CN116451977B

CN116451977B - Method and device for planning, designing and plant construction of green land carbon sink function improvement

Info

Publication number: CN116451977B
Application number: CN202310700460.4A
Authority: CN
Inventors: 郑曦; 吕英烁
Original assignee: Beijing Forestry University
Current assignee: Beijing Forestry University
Priority date: 2023-06-14
Filing date: 2023-06-14
Publication date: 2023-09-19
Anticipated expiration: 2043-06-14
Also published as: CN116451977A

Abstract

The application relates to the technical field of carbon emission, in particular to a method and a device for planning, designing and plant construction for improving the carbon sink function of green land, wherein the method comprises the following steps: acquiring a green land analysis result of a region to be planned; according to the macro features, the mesoscopic features and the micro features of the area to be planned, respectively establishing a macro influence factor set, a mesoscopic influence factor set and a micro influence factor set; according to green land analysis results, macroscopic influence factor sets, mesoscopic influence factor sets and microscopic influence factor sets, respectively determining key dominant factors and threshold values of the key dominant factors, which influence the improvement of the carbon sink function of the green land under each scale; and respectively carrying out macro city green space system planning, mesoscopic green space plaque design and microscopic plant community construction on the area to be planned according to the key dominant factors, the threshold values of the key dominant factors and the green space analysis results to obtain green space results of all scales. The scheme can provide scientific planning, design and plant community construction guidance for improving carbon sink efficiency in urban green space in multiple scales.

Description

Method and device for planning, designing and plant construction of green land carbon sink function improvement

Technical Field

The embodiment of the application relates to the technical field of carbon emission, in particular to a method and a device for planning, designing and plant construction for improving functions of green land carbon sinks.

Background

In the rapid urban process, urban space and population density are rapidly increased, economic activity intensity is enhanced, land utilization and coverage are greatly changed, and energy consumption causes the increase of artificial emission greenhouse gases mainly containing carbon dioxide, so that climate warming is further aggravated. The urban green land is used as an important near-natural ecological space in the urban area, can fix carbon and release oxygen, effectively relieves the heat island effect, and plays an important role in the process of realizing double carbon.

In recent years, urban green lands have been fully studied on different scales as an effective strategy for carbon sink carriers and carbon emission cancellation. Most researches focus on the macro scale of urban green space patterns, quantize the regional green space layout structure by a landscape pattern index method, and optimize due to important indexes based on a logistic regression model; on a microscopic scale, plant community structures such as stand composition, stand structure, dominant tree species, stand density, site conditions and the like are taken as variables, and the relationship between the plant community structures and dependent variable carbon sink quantity is analyzed through regression.

However, green land plaque scale is less when the mesoscale of the garden green land is involved, and the urban green land functional scale effect requires that the carbon sink optimization scheme should have a systematic and comprehensive view angle, and the complex nonlinear relationship between the green land carbon sink benefit dependent variable and the microscopic scale influence factor independent variable in the macro is concerned, so that the mutual segmentation among different scale optimizations is avoided, and the interaction, multiple causality and hysteresis effect of the nonlinear response relationship are overcome.

Therefore, how to plan, design and construct the green land of the city at the full scale, especially at the mesoscale is a technical problem to be solved currently.

Content of the application

In view of the above, embodiments of the present application provide a method and apparatus for planning, designing and plant construction for improving the carbon sink function of green land, so as to at least partially solve the above-mentioned problems.

According to a first aspect of an embodiment of the present application, there is provided a green land carbon sink function improvement planning, designing and plant constructing method, including: obtaining a green land analysis result of a region to be planned, wherein the green land analysis result is used for indicating an average carbon exchange value of a unit area of the green land in the region to be planned and a benefit value of cooling and humidifying the green land in the region to be planned; establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set respectively according to the macroscopic features, mesoscopic features and microscopic features of the area to be planned; according to the green land analysis result, the macroscopic influence factor set, the mesoscopic influence factor set and the microscopic influence factor set, respectively determining key dominant factors influencing the improvement of the green land carbon sink function under the macroscopic scale, the mesoscopic scale and the microscopic scale and threshold values of the key dominant factors; and carrying out macro city green space system planning, mesoscopic green space plaque design and microscopic plant community construction on the area to be planned from a macro scale, a mesoscopic scale and a microscopic scale according to the key dominant factors, the threshold values of the key dominant factors and the green space analysis result, and obtaining a macro scale planning result, a mesoscopic scale design result and a microscopic scale construction result.

According to a second aspect of the embodiments of the present application, there is provided a green land carbon sink function improving planning, designing and plant building apparatus, including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a green land analysis result of a region to be planned, wherein the green land analysis result is used for indicating an average carbon exchange value of a unit area of a green land in the region to be planned and a benefit value of cooling and humidifying the green land in the region to be planned; the construction module is used for respectively establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set according to the macroscopic features, mesoscopic features and microscopic features of the area to be planned; the determining module is used for respectively determining key dominant factors influencing the improvement of the carbon sink function of the green land under the macro scale, the mesoscale and the micro scale and threshold values of the key dominant factors according to the green land analysis result, the macro influence factor set, the mesoscale influence factor set and the micro influence factor set; and the planning module is used for carrying out macro city green space system planning, mesoscopic green space plaque design and microscopic plant community construction on the area to be planned from a macro scale, a mesoscopic scale and a microscopic scale according to the key dominant factors, the threshold values of the key dominant factors and the green space analysis result, so as to obtain a macro scale planning result, a mesoscopic scale design result and a microscopic scale construction result.

By the technical scheme, urban green space planning, green space plaque design and plant community construction are respectively carried out on the area to be planned from a macroscopic scale, a mesoscale and a microscopic scale, so that urban green space can be scientifically planned and constructed under a full scale, and the green space after adjustment according to the green space planning result, the green space plaque design result and the plant community construction result has better carbon sink capacity, so that better cooling and humidifying benefits are achieved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart showing the steps of a method for planning, designing and plant construction for improving the function of a green land carbon sink according to an embodiment of the present application;

FIG. 2 is a schematic illustration of a vegetation net primary productivity assessment according to an embodiment of the application;

FIG. 3 is a schematic diagram of a thermal island spatial distribution and a cold island spatial distribution according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a macro-scale planning result according to an embodiment of the present application;

FIG. 5 is a schematic illustration of mesoscale design results according to an embodiment of the application;

FIG. 6 is a schematic diagram of a micro-scale construction result according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a green space carbon sink function enhancing planning, design and plant building apparatus according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to better understand the technical solutions in the embodiments of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the present application, shall fall within the scope of protection of the embodiments of the present application.

The implementation of the embodiments of the present application will be further described below with reference to the accompanying drawings.

Planning, designing and plant construction method for improving carbon sink function of green land

Fig. 1 is a flowchart illustrating steps of a method for planning, designing and constructing a plant for improving functions of a green space, according to an embodiment of the present application, as shown in fig. 1, the method for planning, designing and constructing a plant for improving functions of a green space includes:

and 101, acquiring a green land analysis result of the area to be planned.

In order to plan green land in a city, firstly, a region to be planned is defined, and the region to be planned can comprise at least one of a new planning and construction region and an updating region of the city, wherein the new planning and construction region has no green land planning basis, the result of the green land of the city is needed to guide the green land to be constructed, the green land in the updating region of the city has the planning basis, and the updating is needed on the original planning basis according to the specific situation of city development. In order to determine the green land current situation of the area to be planned, the direct carbon sequestration capacity (namely, the average carbon sink per unit area of the green land) and the indirect emission reduction capacity (namely, the cooling and humidifying benefit) are used as indexes of the green land carbon sink function of the research area, green land analysis is carried out on the area to be planned, green land analysis results of the area to be planned are obtained, and the green land analysis results are used for indicating the average carbon sink per unit area of the green land in the area to be planned and the cooling value per unit area of the green land in the area to be planned.

Step 102, establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set respectively according to the macroscopic features, mesoscopic features and microscopic features of the area to be planned.

In order to determine ideal green land indexes of green lands in the area to be planned, corresponding influence factor sets are respectively established according to macroscopic features, mesoscopic features and microscopic features in the area to be planned.

For example, the macro features of the area to be planned may be greenbelt patterns of the area to be planned, the mesoscopic features of the area to be planned may be greenbelt patches of the area to be planned, and the micro features of the area to be planned may be vegetation communities of the area to be planned.

And step 103, respectively determining key dominant factors and thresholds of the key dominant factors affecting the improvement of the carbon sink function of the green land under the macro scale, the mesoscale and the micro scale according to the green land analysis result, the macro influence factor set, the mesoscale influence factor set and the micro influence factor set.

After the macro influence factor set, the mesoscopic influence factor set and the microscopic influence factor set are determined, the green space current situation of the area to be planned, indicated by the green space analysis result, is combined, so that key dominant factors which are concentrated in the influence factors and correspond to the promotion of the carbon sink function of the green space under the macro scale, the mesoscopic scale and the microscopic scale, and the threshold value of the key dominant factors, wherein the threshold value of the key dominant factors corresponds to the ideal key dominant factor index of the area to be planned, can be determined.

And 104, respectively carrying out macro city green space system planning, mesoscopic green space plaque design and microscopic plant community construction on the area to be planned according to the key dominant factors, the threshold values of the key dominant factors and the green space analysis results, and obtaining a macro scale planning result, a mesoscopic scale design result and a microscopic scale construction result.

After the key dominant factors and the threshold values of the key dominant factors are obtained, the green space current situation of the area to be planned, indicated by the green space analysis results, is combined, urban green space planning is conducted on the area to be planned from the macro scale, the mesoscale and the micro scale, planning results of the three scales are further obtained, and carbon sink functions of the urban green space can be improved according to the planning results.

In the embodiment of the application, urban green lands can be optimized under the full scale by planning the urban green lands respectively from the macro scale, the mesoscale and the micro scale, and the green lands after adjustment according to the planning result have better carbon sink capacity and further have better cooling and humidifying benefits.

In one possible manner, the process of obtaining the green space analysis result of the area to be planned may further include: and acquiring vegetation coverage data, climate data and time series data of the area to be planned, inputting the vegetation coverage data, the climate data and the time series data into a remote sensing model, and preliminarily obtaining a vegetation net primary productivity evaluation result. And acquiring vegetation data of the area to be planned, acquiring an actual green land analysis result of the area to be planned according to the vegetation data, and respectively calculating average carbon sinks of at least two green lands in various green lands according to the actual green land analysis result to acquire an actual green land carbon sink calculation result. And then, according to the calculation result of the actual carbon sink of the green land, carrying out optimization correction on the primary result of vegetation net primary productivity evaluation to obtain the accurate evaluation result of vegetation net primary productivity. On the other hand, a satellite image of the area to be planned is acquired, the satellite image is input into an atmosphere correction model, urban ground surface temperature data output by the atmosphere correction model are obtained, and then the spatial distribution of the cooling benefit value of the area to be planned is determined according to the urban ground surface temperature data. And finally, obtaining a green land analysis result of the area to be planned according to the accurate evaluation result of the vegetation net primary productivity and the spatial distribution result of the cooling benefit value.

In order to improve the accuracy of green land analysis results and avoid large-scale manual acquisition of green land data, vegetation coverage data, climate data and time series data of a region to be planned are firstly acquired, the vegetation coverage data are acquired by combining supervision classification and visual correction, the vegetation coverage data are divided into 4 vegetation types of woodlands, forestation lands, grasslands and wetlands, the climate data are acquired by month average temperature data, month total precipitation amount grid data and month solar total radiation data, and coverage change information is acquired by comparing the differences of annual or growing period curves of various change detection indexes; or the slope of the fitting linear function is adopted to reflect the vegetation coverage change trend; or decomposing the NDVI time sequence curve by adopting frequency spectrum analysis so as to detect the change of land coverage and the physical change of the surface vegetation ecological system, further analyzing the relation between the vegetation long time sequence change and the climate and environment change, and obtaining time sequence data. And inputting the data into a remote sensing model, and evaluating the vegetation net primary productivity based on the remote sensing model to preliminarily obtain a vegetation net primary productivity evaluation result. Because the accuracy of the result is to be verified, vegetation data of the area to be planned is acquired in the field through manpower or a device, for example, for different green land types (such as park green land, protection green land, square green land, affiliated green land, ecological green land and the like), a space balance sampling method is adopted, a satellite remote sensing image and a field investigation condition are combined, representative sample lands are selected according to factors such as vegetation coverage degree, community composition, structure and the like, the representative sample lands can be selected by selecting sample lands with higher vegetation coverage degree and different community compositions and structures, such as pure forest, mixed forest, multi-layer mixed forest, shrub, ground cover and water as representative sample lands, so as to obtain a green land actual analysis result, then average carbon sinks of unit areas of at least two green lands in various green lands are calculated respectively, at least two representative sample lands can be selected, 5-7 strip-shaped sample lands with the width of less than 20m are arranged, the representative sample lands with the length of 20m are set, the width of the representative sample lands with the width of the strip-shaped sample lands being smaller than 20m are selected, and the whole sample lands with the width of the representative sample lands being smaller than the whole strip lands are recorded. And then obtaining information such as tree species, breast diameter, tree height, crown width, quantity and the like of arbor, tree species, ground diameter, height, crown width, quantity and the like of shrubs, calculating sample side biomass according to vegetation data such as the area and type of grassland biomass, converting the information into carbon sink quantity through correlation coefficients, respectively calculating average values of carbon sinks of all sample sides of park greenbelts, protection greenbelts and affiliated greenbelts, obtaining average carbon sink values of unit areas of various greenbelts, obtaining actual carbon sink calculation results of the greenbelts, and carrying out optimization correction on the primary result of vegetation net primary productivity assessment according to the results. The specific optimization mode can be that the actual carbon sink calculation result of the green land is compared with the numerical value in the primary evaluation result of the vegetation net primary productivity corresponding to the actual carbon sink calculation result of the green land to obtain the ratio between the actual carbon sink calculation result of the green land and the numerical value in the primary evaluation result of the vegetation net primary productivity, all the numerical values in the primary evaluation result of the vegetation net primary productivity are adjusted according to the ratio to obtain the accurate evaluation result of the vegetation net primary productivity, and the index of the average carbon sink of the unit area of the green land in the area to be planned in the green land analysis result is further determined, wherein the accurate evaluation result of the vegetation net primary productivity is shown in a figure 2. In order to determine the index of the cooling and humidifying benefit of the green land in the area to be planned, firstly, a satellite image of the area to be planned is acquired, the satellite image is input into an atmosphere correction model, urban surface temperature data output by the atmosphere correction model are obtained, and then the spatial distribution of the cooling benefit magnitude of the area to be planned is determined according to the urban surface temperature data, wherein the spatial distribution of the cooling benefit magnitude is shown in fig. 3. For example, before the satellite image is input into the atmosphere correction model, the satellite image may be preprocessed to remove interference of cloud shadows and improve accuracy of output results of the atmosphere correction model. And obtaining a green land analysis result of the area to be planned according to the accurate evaluation result of the vegetation net primary productivity and the spatial distribution of the cooling values. The green land actual analysis result is used for indicating the average carbon sink of the green land in the area to be planned and the actual cooling and humidifying benefit of the green land in the area to be planned.

In the embodiment of the application, the vegetation data of the area to be planned is collected, so that the actual analysis result of the green land in the area to be planned is determined, and then the preliminary vegetation net primary productivity evaluation result output by the remote sensing model is optimized, so that the accuracy of the output result of the remote sensing model can be improved, and further the accuracy of the green land analysis result of the area to be planned is improved.

In one possible approach, the remote sensing model may calculate the net primary productivity assessment of vegetation according to the following formula:

wherein ,indicating photosynthetically active radiation absorbed by picture element x in t month,/->The actual light energy utilization rate of the pixel x in t months is represented, vegetation coverage data, climate data and time sequence data are raster data, and the pixel is the minimum unit of the raster data.

In one possible manner, the process of respectively establishing the macro influence factor set, the mesoscopic influence factor set and the micro influence factor set according to the macro features, the mesoscopic features and the micro features of the area to be planned may further include: calculating the average plaque area, the maximum plaque index, the landscape tendril degree, the landscape diversity, the shannon uniformity, the landscape connectivity, the plaque density, the landscape shape index, the blue-green ratio, lin Caobi, lin Shuibi, the grass-water ratio, the forest land occupation ratio, the canopy degree, the gradient, the ground coverage degree, the arbor-irrigation ratio and the forest trunk density of the green land in the area to be planned, determining macroscopic features, mesoscopic features and microscopic features according to calculation results, and finally establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set according to the macroscopic features, mesoscopic features and microscopic features respectively.

The average plaque area (MPS) is used to compare the degree of aggregation or disruption of different landscapes and also indicates the differences between the various types of landscapes, which can be calculated by the following formula:

where ni represents an overview of class i plaques and Ai represents the total area of class i plaques.

The maximum plaque index (LPI), which is used to determine the dominant plaque type in the landscape, can be calculated by the following formula:

wherein ,for (I)>Is plaque area.

Landscape tendrils (CONTAG), which describe the degree of aggregation or extension of different plaque types in a landscape, can be calculated by the following formula:

where Pi is the percentage of the area occupied by the i-type patches, gik is the number of i-type patches and k-type patches adjacent, and m is the total number of patch types in the landscape.

Landscape diversity (SHDI) can be calculated by the following formula:

where Pi is the percentage of area occupied by plaque of type i.

Shannon uniformity (SHEI) can be calculated by the following formula:

wherein m is the total number of plaque types in the landscape, and Pi is the percentage of the area occupied by the plaque of the i type.

Landscape connectivity (CONNECT) can be calculated by the following formula:

wherein ,c_ijk The connection condition of the patch j and the patch k which are related to the patch type i and are within the critical distance; n is n _i The number of patches of patch type i in the landscape.

Plaque Density (PD) for representing the density of a certain plaque in the landscape, reflecting the overall heterogeneity and fragmentation degree of the landscape and the fragmentation degree of a certain type, reflecting the heterogeneity in the unit area of the landscape, can be calculated by the following formula:

where NP is the number of plaques and A is the plaque area.

Landscape Shape Index (LSI) can be calculated by the following formula:

wherein E is the total length of all plaque boundaries in the landscape, and A is the total area of the landscape.

Blue-greenRatio (A) ₁ ) Representing the duty ratio of the forest land, the grassland and the water area, the method can be calculated by the following formula:

where a is the woodland area, b is the grassland area, and c is the water area.

The forest grass ratio (B) represents the ratio of the forest land to the grass area and can be calculated by the following formula:

where a is the woodland area and b is the grassland area.

The forest water ratio (C) represents the ratio of the forest land to the water area, and can be calculated by the following formula:

where a is the forest land area and c is the water area.

The grass water ratio, which represents the ratio of the area of grass to the area of water, can be calculated by the following formula:

where b is the grass area and c is the water area.

The woodland occupation ratio, which represents the ratio of woodland to the total area of green land plaque, can be calculated by the following formula:

wherein a is the forest land area, and T is the green land plaque area.

The canopy density, gradient, ground coverage, arbor-irrigation ratio and forest dry density can be calculated by combining forest industry secondary regulation actual measurement data with basic community characteristics.

After the calculation result is obtained, macroscopic, mesoscopic and microscopic features are determined from the calculation result. The macro, meso, and micro features each include at least one of average plaque area, maximum plaque index, landscape tendril, landscape diversity, shannon uniformity, landscape connectivity, plaque density, landscape shape index, blue-green ratio, lin Caobi, lin Shuibi, grass to water ratio, woodland occupancy, canopy level, grade, ground coverage, arbor to irrigate ratio, and woodland dry density.

And finally, establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set according to the macroscopic features, the mesoscopic features and the microscopic features respectively.

In the embodiment of the application, the macro influence factor set, the mesoscopic influence factor set and the microscopic influence factor set can be established more finely by calculating the average plaque area, the maximum plaque index, the landscape tendril, the landscape diversity, the shannon uniformity, the landscape connectivity, the plaque density, the landscape shape index, the blue-green ratio, lin Caobi, lin Shuibi, the grass-water ratio, the forest land occupation ratio, the canopy density, the gradient, the ground coverage, the arbor-irrigation ratio and the forest dry density of the green land in the area to be planned, so that the accuracy of urban green land planning is improved.

In one possible manner, according to the green space analysis result, the macroscopic influence factor set, the mesoscopic influence factor set and the microscopic influence factor set, the process of determining the key dominant factor and the threshold value of the key dominant factor affecting the improvement of the green space carbon sink function under the macroscopic scale, the mesoscopic scale and the microscopic scale respectively may further include: taking green land analysis results as dependent variables, taking average plaque area, maximum plaque index, landscape tendril, landscape diversity, shannon uniformity and landscape connectivity as macroscopic independent variables, taking plaque density, landscape shape index, blue-green ratio, lin Caobi, lin Shuibi, grass-water ratio and forest land duty ratio as mesoscopic independent variables, taking depression degree, gradient, ground coverage, qiao Guanbi and forest dry density as microscopic independent variables, then establishing a regression tree model according to the dependent variables, macroscopic independent variables, mesoscopic independent variables and microscopic independent variables, obtaining macroscopic independent variable contribution rate, mesoscopic independent variable contribution rate and microscopic independent variable contribution rate which are output by the regression tree model and affect the improvement of the functions of the carbon sink of the green land under the mesoscopic scale and microscopic scale, and respectively taking the independent variables with the contribution rate of the macroscopic independent variables, the contribution rate of the mesoscopic independent variables and the contribution rate of the microscopic independent variables, which are five in the ranking, as key dominant factors influencing the improvement of the functions of the carbon sink of the green space at the macroscopic scale, key dominant factors influencing the improvement of the functions of the carbon sink of the green space at the mesoscopic scale and key dominant factors influencing the improvement of the functions of the carbon sink of the green space at the microscopic scale, determining the association relation between the functions of the carbon sink of the green space and the change trend of the key dominant factors according to the key dominant factors influencing the improvement of the functions of the carbon sink of the green space at the macroscopic scale, the key dominant factors influencing the improvement of the functions of the carbon sink of the green space at the mesoscopic scale and the microscopic scale, and finally determining the threshold values of the key dominant factors influencing the improvement of the functions of the carbon sink of the green space at the macroscopic scale, the mesoscopic scale and the microscopic scale.

In order to determine key dominant factors and thresholds of key dominant factors affecting the improvement of the carbon sink function of greenhouses under the macro scale, mesoscale and micro scale, the analysis of each influencing factor set is needed, firstly, green house analysis results are taken as dependent variables, average plaque area, maximum plaque index, landscape tendril, landscape diversity, shannon uniformity and landscape connectivity are taken as macroscopic independent variables according to the characteristics of each scale, plaque density, landscape shape index, blue-green ratio, lin Caobi, lin Shuibi, grass-water ratio and woodland occupation ratio are taken as mesoscopic independent variables, the canopy density, gradient, ground coverage, qiao Guanbi and woodland density are taken as microscopic independent variables, an enhanced regression tree model based on machine learning is established, and model fitting can call package realization such as 'carb', 'gbm' and 'dismo' in R4.1.0 software. Since the green space analysis result comprises 2 types of average carbon sink per unit area and cooling and humidifying benefits of the green space in the area to be planned, 6 enhanced regression tree models can be established, wherein the average carbon sink per unit area of the green space in the area to be planned is taken as an enhanced regression tree model of a dependent variable and a macroscopic independent variable, the average carbon sink per unit area of the green space in the area to be planned is taken as an enhanced regression tree model of a dependent variable and a mesoindependent variable, the average carbon sink per unit area of the green space in the area to be planned is taken as an enhanced regression tree model of a dependent variable and a microscopic independent variable, the cooling and humidifying benefit value of the green space in the area to be planned is taken as an enhanced regression tree model of a dependent variable and a mesoindependent variable, and the cooling and humidifying benefit value of the green space in the area to be planned is taken as an enhanced regression tree model of a dependent variable and a microscopic independent variable. And obtaining macroscopic independent variable contribution rate, mesoscopic independent variable contribution rate and microscopic independent variable contribution rate which influence the improvement of the carbon sink function of the green space under the macroscopic scale, mesoscopic scale and microscopic scale output by each regression tree model, wherein independent variables with contribution rates five in the macroscopic independent variable contribution rate, mesoscopic independent variable contribution rate and microscopic independent variable are respectively used as key dominant factors which influence the improvement of the carbon sink function of the green space under the macroscopic scale, key dominant factors which influence the improvement of the carbon sink function of the green space under the mesoscopic scale and key dominant factors which influence the improvement of the carbon sink function of the green space under the microscopic scale. And determining the association relation between the green land carbon sink function promotion and the change trend of the key dominant factor according to the key dominant factor affecting the green land carbon sink function promotion at the macro scale, the key dominant factor affecting the green land carbon sink function promotion at the mesoscale and the key dominant factor affecting the green land carbon sink function promotion at the micro scale, for example, a dependence graph of the key dominant factor and the carbon sink benefit can be manufactured, and the threshold of the key dominant factor affecting the green land carbon sink function promotion at the macro scale, the mesoscale and the micro scale is determined according to the threshold of the key turning point (the position with the largest gradient change) of the key dominant factor and the carbon sink benefit change trend in the dependence graph.

According to the embodiment of the application, the key dominant factors influencing the improvement of the carbon sink function of the green land under the macro scale, the key dominant factors influencing the improvement of the carbon sink function of the green land under the mesoscale and the key dominant factors influencing the improvement of the carbon sink function of the green land under the micro scale can be determined by calculating the independent variable contribution rate through the regression model, and the influence factors with smaller influence are ignored, so that the calculation efficiency can be improved.

In one possible manner, the process of planning, designing and plant construction for the improvement of the functions of the green land carbon sink may further include: selecting 90% of the results output by the regression tree model as evaluation samples, taking the evaluation samples with the deviation degree larger than or equal to the deviation threshold value as negative samples, taking the evaluation samples with the deviation degree smaller than the deviation threshold value as positive samples, and evaluating the regression tree model by the following formula:

wherein ,for the area under the working characteristic curve of the subject, rank _i For the number of the ith sample, M, N is the number of positive samples and the number of negative samples, +.>Is the sum of positive sample numbers, +.>Is positively correlated with the positive evaluation of the regression tree model.

AUC (Area Under Curve) is defined as the area under the ROC (receiver operating characteristic curve) subject working characteristic curve, the merits of the regression tree model can be evaluated, the abscissa of the ROC curve is the false Positive Rate (also called false Positive Rate, 0 Positive Rate), and the ordinate is the true Positive Rate (true class Rate, 1 Positive Rate), and correspondingly, the true Negative Rate (true Negative class Rate, 1 Negative Rate) and the false Negative Rate (false Negative class Rate, 0 Negative Rate). The four indexes are calculated as follows:

(1) False Positive Rate (FPR): the probability that the positive example is determined not to be the true example, namely the probability that the positive example is determined to be the true example in the true negative example.

(2) True Positive Rate (TPR): the probability that the positive example is determined to be the true example is also the probability that the positive example is determined to be the positive example in the true example (i.e., the positive example recall rate).

(3) False Negative Rate (FNR): the probability that the negative example is determined not to be the true negative example, that is, the probability that the true example is determined to be the negative example.

(4) True Negative Rate (TNR): the probability that the negative example is determined to be a true negative example is the probability that the negative example is determined to be a negative example in the true negative example.

By drawing ROC curves of each regression tree model, the areas under the curves are compared to serve as indexes of model quality. The larger the AUC value, the higher the accuracy of the regression tree model.

In the embodiment of the application, byAnd evaluating each regression tree model, so that the quality degree of each regression tree model can be known, and the situation that the regression tree model with too low accuracy is used for obtaining the threshold value of the wrong key dominant factor is avoided.

In one possible manner, according to the key dominant factor, the threshold value of the key dominant factor, and the green space analysis result, the macro city green space system planning, mesoscopic green space plaque design, and microcosmic plant community construction are performed on the area to be planned, and the process of obtaining the macro scale planning result, mesoscopic scale design result, and microcosmic scale construction result may further include: determining an optimized value interval of a macroscopic independent variable according to a key dominant factor influencing the improvement of the function of the green land carbon sink under a macroscopic scale, a threshold value of the key dominant factor influencing the improvement of the function of the green land carbon sink under the macroscopic scale and a green land analysis result; and planning the urban green land from the macro-scale area to be planned according to the optimized value interval of the macro independent variable to obtain a macro-scale planning result.

Urban green land planning is carried out on the area to be planned from the macro scale, green land analysis results can be enabled to approach to the threshold value of a key dominant factor affecting the improvement of the green land carbon sink function under the macro scale as much as possible, and then the optimized value interval of the macro independent variable is determined. The macro scale planning results are shown in fig. 4.

In the embodiment of the application, the optimized value interval of the macroscopic independent variable is determined according to the key dominant factor influencing the elevation of the green land carbon sink function under the macroscopic scale, the threshold value influencing the key dominant factor influencing the elevation of the green land carbon sink function under the macroscopic scale and the green land analysis result, so that the optimized space of the green land under the macroscopic scale can be intuitively reflected, and the urban green land planning of the area to be planned is facilitated.

In one possible manner, according to the key dominant factor, the threshold value of the key dominant factor, and the green space analysis result, the macro city green space system planning, mesoscopic green space plaque design, and microcosmic plant community construction are performed on the area to be planned, and the process of obtaining the macro scale planning result, mesoscopic scale design result, and microcosmic scale construction result may further include: determining an optimized value interval of mesoscopic independent variables according to a key dominant factor influencing the improvement of the carbon sink function of the green space under mesoscopic scale, a threshold value of the key dominant factor influencing the improvement of the carbon sink function of the green space under mesoscopic scale and a macro scale planning result; and carrying out urban green space planning on the area to be planned from mesoscale according to the optimized value interval of the mesoscale independent variable to obtain a mesoscale design result.

In order to obtain the mesoscale design result, the mesoscale planning result can be combined with the macro scale planning result to perform further optimization on the basis of the threshold value of the key dominant factor influencing the improvement of the green land carbon sink function under the mesoscale, and the key dominant factor influencing the improvement of the green land carbon sink function under the mesoscale is adjusted to obtain the mesoscale design result. Mesoscale design results are shown in fig. 5.

In the embodiment of the application, the mesoscale design result is obtained by combining the macro-scale planning result, so that mesoscale optimization can be performed on the basis of the macro-scale planning result, and the rationality of urban green space planning is further improved.

In one possible manner, according to the key dominant factor, the threshold value of the key dominant factor, and the green space analysis result, the macro city green space system planning, mesoscopic green space plaque design, and microcosmic plant community construction are performed on the area to be planned, and the process of obtaining the macro scale planning result, mesoscopic scale design result, and microcosmic scale construction result may further include: determining an optimized value interval of the microscopic independent variable according to a key dominant factor influencing the improvement of the green land carbon sink function at the microscopic scale, a threshold value of the key dominant factor influencing the improvement of the green land carbon sink function at the microscopic scale and a mesoscale design result; and planning urban green land from the micro-scale area to be planned according to the optimized value interval of the micro independent variable to obtain a micro-scale construction result.

In order to obtain a micro-scale construction result, the micro-scale construction result can be obtained by further optimizing the mesoscale design result on the basis of a threshold value of a key dominant factor influencing the elevation of the carbon sink function of the green land under the micro-scale, and adjusting the key dominant factor influencing the elevation of the carbon sink function of the green land under the micro-scale. The microscopic scale construction results are shown in fig. 6.

For example, the microscopic scale construction results can be optimized from a planar structural system of community composition and community density, a vertical structural system of stand composition, stand structure and site conditions.

In the embodiment of the application, the microscale construction result is obtained by combining the mesoscale design result, so that microscale optimization can be performed on the basis of the mesoscale design result, and the rationality of urban green space planning is further improved.

Planning, designing and plant building device for improving carbon sink function of green land

Fig. 7 is a block diagram of a green space carbon sink function enhancing planning, design and plant building apparatus 200 according to an embodiment of the present application, as shown in fig. 7, the green space carbon sink function enhancing planning, design and plant building apparatus 200 may include: an acquisition module 201, a construction module 202, a determination module 203 and a planning module 204.

The obtaining module 201 is configured to obtain a green land analysis result of the area to be planned, where the green land analysis result is used to indicate an average carbon exchange value of a unit area of the green land in the area to be planned, and a benefit value of cooling and humidifying the green land in the area to be planned.

In order to plan green land in a city, firstly, a region to be planned is defined, then, in order to determine the current situation of the green land of the region to be planned, the direct carbon sequestration capacity (namely, the average carbon sink of the unit area of the green land) and the indirect emission reduction capacity (namely, the cooling and humidifying benefits) are used as indexes for enhancing the carbon sink of the green land of a research region, the green land analysis is carried out on the region to be planned, and the green land analysis result of the region to be planned is obtained through an obtaining module 201 and is used for indicating the average carbon sink of the unit area of the green land in the region to be planned and the cooling and humidifying benefits of the green land in the region to be planned.

A construction module 202 is configured to establish a macro influence factor set, a mesoscopic influence factor set and a micro influence factor set according to macro features, mesoscopic features and micro features of the area to be planned, respectively.

In order to determine the ideal greenbelt index for the greenbelt in the area to be planned, the construction module 202 establishes corresponding sets of influence factors according to the macro, mesoscopic and micro features in the area to be planned, respectively.

The determining module 203 is configured to determine, according to the green space analysis result, the macroscopic influence factor set, the mesoscopic influence factor set, and the microscopic influence factor set, a key dominant factor and a threshold value of the key dominant factor that affect the improvement of the green space carbon sink function at the macroscopic scale, the mesoscopic scale, and the microscopic scale, respectively.

After the construction module 202 determines the macroscopic influence factor set, the mesoscopic influence factor set and the microscopic influence factor set, the determination module 203 combines the green space current situation of the to-be-planned area pointed by the green space analysis result to determine key dominant factors in the influence factor set, which correspond to the promotion of the carbon sink function of the green space under the macroscopic scale, the mesoscopic scale and the microscopic scale, and threshold values of the key dominant factors, wherein the threshold values of the key dominant factors correspond to ideal key dominant factor indexes of the to-be-planned area.

The planning module 204 is configured to perform macro city green space system planning, mesoscopic green space plaque design and microscopic plant community construction on the area to be planned according to the key dominant factor, the threshold value of the key dominant factor, and the green space analysis result, to obtain a macro scale planning result, a mesoscopic scale design result, and a microscopic scale construction result.

After the determining module 203 obtains the key dominant factors and the threshold values of the key dominant factors, the planning module 204 performs urban green space planning on the area to be planned according to the macro scale, the mesoscale and the micro scale respectively in combination with the green space current situation of the area to be planned indicated by the green space analysis result, so as to obtain planning results of 3 scales, and the carbon sink function of the urban green space can be improved according to each planning result.

In the embodiment of the application, urban green space planning, green space plaque design and plant community construction are respectively carried out on the areas to be planned from the macro scale, the mesoscale and the micro scale through the planning module 204, so that urban green space can be scientifically planned and constructed under the full scale, and the green space after adjustment has better carbon sink capacity according to the green space planning result, the green space plaque design result and the plant community construction result, thereby having better cooling and humidifying benefits.

It should be noted that, since the above-mentioned planning, design and information interaction and execution process of each module in the green space carbon sink function promotion device are based on the same concept as the above-mentioned planning, design and plant construction method embodiment of the green space carbon sink function promotion, specific content can be referred to the description in the above-mentioned planning, design and plant construction method embodiment of the green space carbon sink function promotion, and will not be repeated here.

Electronic equipment

In this embodiment, an electronic device 300 is provided, as shown in fig. 8, the electronic device 300 may include: a processor (processor) 301, a communication interface (Communications Interface) 302, a memory (memory) 303, and a communication bus 304. Wherein:

processor 301, communication interface 302, and memory 303 perform communication with each other via communication bus 304.

A communication interface 302 for communicating with other electronic devices or servers.

The processor 301 is configured to execute the program 305, and may specifically perform the steps related to the foregoing planning, design and plant construction method embodiments for improving the carbon sink function of the green space.

In particular, program 305 may include program code comprising computer-operating instructions.

The processor 301 may be a CPU or specific integrated circuit ASIC (Application Specific Integrated Circuit) or one or more integrated circuits configured to implement embodiments of the present application. The one or more processors comprised by the smart device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.

A memory 303 for storing a program 305. The memory 303 may comprise a high-speed RAM memory or may further comprise a non-volatile memory (non-volatile memory), such as at least one disk memory.

The program 305 may be specifically configured to cause the processor 301 to execute the planning, design and plant construction method for improving the functions of the green land carbon sink in the foregoing embodiments.

The specific implementation of each step in the program 305 may refer to the corresponding descriptions in the corresponding steps and units in the foregoing green space carbon sink function improving planning, design and plant construction method embodiments, which are not described herein. It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and modules described above may refer to corresponding procedure descriptions in the foregoing method embodiments, which are not repeated herein.

According to the electronic equipment provided by the embodiment of the application, urban green space planning, green space plaque design and plant community construction are respectively carried out on the area to be planned from a macroscopic scale, a mesoscale and a microscopic scale, so that the urban green space can be scientifically planned and constructed under a full scale, and the green space after adjustment according to the green space planning result, the green space plaque design result and the plant community construction result has better carbon sink benefit, so that the electronic equipment has better cooling and humidifying benefit.

Computer storage medium

In this embodiment, a computer readable storage medium is provided storing instructions for causing a machine to perform the planning, design and plant construction methods of greenfield carbon sink function promotion as described herein. Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.

In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present application.

Examples of the storage medium for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communication network.

Computer program product

In this embodiment, a computer program product is provided that includes computer instructions that instruct a computing device to perform operations corresponding to any one of the method embodiments described above.

It should be noted that, according to implementation requirements, each component/step described in the embodiments of the present application may be split into more components/steps, or two or more components/steps or part of operations of the components/steps may be combined into new components/steps, so as to achieve the objects of the embodiments of the present application.

The above-described methods according to embodiments of the present application may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and to be stored in a local recording medium downloaded through a network, so that the methods described herein may be stored on such software processes on a recording medium using a general purpose computer, special purpose processor, or programmable or special purpose hardware such as an ASIC or FPGA. It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a memory component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor, or hardware, performs the methods described herein. Furthermore, when a general purpose computer accesses code for implementing the methods illustrated herein, execution of the code converts the general purpose computer into a special purpose computer for performing the methods illustrated herein.

It should be noted that in the description of the present application, the terms "first," "second," and the like are merely used for convenience in describing the various components or names and are not to be construed as indicating or implying a sequential relationship, relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

It should be noted that, although specific embodiments of the present application have been described in detail with reference to the accompanying drawings, the present application should not be construed as limiting the scope of the present application. Various modifications and variations which may be made by those skilled in the art without the creative effort fall within the protection scope of the present application within the scope described in the claims.

Examples of embodiments of the present application are intended to briefly illustrate technical features of embodiments of the present application so that those skilled in the art may intuitively understand the technical features of the embodiments of the present application, and are not meant to be undue limitations of the embodiments of the present application.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for planning, designing and plant construction for improving the function of a green land carbon sink, comprising the steps of:

obtaining a green land analysis result of a region to be planned, wherein the green land analysis result is used for indicating average carbon sink per unit area of the green land in the region to be planned and cooling and humidifying benefits of the green land in the region to be planned;

establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set respectively according to the macroscopic features, mesoscopic features and microscopic features of the area to be planned;

according to the green land analysis result, the macroscopic influence factor set, the mesoscopic influence factor set and the microscopic influence factor set, respectively determining key dominant factors influencing the improvement of the green land carbon sink function under the macroscopic scale, the mesoscopic scale and the microscopic scale and threshold values of the key dominant factors;

According to the key dominant factors, the threshold values of the key dominant factors and the green space analysis results, carrying out macro city green space system planning, mesoscopic green space plaque design and microscopic plant community construction on the to-be-planned area respectively to obtain macro scale planning results, mesoscopic scale design results and microscopic scale construction results;

the obtaining the green land analysis result of the area to be planned includes:

acquiring vegetation coverage data, climate data and time sequence data of the area to be planned;

inputting the vegetation coverage data, the climate data and the time series data into a remote sensing model to preliminarily obtain a vegetation net primary productivity evaluation result, wherein the vegetation net primary productivity evaluation result is used for indicating average carbon sink per unit area of the area to be planned;

acquiring vegetation data of the area to be planned, and acquiring an actual analysis result of the green land in the area to be planned according to the vegetation data, wherein the vegetation data at least comprises vegetation coverage, community composition and structure, and the actual analysis result of the green land is used for correcting an average carbon sink of the green land in the area to be planned in an actual unit area and the actual cooling and humidifying benefit of the green land in the area to be planned;

According to the green land actual analysis result, calculating average carbon sink quantity per unit area of at least two green lands in various green lands respectively to obtain a green land actual carbon sink calculation result;

correcting the vegetation net primary productivity assessment result according to the green land actual carbon sink calculation result to obtain a vegetation net primary productivity accurate assessment result;

acquiring a satellite image of the area to be planned, inputting the satellite image into an atmosphere correction model, and acquiring urban surface temperature data output by the atmosphere correction model;

determining the spatial distribution of the cooling benefit values of the area to be planned according to the urban surface temperature data;

obtaining a green land analysis result of the area to be planned according to the vegetation net primary productivity accurate evaluation result and the spatial distribution of the cooling benefit magnitude;

and respectively establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set according to the macroscopic feature, mesoscopic feature and microscopic feature of the area to be planned, wherein the method comprises the following steps:

calculating the average plaque area, the maximum plaque index, the landscape tendril, the landscape diversity, the shannon uniformity, the landscape connectivity, the plaque density, the landscape shape index, the blue-green ratio, lin Caobi, lin Shuibi, the grass-water ratio, the forest land occupation ratio, the canopy density, the gradient, the land coverage, the arbor-irrigation ratio and the forest dry density of the green land in the area to be planned;

Determining the macro, mesoscopic and micro features from the calculation result, wherein the macro, mesoscopic and micro features respectively comprise at least one of the average plaque area, the maximum plaque index, the landscape tendril, the landscape diversity, the shannon uniformity, the landscape connectivity, the plaque density, the landscape shape index, the bluish green ratio, the Lin Caobi, the Lin Shuibi, the grass water ratio, the woodland occupancy, the canopy density, the slope, the ground coverage, the arbor-irrigation ratio and the woodland dry density;

establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set according to the macroscopic feature, the mesoscopic feature and the microscopic feature respectively;

the determining key dominant factors influencing the improvement of the carbon sink function of the green land under the macro scale, the mesoscale and the micro scale and the threshold values of the key dominant factors according to the green land analysis result, the macro influence factor set, the mesoscale influence factor set and the micro influence factor set respectively comprises the following steps:

taking the green land analysis result as a dependent variable, the average plaque area, the maximum plaque index, the landscape tendril, the landscape diversity, the shannon uniformity and the landscape connectivity as macroscopic independent variables, the plaque density, the landscape shape index, the blue-green ratio, the Lin Caobi, the Lin Shuibi, the grass water ratio and the forest land occupation ratio as mesoscopic independent variables, and the canopy density, the gradient, the land coverage, the arbor-irrigation ratio and the forest dry density as microscopic independent variables;

Establishing a regression tree model according to the dependent variable, the macroscopic independent variable, the mesoscopic independent variable and the microscopic independent variable;

acquiring macroscopic independent variable contribution rate, mesoscopic independent variable contribution rate and microscopic independent variable contribution rate which influence the improvement of the green space carbon sink function under the macroscopic scale, mesoscopic scale and microscopic scale output by the regression tree model;

the contribution rate of the macroscopic independent variable, the contribution rate of the mesoscopic independent variable and the contribution rate of the microscopic independent variable are respectively used as key dominant factors influencing the improvement of the carbon sink function of the green space at a macroscopic scale, key dominant factors influencing the improvement of the carbon sink function of the green space at a mesoscopic scale and key dominant factors influencing the improvement of the carbon sink function of the green space at a microscopic scale;

determining the association relation of the green land carbon sink function and the change trend of the key dominant factor according to the key dominant factor affecting the green land carbon sink function promotion at the macro scale, the key dominant factor affecting the green land carbon sink function promotion at the mesoscale and the key dominant factor affecting the green land carbon sink function promotion at the micro scale;

determining thresholds of key dominant factors affecting the improvement of the carbon sink function of the green land under the macro scale, the mesoscale and the micro scale according to the association relation;

And respectively planning the urban green space of the area to be planned from a macro scale, a mesoscale and a micro scale according to the key dominant factor, the threshold value of the key dominant factor and the green space analysis result to obtain a macro scale planning result, a mesoscale design result and a micro scale plant community construction result, wherein the method comprises the following steps:

determining an optimized value interval of the macroscopic independent variable according to the key dominant factor influencing the improvement of the green land carbon sink function under the macroscopic scale, the threshold value of the key dominant factor influencing the improvement of the green land carbon sink function under the macroscopic scale and the green land analysis result;

according to the optimized value interval of the macro independent variable, urban green land planning is conducted on the area to be planned from a macro scale, and the macro scale planning result is obtained;

determining an optimized value interval of the mesoscopic independent variable according to the key dominant factor influencing the improvement of the green land carbon sink function at the mesoscopic scale, the threshold value of the key dominant factor influencing the improvement of the green land carbon sink function at the mesoscopic scale and the macro-scale planning result;

according to the optimized value interval of the mesoscopic independent variable, urban green land plaque design is carried out on the area to be planned from mesoscopic scale, and the mesoscopic scale design result is obtained;

Determining an optimized value interval of the microscopic independent variable according to the key dominant factor influencing the improvement of the green land carbon sink function at the microscopic scale, the threshold value of the key dominant factor influencing the improvement of the green land carbon sink function at the microscopic scale and the microscopic scale construction result;

and constructing urban green land plant communities from the microscale to the area to be planned according to the optimized value interval of the microscale independent variable to obtain the microscale construction result.

2. The method of claim 1, wherein the remote sensing model calculates the net primary productivity assessment of vegetation according to the formula:

wherein ,indicating photosynthetically active radiation absorbed by picture element x in t month,/->And the actual light energy utilization rate of the pixel x in t months is represented, the vegetation coverage data, the climate data and the time series data are raster data, and the pixel is the minimum unit of the raster data.

3. The method according to claim 1, wherein the method further comprises:

selecting 90% of the results output by the regression tree model as evaluation samples, taking the evaluation samples with the deviation degree larger than or equal to the deviation threshold value as negative samples, and taking the evaluation samples with the deviation degree smaller than the deviation threshold value as positive samples;

The regression tree model is evaluated by the following formula:

wherein ,for the area under the working characteristic curve of the subject, rank _i Is the firstiThe serial numbers of the bar samples, M, N are the number of positive samples and the number of negative samples, +.>Is the sum of positive sample numbers, +.>Is positively correlated with the positive evaluation of the regression tree model.

4. A green space carbon sink function enhancing planning, design and plant construction device, comprising:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a green land analysis result of a region to be planned, wherein the green land analysis result is used for indicating an average carbon exchange value of a unit area of a green land in the region to be planned and a benefit value of cooling and humidifying the green land in the region to be planned;

the construction module is used for respectively establishing a macroscopic influence factor set, a mesoscopic influence factor set and a microscopic influence factor set according to the macroscopic features, mesoscopic features and microscopic features of the area to be planned;

the determining module is used for respectively determining key dominant factors influencing the improvement of the carbon sink function of the green land under the macro scale, the mesoscale and the micro scale and threshold values of the key dominant factors according to the green land analysis result, the macro influence factor set, the mesoscale influence factor set and the micro influence factor set;

The planning module is used for carrying out macro city green space system planning, mesoscopic green space plaque design and microscopic plant community construction on the area to be planned from a macro scale, a mesoscopic scale and a microscopic scale according to the key dominant factors, the threshold values of the key dominant factors and the green space analysis result, so as to obtain a macro scale planning result, a mesoscopic scale design result and a microscopic scale construction result;

the acquisition module is used for acquiring vegetation coverage data, climate data and time sequence data of the area to be planned;

the construction module is used for calculating the average plaque area, the maximum plaque index, the landscape tendril, the landscape diversity, the shannon uniformity, the landscape connectivity, the plaque density, the landscape shape index, the blue-green ratio, lin Caobi, lin Shuibi, the grass-water ratio, the forest land occupation ratio, the canopy density, the gradient, the land coverage, the arbor-irrigation ratio and the forest trunk density of the green land in the area to be planned;

a determining module, configured to use the green land analysis result as a dependent variable, the average plaque area, the maximum plaque index, the landscape tendril, the landscape diversity, the shannon uniformity, and the landscape connectivity as macroscopic independent variables, the plaque density, the landscape shape index, the blue-green ratio, lin Caobi, lin Shuibi, the grass water ratio, and the forest land occupancy as mesoscopic independent variables, and the canopy density, the gradient, the land coverage, the arbor-irrigation ratio, and the forest dry density as microscopic independent variables;

The planning module is used for determining an optimized value interval of the macroscopic independent variable according to the key dominant factor influencing the elevation of the carbon sink function of the green land under the macroscopic scale, the threshold value of the key dominant factor influencing the elevation of the carbon sink function of the green land under the macroscopic scale and the green land analysis result;