" model will be adopted to increase the branch road density to 8km / km 2 In areas that still need improvement (such as old urban areas), we will promote the renovation of old residential communities to accommodate the elderly, and achieve 55 nursing beds per thousand elderly people by 2035. At the same time, we will promote permeable pavement and rooftop greening to alleviate the heat island effect. In key improvement areas (such as western urban areas), we will build a "one horizontal and two vertical" expressway network with a road network density target of ≥30km / km. 2 , and rely on the Horqin New District to introduce the electronic information industry and build a 15-minute living circle.

[0074] This method, which integrates remote sensing technology to dynamically monitor the urban land surface environment, acquires raw datasets of population, historical remote sensing data, urban transportation, employment, housing, and travel data from a target city. These datasets are then cleaned and standardized to generate a standardized urban spatial dataset encompassing ecological, living, and demographic characteristics. Based on this dataset, the urban development boundary is segmented, evaluation units are divided, and a multidimensional evaluation index system is designed. Based on this evaluation index system, each indicator is calculated and weighted to produce a three-tiered evaluation scale for assessing spatial quality. Finally, based on the three-tiered evaluation scale, targeted quality improvement measures are formulated for each region. By integrating and standardizing multi-source data, this method constructs an urban spatial quality assessment system encompassing multiple dimensions: ecology, living, and population, achieving a complete technical closed-loop process from data collection to zoning-based policy implementation. This method can accurately identify differences in urban spatial quality, providing a quantitative basis for urban planning, helping to optimize resource allocation, improve facility layout, mitigate the urban heat island effect, and promote refined urban spatial governance and sustainable development, thereby improving residents' well-being.

[0075] In one embodiment, data cleaning and standardization processing is performed on the original data set to generate a standardized urban spatial data set containing ecological environment characteristics, living circle characteristics, and population characteristics, which may include the following steps:

[0076] Step S201 : unify the format of the original data set and process missing values ​​to obtain a complete data set.

[0077] In step S202, the linear spectral decomposition model and the VIS model are used to classify the preprocessed complete data set and extract ecological environment characteristic parameters; the ecological environment characteristic parameters include vegetation coverage, inverted surface temperature, urban heat island, urban impermeability, soil moisture, night light index and air quality index.

[0078] Step S203: Based on the complete data set and combined with industry standards, a spatial correlation analysis is performed on the living circle facilities and population distribution data, and the facility coverage rate is calculated using a formula.

[0079] Step S204 , spatially gridding the population distribution data, calculating population density and mobility characteristics, and combining the facility coverage rate and nighttime lighting index to obtain living circle characteristics and population characteristic parameters.

[0080] Step S205 , integrating and standardizing the ecological environment characteristic parameters, living circle characteristics, and population characteristic parameters to generate a multi-dimensional urban space dataset.

[0081] Furthermore, the original dataset was first formatted and missing values ​​processed. Interpolation was used to fill in missing population data and remove invalid pixels from the remote sensing images, resulting in a complete dataset with a consistent structure. The preprocessed remote sensing images were then classified using the linear spectral unmixing (LSMA) model and the visual inertial spectrum (VIS) model, separating components such as high- and low-albedo impervious surfaces, vegetation, and bare soil. Seven ecological and environmental parameters, such as vegetation cover and surface temperature, were extracted using the NDVI pixel binary model and a single-window algorithm. Subsequently, based on the complete dataset, ArcGIS buffer analysis (15 / 10 / 5-minute walking radius) was used to calculate the coverage rate of living area facilities (coverage rate = theoretical demand / number of facilities within the buffer zone × 100%). Population data was then gridded to generate population density and mobility maps. These maps, combined with the Night Light Index (NLI) representing economic activity intensity, generated living area and population characteristic parameters. Finally, the three characteristic parameters were dimensionally normalized using the Z-score normalization formula, resulting in an integrated multidimensional urban spatial dataset encompassing ecological, living area, and population dimensions.

[0082] This implementation standardizes and structures urban spatial data through multi-source data preprocessing and feature extraction, addressing issues such as heterogeneous raw data formats, missing value interference, and dimensional discrepancies. Utilizing remote sensing interpretation and spatial analysis techniques, it accurately extracts key ecological and environmental parameters and the characteristics of living area infrastructure coverage, quantifying population distribution and mobility patterns. This significantly improves the accuracy and efficiency of urban spatial analysis, providing a scientific and quantitative basis for urban planning, environmental management, and public policymaking.

[0083] In one embodiment, the vegetation coverage and the inverted surface temperature can be calculated using the following formula:

[0084]

[0085] NDVI = (NIR-R) / (NIR+R)

[0086] Among them, vfc represents vegetation coverage, NDVI represents normalized vegetation index, and NDVI soil Indicates the NDVI value of bare soil or no vegetation cover area, NDVI veg It represents the NDVI value of pure vegetation pixels, NIR represents the near-infrared channel reflectance, and R represents the visible light channel reflectance.

[0087] Ts=[a·(1-CD)+(b·(1-CD)+C+D)·T6-D·T a ]

[0088] C=τ×ε

[0089] D = (1-τ) [1 + τ (1-ε)]

[0090] Where Ts represents the inverted surface temperature, a and b represent constants, a = -67.355351, b = 0.458606, C and D represent intermediate parameters, T6 represents the brightness temperature on the star, T a represents the average atmospheric temperature, τ represents the atmospheric transmittance, and ε represents the surface emissivity.

[0091] Preferably, T a =19.2704+0.91118T0, where T0 represents the near-surface atmospheric temperature.

[0092] Preferably, the atmospheric transmittance τ needs to be obtained by first calculating the atmospheric water vapor content. The calculation method is as follows:

[0093]

[0094] τ=f(w)+f(d)

[0095] Where w represents the atmospheric water vapor content, ρ 19and ρ2 represent the 19th and 2nd bands of MODIS data, respectively. f(w) and f(d) represent the atmospheric transmittance and atmospheric transmittance correction terms calculated based on the atmospheric water vapor content.

[0096] Furthermore, to invert the surface temperature, the radiation intensity value should be converted into the corresponding brightness temperature:

[0097] T thermal =K2 / ln(1+K1 / L thermal )

[0098] Among them, T thermal represents the brightness temperature of the thermal infrared band of LandsatOLI data, K1 and K2 represent constant coefficients, L thermal Indicates the thermal radiation brightness value, LandsatOLI data source: K1 = 774.89, K2 = 1321.08.

[0099] This embodiment achieves the quantitative inversion of vegetation coverage and surface temperature through the above formula, providing key parameters for urban land surface environment monitoring. The vegetation coverage formula is based on the difference in spectral reflectance characteristics, effectively separating vegetation and non-vegetated areas, and quantifying the quality of the ecological base; the single-window algorithm combines atmospheric parameters and surface emissivity to improve the accuracy of surface temperature inversion and accurately depict the urban heat island effect. The combination of the two provides a scientific basis for evaluating urban ecological health (such as the rationality of green space distribution and thermal environment comfort), supports the quantitative analysis of the ecological environment dimension in urban space quality evaluation, and helps to optimize green space systems and formulate heat island mitigation strategies in urban planning.

[0100] In one embodiment, segmenting the urban development boundary based on the urban spatial dataset, dividing the evaluation units, and designing a multi-dimensional evaluation index system may include the following steps:

[0101] In step S301, single-factor index measurement and standardization are performed on the urban spatial dataset, and indicators related to the ecological environment, living circle construction, and population characteristics are screened to obtain a three-level evaluation index set; the three-level evaluation index set includes multiple first-level indicators, each first-level indicator is associated with multiple second-level indicators, and each second-level indicator is associated with multiple third-level indicators.

[0102] Step S302 : Using a topological analysis tool according to the three-level evaluation index set, the urban development boundary is segmented based on the urban branch road network and residential neighborhood standards to obtain spatially discretized evaluation units.

[0103] Step S303 : Using a hierarchical mapping method, the evaluation units are matched with the first-level indicators, the second-level indicators, and the third-level indicators to obtain an indicator hierarchical mapping relationship.

[0104] Step S304: constructing an evaluation system framework according to the indicator hierarchical mapping relationship; the evaluation system framework includes a hierarchical structure of primary indicators, associated secondary indicators, and tertiary indicators.

[0105] Step S305 , determining a minimum technical feature set based on the evaluation system framework; the minimum technical feature set includes the indicator hierarchical mapping relationship and key features of the hierarchical structure.

[0106] Step S306: Analyze and adjust the technical subject concept of the minimum technical feature set to obtain a higher-level technical concept.

[0107] Step S307: If the scope of the higher-level technical concept is larger than the evaluation system framework, the indicator hierarchy mapping relationship is updated to obtain an optimized evaluation indicator system.

[0108] Specifically, we first calculated single-factor indicators for the urban spatial dataset, obtained basic indicator values ​​such as vegetation coverage, facility coverage, and population density through remote sensing interpretation, spatial analysis, and statistical calculations, and used the Z-score standardization method to eliminate dimensional differences; then, we screened indicators related to the ecological environment (such as vegetation coverage and heat island intensity), living circle construction (such as per capita land area in a 15-minute circle and junior high school coverage), and population characteristics (such as density and urbanization rate), and constructed a three-level evaluation indicator set consisting of first-level indicators (4 items), second-level indicators (13 items), and third-level indicators (48 items). Subsequently, we used the ArcGIS topology analysis tool to analyze the urban branch network and residential neighborhood standards (1,000-3,000 people / unit, 2-4hm² / km²). 2 / unit) to divide the urban development boundary and generate spatially discretized evaluation units (such as 1702 units in Hohhot).

[0109] Using a hierarchical mapping approach, a spatial-attribute correspondence is established between each evaluation unit and the three-level indicator system, forming a hierarchical mapping relationship of "evaluation unit-first-level indicator-second-level indicator-third-level indicator." Based on this relationship, an evaluation system framework with a hierarchical structure is constructed. By extracting the key features of the indicator hierarchical mapping relationship and the hierarchical structure, a minimum technical feature set is determined. By analyzing the minimum technical feature set, the technical theme concepts are adjusted to match the superordinate technical concepts (such as "urban space quality assessment"). If the superordinate concept covers a larger scope than the existing framework, the indicator hierarchical mapping relationship is optimized, and related indicators are added or adjusted, ultimately forming a logically complete and comprehensive evaluation indicator system.

[0110] This embodiment achieves the structuring and standardization of urban space quality evaluation through the technical path of "indicator measurement-screening-spatial segmentation-hierarchical mapping-framework optimization." The three-level indicator system systematically covers the multi-dimensional characteristics of ecology, living circle, and population. Topological segmentation ensures that the evaluation units match the actual management units of the city. The hierarchical mapping and framework optimization mechanism guarantee the scientific nature and adaptability of the indicator system. This process solves the problems of indicator fragmentation and inconsistent spatial scale in traditional evaluation, provides an operational technical framework for the quantitative assessment of urban space quality, and supports the accuracy of subsequent evaluation grading and zoning policy implementation.

[0111] In one embodiment, the following steps may be included:

[0112] Step S401: Obtain the green space coverage rate, urban heat island intensity and pollutant concentration of the ecological environment indicators in the evaluation index system.

[0113] Step S402: Obtain the service facility density and walking range in the living circle construction indicators.

[0114] Step S403: extracting the age structure and permanent population distribution from the population characteristic indicators.

[0115] Step S404: Calculate the comprehensive ecological environment index based on the green space coverage rate, urban heat island intensity, and pollutant concentration; the comprehensive ecological environment index is generated through a weighted fusion algorithm.

[0116] Step S405: Input the service facility density and walking range into the spatial network model to calculate the comprehensive index of the living circle.

[0117] Step S406: Integrate the age structure and the permanent population distribution and use a standardized method to generate a population vitality coefficient.

[0118] Step S407: Determine the comprehensive carrying capacity of the region using the analytic hierarchy process based on the comprehensive ecological environment index and the comprehensive living circle index.

[0119] Step S408: Dynamically adjust the weight distribution of regional comprehensive carrying capacity using the population vitality coefficient, and use the natural discontinuity method combined with planning target division to obtain a three-level evaluation grade.

[0120] First, ecological and environmental indicators were extracted from the evaluation index system, including green space coverage obtained through remote sensing interpretation, urban heat island intensity (surface temperature) inverted using a single-window algorithm, and pollutant concentrations (such as PM2.5) obtained from monitoring data. Simultaneously, indicators for the development of a living environment were collected, including service facility density (such as the number of shopping malls per square kilometer) and walkability (coverage ratio of 15-minute, 10-minute, and 5-minute buffer zones). Demographic characteristics were also extracted, including age structure distribution (such as the proportion of the population in each age group) and gridded distribution data of the permanent population. A weighted fusion algorithm was then used to calculate a comprehensive ecological and environmental index. The service facility density and walkability were then input into a spatial network model (such as ArcGIS Network Analyst) to generate a comprehensive living environment index. The age structure and permanent population distribution were then integrated using a standardized method to generate a population vitality coefficient. The analytic hierarchy process (AHP) was used to construct a judgment matrix, determine the weights of the comprehensive index of ecological environment and living circle, and calculate the comprehensive carrying capacity of the region. Finally, the population vitality coefficient was introduced to dynamically adjust the weight distribution. The natural discontinuity method (Jenks) was used in combination with urban planning objectives (such as livability standards) to divide the evaluation units into three evaluation levels: superior quality area, basic comfort area, and area that needs improvement.

[0121] This embodiment achieves a scientific and quantitative assessment of urban space quality through the integration of multi-dimensional indicators and a dynamic weight adjustment mechanism. The ecological environment comprehensive index quantifies the quality of the natural environment, the living circle comprehensive index reflects the spatial accessibility of service facilities, and the population vitality coefficient characterizes the population distribution and structural characteristics. The three are organically integrated through the hierarchical analysis method to ensure that the assessment results take into account the ecological foundation, service capabilities, and population needs. The natural discontinuity method, combined with the hierarchical classification of planning objectives, provides clear guidance for the differentiated governance of urban space, effectively supports the planning and management process of "precise problem identification - scientific resource allocation - dynamic optimization strategy", and enhances the targeted and effective improvement of urban space quality.

[0122] In one embodiment, based on the three-level evaluation level and targeting the spatial quality characteristics of different areas, developing targeted quality improvement strategies may include the following steps:

[0123] Step S501: Calculate the spatial quality comprehensive index of different evaluation units using a formula according to the three-level evaluation level.

[0124] Step S502 : Based on the comprehensive index of space quality, the evaluation unit is divided into a superior quality zone, a basic comfort zone, and a zone that needs improvement.

[0125] Step S503: formulate differentiated quality improvement strategies based on the different spatial quality characteristics corresponding to the superior quality zone, the basic comfort zone, and the zone that needs improvement.

[0126] First, based on the three-level evaluation scale, a formula was used to calculate the comprehensive spatial quality index (Q) of each evaluation unit. The Q values ​​were clustered using the natural breaks method (Jenks), and the evaluation units were divided into three categories: superior quality areas (Q ≥ 0.643726), basic comfort areas (0.5003 ≤ Q < 0.643726), and areas that need improvement (Q < 0.5003). This clearly defined the spatial quality levels of different areas. Differentiated countermeasures were formulated based on the spatial quality characteristics of each level of area: superior quality areas (such as the Changle Palace area) focused on optimizing local facilities and adding small sports venues to address shortcomings in public fitness; basic comfort areas (such as the Yongtai City area) strengthened educational and medical facilities, built new primary schools, and increased road network density; areas that need improvement (such as the old city) promoted ecological restoration and urban renewal, promoted permeable pavement to mitigate the urban heat island effect, and optimized population density and functional layout.

[0127] This example achieves refined grading of urban space through a quantitative spatial quality comprehensive index, transforming abstract quality assessments into actionable spatial zoning. Differentiated countermeasures are closely aligned with the core issues of each tiered area, such as "improving quality within excellence" in superior quality areas, "balancing and addressing shortcomings" in basic comfort areas, and "systemic reconstruction" in areas that require improvement. This approach avoids the extensive model of traditional planning, using data-driven decision-making to improve the precision of urban governance and the efficiency of resource allocation, providing a scientific path for promoting sustainable urban development and enhancing the well-being of residents.

[0128] In one embodiment, the following formula can be used to calculate the comprehensive index of spatial quality of different evaluation units:

[0129] Q=0.31P+0.25L 15 +0.24L 10 +0.20L5

[0130] Among them, Q represents the comprehensive index of spatial quality, P represents the comprehensive index of urban land surface environment, L 15 Indicates the comprehensive index of 15-minute living circle, L 10 L represents the comprehensive index of the 10-minute living circle, and L5 represents the comprehensive index of the 5-minute living circle.

[0131] The calculation formula of this embodiment realizes the quantitative evaluation of urban space quality through weighted integration of multi-dimensional indexes. The multi-scale integration of ecological environment and living circle construction comprehensively covers the urban natural environment and humanistic service elements, avoiding the one-sidedness of single indicator evaluation. Standardized weight distribution ensures the scientific comparability of indicators of different dimensions, so that the evaluation results can accurately reflect the differences in regional space quality. This index provides a quantitative basis for the hierarchical evaluation and planning decision-making of urban space, supports the precise formulation of differentiated strategies such as optimization of quality-superior areas, balanced development of basic comfort areas, and system improvement of areas that need improvement, and improves the scientific nature and effectiveness of urban planning and management.

[0132] In one embodiment, Figure 2 As shown, the present application also provides a system for dynamic monitoring of urban land surface environment integrating remote sensing technology, which may include:

[0133] The data acquisition and processing module 601 is used to obtain the original data set of the target city; the original data set includes population big data, historical remote sensing data, urban traffic big data, employment and residence big data, and travel big data; it is also used to clean and standardize the original data set to generate a standardized urban spatial data set that includes ecological environment characteristics, living circle characteristics, and population characteristics.

[0134] The indicator system construction module 602 is used to segment the urban development boundary of the city according to the urban spatial data set, divide the evaluation units and design a multi-dimensional evaluation indicator system.

[0135] The space grade classification module 603 is used to measure and determine the weight of each evaluation index based on the evaluation index system to obtain a three-level evaluation grade for evaluating space quality.

[0136] The zoning strategy generation module 604 is used to formulate targeted quality improvement strategies based on the spatial quality characteristics of different regions based on the three-level evaluation levels.

[0137] The data acquisition and processing module of the aforementioned urban land surface environment dynamic monitoring system, which integrates remote sensing technology, is responsible for acquiring big data on population, historical remote sensing data, urban transportation, employment and residence, and travel from multiple sources. Data is cleaned through format standardization, missing value imputation, and remote sensing image preprocessing. Linear spectral decomposition models and spatial analysis algorithms are then used to extract ecological and environmental characteristics (such as vegetation coverage and urban heat island intensity), living area characteristics (facility coverage), and population characteristics (density distribution). After standardization, a structured urban spatial dataset is generated. The indicator system construction module, based on the dataset, utilizes topological analysis tools to divide evaluation units according to urban branch road networks and residential neighborhood standards, and designs a three-tiered evaluation indicator system covering ecological environment, living area development, and population and transportation. The spatial grading module uses principal component analysis and the analytic hierarchy process to determine indicator weights, calculate a comprehensive spatial quality index, and incorporate the natural breaks method to categorize the evaluation into three levels: superior quality, basic comfort, and areas requiring improvement. The zoning strategy generation module formulates differentiated improvement strategies based on the characteristics of each tier, such as optimizing facility allocation in superior quality areas and promoting ecological restoration and facility construction in areas requiring improvement. By integrating and standardizing multi-source data, this system has established an urban space quality assessment system encompassing multiple dimensions, including ecology, living environments, and population. This system provides a comprehensive technical closed-loop from data collection to zoning policy implementation. This system can accurately identify differences in urban space quality, providing a quantitative basis for urban planning. This helps optimize resource allocation, improve facility layout, mitigate the urban heat island effect, and promote refined urban space management and sustainable development, ultimately enhancing the well-being of residents.

[0138] In one embodiment, Figure 3 、 Figure 4 and Figure 5 As shown, this application also provides a solution for constructing a multi-scale evaluation system for the living circle, which may include:

[0139] For example, Figure 3 As shown in the figure, a 17-item evaluation index system was constructed, focusing on the 15-minute living circle, covering residential area indicators, public management and service facilities, commercial service facilities, and other dimensions. The weights were determined using the Analytic Hierarchy Process (AHP), such as junior high school coverage rate (weight 0.35) and per capita residential land area (weight 0.28), focusing on assessing the spatial accessibility and service level of city-level public service facilities.

[0140] For example, Figure 4 As shown, focusing on the 10-minute living circle, 12 indicators such as primary school coverage rate, accessibility of vegetable markets, and number of medium-sized sports venues were selected, and a weight calculation method was used to highlight key factors. Among them, the weight of primary school coverage rate reached 0.73, emphasizing the core position of basic education facilities in daily services.

[0141] For example, Figure 5 As shown in the figure, 11 indicators are set for the 5-minute living circle, including kindergarten coverage, community commercial outlet density, and the number of elderly day care centers. After weighting, the kindergarten coverage rate is weighted 0.31, reflecting the direct impact of community-level basic services on residents' convenience. Three accompanying figures form a gradient assessment standard based on the scale of different living circles, providing a quantitative basis for the refined evaluation of urban space quality.

[0142] Preferably, the floor area ratio of residential land is the ratio of the sum of the above-ground building areas of residential buildings and their supporting facilities within the living circle to the total area of ​​residential land.

[0143] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0144] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the urban land surface environment dynamic monitoring method and system integrating remote sensing technology as described above are implemented.

[0145] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0146] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0147] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A method for dynamic monitoring of urban land surface environment integrating remote sensing technology, characterized in that: The method comprises: Obtaining the original data set of the target city; the original data set includes population big data, historical remote sensing data, urban traffic big data, employment and residence big data, and travel big data; Performing data cleaning and standardization on the original data set to generate a standardized urban spatial data set containing ecological environment characteristics, living circle characteristics, and population characteristics; Segmenting the urban development boundary of the city based on the urban spatial data set, dividing the evaluation units and designing a multi-dimensional evaluation index system; Based on the evaluation index system, each evaluation index is measured and weighted to obtain a three-level evaluation grade for evaluating space quality; Based on the three-level evaluation grades, targeted quality improvement measures are formulated for the spatial quality characteristics of different areas.

2. The method according to claim 1, characterized in that The data cleaning and standardization processing of the original data set to generate a standardized urban spatial data set containing ecological environment characteristics, living circle characteristics and population characteristics includes: Unifying the format of the original data set and processing missing values ​​to obtain a complete data set; The pre-processed complete dataset was classified using a linear spectral decomposition model and a VIS model to extract ecological and environmental characteristic parameters, including vegetation coverage, inverted surface temperature, urban heat island, urban impermeability, soil moisture, nighttime light index, and air quality index. Based on the complete data set and combined with industry standards, a spatial correlation analysis is conducted on living circle facilities and population distribution data, and the facility coverage rate is calculated using a formula; Performing spatial gridding processing on the population distribution data, calculating population density and flow characteristics, and combining the facility coverage rate and night light index to obtain living circle characteristics and population characteristic parameters; The ecological environment characteristic parameters, living circle characteristics and population characteristic parameters are integrated and standardized to generate a multi-dimensional urban space dataset.

3. The method according to claim 2, characterized in that The vegetation coverage and inverted surface temperature are calculated using the following formula: NDVI = (NIR-R) / (NIR+R) Among them, vfc represents vegetation coverage, NDVI represents normalized vegetation index, and NDVI soil Indicates the NDVI value of bare soil or no vegetation cover area, NDVI veg It represents the NDVI value of pure vegetation pixels, NIR represents the near-infrared channel reflectance, and R represents the visible light channel reflectance; Ts=[a·(1-C-D)+(b·(1-C-D)+C+D)·T6-D·T a ] C=τ×ε D = (1-τ) [1 + τ (1-ε)] Where Ts represents the inverted surface temperature, a and b represent constants, a = -67.355351, b = 0.458606, C and D represent intermediate parameters, T6 represents the brightness temperature on the star, T a represents the average atmospheric temperature, τ represents the atmospheric transmittance, and ε represents the surface emissivity.

4. The method according to claim 1, wherein The urban development boundary of the city is segmented according to the urban spatial data set, evaluation units are divided, and a multi-dimensional evaluation index system is designed, including: Single-factor index measurement and standardization processing are performed on the urban spatial data set, and indicators related to ecological environment, living circle construction and population characteristics are screened to obtain a three-level evaluation index set; the three-level evaluation index set includes multiple first-level indicators, each of the first-level indicators is associated with multiple second-level indicators, and each of the second-level indicators is associated with multiple third-level indicators; Using the topological analysis tool according to the three-level evaluation index set, the urban development boundary is segmented based on the urban branch road network and residential neighborhood standards to obtain spatially discretized evaluation units; A hierarchical mapping method is used to correspond the evaluation unit with the first-level indicator, the second-level indicator and the third-level indicator to obtain an indicator hierarchical mapping relationship; Constructing an evaluation system framework according to the indicator hierarchical mapping relationship; the evaluation system framework includes a hierarchical structure of primary indicators, associated secondary indicators and tertiary indicators; Determining a minimum technical feature set based on the evaluation system framework; the minimum technical feature set includes the indicator hierarchical mapping relationship and key features of the hierarchical structure; Analyze and adjust the technical subject concept of the minimum technical feature set to obtain a higher-level technical concept; If the scope of the upper-level technical concept is larger than the evaluation system framework, the indicator hierarchy mapping relationship is updated to obtain an optimized evaluation indicator system.

5. The method according to claim 1, wherein The evaluation index system is used to measure and determine the weight of each evaluation index, and obtain a three-level evaluation grade for evaluating space quality, including: Obtaining the green space coverage rate, urban heat island intensity and pollutant concentration of the ecological environment indicators in the evaluation index system; Obtain the service facility density and walking distance in the living circle construction indicators; Extracting the age structure and resident population distribution from the demographic characteristic indicators; Calculating an ecological environment comprehensive index based on the green space coverage rate, urban heat island intensity and pollutant concentration; the ecological environment comprehensive index is generated by a weighted fusion algorithm; The density of service facilities and the walking range are input into the spatial network model to calculate the comprehensive index of the living circle; The age structure and resident population distribution are integrated and a population vitality coefficient is generated using a standardized method; Determine the regional comprehensive carrying capacity using the analytic hierarchy process based on the comprehensive ecological environment index and the comprehensive living circle index; The population vitality coefficient is used to dynamically adjust the weight distribution of the comprehensive carrying capacity of the region, and the natural discontinuity method is combined with the planning target division to obtain a three-level evaluation grade.

6. The method according to claim 1, characterized in that Based on the three-level evaluation level and targeting the spatial quality characteristics of different areas, targeted quality improvement measures are formulated, including: Calculate the spatial quality comprehensive index of different evaluation units using a formula according to the three-level evaluation level; Dividing the evaluation unit into a superior quality zone, a basic comfort zone, and an area that needs improvement based on the comprehensive spatial quality index; Based on the different spatial quality characteristics corresponding to the superior quality zone, basic comfort zone and area that needs improvement, differentiated quality improvement measures are formulated.

7. The method according to claim 6, characterized in that The following formula is used to calculate the comprehensive index of spatial quality of different evaluation units: Q=0.31P+0.25L 15 +0.24L 10 +0.20L5 Among them, Q represents the comprehensive index of spatial quality, P represents the comprehensive index of urban land surface environment, L 15 Indicates the comprehensive index of 15-minute living circle, L 10 L represents the comprehensive index of the 10-minute living circle, and L5 represents the comprehensive index of the 5-minute living circle.

8. The urban land surface environment dynamic monitoring system integrating remote sensing technology is characterized by: The system comprises: The data acquisition and processing module is used to obtain the original data set of the target city; the original data set includes population big data, historical remote sensing data, urban traffic big data, employment and residence big data, and travel big data; it is also used to clean and standardize the original data set to generate a standardized urban spatial data set that includes ecological environment characteristics, living circle characteristics, and population characteristics; An indicator system construction module is used to segment urban development boundaries based on the urban spatial data set, divide evaluation units, and design a multi-dimensional evaluation indicator system; A space grading module is used to measure and determine the weight of each evaluation indicator based on the evaluation indicator system to obtain a three-level evaluation grade for evaluating space quality; The zoning strategy generation module is used to formulate targeted quality improvement strategies based on the spatial quality characteristics of different areas based on the three-level evaluation levels.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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