A method for estimating urban carbon emissions based on multi-source remote sensing images

Abstract

The application discloses a kind of city carbon emission estimation methods based on multi-source remote sensing image, belong to city carbon emission estimation field, this method includes according to historical city night light remote sensing image data set and carbon emission data, fitting the relationship of night light intensity and carbon emission of implementation area, obtain the carbon emission basic estimation model of implementation area;Using carbon emission basic estimation model, obtain the basic carbon emission of night light remote sensing image corresponding to the city to be measured;According to night light remote sensing image of the city to be measured, the area proportion of each type building in grid unit and the area proportion of low-rise building are calculated respectively;The basic carbon emission of night light remote sensing image corresponding to the city to be measured is corrected, and the carbon emission in each grid is obtained.The present application solves the problem that the existing method cannot effectively integrate the advantages of multi-source remote sensing data, the calculation of key parameters is easily disturbed, which further restricts the reliability of carbon emission estimation result.

Description

Technical Field

[0001] This invention belongs to the field of urban carbon emission estimation, and in particular relates to a method for estimating urban carbon emissions based on multi-source remote sensing images. Background Technology

[0002] As the core carriers of carbon emissions, cities require precise and dynamic accounting of their carbon emissions as a key prerequisite for the formulation of emission reduction policies and the evaluation of their effects, but existing methods are insufficient to meet the needs.

[0003] Existing methods have significant bottlenecks. Traditional statistical methods often use administrative regions as the accounting unit, which not only has low spatial resolution and significant time lag in the data, but also relies on manual surveys to supplement the data, failing to reflect the spatial heterogeneity of carbon emissions at the grid scale. Existing remote sensing methods suffer from problems such as coarse land cover classification, simplified nighttime light remote sensing processing without eliminating outlier interference, neglecting the impact of building height and function type on carbon emissions, and lacking dynamic correction mechanisms, resulting in insufficient estimation accuracy. At the same time, existing methods fail to effectively integrate the advantages of multi-source remote sensing data, and the calculation of key parameters (such as building height) is easily affected by interference, further restricting the reliability of carbon emission estimation results. Summary of the Invention

[0004] To address the aforementioned shortcomings in existing technologies, this invention provides a method for estimating urban carbon emissions based on multi-source remote sensing images. This method solves the problem that existing methods fail to effectively integrate the advantages of multi-source remote sensing data, and the calculation of key parameters is easily affected by interference, which further restricts the reliability of carbon emission estimation results.

[0005] To achieve the aforementioned objectives, the present invention employs the following technical solution: a method for estimating urban carbon emissions based on multi-source remote sensing images, comprising:

[0006] The implementation area is divided into grid cells, and the historical city night light remote sensing image dataset of the implementation area and the carbon emission data corresponding to each grid cell in each historical city night light remote sensing image are obtained.

[0007] Based on the historical city night light remote sensing image dataset and the carbon emission data corresponding to each grid cell in the night light remote sensing images of each historical city, the relationship between night light intensity and carbon emission in the implementation area is fitted to obtain the basic estimation model of grid cell carbon emission in the implementation area.

[0008] Acquire nighttime light remote sensing images of the city to be measured in the implementation area, and use the grid cell carbon emission basic estimation model to obtain the basic carbon emission of each grid cell;

[0009] Based on the nighttime light remote sensing images of the city to be tested, the area proportion of each type of building and the area proportion of low-rise buildings are calculated for each grid cell.

[0010] The basic carbon emissions of each grid unit are corrected based on the area ratio of low-rise buildings, the area ratio of each type of building, and the building carbon emission coefficient of each type of building to obtain the carbon emissions of each grid unit.

[0011] The carbon emissions of each grid cell are summed to obtain the city's total carbon emissions.

[0012] Furthermore, the basic estimation model for the carbon emissions of the grid cells in the implementation area is specifically as follows:

[0013] The following processing was performed on the nighttime light remote sensing images of each historical city:

[0014] The distribution range of brightness regions in historical city nighttime light remote sensing images is expanded by power transform to obtain expanded remote sensing images;

[0015] The expanded remote sensing image is normalized to obtain a normalized remote sensing image;

[0016] The normalized remote sensing image is smoothed using a Gaussian template, and the gradient value of the smoothed remote sensing image is obtained.

[0017] Calculate the total amount of nightlight coefficient based on the gradient value;

[0018] The night light intensity is calculated in units of grid cells based on the total night light coefficient.

[0019] Based on the nighttime light intensity of each grid cell in the nighttime light remote sensing images of each historical city and the corresponding carbon emission data of each grid cell in the nighttime light remote sensing images of each historical city, a relationship fitting is performed to obtain a basic estimation model of the carbon emission of the grid cells in the implementation area.

[0020] Furthermore, the grayscale value of each pixel in the expanded remote sensing image is:

[0021]

[0022] in, The grayscale value of a pixel in the expanded remote sensing image; The grayscale values ​​of pixels in a remote sensing image of a historic city at night; This is the expansion factor; The horizontal coordinates of the pixels; is the vertical coordinate of the pixel.

[0023] Furthermore, the expression for the luminous intensity is:

[0024]

[0025]

[0026]

[0027] in, The intensity of nighttime light; This represents the total number of pixels in a grid unit. For the raster unit in the normalized remote sensing image Line number The grayscale value of the column pixels; This represents the total number of pixel rows within a raster cell. The total number of pixel columns within a grid cell; The gradient value of the smoothed remote sensing image; This is a convolution operation; It is a Gaussian function; The grayscale value of the normalized remote sensing image; For gradient operators; To take the absolute value; For the raster unit in the expanded remote sensing image Line number The grayscale value of the column pixels; This refers to the minimum grayscale value of a pixel within a raster cell in the expanded remote sensing image. This represents the maximum grayscale value of a pixel within a raster cell in the expanded remote sensing image. Let be the variance of the Gaussian function.

[0028] Furthermore, the expression for the basic estimation model of carbon emissions from the grid cell is as follows:

[0029]

[0030] in, The base carbon emissions of a grid cell; The intensity of the grid luminescence; and All are exponential parameters; A, B, C These are the fitting parameters; This is the segmentation threshold.

[0031] Furthermore, the calculation of the area proportion of each type of building and the area proportion of low-rise buildings is specifically as follows:

[0032] For each building, the height of the first building is calculated using the collinearity equation by combining the left and right parallax of the stereo image pairs with the image focal length and baseline distance:

[0033]

[0034] in, The first building height; The focal length of the image; The baseline distance between stereo images; The pixel coordinates of the building's top on the left image; The pixel coordinates of the building's top on the right image;

[0035] For each building, the phase difference between two SAR images of the same area was measured using the InSAR synthetic aperture radar interferometry method, and the second building height was calculated.

[0036]

[0037] in, This is the second building height; For SAR wavelength; Phase difference; Angle of incidence;

[0038] The final building height is determined based on the first and second building heights of each building.

[0039]

[0040] in, For the first The final building height; This is the weighting coefficient for the height of the first building; This is the weighting coefficient for the second building height; Index the buildings;

[0041] Calculate the building's longitudinal density factor based on the final building height; and calculate the area ratio of the low-rise buildings based on the building's longitudinal density factor.

[0042]

[0043]

[0044] in, This represents the area percentage of low-rise buildings within a grid unit. This refers to the area parameters of buildings within a grid cell; This refers to the area parameters of low-rise buildings within a grid cell; For the first grid cell Longitudinal density factor of a building; Density threshold;

[0045] Calculate the area percentage of each building type within each grid cell.

[0046] Furthermore, the expression for the carbon emissions of each grid cell is as follows:

[0047]

[0048]

[0049]

[0050] in, Carbon emissions per grid cell; The base carbon emissions of a grid cell; This represents the area ratio coefficient of low-rise buildings; This represents the area percentage of low-rise buildings within a grid unit. For the first Weighting coefficients for each building type; For the first grid cell Area percentage of each building type; The time factor; Index for building types; Total number of building types; The historical period in which it was introduced; For grid cells Compared to the previous year The actual increase in carbon emissions in -1 year; For grid cells Actual carbon emissions in a year; For grid cells -1 year's actual carbon emissions.

[0051] Furthermore, the expression for the city's carbon emissions is as follows:

[0052]

[0053] in, For urban carbon emissions; For the first Carbon emissions per grid cell; This represents the total number of grid cells.

[0054] The beneficial effects of this invention are as follows: It expands the distribution range of brightness grayscale values ​​in nighttime light remote sensing images, which is beneficial for accurately describing brightness changes. It reflects the intensity of spatial changes in nighttime light remote sensing images and considers the impact of the intensity of spatial changes. It obtains the longitudinal density parameters of buildings through remote sensing technology and considers the impact of building density on carbon emissions. Based on the estimation of carbon emissions considering only remote sensing data, it further accurately estimates urban carbon emissions based on building data and historical data of urban carbon emissions. It accurately segments ground objects by combining high-resolution optical remote sensing images with deep learning, optimizes the nighttime light remote sensing image processing workflow, and uses the technical advantages of integrating stereo image pairs and SAR (Synthetic Aperture Radar) remote sensing images to calculate building height and correct building type emission coefficients. It also introduces a time factor to achieve dynamic estimation, breaking through the existing accuracy bottleneck. This scheme can locate high-emission hotspots to support precise emission reduction decisions, relies on remote sensing big data and automated algorithms to reduce accounting costs and achieve high-frequency accounting, and its multi-source remote sensing fusion framework has strong universality. Attached Figure Description

[0055] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0056] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0057] like Figure 1 As shown, in one embodiment of the present invention, a method for estimating urban carbon emissions based on multi-source remote sensing images includes:

[0058] The implementation area is divided into grid cells, and the historical city night light remote sensing image dataset of the implementation area and the carbon emission data corresponding to each grid cell in each historical city night light remote sensing image are obtained.

[0059] Based on the historical city night light remote sensing image dataset and the carbon emission data corresponding to each grid cell in the night light remote sensing images of each historical city, the relationship between night light intensity and carbon emission in the implementation area is fitted to obtain the basic estimation model of grid cell carbon emission in the implementation area.

[0060] Acquire nighttime light remote sensing images of the city to be measured in the implementation area, and use the grid cell carbon emission basic estimation model to obtain the basic carbon emission of each grid cell;

[0061] Based on the nighttime light remote sensing images of the city to be tested, the area proportion of each type of building and the area proportion of low-rise buildings are calculated for each grid cell.

[0062] The basic carbon emissions of each grid unit are corrected based on the area ratio of low-rise buildings, the area ratio of each type of building, and the building carbon emission coefficient of each type of building to obtain the carbon emissions of each grid unit.

[0063] The carbon emissions of each grid cell are summed to obtain the city's total carbon emissions.

[0064] In this embodiment, the present invention utilizes high-resolution optical remote sensing images combined with deep learning to accurately segment ground features, optimizes the nighttime light remote sensing image processing workflow, and leverages the technical advantages of integrating stereo image pairs and SAR (Synthetic Aperture Radar) remote sensing images to calculate building heights and correct building type emission coefficients. It also introduces a time factor to achieve dynamic estimation, overcoming existing accuracy bottlenecks. This solution can locate high-emission hotspots to support precise emission reduction decisions, reduces accounting costs and achieves high-frequency accounting by relying on remote sensing big data and automated algorithms. Its multi-source remote sensing fusion framework has strong universality and can provide technical support for the establishment of my country's carbon accounting standards, the implementation of the "dual-carbon" strategy, and global climate governance.

[0065] The remote sensing images used had a resolution of 0.92 meters, and each image was a 1km×1km grid unit.

[0066] The basic estimation model for the carbon emissions of the grid cells in the implementation area is as follows:

[0067] The following processing was performed on the nighttime light remote sensing images of each historical city:

[0068] The distribution range of brightness regions in historical city nighttime light remote sensing images is expanded by power transform to obtain expanded remote sensing images;

[0069] The expanded remote sensing image is normalized to obtain a normalized remote sensing image;

[0070] The normalized remote sensing image is smoothed using a Gaussian template, and the gradient value of the smoothed remote sensing image is obtained.

[0071] Calculate the total amount of nightlight coefficient based on the gradient value;

[0072] The night light intensity is calculated in units of grid cells based on the total night light coefficient.

[0073] Based on the nighttime light intensity of each grid cell in the nighttime light remote sensing images of each historical city and the corresponding carbon emission data of each grid cell in the nighttime light remote sensing images of each historical city, a relationship fitting is performed to obtain a basic estimation model of the carbon emission of the grid cells in the implementation area.

[0074] The grayscale values ​​of each pixel in the expanded remote sensing image are:

[0075]

[0076] in, The grayscale value of a pixel in the expanded remote sensing image; The grayscale values ​​of pixels in a remote sensing image of a historic city at night; This is the expansion factor; The horizontal coordinates of the pixels; is the vertical coordinate of the pixel.

[0077] The expression for the intensity of the night light is:

[0078]

[0079]

[0080]

[0081] in, The intensity of nighttime light; This represents the total number of pixels in a grid unit. For the raster unit in the normalized remote sensing image Line number The grayscale value of the column pixels; This represents the total number of pixel rows within a raster cell. The total number of pixel columns within a grid cell; The gradient value of the smoothed remote sensing image; This is a convolution operation; It is a Gaussian function; The grayscale value of the normalized remote sensing image; For gradient operators; To take the absolute value; For the raster unit in the expanded remote sensing image Line number The grayscale value of the column pixels; This refers to the minimum grayscale value of a pixel within a raster cell in the expanded remote sensing image. This represents the maximum grayscale value of a pixel within a raster cell in the expanded remote sensing image. Let be the variance of the Gaussian function.

[0082] In this embodiment, the processing of urban nighttime light remote sensing images includes the following steps:

[0083] (1) The light information is mainly in the brightness region, and the power transformation expands the distribution range of the brightness region: ; The original grayscale value of the night-light image. The transformed grayscale value;

[0084] (2) To facilitate subsequent normalization processing, the normalization process is as follows: ;

[0085] (3) The coefficient is larger in the blocky continuous bright area (the population density of this area is high). At the same time, to eliminate the influence of outliers, a Gaussian template is used to smooth y, and then the gradient value is calculated to obtain ,by The asterisk (*) is used as a coefficient in the calculation of total luminescence. It represents a convolution operation. The calculation of luminescence intensity uses a grid as the basic unit. N is the total number of raster pixels, where the Gaussian function is... , .

[0086] The expression for the basic estimation model of carbon emissions from the grid cell is as follows:

[0087]

[0088] in, The base carbon emissions of a grid cell; The intensity of the grid luminescence; and All are exponential parameters; A, B, C These are the fitting parameters; This is the segmentation threshold.

[0089] In this embodiment, a basic estimation formula is constructed based on the piecewise functional relationship between nighttime light intensity and carbon emissions:

[0090]

[0091] in, Carbon emissions (unit: 10,000 tons). This represents the nighttime luminescence intensity value.

[0092] ( When it is smaller, Follow Increase rapidly ( When the value is large, Follow The increase is slow; A, B, C For fitting parameters, A =0.576, B =207.841, C =201.51, =5.76, 2.01.

[0093] The calculation of the area proportion of each type of building and the area proportion of low-rise buildings is as follows:

[0094] For each building, the height of the first building is calculated using the collinearity equation by combining the left and right parallax of the stereo image pairs with the image focal length and baseline distance:

[0095]

[0096] in, The first building height; The focal length of the image; The baseline distance between stereo images; The pixel coordinates of the building's top on the left image; The pixel coordinates of the building's top on the right image;

[0097] For each building, the phase difference between two SAR images of the same area was measured using the InSAR synthetic aperture radar interferometry method, and the second building height was calculated.

[0098]

[0099] in, This is the second building height; For SAR wavelength; Phase difference; Angle of incidence;

[0100] The final building height is determined based on the first and second building heights of each building.

[0101]

[0102] in, For the first The final building height; This is the weighting coefficient for the height of the first building; This is the weighting coefficient for the second building height; Index the buildings;

[0103] Calculate the building's longitudinal density factor based on the final building height; and calculate the area ratio of the low-rise buildings based on the building's longitudinal density factor.

[0104]

[0105]

[0106] in, This represents the area percentage of low-rise buildings within a grid unit. This refers to the area parameters of buildings within a grid cell; This refers to the area parameters of low-rise buildings within a grid cell; For the first grid cell Longitudinal density factor of a building; Density threshold;

[0107] Calculate the area percentage of each building type within each grid cell.

[0108] In this embodiment, the building height is calculated using remote sensing images:

[0109] (1) The building height H is calculated using the collinearity equation by combining the "left-right parallax" (positional deviation of the same building on the left and right images) of the stereo image pair with the image focal length and baseline distance (distance between the centers of the left and right cameras). The formula is as follows: ;

[0110] Parameter definition: The image focal length (known, provided by satellite sensor parameters); : Baseline distance between stereo image pairs (known, obtained from satellite image orbit parameters); , : Pixel coordinates of the building top in the left and right images.

[0111] (2) The formula for calculating building height is as follows: (This is based on the phase difference between two SAR images of the same area measured by InSAR.)

[0112] ;

[0113] (3) Combining the results of (1) and (2) above, determine the final height:

[0114]

[0115] in, , .

[0116] (4) Calculate the building's longitudinal density factor:

[0117] ;

[0118] Will Less than The summation is obtained to all Accumulation ;

[0119] Calculate the area occupied by low-rise buildings: .

[0120] The expression for the carbon emissions of each grid cell is as follows:

[0121]

[0122]

[0123]

[0124] in, Carbon emissions per grid cell; The base carbon emissions of a grid cell; This represents the area ratio coefficient of low-rise buildings; This represents the area percentage of low-rise buildings within a grid unit. For the first Weighting coefficients for each building type; For the first grid cell Area percentage of each building type; The time factor; Index for building types; Total number of building types; The historical period in which it was introduced; For grid cells Compared to the previous year The actual increase in carbon emissions in -1 year; For grid cells Actual carbon emissions in a year; For grid cells -1 year's actual carbon emissions.

[0125] In this embodiment, building type correction:

[0126] To classify buildings, These represent the percentage of area of ​​industrial buildings, retail businesses, office buildings, and residential buildings within a grid cell, respectively, while ωi represents the carbon emission coefficients of industrial buildings, retail businesses, office buildings, and residential buildings. , , , .

[0127] The expression for the city's carbon emissions is:

[0128]

[0129] in, For urban carbon emissions; For the first Carbon emissions per grid cell; This represents the total number of grid cells.

[0130] In this embodiment, considering the interannual variation trend of carbon emissions, a time factor is introduced ( ):

[0131]

[0132] in, The values ​​of carbon emissions change between two consecutive years within the previous five years are expressed, among which, .

[0133] The revised grid carbon emissions are:

[0134] .

Claims

1. A method for estimating urban carbon emission based on multi-source remote sensing images, characterized in that, The method comprises the following steps: dividing the implementation area into grid cells, and obtaining a historical urban night light remote sensing image dataset of the implementation area and carbon emission data corresponding to each grid cell in each historical urban night light remote sensing image; fitting a relationship between the night light intensity and the carbon emission of the implementation area according to the historical urban night light remote sensing image dataset and the carbon emission data corresponding to each grid cell in each historical urban night light remote sensing image, and obtaining a basic grid cell carbon emission estimation model of the implementation area; obtaining a to-be-measured urban night light remote sensing image of the implementation area, and obtaining the basic carbon emission of each grid cell by using the basic grid cell carbon emission estimation model; the expression of the basic grid cell carbon emission estimation model is as follows: wherein, is the base carbon emission for a grid cell; is the grid night light intensity; and are both exponential segment parameters; A、 B, C is the fitting parameter; is the segment threshold; calculating the area proportion of each type of building and the area proportion of low-rise buildings for each grid cell according to the to-be-measured urban night light remote sensing image; the calculation of the area proportion of each type of building and the area proportion of low-rise buildings is specifically as follows: calculating the first building height of each building by using the collinearity equation according to the left and right parallax of the stereo pair, the image focal length and the baseline distance: wherein, is the first building height; is the image focal length; is the stereo pair baseline distance; is the pixel coordinate of the building top on the left image; is the pixel coordinate of the building top on the right image; calculating the second building height of each building by using the InSAR (Interferometric Synthetic Aperture Radar) method to measure the phase difference of two SAR (Synthetic Aperture Radar) images of the same area: wherein, is a second building height; is a SAR wavelength; is a phase difference; is an angle of incidence; determining the final building height according to the first building height and the second building height of each building; wherein, is the final building height for the th building; is a weight coefficient for the first building height; is a weight coefficient for the second building height; is the building index; calculating the building longitudinal density factor according to the final building height, and calculating the area proportion of low-rise buildings according to the building longitudinal density factor: wherein, is the area proportion of low-rise buildings within the grid cell; is the area parameter of buildings within the grid cell; is the area parameter of low-rise buildings within the grid cell; is the longitudinal density factor of the i-th building within the grid cell; is the longitudinal density factor of the i-th building within the grid cell; is the density threshold value; counting the area proportion of each building type in each grid cell; correcting the basic carbon emission of each grid cell according to the area proportion of low-rise buildings, the area proportion of each type of building and the building carbon emission coefficient of each type of building, and obtaining the carbon emission of each grid cell; the expression of the carbon emission of each grid cell is as follows: wherein, is the carbon emission of the grid cell; is the base carbon emission of the grid cell; is the area proportion coefficient of low-rise buildings; is the area proportion of low-rise buildings in the grid cell; is the weight coefficient of the th building type; is the area proportion of the th building type in the grid cell; is the time factor; is the building type index; is the total number of building types; is the introduced historical age; is the actual carbon emission increment of the grid cell in the th year compared to the -1th year; is the actual carbon emission of the grid cell in the th year; is the actual carbon emission of the grid cell in the -1th year; accumulating the carbon emission of each grid cell to obtain the urban carbon emission.

2. The method of claim 1, wherein, The basic grid cell carbon emission estimation model of the implementation area is obtained by specifically: performing the following processing on each historical urban night light remote sensing image: extending the distribution range of the bright area in the historical urban night light remote sensing image by using power transformation to obtain an extended remote sensing image; performing normalization processing on the extended remote sensing image to obtain a normalized remote sensing image; performing smoothing processing on the normalized remote sensing image by using a Gaussian template, and calculating the gradient value of the smoothed remote sensing image; calculating the total night light coefficient according to the gradient value; calculating the night light intensity in units of grid cells according to the total night light coefficient; fitting the relationship according to the night light intensity of each grid cell in each historical urban night light remote sensing image and the carbon emission data corresponding to each grid cell in each historical urban night light remote sensing image, and obtaining the basic grid cell carbon emission estimation model of the implementation area.

3. The method of claim 2, wherein, The gray value of each pixel in the extended remote sensing image is as follows: wherein, is a gray value of a pixel in the expanded remote sensing image; is a gray value of a pixel in the historical urban night light remote sensing image; is an expansion coefficient; is a horizontal coordinate of the pixel; is a vertical coordinate of the pixel.

4. The method of claim 2, wherein, The expression of the night light intensity is as follows: wherein, is the night light intensity; is the total number of pixels in a grid cell; is the normalized gray value of the pixel in the i-th row and j-th column of the grid cell in the remote sensing image; is the normalized gray value of the pixel in the i-th row and j-th column of the grid cell in the remote sensing image; is the normalized gray value of the pixel in the i-th row and j-th column of the grid cell in the remote sensing image; is the total number of pixel rows in a grid cell; is the total number of pixel columns in a grid cell; is the gradient value of the smoothed remote sensing image; is the convolution operation; is the Gaussian function; is the normalized gray value of the pixel in the remote sensing image; is the gradient operator; is the absolute value; is the normalized gray value of the pixel in the i-th row and j-th column of the grid cell in the remote sensing image; is the normalized gray value of the pixel in the i-th row and j-th column of the grid cell in the remote sensing image; is the normalized gray value of the pixel in the i-th row and j-th column of the grid cell in the remote sensing image; is the minimum gray value of the pixel in the grid cell in the extended remote sensing image; is the maximum gray value of the pixel in the grid cell in the extended remote sensing image; is the variance of the Gaussian function.

5. The method of claim 1, wherein, The expression of the urban carbon emission is as follows: wherein, is the urban carbon emissions; is the carbon emissions of the grid cell; is the total number of grid cells.

Images