Decoupling analysis method for land utilization carbon emission and ecological environment quality

Abstract

The invention discloses a decoupling analysis method for land utilization carbon emission and ecological environment quality, and relates to the technical field of carbon emission, and the method comprises the following steps: employing a land utilization carbon emission analysis model to estimate the total amount of land utilization carbon emission based on a land coverage data set and an ODIAC data set; based on the MODIS remote sensing data set, an ecological environment quality evaluation model is adopted to quantify the ecological environment quality, and an ecological environment quality index is obtained; performing decoupling analysis on the total land utilization carbon emission and the ecological environment quality index to obtain a decoupling feature type; and decoupling optimization strategies of different regions are designed according to the decoupling feature types. According to the method, land utilization carbon emission estimation, ecological environment quality evaluation and decoupling analysis technologies are combined, so that an effective technical means is provided for evaluating the space-time decoupling relationship between land utilization carbon emission and ecological environment quality, and a scientific basis is provided for formulating a regional sustainable development strategy.

Description

Technical Field

[0001] The present application relates to the field of carbon emission technology, and in particular to a decoupling analysis method for land use carbon emissions and ecological environment quality. Background Art

[0002] In the context of rapid urbanization and economic development, substantial changes in land use have become a key factor affecting the global carbon cycle and ecological environment quality. In the face of the global challenge of climate change, it is necessary to analyze the spatiotemporal decoupling characteristics between land use carbon emissions and ecological environment quality.

[0003] In the prior art, Chinese patent CN116503222A discloses a carbon emission remote sensing detection method based on the study of urbanization coupling relationships. It uses many years of high-resolution multi-source satellite remote sensing data to analyze the changes in the structure, quantity and category of historical land use data to identify the dynamic characteristics of the spatiotemporal pattern of carbon emissions in the long-term urbanization process; uses cellular automata as tools, combines multi-agents and GIS to build an expansion model of urban construction land, simulates and predicts the expansion law of urban construction land; and calculates the carbon emissions caused by land use changes in the expansion of construction land.

[0004] However, the above-mentioned existing technologies only consider the carbon emissions caused by land use changes during the expansion of construction land, lack of effective means to evaluate the spatiotemporal decoupling relationship between land use carbon emissions and ecological environmental quality, and thus lack of scientific basis for formulating regional sustainable development strategies. Summary of the invention

[0005] This application provides a decoupling analysis method for land use carbon emissions and ecological environment quality, which is used to solve the problem that the existing technology lacks effective means to evaluate the spatiotemporal decoupling relationship between land use carbon emissions and ecological environment quality, and thus lacks a scientific basis for formulating regional sustainable development strategies.

[0006] On the one hand, the present application provides a decoupling analysis method for land use carbon emissions and ecological environment quality, comprising the following steps:

[0007] Step 1: Obtain land cover dataset, ODIAC dataset and MODIS remote sensing dataset.

[0008] Step 2: preprocessing and temporal and spatial scale division of the land cover dataset, the ODIAC dataset and the MODIS remote sensing dataset.

[0009] Step three: Based on the land cover dataset and the ODIAC dataset, a land use carbon emission analysis model is used to estimate the total land use carbon emission.

[0010] Step 4: Based on the MODIS remote sensing data set, an ecological environment quality assessment model is used to quantify the ecological environment quality to obtain an ecological environment quality index.

[0011] Step five, using a decoupling analysis model to perform a decoupling analysis on the total land use carbon emissions and the ecological environment quality index to obtain a decoupling feature type.

[0012] Step six, designing decoupling optimization strategies for different regions according to the decoupling feature types.

[0013] In a possible implementation, in step 2, the preprocessing includes: mask processing, projection conversion processing, and cloud removal processing.

[0014] The time and space scale division adopts a predetermined time and space scale.

[0015] In a possible implementation, the land cover data set includes: forest data, grassland data, shrub data, wetland data, water area data, and bare land data.

[0016] In a possible implementation, in step three, the land use carbon emission analysis model includes a direct carbon emission estimation model and an indirect carbon emission estimation model.

[0017] The direct carbon emission estimation model is used to estimate direct carbon emissions according to the land cover dataset to obtain the total direct carbon emissions.

[0018] The indirect carbon emissions estimation model is used to estimate indirect carbon emissions according to the ODIAC data set to obtain the total indirect carbon emissions.

[0019] The sum of the total direct carbon emissions and the total indirect carbon emissions is the total land use carbon emissions.

[0020] In a possible implementation, the direct carbon emission estimation model is shown as follows:

[0021] E k =Σe i =ΣT i δ i .

[0022] Among them, E k represents the total direct carbon emissions, e i represents the direct carbon emissions corresponding to the i-th land use type, T i represents the area corresponding to the i-th land use type, δ i Represents the carbon emission coefficient corresponding to the i-th land use type.

[0023] The indirect carbon emission estimation model is as follows:

[0024] E=ΣE i .

[0025] Where E represents the total amount of indirect carbon emissions, E i Represents the indirect carbon emissions in the i-th month.

[0026] In a possible implementation, in step 4, the ecological environment quality assessment model adopts a remote sensing ecological index model.

[0027] The remote sensing ecological index model is used to evaluate the greenness index, humidity index, heat index and dryness index according to the MODIS remote sensing data set, and the greenness index, humidity index, heat index and dryness index are normalized and principal component analyzed to obtain the ecological environment quality index.

[0028] In a possible implementation, the greenness index is calculated as shown in the following formula:

[0029]

[0030] Among them, NDVI represents the greenness index, ρ NIR , Red They respectively represent the reflectance of the near-infrared band and the red band of the image in the MODIS remote sensing dataset.

[0031] The calculation of the humidity index is shown in the following formula:

[0032] WET=0.1147ρ Red +0.2489ρ NIR1 +0.2408ρ Blue +0.3132ρ Green

[0033] -0.3122ρ NIR2 -0.6416ρ SWIR1 -0.5087ρ SWIR2 .

[0034] Among them, WET represents the humidity index, ρ NIR1 , NIR2 , Blue , Green , SWIR1 , SWIR2 They respectively represent the reflectance of the near infrared 1 band, near infrared 2 band, blue band, green band, shortwave infrared 1 band, and shortwave infrared 2 band of the images in the MODIS remote sensing dataset.

[0035] The calculation of the heat index is shown in the following formula:

[0036] LST=0.02×DN S -273.15.

[0038] Among them, LST represents the heat index, DN S Represents the grayscale value of the image in the MODIS remote sensing dataset.

[0039] The calculation of the dryness index is shown in the following formula:

[0040] NDBSI=(SI+IBI) / 2.

[0041] Among them, NDBSI stands for dryness index, SI stands for soil index, and IBI stands for comprehensive index.

[0042] The calculation of comprehensive index and soil index is shown in the following two formulas:

[0043]

[0044] In a possible implementation, in step five, the decoupling analysis model adopts the Tapio decoupling model.

[0045] The Tapio decoupling model is shown as follows:

[0046]

[0047] Among them, ε represents the decoupling feature type, ΔCE represents the change in total carbon emissions from land use, and CE i represents the total carbon emissions from land use in the i-th period, CE0 represents the total carbon emissions from land use in the initial period, ΔEI represents the change in the ecological environment quality index, and EI i It represents the ecological environment quality index of the ith period, and EI0 represents the ecological environment quality index of the initial period.

[0048] A decoupling analysis method of land use carbon emissions and ecological environment quality in this application has the following advantages:

[0049] By combining land use carbon emission estimation, ecological environment quality assessment and decoupling analysis technology, the total land use carbon emissions and the ecological environment quality index were decoupled and analyzed to obtain the decoupling characteristic type, filling the gap in the study of the spatiotemporal decoupling mechanism between land use carbon emissions and ecological environment quality, providing an effective technical means for evaluating the spatiotemporal decoupling relationship between land use carbon emissions and ecological environment quality, and providing a scientific basis for the formulation of regional sustainable development strategies.

[0050] Through the division of time and space scales, the decoupling relationship between land use carbon emissions and ecological environmental quality at fine scales (such as counties, districts or finer scales) can be analyzed to improve the accuracy of decoupling analysis.

[0051] By using the direct carbon emission estimation model to estimate direct carbon emissions and the indirect carbon emission estimation model to estimate indirect carbon emissions, the accuracy of the estimation of total land use carbon emissions is improved.

[0052] The proposed ecological environment quality assessment model adopts the remote sensing ecological index model, which improves the reliability of the ecological environment quality index.

[0053] The proposed decoupling analysis model adopts the Tapio decoupling model to improve the meticulousness and stability of the decoupling analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flow chart of a method for decoupling analysis of land use carbon emissions and ecological environment quality provided in an embodiment of the present application;

[0056] Figure 2 The decoupling pattern of land use carbon emissions and ecological environment quality in counties of Shaanxi Province in 2005 provided in the embodiments of this application;

[0057] Figure 3 The decoupling pattern of land use carbon emissions and ecological environment quality in counties of Shaanxi Province in 2010 provided in the embodiments of this application;

[0058] Figure 4 The decoupling pattern of land use carbon emissions and ecological environment quality in counties of Shaanxi Province in 2015 provided in the embodiments of this application;

[0059] Figure 5 The decoupling pattern of land use carbon emissions and ecological environment quality in counties of Shaanxi Province in 2020 provided in the embodiment of this application. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] like Figure 1As shown, the embodiment of the present application provides a decoupling analysis method for land use carbon emissions and ecological environment quality, comprising the following steps:

[0062] Step 1: Obtain land cover dataset, ODIAC dataset and MODIS remote sensing dataset.

[0063] Step 2: preprocessing and temporal and spatial scale division of the land cover dataset, the ODIAC dataset and the MODIS remote sensing dataset.

[0064] Step three: Based on the land cover dataset and the ODIAC dataset, a land use carbon emission analysis model is used to estimate the total land use carbon emission.

[0065] Step 4: Based on the MODIS remote sensing data set, an ecological environment quality assessment model is used to quantify the ecological environment quality to obtain an ecological environment quality index.

[0066] Step five, using a decoupling analysis model to perform a decoupling analysis on the total land use carbon emissions and the ecological environment quality index to obtain a decoupling feature type.

[0067] Step six, designing decoupling optimization strategies for different regions according to the decoupling feature types.

[0068] Specifically, in this embodiment, the land cover dataset adopts the China Land Cover Dataset (CLCD), the ODIAC dataset adopts the ODIAC carbon dioxide emission dataset of the PIE-Engine platform, and the MODIS remote sensing dataset adopts the MOD09A1, MOD11A2, and MOD13A1 datasets of the Google Earth Engine (GEE) platform.

[0069] Exemplarily, in step 2, the preprocessing includes: mask processing, projection conversion processing, and cloud removal processing.

[0070] The time and space scale division adopts a predetermined time and space scale.

[0071] Specifically, in this embodiment, the predetermined spatiotemporal scale adopts the county scale from 2000 to 2020 to analyze the decoupling relationship between land use carbon emissions and ecological environment quality at the county scale.

[0072] This example takes the decoupling relationship between land use carbon emissions and ecological environment quality at the county level in Shaanxi Province as a research case.

[0073] Exemplarily, the land cover data set includes: forest data, grassland data, shrub data, wetland data, water area data, and bare land data.

[0074] Specifically, in this embodiment, forest data, grassland data, shrub data, wetland data, water data, and bare land data are used to estimate the total direct carbon emissions.

[0075] Exemplarily, in step three, the land use carbon emission analysis model includes a direct carbon emission estimation model and an indirect carbon emission estimation model.

[0076] The direct carbon emission estimation model is used to estimate direct carbon emissions according to the land cover dataset to obtain the total direct carbon emissions.

[0077] The indirect carbon emissions estimation model is used to estimate indirect carbon emissions according to the ODIAC data set to obtain the total indirect carbon emissions.

[0078] The sum of the total direct carbon emissions and the total indirect carbon emissions is the total land use carbon emissions.

[0079] Exemplarily, the direct carbon emission estimation model is shown as follows:

[0080] E k =Σe i =ΣT i δ i .

[0081] Among them, E k represents the total direct carbon emissions, e i represents the direct carbon emissions corresponding to the i-th land use type, T i represents the area corresponding to the i-th land use type, δ i Represents the carbon emission coefficient corresponding to the i-th land use type.

[0082] The indirect carbon emission estimation model is as follows:

[0083] E=ΣE i .

[0084] Where E represents the total amount of indirect carbon emissions, E i Represents the indirect carbon emissions in the i-th month.

[0085] Specifically, in this embodiment, the carbon emission coefficients of forest data, grassland data, shrub data, wetland data, water data, and bare land data are respectively: -0.0644 kg·m -2 ·a -1 、-0.0022kg·m -2 ·a -1 、-0.0230kg·m -2 ·a -1 、-0.000006132kg·m -2 ·a-1 、-0.0253kg·m -2 ·a -1 、-0.0005kg·m -2 ·a -1 , where the unit is kilograms per square meter per year.

[0086] The indirect carbon emission estimation model is as follows:

[0087] E=ΣE i .

[0088] Where E represents the total amount of indirect carbon emissions, E i Represents the indirect carbon emissions in the i-th month.

[0089] Specifically, in this embodiment, due to the lack of long-term county-level energy consumption statistics in Shaanxi Province, the corresponding carbon emission data in the ODIAC carbon dioxide emission data set is obtained as the indirect carbon emissions of the cultivated land data and the building data.

[0090] Exemplarily, in step four, the ecological environment quality assessment model adopts a remote sensing ecological index model.

[0091] The remote sensing ecological index model is used to evaluate the greenness index, humidity index, heat index and dryness index according to the MODIS remote sensing data set, and the greenness index, humidity index, heat index and dryness index are normalized and principal component analyzed to obtain the ecological environment quality index.

[0092] Exemplarily, the greenness index is calculated as shown below:

[0093]

[0094] Among them, NDVI represents the greenness index, ρ NIR , Red They respectively represent the reflectance of the near-infrared band and the red band of the image in the MODIS remote sensing dataset.

[0095] The calculation of the humidity index is shown in the following formula:

[0096] WET=0.1147ρ Red +0.2489ρ NIR1 +0.2408ρ Blue +0.3132ρ Green

[0097] -0.3122ρ NIR2 -0.6416ρ SWIR1 -0.5087ρ SWIR2 .

[0098] Among them, WET represents the humidity index, ρ NIR1 , NIR2 , Blue , Green , SWIR1 , SWIR2 They respectively represent the reflectance of the near infrared 1 band, near infrared 2 band, blue band, green band, shortwave infrared 1 band, and shortwave infrared 2 band of the images in the MODIS remote sensing dataset.

[0099] The calculation of the heat index is shown in the following formula:

[0100] LST=0.02×DN S -273.15.

[0102] Among them, LST represents the heat index, DN S Represents the grayscale value of the image in the MODIS remote sensing dataset.

[0103] The calculation of the dryness index is shown in the following formula:

[0104] NDBSI=(SI+IBI) / 2.

[0105] Among them, NDBSI stands for dryness index, SI stands for soil index, and IBI stands for comprehensive index.

[0106] The calculation of comprehensive index and soil index is shown in the following two formulas:

[0107]

[0108] Specifically, in this embodiment, after calculating the heat index, the raster resampling tool is used to resample its resolution to 500 meters to ensure the consistency of the resolution of the green index, humidity index, heat index and dryness index in the remote sensing ecological index model. Due to the different dimensions of different indicators, this embodiment uses the range normalization method to normalize the green index, humidity index, heat index and dryness index, and performs principal component analysis on the four normalized indicators to obtain the ecological environment quality index. The higher the ecological environment quality index, the better the ecological environment quality.

[0109] In a possible embodiment, the ecological environment quality index data within the administrative boundary of Shaanxi Province obtained in this application is subjected to correlation analysis with the high-resolution ecological environment quality data set of China, and the correlation coefficient and root mean square error are calculated, wherein the correlation coefficient R 2 The value of the remote sensing ecological index model is 0.89, and the root mean square error (RMSE) is 0.07, which shows that the remote sensing ecological index model has high reliability and accuracy as the ecological environment quality assessment model.

[0110] Exemplarily, in step five, the decoupling analysis model adopts the Tapio decoupling model.

[0111] The Tapio decoupling model is shown as follows:

[0112]

[0113] Among them, ε represents the decoupling feature type, ΔCE represents the change in total carbon emissions from land use, and CE i represents the total carbon emissions from land use in the i-th period, CE0 represents the total carbon emissions from land use in the initial period, ΔEI represents the change in the ecological environment quality index, and EI i It represents the ecological environment quality index of the ith period, and EI0 represents the ecological environment quality index of the initial period.

[0114] Specifically, in this embodiment, the decoupling feature types are divided into 8 types, as shown in Table 1:

[0115] Table 1 Decoupling feature types

[0116]

[0117] In this embodiment, 5 years are set as the periodic interval, and the baseline years are 2000, 2005, 2010, and 2015, respectively. The decoupling characteristic types at the county level in Shaanxi Province in 2005, 2010, 2015, and 2020 are statistically analyzed. From 2005 to 2020, the decoupling characteristic types of the total land use carbon emissions and the ecological environment quality index in counties of Shaanxi Province are mainly concentrated in strong negative decoupling (SND), expansive negative decoupling (END), expansive coupling (EC), weak decoupling (WD), and recessive decoupling (RD). Among them, the main decoupling characteristic types in 2005, 2010, 2015, and 2020 are expansive negative decoupling (END), expansive negative decoupling (END), expansive negative decoupling (END), and weak decoupling (WD), respectively. There are significant temporal changes in the decoupling types, with changes occurring in more than 95% of the counties. The overall trend is deterioration from 2005 to 2010, improvement from 2010 to 2015, and continued deterioration from 2015 to 2020.

[0118] Specifically, Figures 2 to 5The figure shows the decoupling pattern of land use carbon emissions and ecological environment quality in counties of Shaanxi Province from 2005 to 2020. In 2005, the decoupling trend of land use carbon emissions and RSEI in counties of Shaanxi Province was relatively the best, but the overall situation was still not ideal, and there was no obvious spatial distribution pattern. Although the number of counties showing decoupling was the largest in 2005 among all years, it accounted for only 28.04%. Most counties were in a negative decoupling state, with the expansionary negative decoupling (END) type being the main type, accounting for 50.47%. In 2010, the decoupling trend deteriorated to the worst, with 95 counties showing negative decoupling, accounting for nearly 90%, almost covering the entire study area. Most of the counties that showed decoupling in 2005 (83.33%) turned to negative decoupling. There were only 11 decoupled counties left, mainly distributed in the northeast of northern Shaanxi and the northern edge of southern Shaanxi. In 2015, the decoupling trend between southern Shaanxi and Guanzhong improved, while that in northern Shaanxi deteriorated. The number of counties with strong negative decoupling (SND) type decreased significantly, from 45 to 17. The number of counties with weak decoupling (WD) type increased significantly to 24, mainly transformed from strong negative decoupling (SND) and expansionary negative decoupling (END) types. The number of counties with expansionary coupling (EC) type also increased significantly, all of which were transformed from negative decoupling counties (END-EC: 12, WND-EC: 1, SND-EC: 6). In 2020, the overall decoupling trend deteriorated again, concentrated in southern Shaanxi and Guanzhong, while northern Shaanxi improved over a large area. The number of counties with strong negative decoupling (SND) type reached 52, mainly distributed in southern Shaanxi and central and southern Guanzhong. The number of decoupled counties is the same as in 2015, both 24, but the distribution is more concentrated in northern Shaanxi and northern Guanzhong. Overall, the decoupling trend in Shaanxi Province is fluctuating and deteriorating, and the overall outlook is not optimistic.

[0119] Specifically, in step six, the specific process of designing the decoupling optimization strategy for different regions according to the decoupling feature type is as follows:

[0120] In this embodiment, strong negative decoupling (SND) is the most unfavorable decoupling characteristic type in Shaanxi Province, accounting for nearly half of the counties, mainly distributed in southern Shaanxi, central and southern Guanzhong. In the strong negative decoupling (SND) type counties, the decline in the ecological environment quality index is related to the increase in the total carbon emissions from land use, indicating that the rapid development of the social economy has led to environmental degradation. However, the advantage of these counties is that they have high vegetation coverage and have great potential to improve the decoupling situation. Therefore, strategies such as restricting the development of high-emission industries, strengthening ecosystem management, strictly observing the ecological protection red line, and developing carbon sink industries can be adopted to improve the decoupling status of these counties.

[0121] In this example, weak decoupling (WD) is the prevailing decoupling characteristic type in Shaanxi Province in 2020, second only to strong decoupling (SD) in terms of effectiveness, and is concentratedly distributed, mainly in the northern part of Guanzhong and the central and northern Shaanxi regions. Although the total land use carbon emissions and the ecological environment quality index in weakly decoupled (WD) counties both showed positive growth rates, the growth rate of the ecological environment quality index was faster, reflecting a good development status, indicating that the region attaches great importance to environmental protection in socio-economic growth. In order to promote the transition from weakly decoupled (WD) counties to strongly decoupled (SD) counties, necessary measures should be taken to maintain the improvement of ecological environment quality while reducing land use carbon emissions. This requires the implementation of strategies such as controlling urban expansion, establishing ecological corridors, developing renewable resources, and planning green infrastructure.

[0122] In this example, the number of counties with expansive negative decoupling (END) is the same as that of counties with weak decoupling (WD), and they are dispersed. Expansive negative decoupling (END) is considered to be a less favorable decoupling situation, and the ecological environment quality index is positive, but the total land use carbon emissions are growing faster. This shows that the efforts of these counties to protect the ecological environment during the socio-economic development process have failed to offset the increase in carbon emissions. The main challenge of transitioning counties with expansive negative decoupling (END) to counties with weak decoupling (WD) or strong decoupling (SD) is to mitigate or control the incremental land use carbon emissions driven by social and economic progress. To address this challenge, strategies such as adjusting industrial structure, promoting energy construction and emission reduction technologies, optimizing the energy supply framework, and raising public awareness through educational campaigns can be implemented.

[0123] In this example, only Ningqiang County in southern Shaanxi belongs to the recessive decoupling (RD) type county, and its ecological environment quality has been deteriorating, although at a slow rate. Ningqiang County is located in the Qinling Mountains, with a mild and humid climate, abundant rainfall, and rich forest resources. The protection and strengthening of the ecological environment is the key to optimizing the decoupling situation in the county. Measures such as protecting core ecological areas, restoring ecosystems, implementing ecological compensation mechanisms, and developing ecotourism may be effective.

[0124] In this example, there are 8 EC-type counties, 4 of which are located in the middle of Yan'an, and the remaining 4 are distributed in the Guanzhong region. Although the ecological environment quality of these EC-type counties has improved, land use carbon emissions have increased at the same rate. For the transformation of EC-type counties to SD-type counties, the improvement of the socio-economic development model is the most important. Therefore, adopting a circular economy model, establishing a carbon emission incentive and penalty system, optimizing the land management system, and promoting an ecological agriculture strategy can play a key role.

[0125] The embodiments of the present application combine land use carbon emission estimation, ecological environment quality assessment and decoupling analysis technology to perform decoupling analysis on the total land use carbon emissions and the ecological environment quality index, and obtain decoupling feature types, filling the gap in the research on the spatiotemporal decoupling mechanism between land use carbon emissions and ecological environment quality, providing an effective technical means for evaluating the spatiotemporal decoupling relationship between land use carbon emissions and ecological environment quality, and providing a scientific basis for the formulation of regional sustainable development strategies.

[0126] Through the division of time and space scales, the decoupling relationship between land use carbon emissions and ecological environmental quality at fine scales (such as counties, districts or finer scales) can be analyzed to improve the accuracy of decoupling analysis.

[0127] By using the direct carbon emission estimation model to estimate direct carbon emissions and the indirect carbon emission estimation model to estimate indirect carbon emissions, the accuracy of the estimation of total land use carbon emissions is improved.

[0128] The proposed ecological environment quality assessment model adopts the remote sensing ecological index model, which improves the reliability of the ecological environment quality index.

[0129] The proposed decoupling analysis model adopts the Tapio decoupling model to improve the meticulousness and stability of the decoupling analysis.

[0130] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A decoupling analysis method for land use carbon emissions and ecological environment quality, characterized in that: The following steps are involved: Step 1: Obtain land cover dataset, ODIAC dataset and MODIS remote sensing dataset; Step 2: preprocessing and temporal and spatial scale division of the land cover dataset, the ODIAC dataset and the MODIS remote sensing dataset; Step 3, based on the land cover dataset and the ODIAC dataset, using a land use carbon emission analysis model to estimate the total land use carbon emission; Step 4: Based on the MODIS remote sensing data set, an ecological environment quality assessment model is used to quantify the ecological environment quality to obtain an ecological environment quality index; Step 5, using a decoupling analysis model to perform decoupling analysis on the total land use carbon emissions and the ecological environment quality index to obtain a decoupling feature type; Step six, designing decoupling optimization strategies for different regions according to the decoupling feature types.

2. The decoupling analysis method of land use carbon emissions and ecological environment quality according to claim 1 is characterized in that: In step 2, the preprocessing includes: mask processing, projection conversion processing, and cloud removal processing; The time and space scale division adopts a predetermined time and space scale.

3. The decoupling analysis method of land use carbon emissions and ecological environment quality according to claim 1 is characterized in that: The land cover data set includes: forest data, grassland data, shrub data, wetland data, water area data, and bare land data.

4. The decoupling analysis method of land use carbon emissions and ecological environment quality according to claim 1 is characterized in that: In step 3, the land use carbon emission analysis model includes a direct carbon emission estimation model and an indirect carbon emission estimation model; Using the direct carbon emission estimation model to estimate direct carbon emissions based on the land cover dataset, to obtain the total direct carbon emissions; Using the indirect carbon emission estimation model to estimate indirect carbon emissions according to the ODIAC data set, to obtain the total amount of indirect carbon emissions; The sum of the total direct carbon emissions and the total indirect carbon emissions is the total land use carbon emissions.

5. The decoupling analysis method of land use carbon emissions and ecological environment quality according to claim 4 is characterized in that: The direct carbon emission estimation model is as follows: E k =In i =ΣT i d i , Among them, E k represents the total direct carbon emissions, e i represents the direct carbon emissions corresponding to the i-th land use type, T i represents the area corresponding to the i-th land use type, δ i represents the carbon emission coefficient corresponding to the i-th land use type; The indirect carbon emission estimation model is as follows: E=ΣE i , Where E represents the total amount of indirect carbon emissions, E i Represents the indirect carbon emissions in the i-th month.

6. The decoupling analysis method of land use carbon emissions and ecological environment quality according to claim 1 is characterized in that: In step 4, the ecological environment quality assessment model adopts a remote sensing ecological index model; The remote sensing ecological index model is used to evaluate the greenness index, humidity index, heat index and dryness index according to the MODIS remote sensing data set, and the greenness index, humidity index, heat index and dryness index are normalized and principal component analyzed to obtain the ecological environment quality index.

7. The decoupling analysis method of land use carbon emissions and ecological environment quality according to claim 6 is characterized in that: The calculation of the greenness index is shown in the following formula: Among them, NDVI represents the greenness index, ρ NIR , Red They represent the reflectance of the near-infrared band and the red band of the images in the MODIS remote sensing dataset respectively; The calculation of the humidity index is shown in the following formula: Among them, WET represents the humidity index, ρ NIR1 , NIR2 , Blue , Green , SWIR1 , SWIR2 They represent the reflectance of near infrared 1 band, near infrared 2 band, blue band, green band, short wave infrared 1 band, and short wave infrared 2 band of the images in the MODIS remote sensing dataset respectively; The calculation of the heat index is shown in the following formula: LST=0.02×DN S -273.15, Among them, LST represents the heat index, DN S Represents the grayscale value of the image in the MODIS remote sensing dataset; The calculation of the dryness index is shown in the following formula: NDBSI=(SI+IBI) / 2, Among them, NDBSI stands for dryness index, SI stands for soil index, and IBI stands for comprehensive index; The calculation of comprehensive index and soil index is shown in the following two formulas:

8. The decoupling analysis method of land use carbon emissions and ecological environment quality according to claim 1 is characterized in that: In step 5, the decoupling analysis model adopts the Tapio decoupling model; The Tapio decoupling model is shown as follows: Among them, ε represents the decoupling feature type, ΔCE represents the change in total carbon emissions from land use, and CE i represents the total carbon emissions from land use in the i-th period, CE0 represents the total carbon emissions from land use in the initial period, ΔEI represents the change in the ecological environment quality index, and EI i It represents the ecological environment quality index of the ith period, and EI0 represents the ecological environment quality index of the initial period.

Images