CN116109191A - Urban department carbon emission estimation method and system based on satellite observation and GIS - Google Patents

Urban department carbon emission estimation method and system based on satellite observation and GIS Download PDF

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CN116109191A
CN116109191A CN202310116398.4A CN202310116398A CN116109191A CN 116109191 A CN116109191 A CN 116109191A CN 202310116398 A CN202310116398 A CN 202310116398A CN 116109191 A CN116109191 A CN 116109191A
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崔远政
王磊
段学军
施开放
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Nanjing Institute of Geography and Limnology of CAS
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Abstract

The invention discloses a city department carbon emission estimation method and system based on satellite observation and GIS, the invention uses night light and month average data, city function partition information data, interest point data, and city function partition carbon emission annual data of industry, traffic, resident life, service industry and other city function partitions obtained by statistical accounting of a domestic professional noctilucent remote sensing satellite sensor with higher spatial resolution, and can build a high-definition grid industry, traffic, resident life, service industry and other departments carbon emission database by preprocessing satellite observation data and synthesizing objective data such as a multi-source satellite observation and geographic information system, and combines city function partition information and landThe spatial heterogeneity of carbon emission distribution of different urban functional partitions is fully considered by the physical weighted regression model, so that the carbon emission is more accurate in spatial expression, and the CO of urban departments can be greatly improved as a result 2 The accuracy of the emission estimation.

Description

Urban department carbon emission estimation method and system based on satellite observation and GIS
Technical Field
The invention belongs to the field of geographic information and satellite remote sensing image information extraction and application, and particularly relates to a method and a system for estimating carbon emission of urban departments based on satellite observation and GIS.
Background
Carbon dioxide emissions from the combustion of fossil fuels have become the most important climate change problem worldwide. Carbon dioxide emissions are mainly from man-made industrial activities, traffic, services, residential life and other sources. The total carbon emission of the city is accumulated by the emission of a plurality of functional partitions (including industry, traffic, resident life, service industry and the like), so that the carbon dioxide emission of different functional partitions in the city is more detailed, and the government can be helped to formulate more effective carbon dioxide emission reduction policies.
In the city planning concept, the city functional partition is an area for reasonably utilizing the land and natural conditions to avoid mutual interference of industrial production, transportation and resident life, and the city is divided according to functions, and the functional partition is usually carried out on the basis of evaluating and selecting the land of the city. In general, carbon dioxide emissions from different urban functional areas are mainly from statistical data. However, due to the lack of spatial information inside the city, there is a difficulty in estimating functionally partitioned carbon dioxide emissions at high spatial resolution scales.
Since night light data of satellite observation reflects the intensity of human activities to some extent, many studies have employed night light intensity (NTL) data in satellite observation to estimate carbon dioxide emissions, and have employed "top-down" techniques to spatially map carbon dioxide emissions. Meanwhile, because different functional partitions (industry, traffic, resident life, service industry and the like) are located in different city functional partitions, large space difference exists in carbon emission in cities, and the research on high-definition carbon emission data of different departments such as industry, traffic, resident life, service industry and the like is relatively less in the current comprehensive satellite observation data and interest point density data establishment.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the method and the system for estimating the carbon emission of the urban departments based on satellite observation and GIS are provided to solve the problems existing in the prior art.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention provides a city department carbon emission estimation method based on satellite observation and GIS, which comprises the following steps:
s1, acquiring and preprocessing moon night light intensity data based on a remote sensing satellite sensor of Lopa nationality 1, and obtaining night light annual average intensity data through calculation; then resampling the night light annual average intensity data, the interest point data of all departments (including industry, traffic, resident life and service industry) of the national city and the functional partition (industry, traffic, resident life and service industry) data of the national city to the specified grid space resolution as follows: 300m×300m;
s2, calculating night light annual average intensity grid data based on urban functional partitions according to the night light annual average intensity data in the step S1 and the national urban functional partition data;
based on the national urban administrative region division, the night light annual average intensity data of the urban functional partition of the urban scale is calculated in the step S1;
creating a fishing net based on the interest point data of all departments of the national city in the step S1, calculating the interest point density of the net scale of all departments, and then calculating the interest point density of different departments of the grade city scale by combining the administrative region division of the national grade city;
s3, statistics-based CO of all departments of national city 2 Emission data, namely establishing CO (carbon monoxide) based on geographic weighted regression urban departments according to the point of interest density of different departments of the urban scale and the night light annual average intensity data based on urban functional partitions, which are obtained in the step S2 2 Emission estimation model, which determines the optimal spatial weight of the model by adjusting bandwidth, and further calculates and obtains urban functional partition CO of national grade city scale 2 Regression coefficients of emissions;
s4, aiming at different local cities, inputting the city-based obtained in the step S2Night light annual average intensity grid data and interest point density grid data of city functional partitions and CO (carbon monoxide) is utilized 2 Regression coefficients obtained by emission estimation models are calculated to obtain grid points CO of each department 2 Emission of initial results, calculated CO for it 2 Correcting the initial emission to obtain grid point CO of each final department 2 And (5) discharging results.
On the other hand, the invention also provides a city department carbon emission estimation system based on satellite observation and GIS, which comprises:
a preprocessing module configured to perform the following actions: acquiring and preprocessing the moon night light intensity data based on a remote sensing satellite sensor, and obtaining night light annual average intensity data through calculation; resampling the night light annual average intensity data, the national city interest point data of each department and the national city functional partition data to the specified grid space resolution;
a data calculation module configured to perform the following actions:
calculating night light annual average intensity grid data based on urban functional partitions according to the night light annual average intensity data and the national urban functional partition data;
calculating the night light annual average intensity data of the urban functional partitions of the ground city scale based on the national ground city administrative region division and the night light annual average intensity data;
creating a fishing net based on the interest point data of each department of the national city, calculating the interest point density of the grid scale of each department, and then calculating the interest point density of different departments of the grade city scale by combining the administrative region division of the national grade city;
CO 2 an emission estimation model calculation module configured to perform the actions of: statistics-based national city departments CO 2 Emission data, namely establishing CO (carbon monoxide) based on geographic weighted regression urban departments according to the point of interest density of different departments of the urban scale and the night light annual average intensity data based on urban functional partitions, which are obtained in the step S2 2 Emission estimation model, which determines the optimal spatial weight of the model by adjusting bandwidth, and calculates and obtains the national gradeCity scale city function partition CO 2 Regression coefficients of emissions;
CO 2 an emission lattice product calculation unit configured to perform the following actions: aiming at cities of different land grades, night light annual average intensity grid data and interest point density grid data based on urban functional partitions are input, and CO is utilized 2 Regression coefficients obtained by emission estimation models are calculated to obtain grid points CO of each department 2 Emission of initial results, calculated CO for it 2 Correcting the initial emission to obtain grid point CO of each final department 2 And (5) discharging results.
Compared with the prior art, the invention adopts the technical problems and has the following beneficial technical effects:
the invention uses the night light month data, the city function partition information data, the interest point data and the city function partition carbon emission annual data obtained by statistical accounting of the domestic satellite sensor observed by the domestic satellite sensor with higher spatial resolution, can establish a high-definition grid function partition carbon emission database by preprocessing satellite observation data and synthesizing objective data such as a multi-source satellite observation and geographic information system, and fully considers the spatial heterogeneity of the carbon emission distribution of different functional partitions of a city by combining the city function partition information and a geographic weighted regression model, so that the function partition carbon emission is more accurate in spatial expression. The estimation accuracy of the CO2 emission of the functional partition can be greatly improved.
In addition, the domestic night light satellite data Lopa 1 (Luojia 1-01) and the current foreign night light data (NPP-VIIRS and DMSP-OLS satellite data) are utilized to have higher spatial resolution advantages, so that the established high-definition grid department carbon emission data is superior to the department (industry, traffic, resident life and service industry) carbon emission data generated by the current foreign night light data in terms of spatial resolution or estimation accuracy. The method improves the accuracy of the estimation method of the carbon dioxide emission of the functional partitions, and is helpful for making policies for relieving the carbon dioxide emission of urban departments.
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FIG. 1 is a flow chart of a method for estimating carbon emission of urban departments based on satellite observation and GIS.
FIG. 2 is a block diagram of a system for estimating carbon emissions in urban departments based on satellite observations and GIS in accordance with the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, specific examples are set forth below, along with the accompanying drawings.
Aspects of the invention are described herein with reference to the drawings, in which there are shown many illustrative embodiments. The embodiments of the present invention are not limited to the embodiments described in the drawings. It is to be understood that this invention is capable of being carried out by any of the various concepts and embodiments described above and as such described in detail below, since the disclosed concepts and embodiments are not limited to any implementation. Additionally, some aspects of the disclosure may be used alone or in any suitable combination with other aspects of the disclosure.
As shown in fig. 1, the method flow chart of the present invention comprises the steps of:
s1, preprocessing night light intensity moon data, nationwide city interest point data of departments (industry, traffic, resident life and service industry) and city function partition (including industry, traffic, resident life and service industry) data which are acquired based on a remote sensing satellite sensor of Lojia 1 (Luojia 1-01); satellite No. 1 of Lopa is a night illumination remote sensing satellite sensor of the first specialty in the world, and transmits in 2018, 6 and 2 days; it is developed by university of Wuhan and Changchun long-light aerospace technology company. Compared with two night light products DMSP/OLS (about 1000 meters) and Suomi-NPPVIIRS (about 500 meters) used in the past institute, the spatial resolution of Lopa 1 is finer (about 130 meters), the scanning range is 250 km, and the quantization degree is 14 bits. Most of the existing department carbon emission data estimated by satellite data mostly adopts DMSP-OLS and Suomi-NPPVIIRS night lamplight data, so that the resolution of the product is generally inferior to the spatial resolution (more than 500 m) of the lamplight data. Therefore, the newly released domestic remote sensing satellite sensor Lopa 1 with higher spatial resolution is more suitable for establishing high-definition carbon dioxide emission lattice products of different urban departments because of the advantages of more excellent spatial resolution and radiation resolution.
As a specific embodiment of the present invention, step S1 specifically includes the following substeps:
s101, denoising preprocessing is carried out on night light intensity moon data acquired by a satellite with the Lopa nationality of No. 1, all the night light intensity moon data smaller than 1 are regarded as noise, and an effective night light area larger than or equal to 1 is reserved, wherein the effective night light area is represented by the following formula:
Figure BDA0004078707960000041
wherein z is p Intensity value x of night light denoising of grid p p The original intensity value of night light of the grid p.
S102, correcting abnormal values by using a threshold neighborhood average method, wherein in order to further reduce the overflow effect of part of lamplight or the occurrence of extreme abnormal values, the invention takes provincial level as a scale, takes the maximum radiation value of lamplight of a provincial airport or port aiming at each provincial as the threshold value of lamplight of the provincial area, and processes the threshold neighborhood average method for the area exceeding the threshold value by using the threshold neighborhood average method operator as follows:
Figure BDA0004078707960000042
and S103, carrying out average calculation on the night and month lamplight intensity data to obtain night lamplight intensity annual average data.
S104, resampling night light image data, POI (point of interest) data and national city functional partition data to keep space consistency, uniformly adopting WGS84-Albers projection based on ArcGIS10.5 software to avoid errors because the POI data is point data, and resampling all data to 300m multiplied by 300m of spatial resolution by using a nearest neighbor sampling method.
The POI data includes malls, restaurants and toursThe data of scenic spots, hotels, recreational places and the like are provided by Gaoder corporation of an online map service provider, and various places of interest points are classified and combined into four major categories by combining the functional partition classification of carbon emission, including urban functional partitions of industry, traffic, urban residents, service and the like. The data of administrative division of China are downloaded to the national geographic information center website (http:// www.ngcc.cn/ngcc /). CO calculated from bottom to top by each functional partition (industry, traffic, city residents and service) of China city 2 Emission data is provided by the chinese urban greenhouse gas working group.
S2, calculating grid night light intensity annual average data in urban functional subareas such as industry, traffic, resident life, service industry and the like based on grid scales; calculating night light intensity annual average data of each city functional partition (industry, traffic, resident life and service industry) in the whole country based on the district division of the ground level city; based on the national interest point data, the grid scale interest point density of different departments of industry, traffic, resident life and service industry is calculated, and then the interest point density of different departments (industry, traffic, resident life and service industry) in each city is calculated by combining the national land-level municipal administration division.
As a specific embodiment of the present invention, step S1 specifically includes the following sub-steps:
s201, further extracting night light intensity annual average data based on urban function partition data as a mask, and calculating the function partition night light intensity annual average data of each grid, wherein the formula is as follows:
Figure BDA0004078707960000051
wherein a is i Information value, FZ, of urban functional partitions (industry, traffic, resident life, service industry) of grids w For the city function partition to be the value to which w belongs, SNTL s,w Night light intensity for grid s belonging to functional partition w;
s202, calculating the interest point density of different departments of the grid scale of industry, traffic, resident life and service, wherein the interest point density is calculated by adopting a nuclear density estimation method on the basis of creating a fishing grid, and the following formula is adopted:
Figure BDA0004078707960000052
wherein D (x) is POI density index, h is threshold, n is the number of points within the threshold range, (x-x) q ) 2 +(y-y q ) 2 For the point of interest coordinates (x q ,y q ) And square of the distance between grid coordinates (x, y) within the area;
s203, calculating the interest point density of different departments of industry, traffic, resident life and service industry of all the national district cities by combining the district urban administrative division.
S3, calculating and obtaining CO of all departments (industry, traffic, resident life and service industry) of the national city based on statistics 2 Emission data, namely establishing CO based on geographic weighted regression according to urban functional partition (industry, traffic, resident life and service industry) interest point density and night light intensity annual average data of the urban functional partition with the grade and city scale obtained in the step S2 2 Emission estimation model, which determines the optimal spatial weight of the model by adjusting bandwidth, and further calculates and obtains urban functional partition CO of national grade city scale 2 Regression coefficients of emissions; the geographic weighted regression model carries out local regression on the space variables, and according to different space bandwidths and kernel functions, the generalized least square method is combined to analyze the space heterogeneity among the variables.
The CO based on geographic regression weighting 2 The emission estimation model is as follows:
Figure BDA0004078707960000053
wherein y is i,j Estimated carbon emission for city i sector j, (u) i ，v i ) Is the geographic coordinates of city i; beta 0,j (u i ,v i ) For intercept of city i department j, beta k,j (u i ，v i ) Regression parameters for the kth explanatory variable of city i department j; x is X k,i,j Explaining the variable value for the kth of the department j of the city i; beta 0,j (u i ，v i ) Is a constant term; m is the number of explanatory variables; epsilon i,j Is a random error.
Opposite department CO 2 The emission estimation model determines the model optimal spatial weight by adjusting the bandwidth as follows:
Figure BDA0004078707960000061
wherein b represents the bandwidth,
Figure BDA0004078707960000062
representing the distance, w, between the sample point v and the regression point u uv Is the weight.
S4, inputting night light intensity annual average data of grid scale of urban functional partitions such as industry, traffic, resident life, service industry and the like aiming at different local cities, and utilizing CO (carbon monoxide) to obtain urban functional partition interest point density of the grid scale 2 Regression coefficients obtained by the emission estimation model are calculated to obtain grid points CO of departments of urban industry, traffic, resident life, service industry and the like 2 Emission of initial results, calculated CO for it 2 Correcting the initial discharge amount to obtain final high-definition grid point CO for industries, traffic, resident life, service industry and the like 2 And (5) discharging results.
CO weighted according to geographic regression 2 The invention performs space visualization on the variable coefficients of all ground cities according to the ArcGIS software platform.
Based on each grid (resampling to 300m x 300 m), based on functional partition (industry, traffic, urban residents, services) NTL data and each functional partition (industry, traffic, urban residents, services) of each grid, point of interest (POI) density, combined with CO based on geographic weighted regression 2 Coefficient of each variable of each city calculated by emission estimation model, calculationCO of different departments of each grid in each city 2 Initial discharge.
GY i，g，j ＝β 0，j (u i ，v i )/f i，j +β 1，j (u i ，v i )X 1，i，g，j +β 2，j (u i ，v i )X 2，i，g，j +ε i，j /f i，j ；
Wherein GY i，g，j Representing the predicted department j carbon emissions for a particular grid element g in city i, (u) i ，v i ) Is the geographic coordinates of city i; beta 0，j (u i ，v i ) For intercept of city i department j, beta 1，j (u i ，v i ) And beta 2，j (u i ，v i ) Regression parameters for two explanatory variables (night light data and point of interest density) for city i department j; x is X 1，i，g，j And X 2，i，g，j Two interpretation variable values (night light data and point of interest density) for a particular grid element g of city i department j; beta 0，j (u i ，v i ) Is a constant term; f (f) i，j The number of the specific grids of the departments j in the city i; epsilon ij Is a random error.
For a particular city, the addition of the grid-based department carbon emissions within the city estimated based on the result of the geo-weighted regression model estimation within its administrative boundaries may not be exactly equal to the original statistically calculated city department emissions, although the emissions variance is typically small, and further adjustments to the grid-based department carbon emissions are required to bring the total grid-based department carbon emissions within any city boundary into perfect agreement with the total department carbon emissions value for that city accounting statistic.
CO for each grid industry, traffic, resident life, service industry and other departments in each city 2 The initial discharge amount is corrected as follows:
GE i，g，j ＝GY i，g，j ×RY i，j /CY i，j ；
wherein GE i,g,j Representing a particular grid element g in city iFinal correction department (industry, traffic, resident life, service industry) j carbon emission, GY i,g,j Representing the predicted department j carbon emissions, RY, for a particular grid element g in city i i,j Representing the predicted total carbon emissions, CY, for section j of the city i,j Representing the statistically calculated emissions from section j of city i.
The night light intensity data is extracted based on the information of different functional partitions of the city, and the carbon dioxide emission condition of the real functional partition is improved and reflected. Furthermore, compared to CO partitioning different functions by night light data alone in most other studies 2 Emission downscaling grid allocation we will introduce POI density values based on direct correlation of high spatial resolution grid scale as another important factor to participate in sector (industrial, traffic, residential, service) CO 2 Emission estimation and downscaling grid allocation scale. In the application of the estimation model, the invention uses a geographic weighted regression model (Geographically weighted regression) to carry out regression on the spatial variable, and the model can improve the traditional regression method and can reveal the spatial heterogeneity of different variables in different cities. Compared with the problem of space non-stationarity which cannot be solved by the traditional global regression, the geographic weighted regression model can calculate the variable coefficients aiming at different cities, and can greatly improve the functional partition CO 2 The accuracy of the emission estimation.
As shown in fig. 2, the present invention further provides a system for estimating carbon emission in urban departments based on satellite observation and GIS, comprising: the preprocessing module, the data calculation module includes: urban functional partition (industry, traffic, resident life and service industry) based land-level urban night light intensity annual average data calculation unit, urban functional partition (industry, traffic, resident life and service industry) based grid night light intensity annual average data calculation unit, department (industry, traffic, resident life and service industry) interest point density calculation unit and department CO 2 Emission estimation model calculation unit, high-definition department CO 2 And an exhaust lattice product calculating unit. Wherein:
a preprocessing module configured to perform the following actions: night lamplight based on satellite acquisitionIntensity month average data, city each department interest point data, city function partition data, city department CO 2 Preprocessing emission statistical data;
a data calculation module comprising:
the grid night light intensity annual average data calculation unit based on urban functional partitions (industry, traffic, resident life and service industry) is configured to perform the following actions: calculating night light intensity annual average data of grid scales in different city functional subareas;
the urban night light intensity annual average data calculation unit based on urban functional partitions (industry, traffic, resident life and service industry) is configured to perform the following actions: calculating night light intensity annual average data of each city in the range of different city functional partitions based on the division of the ground level municipal administration areas;
a department (industry, traffic, residential life, service industry) point of interest density calculation unit configured to perform the following actions: creating a fishing net based on national interest point data, calculating the interest point density of different departments of industry, traffic, resident life and service industry, and then calculating the interest point density of different departments (industry, traffic, resident life and service industry) in each city by combining national grade municipal administration division;
department (industry, traffic, resident life, service) CO 2 An emission estimation model calculation unit configured to perform the following actions: statistics-based CO of various departments (industry, traffic, resident life and service industry) of national cities 2 Emission data, namely establishing CO based on geographic weighted regression according to the point of interest density and night light intensity annual average data of national grade city scale urban functional partitions (industry, traffic, resident life and service industry) obtained in the step S2 2 And the emission estimation model is used for determining the optimal spatial weight of the model by adjusting the bandwidth, and further calculating and obtaining the regression coefficient of the CO2 emission of the urban functional partition of the national grade city scale.
High definition sector (industry, traffic, resident life, service) CO 2 An emission lattice product calculation unit configured to perform the following actions: for different placesGrade city, input night light intensity annual average data of grid scale of urban functional partitions such as industry, traffic, resident life, service industry and the like, and urban functional partition interest point density of grid scale, and utilize CO 2 Regression coefficients obtained by the emission estimation model are calculated to obtain grid points CO of departments of urban industry, traffic, resident life, service industry and the like 2 Emission of initial results, calculated CO for it 2 Correcting the initial discharge amount to obtain final high-definition grid point CO for industries, traffic, resident life, service industry and the like 2 And (5) discharging results.
While the invention has been described in terms of preferred embodiments, it is not intended to be limiting. Those skilled in the art will appreciate that various modifications and adaptations can be made without departing from the spirit and scope of the present invention. Accordingly, the scope of the invention is defined by the appended claims.

Claims (10)

1. A city department carbon emission estimation method based on satellite observation and GIS is characterized by comprising the following steps:
s1, acquiring and preprocessing month-average night light intensity data based on a remote sensing satellite sensor, and obtaining night light annual average intensity data through calculation; resampling the night light annual average intensity data, the national city interest point data of each department and the national city functional partition data to the specified grid space resolution;
s2, calculating night light annual average intensity grid data based on urban functional partitions according to the night light annual average intensity data in the step S1 and the national urban functional partition data;
based on the national urban administrative region division, the night light annual average intensity data of the urban functional partition of the urban scale is calculated in the step S1;
creating a fishing net based on the interest point data of all departments of the national city in the step S1, calculating the interest point density of the net scale of all departments, and then calculating the interest point density of different departments of the grade city scale by combining the administrative region division of the national grade city;
s3, statistics-based CO of all departments of national city 2 Emission data, namely establishing CO (carbon monoxide) based on geographic weighted regression urban departments according to the point of interest density of different departments of the urban scale and the night light annual average intensity data based on urban functional partitions, which are obtained in the step S2 2 Emission estimation model, which determines the optimal spatial weight of the model by adjusting bandwidth, and further calculates and obtains urban functional partition CO of national grade city scale 2 Regression coefficients of emissions;
s4, inputting night light annual average intensity grid data and interest point density grid data based on city functional partitions, which are obtained in the step S2, aiming at different local cities, and utilizing CO 2 Regression coefficients obtained by emission estimation models are calculated to obtain grid points CO of each department 2 Emission of initial results, calculated CO for it 2 Correcting the initial emission to obtain grid point CO of each final department 2 And (5) discharging results.
2. The method for estimating carbon emissions in urban departments based on satellite observation and GIS according to claim 1, wherein in step S1, the night light intensity annual average data is obtained by:
s101, denoising preprocessing is carried out on night light intensity moon average data acquired by satellites, pixel values smaller than 1 in the night light intensity moon average data are regarded as noise, and the pixel values are reserved to be used as effective night light areas with the pixel values larger than or equal to 1, wherein the effective night light areas are represented by the following formula:
Figure FDA0004078707950000011
wherein z is p Intensity value x of night light denoising of grid p p The original intensity value of night light of the grid p is obtained;
s102, correcting an abnormal value by using a threshold neighborhood average method, wherein the threshold neighborhood average method operator has the following formula:
Figure FDA0004078707950000012
s103, carrying out average calculation on the night and month lamplight intensity data to obtain night lamplight intensity annual average data;
s104, resampling night light image data, POI data and national city functional partition data, uniformly adopting WGS84-Albers projection based on ArcGIS10.5 software, and resampling all data to specified spatial resolution by using a nearest neighbor sampling method.
3. The method for estimating carbon emissions in urban departments based on satellite observation and GIS according to claim 1, wherein in step S1, the remote sensing satellite sensor is a satellite No. 1 of the juga.
4. The method for estimating carbon emissions in urban departments based on satellite observation and GIS according to claim 1, wherein each department includes industry, traffic, resident life, service, etc.; the city functional partition includes: industrial, traffic, residential, service, and other functional areas.
5. The method for estimating carbon emissions in urban departments based on satellite observations and GIS according to claim 1, characterized in that the spatial resolution of the grid is: 300m×300m.
6. The method for estimating carbon emissions in urban departments based on satellite observation and GIS according to claim 2, characterized in that step S2 comprises the following sub-steps:
s201, further extracting night light intensity annual average data based on urban function partition data as a mask, and calculating the function partition night light intensity annual average data of each grid, wherein the formula is as follows:
Figure FDA0004078707950000021
wherein a is i City function partition information value for grid s, FZ w For the city function partition to be the value to which w belongs, SNTL s,w Night light intensity for grid s belonging to functional partition w;
s202, calculating the interest point density of different departments of the grid scale of industry, traffic, resident life and service, wherein the interest point density is calculated by adopting a nuclear density estimation method on the basis of creating a fishing grid, and the following formula is adopted:
Figure FDA0004078707950000022
wherein D (x) is POI density index, h is threshold, n is the number of points within the threshold range, (x-x) q ) 2 +(y-y q ) 2 For the point of interest coordinates (x q ,y q ) And square of the distance between grid coordinates (x, y) within the area;
s203, calculating the interest point density of different departments of all the national district cities by combining the district city administrative division.
7. The method for estimating carbon emissions in urban departments based on satellite observations and GIS according to claim 6, wherein in step S3, said geographic regression weighting-based departments CO 2 The emission estimation model is as follows:
Figure FDA0004078707950000023
wherein y is i,j Estimated carbon emission for city i sector j, (u) i ，v i ) Is the geographic coordinates of city i; beta 0,j (u i ,v i ) For intercept of city i department j, beta k,j (u i ，v i ) Regression parameters for the kth explanatory variable of city i department j; x is X k,i,j Explaining the variable value for the kth of the department j of the city i; beta 0，j (u i ，v i ) Is a constant term; m is the number of explanatory variables;ε i，j is a random error.
8. The method for estimating carbon emissions in urban departments based on satellite observations and GIS according to claim 7, characterized in that in step S3, the optimal spatial weight of the model is determined by adjusting the bandwidth, as follows:
Figure FDA0004078707950000031
wherein b represents the bandwidth,
Figure FDA0004078707950000032
representing the distance, w, between the sample point v and the regression point u uv Is the weight.
9. The method for estimating carbon emissions in urban departments based on satellite observation and GIS according to claim 8, characterized in that step S4 comprises the following sub-steps:
s4.1, calculating the CO of each grid department in each city 2 Initial discharge amount, the following formula:
GY i，g，j ＝β 0，j (u i ，v i )/f i，j +β 1，j (u i ，v i )X 1，i，g，j +β 2，j (u i ，v i )X 2，i，g，j +ε i，j /f i，j ；
wherein GY i，g，j Representing the predicted department j carbon emissions for a particular grid element g in city i, (u) i ，v i ) Is the geographic coordinates of city i; beta 0，j (u i ，v i ) For intercept of city i department j, beta 1，j (u i ，v i ) And beta 2，j (u i ，v i ) Regression parameters of night light data and interest point density of the city i department j are respectively; x is X 1，i，g，j And X 2，i，g，j Night light data and interest point density of specific grid units g of city i department j respectivelyVariable values; beta 0，j (u i ，v i ) Is a constant term; f (f) i，j The number of the specific grids of the departments j in the city i; epsilon i，j Is a random error;
s4.2, CO for each grid department in each city 2 The initial discharge amount is corrected as follows:
GE i，g，j ＝GY i，g，j ×RY i，j /CY i，j ；
wherein GE i，g，j Representing the final corrected department j carbon emissions, GY, of a particular grid element g in city i i，g，j Representing the predicted department j carbon emissions, RY, for a particular grid element g in city i i，j Representing the predicted total carbon emissions, CY, for section j of the city i，j Representing the statistically calculated emissions from section j of city i.
10. Urban sector carbon emission estimation system based on satellite observation and GIS, characterized by comprising:
a preprocessing module configured to perform the following actions: acquiring and preprocessing the moon night light intensity data based on a satellite remote sensing sensor, and obtaining night light annual average intensity data through calculation; resampling the night light annual average intensity data, the national city interest point data of each department and the national city functional partition data to the specified grid space resolution;
a data calculation module configured to perform the following actions:
calculating night light annual average intensity grid data based on urban functional partitions according to the night light annual average intensity data and the national urban functional partition data;
calculating the night light annual average intensity data of the urban functional partitions of the ground city scale based on the national ground city administrative region division and the night light annual average intensity data;
creating a fishing net based on the interest point data of each department of the national city, calculating the interest point density of the grid scale of each department, and then calculating the interest point density of different departments of the grade city scale by combining the administrative region division of the national grade city;
CO 2 an emission estimation model calculation module configured to perform the actions of: statistics-based national city departments CO 2 Emission data, namely establishing CO (carbon monoxide) based on geographic weighted regression urban departments according to the point of interest density of different departments of the urban scale and the night light annual average intensity data based on urban functional partitions, which are obtained in the step S2 2 Emission estimation model, which determines the optimal spatial weight of the model by adjusting bandwidth, and further calculates and obtains urban functional partition CO of national grade city scale 2 Regression coefficients of emissions;
CO 2 an emission lattice product calculation unit configured to perform the following actions: aiming at cities of different land grades, night light annual average intensity grid data and interest point density grid data based on urban functional partitions are input, and CO is utilized 2 Regression coefficients obtained by emission estimation models are calculated to obtain grid points CO of each department 2 Emission of initial results, calculated CO for it 2 Correcting the initial emission to obtain grid point CO of each final department 2 And (5) discharging results.
CN202310116398.4A 2023-02-15 2023-02-15 Urban department carbon emission estimation method and system based on satellite observation and GIS Pending CN116109191A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116415110A (en) * 2023-06-05 2023-07-11 武汉大学 Method for carrying out carbon emission partition gridding based on multisource remote sensing density data
CN117034588A (en) * 2023-07-31 2023-11-10 广东省科学院广州地理研究所 Industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116415110A (en) * 2023-06-05 2023-07-11 武汉大学 Method for carrying out carbon emission partition gridding based on multisource remote sensing density data
CN116415110B (en) * 2023-06-05 2023-08-15 武汉大学 Method for carrying out carbon emission partition gridding based on multisource remote sensing density data
CN117034588A (en) * 2023-07-31 2023-11-10 广东省科学院广州地理研究所 Industrial carbon emission space simulation method and system based on noctilucent remote sensing and interest points

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