CN117150170A - Method and device for calculating carbon emission of land utilization in night lamplight data auxiliary area - Google Patents

Method and device for calculating carbon emission of land utilization in night lamplight data auxiliary area Download PDF

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CN117150170A
CN117150170A CN202310475573.9A CN202310475573A CN117150170A CN 117150170 A CN117150170 A CN 117150170A CN 202310475573 A CN202310475573 A CN 202310475573A CN 117150170 A CN117150170 A CN 117150170A
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符静
粟宝玲
罗灿莹
杨立国
郑文武
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Hengyang Normal University
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Abstract

The application provides a calculation method and a device for carbon emission of land utilization in an auxiliary area of night lamplight data, wherein the method comprises the following steps: selecting a research area of at least one city, and acquiring night light data, energy consumption data, land utilization data and auxiliary research data of the research area; estimating the carbon emission of the land utilization in the research area according to the land utilization data and the energy consumption data; extracting construction land in the research area according to night lamplight data, and calculating total energy consumption carbon emission and construction land carbon emission of the research area; judging the spatial attribute and the significance of the carbon emission of the land in the research area; the driving factors affecting the land use carbon emissions of the area of investigation are identified and analyzed. The application discloses the evolution characteristics of the land utilization and the carbon emission in the research area on the space-time scale based on night lamplight data exploration and analysis, provides scientific basis for ecological protection and development of the research area, and further improves the accuracy of measuring and calculating the land utilization and the carbon emission in the research area.

Description

Method and device for calculating carbon emission of land utilization in night lamplight data auxiliary area
Technical Field
The invention belongs to the technical field of carbon emission calculation, and particularly relates to a method and a device for calculating carbon emission of a night lamplight data auxiliary area land utilization.
Background
Global warming has a significant impact on regional ecology and sustainable development of human society, an international challenge facing humans in the 21 st century. The main driving factor for this phenomenon is carbon dioxide (CO 2 ) And (5) discharging. IPCC indicates CO in the atmosphere 2 The concentration increases by 1.9ppm each year, further exacerbating climate warming. Land utilization and land cover change (LUCC) are important sources of carbon emissions, accounting for about one third of the total carbon emissions from human activities since the industrial revolution. In view of the important feedback mechanisms of land systems and atmospheric systems, it is important to evaluate in depth the interactions between land use changes and carbon emissions.
There is a great deal of literature discussing the relationship between land utilization and carbon emissions, which is one of the main artifacts of the greenhouse effect. There are significant differences in the biological species of different land types, and the different land types behave differently in the carbon cycle. Wherein, the construction land and the cultivated land mainly discharge carbon, and the forest land, the water area and the like mainly have absorption effect on carbon. While variations between different land use types may result in variations in biological species and thus in variations in regional land use carbon emissions. In the measurement of the carbon emission amount of land use, an indirect method and a direct method are generally employed. Namely, the types of woodland, grassland and the like are directly measured and calculated by using the carbon emission coefficient, and the carbon emission amount generated by energy consumption of the construction land is indirectly represented. And the carbon emission coefficient of land utilization and the carbon emission coefficient of energy consumption are not uniform, and all adopt empirical values.
However, when the existing method adopts an empirical value to calculate the carbon emission amount of the land, the interaction between influencing factors is easily ignored, and errors are easily generated.
Disclosure of Invention
The application aims to solve the technical problem of easily neglecting interaction among influencing factors and easily generating errors when an empirical value is adopted to calculate the carbon emission of the land in the night lamplight data auxiliary area.
In order to achieve the above purpose, the application provides a method and a device for calculating carbon emission of land utilization in a night light data auxiliary area, wherein the method comprises the following steps:
selecting a research area comprising a plurality of cities, and acquiring night light data, energy consumption data, land utilization data and auxiliary research data of the research area;
estimating land use carbon emissions in the research area based on land use data and the energy consumption data;
extracting construction land in the research area according to the night light data and the land utilization data, and calculating total energy consumption carbon emission and construction land carbon emission of the research area according to the night light data, the energy consumption data and the auxiliary research data;
Judging the spatial attribute and the significance of the carbon emission of the land in the research area through exploratory spatial data analysis;
the driving factors affecting the utilization of carbon emissions in the investigation region are identified and analyzed by the geographic detector.
Optionally, the extracting the construction land in the research area according to the night light data, the energy consumption data and the land utilization data specifically includes:
counting the pixel brightness values of the remote sensing image in the night lamplight data and accumulating to obtain the accumulated area value of the pixel brightness of the remote sensing image;
comparing the accumulated area value of the pixel brightness of the remote sensing image with the construction land area in each provincial and urban statistical annual survey in the research area according to the land utilization data, and taking the pixel brightness value of the remote sensing image with the closest area as an optimal threshold value;
and taking the optimal threshold value as a critical point, judging the part of the research area, in which the image gray value is larger than the optimal threshold value, as a construction land range of the city, and extracting the construction land.
Optionally, the calculating the total energy consumption carbon emission of the research area specifically includes:
and determining carbon emission coefficients of various energy sources, and estimating an approximate value of the total energy consumption carbon emission by combining the energy consumption data.
Optionally, the calculating the carbon emission of the construction land of the research area according to the night light data, the energy consumption data and the auxiliary research data specifically includes:
dividing the research area according to the auxiliary research data and the district boundaries of the cities, and selecting night light data corresponding to different cities in the same year;
dividing each city into a plurality of county areas and urban areas to obtain night light data of each county area and urban area;
and measuring and calculating the energy consumption and carbon emission of each county and district and municipal district.
The energy consumption carbon emission is the ratio of the total carbon emission of the city to the pixel brightness value of the city remote sensing image multiplied by the total night light data brightness value of one county.
Optionally, the acquiring auxiliary data of the research area specifically includes: extracting space vector data of different scales of each city by using a space data processing tool; the set of space vector data forms auxiliary study data.
Optionally, the distinguishing the spatial attribute and the significance of the carbon emission of the land in the research area through exploratory spatial data analysis specifically includes:
Calculating a global space autocorrelation value and a local space autocorrelation value of the carbon emission of the land utilization between each county and the urban district according to the carbon emission of the land utilization of each county and the urban district;
revealing the spatial attribute and the significance of the carbon emission of the land utilization according to the global spatial autocorrelation value;
and carrying out clustering inspection on the local space autocorrelation to obtain local similarity and difference degree between the carbon emission of each county region and the carbon emission of the urban district in the research region.
Optionally, the identifying and analyzing the driving factors affecting the research area by using carbon emission through the geographic detector specifically includes:
detecting the space difference of the variables through a factor detector, and exploring the interpretation strength of the factors on the space difference of the variables;
interaction between the two influencing factors is detected by an interaction detector, and whether the interpretation power of the interaction on the variable changes is judged.
In order to achieve the above object, the present application also provides an apparatus for studying regional land utilization carbon emission based on night light data, comprising:
and a data statistics module: selecting a research area, and acquiring night light data, energy consumption data, land utilization data and auxiliary research data of the research area;
The calculation module: estimating the carbon emission of the land utilization in the research area according to the land utilization data and the energy consumption data, extracting the construction land in the research area according to the night lamplight data, and calculating the total energy consumption carbon emission and the carbon emission of the construction land in the research area;
exploratory spatial analysis module: judging the spatial attribute and the significance of the carbon emission of the land in the research area through exploratory spatial data;
geographic detector module: the driving factors affecting the utilization of carbon emissions in the study area are identified and analyzed.
Optionally, the geographic detector module includes:
factor detector: detecting the space difference of the variables, and exploring the interpretation strength of factors on the space difference of the variables;
interaction detector: interaction between the two influencing factors is detected, and whether the interpretation power of the interaction on the variable is changed is judged.
The invention has the beneficial effects that based on NPP-VIIRS night lamplight data, from county scale, the exploratory space analysis method is adopted, and the land utilization and the evolution characteristics of carbon emission in space of a research area in a certain period of time are revealed; the geographical detector is used for discussing carbon emission driving factors of the region, scientific basis is provided for ecological protection and development of the research region, and accuracy of measurement and calculation of carbon emission of the soil of the research region is further improved.
Drawings
FIG. 1 is a schematic flow chart of a method for calculating carbon emission of a night light data auxiliary area land according to the present invention;
fig. 2 is a schematic diagram of a construction land extracted in step (3) in a calculation method for carbon emission of a night light data auxiliary area land;
FIG. 3 is a schematic diagram of interaction modes of interaction detectors in a calculation method of carbon emission of a night light data auxiliary area land utilization according to the present invention;
fig. 4 is a schematic diagram of land utilization transfer change of a research area in 2010-2020 in a calculation method of carbon emission of a night light data auxiliary area provided by the invention;
fig. 5 is a schematic diagram of a change area of a land utilization type in 2010-2020 in a research area in a night light data auxiliary area land utilization carbon emission calculation method;
fig. 6 is a schematic diagram of carbon emission of a research area in a county region in 2010-2020 in a night light data auxiliary area land utilization carbon emission calculation method;
fig. 7 is a schematic diagram of carbon emission level of a research area in county region land utilization in 2010-2020 in a night light data auxiliary area land utilization carbon emission calculation method provided by the invention;
Fig. 8 is a schematic diagram of carbon emission LISA clustering of a research area in 2010-2020 in a method for calculating carbon emission of night light data auxiliary area land according to the present invention;
fig. 9 is a schematic diagram of a carbon emission driving factor of a research area in 2010-2020 in a method for calculating carbon emission of a night light data auxiliary area land;
fig. 10 is a schematic diagram of an interaction detection result of a research area in a method for calculating the carbon emission of a night light data auxiliary area land.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings in the preferred embodiments of the present invention. In the drawings, the same or similar reference numerals refer to the same or similar components or components having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the invention. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
In the description of the present application, it should be noted that, unless explicitly stated and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be fixedly connected, or indirectly connected through intermediaries, for example, or may be in communication with each other between two elements or in an interaction relationship between the two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present application, it should be understood that the terms "upper," "lower," "front," "rear," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present application and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
The terms first, second, third and the like in the description and in the claims and in the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein.
Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or maintenance tool that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or maintenance tool.
As shown in fig. 1, the application provides a method for calculating carbon emission of a night light data auxiliary area land, which comprises the following steps: (1) Selecting a research area containing at least one city, and acquiring night light data, energy consumption data, land utilization data and auxiliary research data of the research area; (2) Estimating the carbon emission of the land utilization in the research area according to the land utilization data and the energy consumption data; (3) And (5) extracting the construction land in the research area according to the night lamplight data, and calculating the total energy consumption carbon emission and the construction land carbon emission of the research area.
Specifically, in step (1), a study area is selected, typically a region of the geographic location containing multiple cities is targeted for computational study. In the present application, the area of investigation is selected to be the loop of the ecological economic circle of the Dongting lake. The ecological economic circle of the ring Dongting lake (hereinafter referred to as the ring Dongting lake region) takes the Dongting lake as the center, is between 111 DEG 53 'and 113 DEG 05' E and 28 DEG 44 'and 29 DEG 35' N, and relates to Yiyang city, yueyang city, changde city, changsha city 4 and Hubei province state city of Hunan province, and the total area of the land is 6.05X104 km, which is 33 county-level administrative regions (county, county-level city and region) (hereinafter referred to as county collectively) 2 In 2020, the general population was 2029.74 ×104 people and the urbanization rate was 57% at the end of the year. The climate conditions in the environment are superior, the land utilization types are various, and the areas of cultivated lands, forest lands and water areas are large. The area is covered by agriculture,Mainly in pasturing and fishery, is one of the important grain production bases in China. In 2014, the ecological economy planning area of the cave lake is formally started and implemented, and the method has important significance for regional energy conservation and emission reduction. With the implementation of ecological environment protection measures such as ecological restoration of wetland, comprehensive environmental remediation and the like in the Dongting lake area, the ecological construction of the research area has the initial effect, but the problems of reduced atmospheric environment quality, damaged ecological system, serious water pollution and the like still exist. Therefore, the system analyzes the space-time evolution characteristics and influence factors of carbon emission of the land in the annular cave lake region, and provides scientific basis and decision support for the ecological protection and development of the annular cave lake region aiming at carbon emission reduction.
Further, in the step (1), night light data, energy consumption data, land utilization data and auxiliary research data of a research area are acquired, wherein in the application, a global 500m resolution 'NPP-VIIRS-like' night light data set is used as the night light data, the data are based on automatic encoder model integration calibration DMSP-OLS (2000-2012) and NPP-VIIRS (2013-2020) month night light data of a convolutional neural network, and on the basis, a 'NPP-VIIRS-like' night light expansion time sequence (2000-2020) is acquired, and the data have good precision and time-space consistency. Meanwhile, the data set filters initial sunlight, moon light, cloud and fog pixels and background noise such as lamplight, flame and aurora, and detail information in the city can be clearly reflected. In the application, 11 pieces of 'NPP-VIIRS' night light data are selected, the ArcGIS software is used for cutting out night light images of the ring-hole-court lake region, and the size of each grid unit is considered to be reduced along with the increase of latitude, so that the combined image is projected by using a Lanbert equal-area projection method, resampling is carried out with the spatial resolution of 1km, and the influence of grid unit change is reduced.
In the application, land utilization data is divided into two types, one is remote sensing image interpretation data; one is government agency statistics yearbook data. The application uses remote sensing image interpretation data for calculating land utilization carbon emission, then uses construction land carbon emission, uses construction land area extracted by night light data and area corresponding to construction land area in annual-image data, further calculates specific gravity of light values of each county in each city as specific gravity of energy carbon emission of each county in total emission of each city based on the obtained area range, and multiplies the total energy carbon emission of each city to obtain the carbon emission of construction land of each county.
The auxiliary research data are statistical annual-differentiation data (land utilization data such as construction land area, driving factor data and the like which can be obtained by government departments), other administrative area vector data, topographic data and the like. And the construction land carbon emission calculation relates to night lamplight data, land utilization construction land area in the statistics annual survey, energy consumption data and administrative area vector data.
Correspondingly, the energy consumption data is the consumption of raw coal, fuel gas, natural gas and other energy sources in each district and city of the research area, and is used for calculating the energy consumption carbon emission in a partition mode. These energy consumption statistics are derived from statistical annual certificates for each local market.
The land utilization data in the application are the land utilization data of Hunan province in five periods of 2010, 2013, 2015, 2018 and 2020, which are sourced from data resource centers of China academy of sciences, correspondingly, the data of a research area are obtained in ArcGIS by using a cutting tool, and the occupied area of each land utilization type is counted, so that the calculation of the carbon emission of the land utilization of the research area is carried out later.
The auxiliary study data included: administrative district vector data. Wherein the administrative district vector data is from a national geographic data center. And (3) defining according to the range of the ring-hole court lake region, and extracting space vector data of different scales of 5 markets, namely Changde market, yueyang market, yiyang market, jingzhou market and Changsha market, by using a space data processing tool of the ArcGIS as auxiliary research data.
In step (2), the carbon emissions of the land use in the research area are estimated from the land use data and the energy consumption data, and the conversion conditions of the land use type are firstly counted by using a land use transfer matrix. The land utilization transfer matrix is essentially the application of a Markov model on land utilization change, not only can quantitatively indicate the conversion condition among different land utilization types, but also can reveal the transfer rate among different land utilization types. The transformation condition of each land type in the ecological economic region of the ring-hole-family lake in 2010-2020 is disclosed by means of a land utilization transfer matrix, and the expression is as follows:
Wherein: s is S ij The area of the ith land use type converted into j land types in km in the research period is represented 2 。
Woodland, unused land, grassland and water areas absorb carbon during the carbon cycle, representing carbon sinks; carbon emissions are produced in cultivated and construction lands and are expressed as a carbon source. While land use carbon emissions are primarily estimated for these six main land use types of carbon emissions. The carbon emission of the construction land is mainly carbon emission generated by counting energy consumption in human social activities, and the carbon emission of the construction land is replaced by the energy consumption carbon emission obtained by simulating a carbon emission simulation model between night lamplight data and the energy consumption carbon emission in the prior literature. At the same time, the carbon emission coefficients of cultivated land, woodland, grassland, water area and unused land obtained according to the prior research result are respectively 0.4970t/hm 2 、-0.6440t/hm 2 、-0.0205t/hm 2 、-0.0230t/hm 2 、-0.0050t/hm 2 . The calculation formula of the carbon emission of the land is as follows:
wherein C is e Represents the total amount of carbon emissions; e, e i Representing the carbon emissions of the type i land use; a is that i A footprint represented as a type i land use; alpha i Expressed as carbon emission coefficients for various land use types. C (C) i Representing the carbon emissions produced by the energy consumption of each county and city.
In the step (3), the construction land in the research area is extracted according to night light data, and the method specifically comprises the following steps: counting the pixel brightness values of the remote sensing image in the night lamplight data and accumulating to obtain the accumulated area value of the pixel brightness of the remote sensing image; comparing the accumulated area value of the pixel brightness of the remote sensing image with the construction land area in each provincial and urban statistical annual survey in the research area, and taking the pixel brightness value of the remote sensing image with the closest area as an optimal threshold value; taking the optimal threshold value as a critical point, and judging the part of the research area, in which the image gray value is larger than the optimal threshold value, as a construction land range of the city; and (5) extracting the construction land.
It is understood that the method of threshold value is used to extract the construction land of the research area in the application. Firstly, counting the area (representing construction land) of pixel brightness (DN) value of each remote sensing image in night light image, accumulating, comparing DN accumulated area value with construction land area in each provincial and urban statistical annual-light, obtaining the optimal threshold value when the two areas are closest, taking the threshold value as a critical point, and judging the part of the image gray value larger than the threshold value as the construction land range of the city. Finally, the construction land is extracted based on the image. As shown in fig. 2, the present study utilized NPP-VIIRS noctilucent data to extract the land area for the construction of the ecological economic zone of the cave lake. In the whole, the areas with higher lamplight values are mainly located in Yueyang urban jurisdiction, changde urban jurisdiction, jingzhou urban jurisdiction and Wangguang urban area. And the lamplight value and range of the research area are increased year by year, which indicates that the construction land of the ring-hole court lake area is continuously expanded.
Calculating total energy consumption carbon emission of the research area specifically comprises the following steps: and determining carbon emission coefficients of various energy sources, and estimating an approximate value of the total energy consumption carbon emission by combining the energy consumption data. The amount of carbon emissions generated by the energy consumption is taken as the actual amount of carbon emissions in the region. In the application, the total energy consumption carbon emission is estimated by adopting a 'top-down' calculation method provided by IPCC. The carbon emission coefficients of various energy sources are determined by IPCC (national greenhouse gas inventory guidelines) (2006). An approximation of the carbon emissions is estimated in combination with the statistical data relating to the energy source. The carbon emission of the ecological economic rings of the ring-hole lake is calculated by using the energy consumption statistical data of each county (city and district), and the calculation formula is as follows:
in the formula C j The total carbon emission of j years; i is an energy source type; e (E) i The consumption of the energy i terminal is calculated; the unit is: kg. m is m 3 、kw·h；B i The conversion coefficient of the i-th energy standard coal is as follows: kgce/kg, kgce/m 3 、kgce/(kw·h)；K i The carbon emission coefficient is kg/kgce of the energy i; k (K) i Values are derived from IPCC carbon emission calculation guidelines default values, standard coal conversion coefficients and carbon emission coefficients for various energy sources are shown in the following table:
wherein the table is the energy consumption carbon emission coefficient, and the natural gas is converted into standard coal to be t×ce/×10 4 m 3 The method comprises the steps of carrying out a first treatment on the surface of the The electric power is converted into standard coal to be t.ce/. Times.10 4 kW.h; the rest are converted into standard coal and are t.ce/t.
Correspondingly, the method for calculating the carbon emission of the construction land of the research area specifically comprises the following steps: dividing a research area by using the district boundaries of cities, and selecting night light data corresponding to different cities in the same year; dividing each city into a plurality of county areas to obtain night light data of each county area; and measuring and calculating the energy consumption and carbon emission of each county and the corresponding city, wherein the energy consumption and carbon emission is the ratio of the total carbon emission of the city to the pixel brightness value of the remote sensing image of one county and the total brightness value of night light data of the county.
Specifically, in the application, a certain correlation exists between the total brightness value (TDN, total DN value) of night light data and the carbon emission. In the application, cities in a research area are divided according to the border of the district, meanwhile, NPP-VIIRS night light data cut out by the city border of each district is utilized, the energy consumption carbon emission of each county (city, district) in the research area is calculated and calculated by utilizing the ratio of the DN value of the county domain of the NPP-VIIRS night light data to the TDN value of the city domain on the basis of the total energy consumption carbon emission of each city in the research area in 2010-2020 calculated by the formula (3), and the formula is as follows:
C i ＝C j /T j ×T i (4)
Wherein: c (C) i Representing the energy consumption carbon emission of the i county region; c (C) j Expressed as the total amount of carbon emissions for j years in the city domain corresponding to the county domain; t (T) j DN total value expressed as light data of the j-year night of the city domain corresponding to the i-county domain; ti represents the sum of the i county domain DNs. The energy consumption and carbon emission amount is the ratio of the total carbon emission amount of the city to the pixel brightness value of the city remote sensing image, namely the proportion or weight of a small area to a large area. For example, expressed herein are: the total emission amount of the large area is multiplied by the total pixel brightness value of the night light image of the small area and the total pixel brightness value of the night light image of the large area, so that the carbon emission amount of the small area can be obtained. It should be noted that the larger the area (e.g., the selected municipal administration area in the present application), the easier it is to obtain the energy consumption data from the statistical annual survey, while the data is difficult to obtain from small areas such as the county administration area.
In this step, the carbon emission amount of each land (i.e., cultivated land, woodland, grass, water area, unused land) needs to be directly calculated based on the formula (2) by using the carbon emission coefficient corresponding to the land. Although construction land is a type of land used, it is generally not recommended to use this direct calculation method to avoid errors.
In the application, the carbon emission of the construction land is mainly the carbon emission generated by energy consumption in the human social activity statistics, and the relation exists between the total DN value (also called gray value or brightness value) of night lamplight and the carbon emission of the energy source, so that the carbon emission of the construction land can be calculated indirectly. Since night light data is related to carbon emission of construction land, the effective DN value accumulation area can be obtained by a threshold method in the application: firstly, counting the coverage area (representing the construction land) of each DN value in night light images, accumulating (accumulating from high to low), comparing with the construction land area of the area in the statistical annual survey, selecting the minimum DN value which is the most similar to the DN value accumulation area and the construction land area in the statistical annual survey as the optimal threshold, and obtaining the construction land space distribution diagram (shown in figure 2) by taking all DN value coverage areas larger than the threshold in the night light images as the construction land area.
Generally, various energy consumption amounts on the scale of a city domain (representing a larger space scale, the application represents a municipal district) can be found, so that the total carbon emission amount of each city involved in the ecological economic circle of the ring-hole lake can be obtained through a formula (3), but the energy consumption amounts on the scale of a county domain (representing the county district, including the municipal district) are difficult to find. In this way, we can reject the image area smaller than the optimal threshold by the optimal threshold determined by the above process, further calculate the ratio (which can be regarded as area ratio in fact) of the total value of DN in county domain to the total value of DN in city domain, so as to obtain the energy consumption ratio (or weight) of each county (city and district) to the city where it is located, and finally multiply the total carbon emission of the city domain obtained by the formula (3) by the energy consumption ratio to obtain the carbon emission of each county (city and district).
The above-mentioned directly calculated carbon emission amounts of cultivated land, woodland, grassland, water area and unused land and indirectly calculated carbon emission amounts of construction land are combined, the carbon source (cultivated land and construction land) is positive, and the carbon sink (woodland, grassland, water area and unused land) is negative, and the total carbon emission amount of each county (district level city, district) can be obtained by adding.
After the calculation is completed, the spatial attribute and the significance of the carbon emission of the land in the research area need to be judged through exploratory spatial data analysis. For this purpose, in the present application, the steps are further included: (4) Judging the spatial attribute and the significance of the carbon emission of the land in the research area through exploratory spatial data analysis; (5) The driving factors affecting the utilization of carbon emissions in the investigation region are identified and analyzed by the geographic detector.
Specifically, the space attribute and the significance of the land utilization carbon emission are disclosed through the global space autocorrelation values of the land utilization carbon emission of the city and the land utilization carbon emission of the county; and carrying out clustering inspection through local space autocorrelation to obtain local similarity and difference degree between the carbon emission of each county and city in the research area.
The Exploratory Spatial Data Analysis (ESDA) is based on geography, statistics and the like, and uses spatial autocorrelation (Spatial Autorrelation) to identify the attribute of spatial data according to spatial relationship, and is mainly divided into:
global spatial autocorrelation (Clobal Moran's I):
clobal Moran's I reveals the spatial attributes and significance of land utilization carbon emissions. The specific calculation is as follows:
wherein: i is represented by Clobal Moran's I, ranging from [ -1,1]. When I>At 0, there is a significant positive correlation between land utilization carbon emissions; when I<At 0, a significant negative correlation is indicated; when i=0, it indicates that there is no correlation. And the larger the absolute value of I, the stronger the correlation between the land utilization carbon emissions. n is the number of administrative areas of the research area county level; x is x i 、x j The carbon emission is used for the land in the research areas i county and j county; w (w) ij Is a space weight matrix of city and county i and j, the adjacency is 1, otherwise, the adjacency is 0;represents an average value of land utilization carbon emissions.
Local spatial autocorrelation:
the LISA is used for clustering test to illustrate the local similarity (positive correlation) and the difference (negative correlation) degree between the carbon emission of a certain city and county and the adjacent city and county in the local research area. The calculation formula is as follows:
Wherein: x is X i 、X j The distribution is the standardized land utilization carbon emission of research areas i county and j county; w (W) ij Is a spatial weight matrix of i county and j county, the same as Clobal Moran's I;represents an average value of land utilization carbon emissions.
In step (5), identifying and analyzing driving factors affecting the utilization of carbon emissions in the research area by means of a geographic probe, comprising in particular: detecting the space difference of the variables through a factor detector, and exploring the interpretation strength of the factors on the space difference of the variables; interaction between the two influencing factors is detected by an interaction detector, and whether the interpretation power of the interaction on the variable changes is judged.
Among them, the geographic detector is a set of statistical methods for detecting spatial heterogeneity, detects driving factors behind geographic variables, and has unique advantages in that the interaction of two factors on dependent variables can be detected. As a powerful tool for factor analysis, the tool has been widely used for analyzing the influence mechanisms of natural environment factors and socioeconomic factors. The application utilizes factor detectors and interaction detectors to identify and analyze driving factors affecting carbon emission of the land utilization of the ecological economic circle of the ring-hole lake.
The factor detector can detect the spatial difference of the variable, and meanwhile explore the interpretation strength of the factor on the spatial difference of the variable. The specific formula is as follows:
Wherein p is the explanatory power of the carbon emission driving factor of the land, and the value range is 0,1]The method comprises the steps of carrying out a first treatment on the surface of the N represents the total sample amount, K is the number of driver layers; sigma (sigma) 2 The variance of carbon emissions for the land utilization throughout the investigation region; n (N) i Is the number of samples in the i driver layer, i (i=1, 2,..k). The larger the p value is, the stronger the explanation of the influence factor on the carbon emission of the land utilization in the research area is.
The interaction detector is used for detecting interaction between two influencing factors and judging whether the interpretation power of the interaction on the variable is increased or decreased. Respectively calculating two factors X 1 And X 2 Q value of (2): q (X) 1 )、q(X 2 ) And compare q (X 1 )、q(X 2 ) And q (X) 1 )∩(X 2 ) And the relationship between them, for determining the manner in which they interact. Five ways of interaction between the two factors are shown in fig. 3.
In conclusion, based on NPP-VIIRS night light data, from county scale, exploratory space analysis method is adopted, and the characteristics of land utilization and carbon emission evolution in space of the ring-cave lake ecological economy in 2010-2020 are revealed. Finally, a geographic detector is used for discussing carbon emission driving factors of the region, and scientific basis is provided for ecological protection and development of the ecological economic region of the ring-hole lake.
On the basis of the embodiment, the application also provides a regional land utilization carbon emission research device based on night light data, which comprises: and a data statistics module: selecting a research area, and acquiring night light data, energy consumption data, land utilization data and auxiliary research data of the research area; the calculation module: estimating the carbon emission of the land utilization in the research area according to the land utilization data and the energy consumption data, extracting the construction land in the research area according to the night lamplight data, and calculating the total energy consumption carbon emission and the carbon emission of the construction land in the research area; exploratory spatial analysis module: judging the spatial attribute and the significance of the carbon emission of the land in the research area through exploratory spatial data; geographic detector module: the driving factors affecting the utilization of carbon emissions in the study area are identified and analyzed.
Wherein the geographic detector module comprises: factor detector: detecting the space difference of the variables, and exploring the interpretation strength of factors on the space difference of the variables; interaction detector: interaction between the two influencing factors is detected, and whether the interpretation power of the interaction on the variable is changed is judged.
The method according to the invention is described in detail below by way of specific examples.
As shown in fig. 4, the circular cave lake region is mainly cultivated land, the forest land is secondary, and the occupied area of grassland and unused land is minimum. 2010-2013, the maximum area of cultivated land (7530.44 km) 2 ) Forest land (5135.70 km) 2 ). Each land utilization type is transferred into an area, and the cultivated land transfer area is 7146.40km 2 Forest land transfer area 5202.357km 2 The area of the water area is 2874.28km 2 The transfer area of the unused land is the smallest (356.18 km) 2 ). In 2013-2015, the condition of the transfer-in and transfer-out area of each land use type is consistent with 2010-2013, and the land use type with the largest transfer-out area is still cultivated land (7876.66 km) 2 ) Woodland (5484.91 km) 2 ). The cultivated land with the largest area (7899.55 km) 2 ) Woodland (5380.44 km) 2 ). In 2015-2018, the area of unused land transfer is the largest compared with the rise in 2010-2013 and 2013-2015. In the cultivated land turning out area (8086.58 km) 2 ) The area ratio of the cultivated land to the forest land is the largest (54.02%), and the area ratio of the cultivated land to the water area is the next (26.45%). In 2018-2020, the whole transfer-in and transfer-out amount of each land use type is reduced, but cultivated land (4617.36 km) 2 ) Woodland (2870.3 km) 2 ) The transfer area is still one or two. Water area (1977.87 km) 2 ) The transfer area is the third, the construction land (1032.71 km) 2 ) Next, the unused area (460.64 km) 2 ) Grasslands (284.54 km) 2 ) The transfer area is minimal. Of the areas of emergence, the cultivated land has the largest area of emergence (4355.69 km) 2 ) Of which there is 2279.33km 2 Is rolled out to be forest land, 1201.84km 2 And turning out of the water area.
In 2010-2020, the most cultivated land in the ring-hole lake area is rotated out of various land utilization types, which is up to 8162.19km 2 Wherein the area ratio of the conversion to the woodland is the largest (52.5%), followed by the water area (26.06%) and the construction land (18%). The forest land is rolled out for a plurality of times, and the total of 5402.33km 2 Wherein about 78.6% of the area is converted to cultivated land and 7.82% is converted to water. The water area and the construction land transfer area are 3284.18km respectively 2 、1349.09km 2 . Grasslands and disuseThe land transfer area is minimal, accounting for about 22.17% of the grassland and 21.7% of the unused land is converted to cultivated land. In the transfer area of the ring-hole lake region, the cultivated land is maximum (7499.59 km) 2 ) 56.23% of the area comes from woodland. The transfer areas of forest lands, water areas, construction lands and unused lands are 5387.4km respectively 2 、3101.12km 2 、2050.5km 2 、633.75km 2 . Wherein, in the transfer area of the forest land, the maximum area (4285.05 km) from the cultivated land to the forest land 2 ) Accounting for 79.54 percent of the total area. The minimum grassland is 593.96km 2 . From this, it is known that the cultivated area of the ecological economic ring of the ring-hole lake is continuously reduced, the construction land area is continuously increased, and the increased part of the construction land is as high as 701.4km 2 71.67% are from cultivated land, indicating that the development of the town of the ecological economic circle of the ring-hole lake is mainly extended by occupying cultivated land.
As shown in fig. 5, there was a significant difference in net change in area between different land use types in the ring-hole lake region in 2010-2020. Wherein, the areas of construction land and unused land are increased, while the areas of cultivated land, woodland, grassland and water area are reduced. The construction land area is 1349.09km 2 To 2050.5km 2 The growth is 52 percent. Increase in unutilized area by 206.94km 2 The growth is 48.49 percent. The cultivated land area is 8162.19km from 2010 2 Reduced to 7499.59km in 2020 2 The reduction is 8.11 percent. 183.06km of water area is reduced 2 Reduced by 5.57%, and reduced by 14.93km respectively in woodland and grass ground 2 And 47.76km 2 The reduction is 0.29% and 7.44%, respectively. Reduced land use type area: cultivated land>Water area>Grassland>And (5) a woodland.
As shown in Table 2, in 2010-2020, the net carbon emission in the Dongting lake area was 2030.88 ×10 in 2010 4 t was increased to 2355.62 ×10 in 2012 4 t, then continue to decrease to 2016 years 2140.97X 10 4 t, and net carbon emissions from 2018 to 2020 are reduced. It can be seen that the land utilization of the study area has a downward trend in the fluctuation of the carbon emissions as a whole. The carbon emission in 2014 is reduced, and the reason is put forward in the plan of ecological economy area of Dongting lake in 2014The implementation of carbon emission reduction in the ecological economic area of the ring-hole lake is promoted to a certain extent. Overall, the carbon absorption of the ecological economic ring of the ring-hole lake is far less than the carbon emission, and the maximum carbon emission of the construction land is (2320.01 ×10) for the example of 2020 4 t) the carbon absorption of the woodland is at most (143.53 multiplied by 10) 4 t). From this, it is found that the carbon emission amount of the construction land is 16 times the carbon absorption amount of the woodland. In contrast, woodland contributes little to carbon sink. Referring to FIG. 5, the net increase in construction land and tilled area as a carbon source is 38.8km 2 The net carbon emission in the research area is in a decreasing trend, mainly because the consumption of crude oil, diesel oil and other energy in the construction land is decreasing year by year.
In the aspect of carbon source, the cultivated area is gradually reduced, and the carbon emission amount is 134.29 multiplied by 10 in 2010 4 t, to 131.08X10 in 2020 4 the lowest point in the year 2018 is 129.79 multiplied by 10 4 t. The carbon emission of the construction land is consistent with the fluctuation trend of the total carbon emission, the land is the most main carbon source of the ecological economic circle of the ring-hole lake, and in the future, the definition of an ecological red line for reasonably controlling the expansion of the construction land is particularly important in the realization of carbon neutralization tasks.
In terms of carbon sequestration, the carbon sequestration potential of the woodland ecosystem is great, accounting for about 98.63% of the total carbon sequestration. This is related to the large footprint of the forest land in the area of investigation compared to other land use types. The overall total carbon sequestration tends to decrease, but the magnitude of the change is not large.
Table 2 2010-2020 environmental Dongting lake ecological economic ring land utilization carbon emission/10 4 t
And (3) obtaining the carbon emission of the land utilization in the county of the ring court lake region 2010-2020 by utilizing the night light data, and carrying out merging statistics on the urban jurisdictions in order to more clearly analyze the change degree of the carbon emission of the land utilization in different county regions of the research region. As can be seen from fig. 6, the carbon emissions in the municipalities and jurisdictions are relatively large, and the municipalities and the counties show significant differences. Taking 2020 as an example, yueyang City and jurisdictionThe carbon emission of the district land is maximum, and the total carbon emission of other municipal districts is approximately 3 times; negative values of carbon emissions occur in the county of anghua and the county of Shimen, probably because these areas have a relatively heavy woodland area. In 2010-2020, the carbon emission of the land utilization of most county areas in the ring-hole lake region is 50 multiplied by 10 4 t is less than or equal to t. And the fluctuation range of the carbon emission of the land utilization in each county is not large, and the trend of slow decrease is presented. Wherein the change is more obvious in the regions of Yueyang City and jurisdiction, changde City and jurisdiction and Yiyang City and jurisdiction, respectively from 995.09 multiplied by 10 4 t、350×10 4 t、274.04×10 4 t is reduced to 904.33 ×10 4 t、188.38×10 4 t、181.67×10 4 t。
The land utilization carbon emission of the county region of the ring-hole court is divided into 6 grades by utilizing a natural break point method. Carbon emission below 0t is negative carbon emission, 0-20×10 4 t is lower carbon emission, 20-50 multiplied by 10 4 t is low carbon emission, 50-100 multiplied by 10 4 t is medium carbon emission, 100-250 multiplied by 10 4 t is higher carbon emission, 250-1000 multiplied by 10 4 t is high carbon emissions. In general, the spatial diversity of each county in the research area is more remarkable, the county is in a gradually-enhanced state from west to east, and each county in southwest is 20×10 4 Below t, the carbon negative emission region and the low carbon emission region are more concentrated. The eastern part reaches 50 multiplied by 10 4 Above t (fig. 7), the high carbon emission zone and the higher carbon emission zone are more concentrated. In 2010, high carbon emissions were mainly located in Yueyang building, changde district, and Heshan district. In 2011, the carbon emission of the whole land utilization of the ring-hole lake region is increased compared with 2010, the medium carbon emission region is increased, and the lower carbon emission region is more concentrated. In 2012, jingzhou and/28583 counties have changed from lower carbon emissions levels to medium carbon emissions and the herdson has decreased from higher carbon emissions levels to higher carbon emissions. In 2013, the carbon emission reaches 100×10 4 the county region above t is mainly located in the river, yueyang building and Heshan. The Yueyang county, pingjiang county, guru city, hunan county, and the sandy city area are all medium carbon emission areas. Other county regions are all 20×10 4 And below t, the carbon emission of the land utilization in Shimen county is negative carbon emission. In 2015, shimen county and AnThe carbon emission amount in the county is smaller than the carbon emission amount and takes on a negative value. Carbon emission of 50X 10 4 the number of county regions above t is reduced compared with 2013. The carbon emission of the land used in the wuling area is reduced to 250 multiplied by 10 4 t is less than or equal to t. The whole carbon emission amount of the county region land of 2018 research has obvious rising trend, and particularly the carbon emission amount of county region of Yueyang city reaches 50 multiplied by 10 4 t is above, and the carbon emission of county regions such as Sha city, xinghua city, yuan Jiang city and the like is increased to 20 multiplied by 10 in 2018 4 t is above. The carbon emission of the land in the county of the part of 2020 is reduced, and the carbon emission of Junshan region, taojiang county and Shishou city tends to be converted into the carbon emission of the region of negative carbon in 2020. The land utilization carbon emission of the Wuling area, yueyang building area and Heshan area is high, and has no obvious change during the research, which is possibly related to the high socioeconomic development level of the urban district, the population concentration, the large energy consumption, the large land occupation area for construction and the petroleum, industrial and power plants which are greatly developed in the Yueyang city in recent years.
In 2015-2020, moran's I values of carbon emission of the land utilization in each county region of the ecological economic circle of the ring-hole lake are all positive values, and the significance test (P < 0.05) is passed (Table 3). This result shows that the research county regions exhibit significant positive correlation between carbon emissions and are all in an aggregation mode, with phases aggregating between high-value regions of carbon emissions and phases aggregating between low-value regions. Moran's I index decreases from 0.132 in 2015 to 0.12 in 2020, and overall decreases. This may be a positive response of the region to national carbon emission reduction policies, resulting from a reduction in energy consumption.
Table 3, 2010-2020, environmental Dongting lake ecological economic ring land utilization carbon emission Moran's I index
As can be seen from the regional Moran's I index of carbon emissions from county land utilization, the environmental economy of the ring-hole lake, county land utilization, is non-significant in most regions of carbon emissions. As shown in FIG. 8, the HH type and LH type concentration regions are mainly distributed in the northeast region of the Dongting lake. The method comprises the following steps of selecting a cloud stream region as a LH aggregation type region, wherein the cloud stream region is a LL aggregation type region in Shimen county in 2010, the LL aggregation type region is 28583 bed county in 2011, and the cloud stream region is changed from the LH aggregation type region to an HH aggregation type region. In 2012, there was no major change from 2011, and the county increased to LL-aggregated region. As in 2013 and 2014, only 9% of county regions show significant forms, yue Yangshi city district cloud stream regions and Linxiang city mainly show HH type and LH type, and LL type is distributed in county of Changde city/28583. HH type in 2015 is mainly distributed in Yueyang building area and Yunxi area, and LH type and LL type are still distributed in Linxiang city. The local autocorrelation results in 2016-2018 are consistent, the HH aggregation type region is mainly cloud stream region, yueyang building region and Linxiang city in Yueyang city, and the LL aggregation type region is mainly distributed in the northwest part of the Dongting lake, including Shimen county,/28583 Gexian and Ling28583 Yueyang county. In 2020, HH type, LH type and LL type are located only in Yunxi region, linxiang city, anhua county and Sancounty. In general, in 2010-2020, HL aggregation type areas do not appear in the ecological economic circles of the ring-hole lake, and the HH aggregation type areas are relatively fixed and are always distributed in the urban jurisdiction of Yueyang city, which is related to the relative development of Yue Yangshi second industry, and the acceleration of the urban process causes the area to be a high-energy consumption area.
In order to detect driving factors of land utilization carbon emission change, the application selects an average value of 8 social factors and 3 natural factors of the ecological economic county scale of the ring-hole lake in 2010-2020, including DEM (X1), gradient (X2), slope direction (X3), land utilization index (X4), population (X5), people's average occupied area (X6), township (X7), first industry occupied ratio (X8), second industry occupied ratio (X9), people's average GDP (X10) and fixed asset investment (X11). The average of 11 indices from 2010 to 2020 was interpolated using ArcGIS tools (fig. 9). With the average carbon emissions of land utilization in the region of 2010-2020 as a dependent variable, the dominant driving factor for land utilization carbon emissions variation was explored using a geographic probe to quantify the individual interpretation and interaction forces of the influencing factors (table 4 and fig. 10).
TABLE 4 q statistics and p values for geographical detection of the Dongting lake area
As can be seen from table 4, the individual explanatory power of each influencing factor on the carbon emission amount of land use is that the human-average GDP (0.94), the urbanization (0.52), the fixed asset investment (0.51) and the first industrial duty (0.39) are important in studying the influence of the carbon emission amount of land use in county and county, and all pass the significance test (< 0.05). The human-average GDP represents the socioeconomic development status level of the region, and the town ratio represents the expansion degree of the construction land. In recent years, the social economy of the ring-hole lake region rapidly develops, the tourism industry and infrastructure construction are greatly developed, the land for town construction is continuously expanded, and the increase of the carbon emission of land utilization is aggravated, so that more emphasis is placed on the town development in the later carbon emission reduction link, and reasonable measures are adopted to ensure the scientific and sustainable development of towns. The effect of slope direction (0.36), people's average occupied area (0.35), slope (0.13) and land utilization degree index (0.12) on carbon emission of the land utilization of the ring cave lake region is also not negligible. And the second industry ratio (0.06), population (0.04) and DEM (0.03) have less influence on the carbon emission utilization of the land in the county of the ring-hole court.
The results of the interaction detector indicate (fig. 10) that each factor has less effect than the combination between any two factors and no attenuation effect, so the land utilization carbon emission change of the study area is the result of the combined effect of the different driving factors. Wherein X is 1 And X 2 、X 1 And X 4 、X 1 And X 6 、X 1 And X 7 Etc. are two-factor enhanced, while the pairwise interactions of other factors are non-linearly enhanced. X is X 1 And X 10 、X 2 And X 6 、X 2 And X 7 、X 2 And X 10 、X 3 And X 6 、X 3 And X 7 、X 3 And X 10 、X 3 And X 11 、X 4 And X 6 、X 4 And X 7 、X 4 And X 10 The interaction between the components is stronger and is more than 90 percent; x is X 1 And X 7 、X 1 And X 11 、X 2 And X 11 、X 3 And X 9 、X 6 And X 8 The interaction degree between the two components is above 50%; x is X 1 And X 8 、X 2 And X 8 、X 3 And X 5 、X 4 And X 8 The interaction degree between the two components is about 40%, so that the effect of each influence factor on the ecological economic circle of the ring-hole lake is not negligible.
According to the application, by utilizing land utilization remote sensing monitoring data and NPP-VIIRS noctilucent data and combining carbon emission correction coefficients and IPCC inventory methods of various land utilization types, the statistical data of the land utilization carbon emission are obtained, and by means of GIS technology, the space-time pattern and influence factors of the land utilization carbon emission of the ring-cave lake ecological economy in 2010-2020 are analyzed by the system, and the main conclusion is as follows:
(1) The main land utilization changes of the ecological economic circle of the ring-hole lake in 2010-2020 are increased construction land, reduced grasslands and reduced cultivated lands. Reduced land use type area: cultivated land > water area > grass land > forest land. Among the various areas of land utilization, the most area of land is transferred out of the ring-hole lake area, and is mainly converted into forest land, and then water area and construction land. The forest land is transformed into cultivated land and water area for a second time. The area of grass and unused land transfer out is minimal. Of the areas of rotation, the rotation area of the cultivated land is the largest, and nearly half of the area comes from the woodland. In the forest land transfer area, the area converted from cultivated land to forest land occupies 79.54% of the total forest land transfer area. In the whole, the cultivated land area of the circular hole-court lake area is continuously reduced, the construction land area is continuously increased, and the urban development of the circular hole-court lake area is mainly expanded by occupying cultivated land.
(2) In the aspect of carbon sources, the cultivated area is gradually reduced, and the carbon emission is also reduced. And the carbon emission of the construction land is the most main carbon source in the circular cave lake region, and the fluctuation trend of the carbon emission is consistent with the whole research region. In the aspect of carbon sink, the total carbon sink in the ring cave lake region tends to decrease, but the change amplitude is not large. Carbon sequestration in the woodland plays an important role in this area. Overall, the carbon absorption of the ecological economic ring of the ring-hole lake is far less than the carbon emission. The net carbon emissions in the 2010-2020 research area are still on the decline. This is mainly related to the reduction of energy consumption in the ring-hole lake region.
(3) The land utilization carbon emissions in the county region of the ring-hole lake show a significant spatial variability. The whole is in a gradually enhanced state from west to east, the carbon emission of the land in Yueyang city is maximum, the total carbon emission of other areas in the city is approximately 3 times, and the carbon emission of the Anhua county and Shimen county is negative. In 2010-2020, the fluctuation range of the carbon emission of the land utilization in each county is not large, and the carbon emission tends to be gradually decreased. The carbon emission of the land used in the county and area of the year 2020 is reduced, and the carbon emission of the junshan region, the Taojiang county, the Shishou city and the like tends to be converted into the carbon emission-negative region. The land utilization of the land in the Wuling zone, yueyang building zone and Heshan zone has higher carbon emission and has no obvious change during the research period.
(4) The Clobal Moran's I value showed a decreasing parabolic trend after increasing, indicating that the land utilization of the ring hole lake area is positively correlated overall. In 2013-2020, the results of local autocorrelation of carbon emission space in most county land utilization are shown to be of a non-significant type, the HH aggregate type is fixed to the Yue Yangshi district, and HL aggregate type areas do not appear.
(5) Among the 11 statistical indexes selected, the average human GDP, the urbanization, the fixed asset investment and the first industrial ratio are dominant influencing factors of carbon emission of the land utilization of the ecological economic circle of the ring-hole lake, q values are all above 0.35, and all pass the l significance test (< 0.05). The factors such as the second industry ratio, population, DEM and the like have small influence on the carbon emission utilization of the land in the county of the ring-hole court lake region. The interaction detection result of any two factors shows that the combination of two different influencing factors can increase the strength of carbon emission of land utilization. Both two-factor enhancement and non-linear enhancement occur. Explaining the effect of each influencing factor on the ring-hole lake region is not negligible.
Areas where the increase rate of the carbon emission of land utilization is large are mainly concentrated in the economic and fast-developing countries. The construction land is a main carbon source for land utilization, carbon emission of the construction land shows an ascending trend, and forest lands and water bodies have a large contribution to total carbon sink of a research area. The net carbon emission of land utilization in yellow river delta areas, zhujiang delta areas, mei river basins and the like is in an ascending trend. Factors such as GDP and city level have a certain influence on the carbon emission of land utilization. The decreasing trend of the carbon reserves in the ecological economic area of the cave lake is mainly caused by the reduction of cultivated land, woodland and the increase of construction land. The research of the application shows that the net carbon emission of the ecological economic circle of the ring-hole lake is in a descending trend in fluctuation, and the average GDP, the average occupied area and the town are dominant factors of the carbon emission of the land utilization.
Based on NPP-VIIRS night lamplight data, the application discloses the characteristics of land utilization and spatial evolution of carbon emission of the ecological economic circle of the Dongting lake in 2010-2020 by adopting a exploratory spatial analysis method from the county scale. Finally, a geographic detector is used for discussing carbon emission driving factors of the region, scientific basis is provided for ecological protection and development of the ecological economic region of the ring-hole lake, and the accuracy of measuring and calculating the carbon emission of the land utilization of the research region is further improved.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of protection of the application is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the application, the steps may be implemented in any order and there are many other variations of the different aspects of one or more embodiments of the application as above, which are not provided in detail for the sake of brevity.
One or more embodiments of the present application are intended to embrace all such alternatives, modifications and variations as fall within the broad scope of the present application. Accordingly, any omissions, modifications, equivalents, improvements and others which are within the spirit and principles of the one or more embodiments of the application are intended to be included within the scope of the application.

Claims (10)

1. The method for calculating the carbon emission of the land utilization in the night light data auxiliary area is characterized by comprising the following steps of:
selecting a research area containing at least one city, and acquiring night light data, energy consumption data, land utilization data and auxiliary research data of the research area;
Estimating land use carbon emissions in the research area based on land use data and the energy consumption data;
and extracting the construction land in the research area according to the night light data and the land utilization data, and calculating the total energy consumption carbon emission and the construction land carbon emission of the research area according to the night light data, the energy consumption data and the auxiliary research data.
2. The night light data-aided area land utilization carbon emission calculation method of claim 1, wherein said extracting a construction land within said research area from said night light data and said land utilization data specifically comprises:
counting the pixel brightness values of the remote sensing image in the night lamplight data and accumulating to obtain the accumulated area value of the pixel brightness of the remote sensing image;
comparing the accumulated area value of the pixel brightness of the remote sensing image with the construction land area in each provincial and urban statistical annual survey in the research area according to the land utilization data, and taking the pixel brightness value of the remote sensing image with the closest area as an optimal threshold value;
and taking the optimal threshold value as a critical point, judging the part of the research area, in which the image gray value is larger than the optimal threshold value, as a construction land range of the city, and extracting the construction land.
3. The night light data auxiliary area land utilization carbon emission calculating method of claim 1, wherein the calculating of the total energy consumption carbon emission of the research area based on the night light data, the energy consumption data and the auxiliary research data specifically comprises:
and determining carbon emission coefficients of various energy sources, and estimating an approximate value of the total energy consumption carbon emission by combining the energy consumption data.
4. The night light data-aided area land utilization carbon emission calculation method of claim 1, wherein calculating the research area construction land carbon emission from the night light data and the aided research data specifically comprises:
dividing the research area according to the auxiliary research data and the district boundaries of the cities, and selecting night light data corresponding to different cities in the same year;
dividing each city into a plurality of county areas and urban areas to obtain night light data of each county area and urban area;
and measuring and calculating the energy consumption carbon emission of each county and urban district, wherein the energy consumption carbon emission comprises the carbon emission of the construction land of the research area.
5. The method for calculating the carbon emission of the night light data auxiliary area land according to claim 4, wherein the energy consumption carbon emission is a ratio of a total carbon emission amount of a city to a luminance value of a remote sensing image pixel of one of county areas multiplied by a total luminance value of the night light data of the county area.
6. The night light data auxiliary area land utilization carbon emission calculating method as defined in claim 1, wherein the acquiring auxiliary research data of the research area comprises:
extracting space vector data of different scales of each city by using a space data processing tool;
the set of space vector data forms auxiliary study data.
7. The night light data-aided region land utilization carbon emission calculation method of claim 1, wherein after said extracting a construction land in said research region from said night light data and said land utilization data, calculating a total energy consumption carbon emission and a construction land carbon emission of said research region from said night light data and said aided research data, further comprises:
judging the spatial attribute and the significance of the carbon emission of the land in the research area through exploratory spatial data analysis;
the driving factors affecting the utilization of carbon emissions in the investigation region are identified and analyzed by the geographic detector.
8. The night light data aided area land utilization carbon emission calculating method of claim 7, wherein said distinguishing the spatial attribute and the significance of the land utilization carbon emission in the research area by exploratory spatial data analysis specifically comprises:
Calculating a global space autocorrelation value and a local space autocorrelation value of the carbon emission of the land utilization between each county and the urban district according to the carbon emission of the land utilization of each county and the urban district;
revealing the spatial attribute and the significance of the carbon emission of the land utilization according to the global spatial autocorrelation value;
and carrying out clustering inspection on the local space autocorrelation values to obtain local similarity and difference degree between the carbon emission of each county and district in the research area and the carbon emission of the urban district.
9. The night light data aided area land utilization carbon emission calculation method of claim 7, wherein said identifying and analyzing by a geographic detector driving factors affecting the research area utilization carbon emission specifically comprises:
the geographic detector comprises a factor detector and an interaction detector, wherein the factor detector is used for detecting the spatial difference of the variable, and meanwhile, the interpretation strength of the factor on the spatial difference of the variable is explored;
and detecting interaction between the two influencing factors by the interaction detector, and judging whether the interpretation power of the interaction on the variable changes or not.
10. A night light data auxiliary area land utilization carbon emission calculating device, comprising:
And a data statistics module: selecting a research area, and acquiring night light data, energy consumption data, land utilization data and auxiliary research data of the research area;
the calculation module: estimating land utilization carbon emission in the research area according to land utilization data and energy consumption data, extracting construction land in the research area according to the night light data, and calculating total energy consumption carbon emission and construction land carbon emission in the research area according to the night light data, the energy consumption data and the auxiliary research data;
exploratory spatial analysis module: judging the spatial attribute and the significance of the carbon emission of the land in the research area through exploratory spatial data;
geographic detector module: the system comprises a factor detector and an interaction detector, wherein the factor detector is used for detecting the spatial difference of the variable and exploring the interpretation strength of the factor on the spatial difference of the variable;
the interaction detector is used for detecting interaction between two influencing factors and judging whether the interaction changes the interpretation power of the variable.
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