A method and system for calculating the stock and temporal and spatial changes of building materials in urban agglomerations

Abstract

The present invention discloses a method and system for calculating the temporal and spatial changes of the building material stock of an urban agglomeration in the field of night light remote sensing technology. The method for calculating the building material stock of an urban agglomeration includes collecting night light remote sensing data in a specified area; inputting the collected night light remote sensing data into a constructed building volume estimation model, and outputting the measured value of the building volume; and calculating the building material stock in the specified area according to the material use intensity coefficient and the measured value of the building volume, so as to analyze the scale and distribution characteristics of the building material stock in the specified area. The present invention can use night light remote sensing data to estimate the building material stock at the cellular scale of an urban agglomeration, truly reflect the spatial structural characteristics of the built-up area, and reflect the internal form of the city.

Description

Technical Field

[0001] The present invention belongs to the technical field of nighttime light remote sensing, and in particular relates to a method and system for calculating the temporal and spatial changes of building material stocks in urban agglomerations. Background Art

[0002] Urbanization is a symbol of the economic development of a country or region, and is the product of the development of human society, economy and culture to a certain period. China's urban agglomerations are undergoing a rapid and large-scale urbanization process, and the consumption of various building resources is increasing day by day, resulting in a large inventory of building materials. With the continuous accumulation of building material stocks (the accumulation of various building materials in buildings within the boundaries of the urban system), people have realized that how to effectively recycle these accumulated resources is the key to solving the problem of resource shortage, achieving sustainable development of resources, and developing a circular economy and a low-carbon economy. First of all, it is necessary to understand the scale, composition and location of the current building material stock. Only by accurately grasping the stock can we correctly analyze its resource benefits and then put forward reasonable resource recycling suggestions.

[0003] Material stock is the amount of material accumulated in the system during the material metabolism process. It contains recyclable secondary resources in the system, and its demolition process will also have an impact on the ecological environment. Therefore, the accounting of building material stock is the basis for research on material flow and resource and environmental effects. At present, various methods are being proposed to calculate the scale of material stock. Traditionally, building material stock is mainly calculated based on statistical data through "top-down" or "bottom-up" methods. In short, the "top-down" method quantifies material inventory by calculating the inflow of inventory and the outflow over time. This method relies on highly aggregated statistical data and is suitable for large-scale accounting over a long period of time. However, this method cannot reflect a key feature of material stock-spatial distribution, so it is not suitable for high-resolution spatial mapping. In addition, this method is time-consuming and labor-intensive, complex to calculate, and difficult to obtain data.

[0004] Therefore, the spatial mapping of material stocks usually adopts a "bottom-up" method driven by spatial data. This method is based on GIS spatial data that can calculate the detailed physical dimensions (area and height) of buildings or infrastructure. Combined with material strength, the material stock can be accurately calculated and spatial mapping can be completed. In addition, in order to illustrate the changes in material accumulation over time, 4D-GIS databases based on the area or volume information of cadastral buildings and infrastructure are also increasingly used in material stock accounting. However, it should be pointed out that due to the difficulty in obtaining the 4D-GIS database involved in this method and the complexity of data processing, the scope of application of this method is often limited to the scale of a single city within a limited time. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a method and system for calculating the stock of building materials and their temporal and spatial changes in urban agglomerations, which can use nighttime light remote sensing data to estimate the stock of building materials at the cellular scale of urban agglomerations, truly reflect the spatial structural characteristics of the built-up areas, and reflect the internal form of the city.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] In a first aspect, a method for calculating the stock of building materials in an urban agglomeration is provided, comprising: collecting nighttime light remote sensing data in a designated area; inputting the collected nighttime light remote sensing data into a constructed building volume estimation model, and outputting the measured value of the building volume; calculating the stock of building materials in the designated area based on the material use intensity coefficient and the measured value of the building volume, so as to analyze the scale and distribution characteristics of the stock of building materials in the designated area.

[0008] Furthermore, the method for constructing the building volume estimation model includes: collecting nighttime light remote sensing data in a specified area and dividing it into a number of cells; counting the total brightness value of the nighttime light in each cell, and classifying and clustering the cells based on the ratio of area to brightness value; calculating the true value of the building volume in the cell based on known urban building outline GIS data; constructing a building volume estimation model based on the total brightness value of the nighttime light in each cell and the true value of the building volume and training it to obtain a building volume estimation model with accuracy that meets the requirements.

[0009] Furthermore, the nighttime light remote sensing data in the designated area is collected and divided into a number of cells, specifically: ArcGIS software is used to delineate the shape units of independent built-up areas for the nighttime light remote sensing data, and by inverting the source nighttime light brightness values, it is regarded as a topographic map of "peaks" and "valleys" with varying elevations, and a watershed algorithm that can analyze local minima and maxima is used to draw the "watershed" of light brightness, determine the local center of human activity, and use this to divide the built-up area units into a number of cells.

[0010] Furthermore, the building volume estimation model is expressed as:

[0011] VOL i =a i ×NTL i (1)

[0012] Among them, VOL i represents the building volume in the cell of the i-th cluster; a i NTL represents the relationship between the total brightness of night lights in the cell in the ith cluster and the building volume; i Represents the total brightness value of the night light in the cell in the i-th cluster.

[0013] Furthermore, the building material inventory in the specified area is calculated using the following formula:

[0014]

[0015] Among them, MS j，k，l represents the building material stock in cell j, k represents the building type, l represents the material type, VOL j represents the building volume in cell j, FH represents the average floor height, θ k It represents the proportion of the volume of brick-concrete or steel-concrete buildings in cell j to the total volume of the building. k，l Indicates the material usage intensity coefficient of material l.

[0016] Secondly, a system for calculating the stock of building materials in an urban agglomeration is provided, including: a data acquisition module for collecting nighttime light remote sensing data in a specified area; a building volume acquisition module for inputting the collected nighttime light remote sensing data into a constructed building volume estimation model, and outputting the measured value of the building volume; a building material stock acquisition module for calculating the stock of building materials in a specified area based on the material use intensity coefficient and the measured value of the building volume, and for analyzing the scale and distribution characteristics of the stock of building materials in a specified area.

[0017] In a third aspect, a method for calculating the spatiotemporal changes in the stock of building materials in an urban agglomeration is provided, comprising: collecting long-term nighttime light remote sensing data of a set duration; using the method for calculating the stock of building materials in an urban agglomeration described in the first aspect, obtaining the stock of building materials in a specified area corresponding to the long-term nighttime light remote sensing data of a set duration, and generating a spatiotemporal data set of the stock of building materials for analyzing the changes in the stock of building materials and revealing the spatiotemporal changes in the stock of building materials.

[0018] In a fourth aspect, a system for calculating the spatiotemporal changes in the stock of building materials in an urban agglomeration is provided, comprising: a time series data acquisition module for collecting long-time series nighttime light remote sensing data of a set duration; a spatiotemporal change module for building material stock, for using the method for calculating the stock of building materials in an urban agglomeration as described in any one of claims 1 to 5, to obtain the stock of building materials in a specified area corresponding to the long-time series nighttime light remote sensing data of a set duration, to generate a spatiotemporal data set of building material stock, for analyzing the changes in the stock of building materials and revealing the spatiotemporal change characteristics of the stock of building materials.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] (1) The present invention inputs the collected nighttime light remote sensing data into the constructed building volume estimation model, and outputs the measured value of the building volume; according to the material use intensity coefficient and the measured value of the building volume, the building material stock in the specified area is calculated, which is used to analyze the scale and distribution characteristics of the building material stock in the specified area. The nighttime light remote sensing data can be used to estimate the building material stock at the cellular scale of the urban agglomeration, truly reflecting the spatial structure characteristics of the built-up area and the internal form of the city;

[0021] (2) The present invention innovates the built-up area unit segmentation method based on nighttime lights. By segmenting the nighttime light data through the watershed, the generated built-up area cell units are more consistent with the spatial structure characteristics of the built-up area and can better reflect the internal form of the urban agglomeration;

[0022] (3) The present invention analyzes the total brightness value of nighttime lights and the building volume, and analyzes the empirical relationship between the two, to construct a new model that can directly use nighttime lights to estimate the building volume at the cellular scale of urban agglomerations. The model is simple and easy to operate, and is relatively easy to promote and apply in other cities, which can promote the development of building material stock estimation in urban agglomerations;

[0023] (4) Based on the constructed building volume estimation model and material use intensity coefficient, the scientific understanding of the spatiotemporal distribution of building material stocks in urban agglomerations will be enhanced, which will serve the research on the development model of urban agglomerations in eastern coastal areas of China. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for calculating the stock and temporal and spatial changes of building materials in an urban agglomeration provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0026] Embodiment 1:

[0027] A method for calculating the stock of building materials in an urban agglomeration comprises: collecting nighttime light remote sensing data in a designated area; inputting the collected nighttime light remote sensing data into a constructed building volume estimation model, and outputting the measured value of the building volume; and calculating the stock of building materials in the designated area according to the material use intensity coefficient and the measured value of the building volume, so as to analyze the scale and distribution characteristics of the stock of building materials in the designated area.

[0028] The main idea of ​​the new method for estimating the building material stock of urban agglomerations at the cellular scale using nighttime light remote sensing data in this embodiment is: first, identifying and extracting the cellular units of the built-up area based on the nighttime light data; second, establishing a mathematical model between the nighttime light brightness and the building volume; finally, using a "bottom-up" approach in combination with the material use intensity coefficient, the building material stock of urban agglomerations in eastern China is calculated, the scale and distribution characteristics of the building material stock of urban agglomerations in eastern China's coastal areas are determined, and their spatiotemporal variation characteristics are explored.

[0029] Step 1: Based on night light data, use the “watershed” method to identify and divide built-up area cells.

[0030] ArcGIS software is used to clearly delineate the shape units of independent built-up areas from nighttime light remote sensing data (data that have undergone relevant preprocessing) instead of using a unified spatial grid. By inverting the source nighttime light brightness values, it is regarded as a topographic map of "peaks" and "valleys" with varying elevations. A watershed algorithm that can analyze local minima and maxima is used to draw the "watershed" of light brightness, determine the local center of human activity, and use this to divide the built-up area units into several cells.

[0031] Step 2: Count the total nighttime light brightness value and the actual building volume value within the cell, analyze the intrinsic relationship between the two, quantify the relationship between the two, build an estimation model for the building volume, and test the model performance.

[0032] Firstly, the total brightness value of night lights in cells is counted, and cells are classified and clustered based on the ratio of area to brightness value. The real value of building volume in cells is calculated based on the known urban building outline GIS data, and the intrinsic relationship between light brightness value and building volume is analyzed. The empirical relationship between them is quantified, and a building volume estimation model is constructed, which can be expressed as:

[0033] VOL i =a i ×NTL i (1)

[0034] Among them, VOL i represents the building volume in the cell of the i-th cluster; a i NTL represents the relationship between the total brightness of night lights in the cell in the ith cluster and the building volume; i Represents the total brightness value of the night light in the cell in the i-th cluster.

[0035] Secondly, the accuracy of the established estimation model is verified using the building volume data of sample cities.

[0036] Step 3: Calculate the building material stock in the study area based on the material use intensity coefficient and building volume estimation model.

[0037] First, the material use intensity coefficient is obtained, and then the building material stock is calculated based on the building volume estimated by the model. The calculation formula can be expressed as:

[0038]

[0039] Among them, MS j，k，l represents the building material stock in cell j; k represents the building type (including brick-concrete structure or steel-concrete structure); l represents the material type; VOL j Represents the building volume in cell j, in m 3 ; FH represents the average floor height, which is 3m according to the Chinese standard building requirements; θ k represents the proportion of the brick-concrete or steel-concrete building volume in cell j to the total building volume; MI k，l Indicates the material use intensity coefficient of material l, the unit is kg / m 2 .

[0040] The present invention adopts the building type classification method based on the number of floors established in the prior art: 1-6 floors are brick-concrete structures, and 7 floors and above are reinforced concrete structures. According to this building type classification method, given the building volume, the θ in the cell is first calculated. k The true value of θ for different clusters k The value is the θ of the cells in the cluster k The average value of the true value. The material use intensity coefficient is shown in Table 1.

[0041] Table 1 Average material use intensity coefficient after calibration (kg / m 2 )

[0042] Building Types cement Steel sand gravel wood brick Total Brick-concrete structure 193.5 24.5 719 561.5 29 623 2150.5 Steel-concrete structure 328 77.5 619 815.5 26.5 125 1991.5

[0043] Step 4: Analyze the scale and distribution characteristics of the building material stock in the study area.

[0044] Step 5: Use the long-term nighttime light dataset to generate a spatiotemporal dataset of the building material stock in the eastern coastal urban agglomeration, analyze the annual changes in the building material stock, reveal the spatiotemporal variation characteristics of the building material stock, and put forward reasonable suggestions for building resource recycling.

[0045] After experimental calculation, among the three urban agglomerations in the eastern coastal areas of my country, the Beijing-Tianjin-Hebei urban agglomeration had the largest initial stock of construction materials in 2000, which was 4.022 billion tons, higher than the Yangtze River Delta urban agglomeration and the Guangdong-Hong Kong-Macao Greater Bay Area (2.462 billion tons and 2.847 billion tons, respectively); by 2020, the construction material stock of the Yangtze River Delta urban agglomeration reached 34.869 billion tons, more than twice that of the Beijing-Tianjin-Hebei urban agglomeration (16.539 billion tons) and the Guangdong-Hong Kong-Macao Greater Bay Area (12.993 billion tons). Overall, from 2000 to 2010, the construction material stock of the three urban agglomerations showed a steady growth trend, with no obvious gap; however, from 2010 to 2020, the construction material stock of the Yangtze River Delta urban agglomeration increased by more than 3 times, and the gap with the other two urban agglomerations widened rapidly.

[0046] The present invention inputs the collected nighttime light remote sensing data into the constructed building volume estimation model, and outputs the measured value of the building volume; according to the material use intensity coefficient and the measured value of the building volume, the building material stock in the specified area is calculated, which is used to analyze the scale and distribution characteristics of the building material stock in the specified area. The nighttime light remote sensing data can be used to estimate the building material stock at the cellular scale of the urban agglomeration, truly reflect the spatial structure characteristics of the built-up area, and reflect the internal form of the city.

[0047] Based on the long-term night-time light remote sensing data (2000-2020), the urban cellular-scale building material stock remote sensing accounting method established in the present invention was used to construct a spatiotemporal dataset of building material stock in my country's eastern coastal urban agglomerations, and then the annual changes in building material stock were analyzed to reveal the spatiotemporal and temporal change characteristics of building material stock.

[0048] The present invention takes the eastern coastal urban agglomeration as the object, innovates the built-up area unit segmentation method based on night lights, and generates built-up area cellular units by dividing the night light data by watershed, which is more in line with the spatial structure characteristics of the built-up area and can better reflect the internal form of the urban agglomeration. The method of the present invention is based on the total brightness value of night lights and building volume of the eastern coastal urban agglomeration. By analyzing the empirical relationship between the two, a new model that can directly use night lights to estimate the building volume at the urban cellular scale is constructed. The model is simple and easy to operate, and is relatively easy to promote and apply in other cities, which can promote the development of building material stock estimation in urban agglomerations.

[0049] Based on the constructed building volume estimation model and material use intensity coefficient, the present invention will construct satellite remote sensing products of the building material stock of China's eastern coastal urban agglomerations. These products will enhance the scientific understanding of the spatiotemporal distribution laws of the building material stock of urban agglomerations and serve the research on the development model of China's eastern coastal urban agglomerations.

[0050] Embodiment 2:

[0051] Based on the method for calculating the stock of building materials in an urban agglomeration described in Example 1, this embodiment provides a system for calculating the stock of building materials in an urban agglomeration, including: a data acquisition module, used to collect nighttime light remote sensing data in a specified area; a building volume acquisition module, used to input the collected nighttime light remote sensing data into a constructed building volume estimation model, and output the measured value of the building volume; a building material stock acquisition module, used to calculate the stock of building materials in a specified area based on the material use intensity coefficient and the measured value of the building volume, and used to analyze the scale and distribution characteristics of the building material stock in the specified area.

[0052] Embodiment three:

[0053] Based on Example 1 and Example 2, this embodiment provides a method for calculating the spatiotemporal changes in the stock of building materials in an urban agglomeration, including: collecting long-term night-time light remote sensing data of a set duration; using the method for calculating the stock of building materials in an urban agglomeration described in Example 1, obtaining the stock of building materials in a specified area corresponding to the long-term night-time light remote sensing data of a set duration, and generating a spatiotemporal data set of the stock of building materials for analyzing the changes in the stock of building materials and revealing the spatiotemporal change characteristics of the stock of building materials.

[0054] Embodiment 4:

[0055] Based on Example 1 to Example 3, this embodiment provides a system for calculating the spatiotemporal changes in the stock of building materials in an urban agglomeration, including: a time series data acquisition module, used to collect long-time series nighttime light remote sensing data of a set duration; a building material stock spatiotemporal change module, used to adopt the method for calculating the stock of building materials in an urban agglomeration as described in any one of Claims 1 to 5, to obtain the stock of building materials in a specified area corresponding to the long-time series nighttime light remote sensing data of a set duration, and to generate a spatiotemporal data set of the building material stock, which is used to analyze the changes in the building material stock and reveal the spatiotemporal change characteristics of the building material stock.

[0056] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for calculating the stock of building materials in an urban agglomeration, characterized in that: include: Collect nighttime light remote sensing data in a designated area; Input the collected nighttime light remote sensing data into the constructed building volume estimation model, and output the measured value of the building volume; Calculate the building material stock in the designated area based on the material use intensity coefficient and the measured value of the building volume, and use it to analyze the scale and distribution characteristics of the building material stock in the designated area; The method for constructing the building volume estimation model comprises: Collect nighttime light remote sensing data in a specified area and divide it into several cells; Count the total brightness of the night lights in each cell, and classify and cluster the cells based on the ratio of area to brightness. According to the known urban building outline GIS data, the real value of the building volume in the cell is calculated; A building volume estimation model is constructed and trained based on the total brightness value of nighttime lights and the true value of the building volume in each cell to obtain a building volume estimation model with satisfactory accuracy. The nighttime light remote sensing data in the designated area is collected and divided into a number of cells. Specifically, ArcGIS software is used to delineate the shape units of independent built-up areas for the nighttime light remote sensing data, and the source nighttime light brightness values ​​are inverted to be regarded as "peaks" and "valleys" with different elevations. A watershed algorithm that can analyze local minima and maxima is used to draw the "watershed" of light brightness, determine the local center of human activities, and use this to divide the built-up area units to form a number of cells.

2. The method for calculating the stock of building materials in an urban agglomeration according to claim 1, characterized in that: The building volume estimation model is expressed as: （1） in, Indicates i The volume of buildings within the cells in the cluster; Indicates i The relationship coefficient between the total brightness of nighttime lights in the cells of a cluster and the building volume; Indicates i The total brightness of the night lights in the cells of the cluster.

3. The method for calculating the stock of building materials in an urban agglomeration according to claim 1, characterized in that: The building material inventory in a specified area is calculated using the following formula: （2） in, represents the building material stock in cell j, k Indicates the building type. l Indicates the material type, Representation Cell j The volume of the building inside, FH represents the average floor height, Representation Cell j The ratio of the volume of internal brick-concrete or steel-concrete buildings to the total volume of the building. Display material l The material use intensity coefficient.

4. A system for calculating the stock of building materials in urban agglomerations, characterized in that: include: Data acquisition module, used to collect nighttime light remote sensing data in a specified area; The building volume acquisition module is used to input the collected nighttime light remote sensing data into the constructed building volume estimation model and output the measured value of the building volume; The building material stock acquisition module is used to calculate the building material stock in the specified area according to the material use intensity coefficient and the measured value of the building volume, and is used to analyze the scale and distribution characteristics of the building material stock in the specified area; The method for constructing the building volume estimation model comprises: Collect nighttime light remote sensing data in a specified area and divide it into several cells; Count the total brightness of the night lights in each cell, and classify and cluster the cells based on the ratio of area to brightness. According to the known urban building outline GIS data, the real value of the building volume in the cell is calculated; A building volume estimation model is constructed and trained based on the total brightness value of nighttime lights and the true value of the building volume in each cell to obtain a building volume estimation model with satisfactory accuracy. The nighttime light remote sensing data in the designated area is collected and divided into a number of cells. Specifically, ArcGIS software is used to delineate the shape units of independent built-up areas for the nighttime light remote sensing data, and the source nighttime light brightness values ​​are inverted to be regarded as "peaks" and "valleys" with different elevations. A watershed algorithm that can analyze local minima and maxima is used to draw the "watershed" of light brightness, determine the local center of human activities, and use this to divide the built-up area units to form a number of cells.

5. A method for calculating the temporal and spatial changes in the stock of building materials in an urban agglomeration, characterized in that: include: Collect long-term nighttime light remote sensing data of a set duration; The method for calculating the building material stock of an urban agglomeration as described in any one of claims 1 to 3 is adopted to obtain the building material stock in a specified area corresponding to the long-term nighttime light remote sensing data of a set duration, and generate a spatiotemporal data set of the building material stock for analyzing the changes in the building material stock and revealing the spatiotemporal change characteristics of the building material stock.

6. A system for calculating the temporal and spatial changes in the stock of building materials in urban agglomerations, characterized in that: include: Time series data acquisition module, used to collect long-time series nighttime light remote sensing data of a set duration; A module for spatiotemporal variation of building material stock is used to adopt the method for calculating the building material stock of an urban agglomeration as described in any one of claims 1 to 3, obtain the building material stock in a specified area corresponding to the long-term nighttime light remote sensing data of a set duration, and generate a spatiotemporal data set of building material stock for analyzing the changes in building material stock and revealing the spatiotemporal variation characteristics of building material stock.

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