A method and system for screening street trees based on a canopy ecological index, and a medium

Abstract

The application relates to a sidewalk tree screening method based on a forest ecological index, a regional environment model is established by using a DEM technology, tree body data of different types of sidewalk trees are measured and recorded, a plurality of tree body data sub-models are established, and simulation calculation is sequentially performed on the tree body data sub-models, a simulation result is analyzed by using a stepwise multiple linear regression, a regional environment model and a sidewalk tree type model are fused, a simulation model is established, the regional environment model is trained by using the tree body data sub-models, simulation model characteristics are obtained, a heat balance coefficient of the simulation model and a tree species growth characteristic value are calculated, a trend curve is estimated, the simulation model trend curve is compared with a reference target model trend curve, and high-quality plants suitable for the target region are determined. When the simulation model is constructed, the heat balance coefficient in the target region range and the growth characteristic value trend of the plants in the target region are considered, so that more stable and longer forest ecological effects can be achieved in the target region.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban street greening, and particularly relates to a sidewalk tree screening method and system based on a tree-lined ecological index and a medium. BACKGROUND

[0002] With the acceleration of urbanization, the heat island effect has a significant impact on urban climate, atmospheric environment, biological habits and urban residents' health, and the intensity of urban heat island increases with the increase of the size of the city. People plan, build and protect urban green spaces in order to promote environmental restoration and mitigate the heat island effect. Road green space, as an important green infrastructure in the city, mainly brings cooling and humidification and shading effects through sidewalk trees on the green belt.

[0003] In order to reduce the impact of the heat island effect on the city, it is necessary to first ensure the vegetation coverage area and appropriately increase the proportion of plant patches. At present, more scholars measure urban green space through two-dimensional plane indicators such as vegetation coverage, green space ratio, normalized difference vegetation index (NDVI), and per capita green space area. In recent years, scholars have begun to analyze urban green space through three-dimensional indicators such as green view rate, green amount measurement and evaluation, which can better reflect the effect of three-dimensional greening in the city.

[0004] Therefore, the patent application No. 201810509102.4 discloses a sidewalk tree screening method based on a thermal comfort index, which includes a measurement stage, a simulation stage and a comparative analysis stage. In the measurement stage, the microclimate factor data of a single test sidewalk tree and the microclimate factor data of the test sidewalk tree in a street environment are input into Rayman software for calculation, respectively, to obtain the PET data and PMV data of the single test sidewalk tree and the PET data and PMV data of the test sidewalk tree in the street environment. In the simulation stage, ENVI met microclimate simulation software is used to model to obtain the PET data and PMV data of each test sidewalk tree in the street environment. In the comparative analysis stage, the data obtained in the measurement stage and the data obtained in the simulation stage are sorted and compared to obtain a sidewalk tree species with good thermal comfort.

[0005] At present, basically all sidewalk tree screening methods only consider the microclimate daily variation data of a single sidewalk tree in summer and the microclimate daily variation data of different tree species of sidewalk trees in a street environment and in summer, construct a corresponding sidewalk tree model by combining it with an environmental model, and only consider the current shading effect without considering the future growth trend of the sidewalk trees and the impact on urban construction. For urban street construction, the comprehensive effect to be achieved in the future should also be considered to make more reasonable planning and sidewalk tree selection.

[0006] To this end, it is necessary to propose a kind of street tree screening method based on the ecological index of forest, reasonably assess the plant growth characteristics of various street trees and the heat balance trend of the target area, and then compare with the characteristics of the ideal target to obtain high-quality plants suitable for the target area. SUMMARY

[0007] To solve the above problems, the present application provides a kind of street tree screening method based on the ecological index of forest, system and medium.

[0008] The technical scheme adopted by the present application is: a kind of street tree screening method based on the ecological index of forest, comprising the following steps,

[0009] Step 1) collect the latitude and longitude data and meteorological data of the target area, and establish a regional environment model using DEM technology;

[0010] Step 2) measure and record the tree data of different kinds of street trees, establish multiple tree data submodels, and simulate and calculate each tree data submodel to obtain simulation results, and use stepwise multiple linear regression analysis to analyze the simulation results, calculate the characteristics of each tree data submodel, and fuse multiple tree data submodels to obtain a street tree species model;

[0011] Step 3) fuse the regional environment model and the street tree species model to establish a simulation model, train the regional environment model with the tree data submodel, obtain the characteristics of the simulation model, calculate the heat balance coefficient and the growth characteristic value of the simulation model, and estimate the trend curve;

[0012] Step 4) set the target heat balance coefficient and the target growth characteristic value, establish a reference target model, estimate the trend curve, extract the characteristics of the simulation model and the corresponding characteristics of the target model, and compare them using IA method, when 0.5

[0013] As a preferred, in step 1), the meteorological data includes light intensity under full light, temperature under full light, wind speed;In step 2), the tree data includes tree height, breast height, branch point height, crown height, leaf growth height, crown width, leaf thickness, leaf area, leaf area index, shade temperature, shade humidity, shade light intensity, each tree data is simulated and calculated, and the simulation results include cooling rate, light shielding rate, humidity increasing rate, shade quality and comprehensive shade effect.

[0014] As a preferred, in step 1), the establishment of the regional environment model comprises the following steps:

[0015] Step 101: Obtain DEM data of the target area, use MapGIS K9 platform to correct the space and coordinate of the DEM data, generate DEM data space coordinates, then cut and triangulate the DEM data to form a surface triangular network, and use the surface triangular network to establish a surface three-dimensional network model;

[0016] Step 102: Obtain the original geological map of the target area, and perform coordinate transformation on the geology to make it consistent with the DEM data space coordinates;

[0017] Step 103: Use MapGIS K9 platform to select and increase or decrease the target area geological data to generate a geological vector map;

[0018] Step 104: Add the vector geological map generated in step 103 to the three-dimensional network model.

[0019] As preferred, in step 2), the tree data sub-model is established by the following steps:

[0020] Step 201: Select a sample tree with good growth conditions, five years of planting age, relatively uniform crown and leaf distribution, and no recent artificial intervention pruning, use a wide-angle digital camera to take 360° horizontal and 270° vertical pictures, and use a laser scanner to scan synchronously during the shooting process to obtain tree images and three-dimensional information;

[0021] Step 202: Use Easy-Model software to stitch and register the tree images, generate three-dimensional image information, and import it into the software provided with the laser scanner, establish the point cloud data of the sample tree, export it, import it into AutoCAD, define the coordinate system of the sample tree in the AutoCAD environment through the point cloud software, then import it into Sketch-Up software to build a sample tree model, correct the three-dimensional information according to the tree structure data obtained in the actual measurement, and construct a tree data sub-model.

[0022] As preferred, in step 2), the characteristics of each tree data sub-model are calculated based on the following equation:

[0023] (1)

[0024] (2)

[0025] Wherein, P is the shading index; T is the cooling rate; L is the shading rate; S is the shading area; C is the heat absorption coefficient; is the light intensity under full light; is the light intensity under shade; T1 is the temperature under full light; T2 is the temperature under shade; K is the correction coefficient, usually 0.5-0.8.

[0026] As preferred, in step 3), the heat balance coefficient calculation formula is:

[0027] ;

[0028] Wherein, K PI is the heat balance coefficient; R is the net radiation energy of the tree species surface (MJ / m 2 d); G is the soil heat flux density (MJ / m 2 d), T2 is the full light temperature 1.5 meters above the ground at a specific time (℃); U is the average wind speed 1.5 meters above the ground (m / s); α is the standard value of solar constant (1353w / m 2 ); △ is the temperature difference value of full light temperature minus shade temperature; is the full light intensity; is the shade light intensity;

[0029] The tree species growth characteristic value calculation formula is:

[0030] ;

[0031] In the formula, is the crown width of the tree at time t; is the crown width of the tree at initial time t0; is the growth speed of the tree at initial time t0; is the growth acceleration.

[0032] As preferred, in step 4), the establishment of the reference target model includes the following steps:

[0033] Step 401: using Sketch-Up software to construct a reference plant model, setting the tree data of the reference plant according to the ideal heat balance coefficient and the target growth characteristic value;

[0034] Step 402: fusing the reference plant model to the regional environment model, setting the layout of the reference plant model in the regional environment model, so that the heat balance coefficient of the regional environment model reaches the target heat balance coefficient, and obtaining the reference target model.

[0035] As preferred, in step 4), the IA method is used to compare the characteristics of the simulation model and the corresponding target model, and the calculation formula is:

[0036]

[0037] In the formula, X pi is the target value; X mi is the simulation value; X p is the average value of the target value: X m is the average value of the measured value, and X pi '=Xpi X p X mi X mi X m The value of IA can reflect the degree of coincidence between the target value and the simulation value; during calculation, the target heat balance coefficient and the target growth characteristic value are substituted into the target value, and the simulation model heat balance coefficient and the tree growth characteristic value are substituted into the simulation value for calculation.

[0038] As preferred, a sidewalk tree screening system based on a forest shelter ecological index comprises

[0039] A data storage module is configured to store the latitude and longitude data, meteorological data, geological data and tree data of a target area, perform simulation calculation on the tree data sub-models, obtain simulation results, and calculate the characteristics of each tree data sub-model by using stepwise multiple linear regression analysis on the simulation results.

[0040] A regional environment construction module is configured to establish a regional environment model by using a DEM technology.

[0041] A plant construction module is configured to establish a plurality of tree data sub-models.

[0042] A screening module is configured to fuse the regional environment model and the sidewalk tree species model, set a target area in a software database to determine the arrangement and species of trees by free combination, and customize the tree data sub-models corresponding to each tree in the target area.

[0043] An analysis module is configured to calculate the simulation model heat balance coefficient and the tree growth characteristic value, estimate the trend curve of the simulation model, estimate the trend curve of a reference target model, extract the characteristics of the simulation model and the corresponding characteristics of the target model, and compare the characteristics by using an IA method.

[0044] As preferred, a computer readable storage medium stores a computer program, the computer program comprises program instructions, and the program instructions enable a processor to execute the method when executed by the processor.

[0045] The present application has the following advantages:

[0046] The application provides a sidewalk tree screening method and system based on a shelterbelt ecological index and a medium, different types and / or ages of sidewalk trees can be made into corresponding tree data sub-models, a large number of tree data sub-models are contained in a sidewalk tree species model, simulation results of multiple indexes are obtained according to tree data simulation calculation, the relationship between the shelterbelt ecological index effect and the relationship between the tree structure index and the shelterbelt ecological index effect is identified and quantified by stepwise multiple linear regression, and the target shelterbelt ecological index effect to be achieved is simulated artificially in advance according to the corresponding relationship through the simulation model, which is beneficial to scientifically select the tree species of sidewalk trees in the target area according to the shelterbelt ecological effect, and provides a scientific basis for establishing an ecological system in line with the shelterbelt ecological effect. Secondly, when simulating the target area model, the heat balance coefficient in the range of the region and the trend of the growth characteristic value of the plant in the target region are considered, which is beneficial to achieve more stable and longer shelterbelt ecological effect in the target region. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 It is a flowchart of a first embodiment of a sidewalk tree screening method based on a shelterbelt ecological index;

[0048] Figure 2 It is a list diagram of meteorological data and tree data measurement data sources;

[0049] Figure 3 It is a list diagram of instruments used for meteorological data and tree data;

[0050] Figure 4 It is a comparison diagram of the heat balance coefficient trend curve of the simulation model and the heat balance coefficient trend curve of the reference target model;

[0051] Figure 5 It is a comparison diagram of the tree species growth characteristic value trend curve of the simulation model and the tree species growth characteristic value trend curve of the reference target model. DETAILED DESCRIPTION

[0052] The features and exemplary embodiments of various aspects of the application will be described in detail below, in order to make the purposes, technical solutions and advantages of the application more clear and apparent, the application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the application, but not to limit the application. The application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only to provide a better understanding of the application by showing examples of the application.

[0053] It should be understood, when used in the specification and the appended claims, the term "comprises" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0054] As shown in Figure 1 The present application provides a flow chart of a first embodiment of a sidewalk tree screening method based on the shade ecological index, which comprises the following steps,

[0055] Step 1) Collect the latitude and longitude data and meteorological data of the target area, and establish a regional environment model by using DEM technology.

[0056] In one embodiment, the target area is a geographical location selected for planting plants for greening, which can be specifically a public area such as a city street, a forest, a campus, a park, or a private garden. A regional environment model is established by using DEM technology to simulate the environmental state of the target area, thereby providing a virtual simulation environment for achieving the predetermined shade ecological index.

[0057] In one embodiment, the meteorological data includes light intensity under full light, temperature under full light, and wind speed. The instruments used for measuring each data are as shown in Figure 2 Figure 3

[0058] In one embodiment, the latitude and longitude data of the target area can be obtained from the national standard provincial and municipal database, or from online navigation software (such as Google Maps, Baidu Maps, and Gaode Maps). The meteorological data can be measured data in a certain time period of the year in the region according to the demand, such as measuring in a specific time period (such as 11:00-15:00) in the whole month of June in the region, generating multiple daily meteorological sub-sets, and freely selecting any representative daily meteorological sub-set to set the daily meteorological change condition of the regional environment model.

[0059] In one embodiment, the meteorological data can be the meteorological measured data of the target area range every day (9:00-17:00) of the previous year, which is recorded once every 1h, recorded in an excel table, and multiple daily meteorological sub-sets are generated, and any representative daily meteorological sub-set can be freely selected to set the daily meteorological change condition of the regional environment model.

[0060] In one embodiment, the establishment of the regional environment model comprises the following steps:

[0061] ​​Step 101: Obtain DEM data of a target area from a Map GIS K9 platform, perform spatial correction and coordinate registration on the DEM data by using the Map GIS K9 platform, generate DEM data spatial coordinates, perform cutting on the DEM data by using a tiling and cutting function of the Map GIS, save the cut DEM data to a database, perform triangulation on the DEM data to form a surface triangular network, and establish a surface three-dimensional network model by using the surface triangular network; set a color for each corner point of the surface three-dimensional network model, obtain a surface element color by color interpolation of corresponding corner points; obtain remote sensing images of the target area with a resolution of 10 m by using Google Earth software, perform geometric correction on the obtained remote sensing images by using ENVI software to obtain remote sensing image data matched with the DEM data, perform enhancement and fusion processing on the remote sensing images to obtain image data in a raster data format, and save the image data to the Map GIS K9 platform database; and add the image data to the three-dimensional surface network model in the form of an image by using the Map GIS K9 platform.

[0062] Step 102: Obtain an original geological map of a target area by using a MapGIS K9 platform, and perform coordinate transformation on the geological map to make it consistent with the DEM data spatial coordinates.

[0063] Step 103: Select and add or subtract target area geological data by using the MapGIS K9 platform, generate a geological vector map, and save the vector map to a database of the MapGIS K9 platform.

[0064] Step 104: Add the vector geological map generated in step 103 to the three-dimensional network model to generate a regional environment model.

[0065] Compared with a traditional environment modeling method, the DEM technology is used to establish a regional environment model, the modeling speed is fast, three-dimensional visualization can be realized, and the target area can be accurately restored.

[0066] Step 2) Measure and record tree data of different types of street trees, establish a plurality of tree data sub-models, and simulate and calculate the tree data sub-models one by one to obtain simulation results, and use stepwise multiple linear regression analysis to analyze the simulation results to calculate the characteristics of each tree data sub-model, and fuse the plurality of tree data sub-models to obtain a street tree type model.

[0067] In one embodiment, establishing a tree data sub-model includes the following steps:

[0068] Step 201: Select a sample tree with good growth conditions, a planting age of five years, relatively uniform crown and leaf distribution, and no recent artificial intervention pruning, take a picture by using a wide-angle digital camera at 360° horizontally and 270° vertically, and simultaneously scan the tree by using a laser scanner during the picture taking process to obtain a tree image and three-dimensional information;

[0069] Step 202: The tree image is spliced and registered by the easy module software to generate three-dimensional image information and is imported into the software provided with the laser scanner to establish point cloud data of the sample plant and export the point cloud data, which is imported into AutoCAD, a coordinate system of the sample plant is defined in the AutoCAD environment through the point cloud software, then the sample plant model is built in the Sketch-Up software, the three-dimensional information is corrected according to the tree structure data obtained in the actual measurement, and the tree data sub-model is constructed.

[0070] In one embodiment, 15 kinds of trees including Bombax ceiba, Magnolia denudata, Ceiba aesculifolia, Bauhinia championii, Khaya nyasica, Terminalia catappa, Acer truncatum, Pisonia aculeata, Cinnamomum camphora, Delonix regia, Tabebuia heterophylla, Lagerstroemia speciosa, Prunus dulcis, Ficus microcarpa and Roystonea regia, which are promoted and applied as the city greening keynote tree species in the Guangzhou Green Space System Planning (2020-2035), can be used to establish their respective tree data sub-models. Each tree data sub-model of the 15 kinds of trees is added to the row tree species model, and one of the plants can be selected to establish different tree data sub-models according to the tree characteristics of the plant in different planting years and added to the row tree species model to form a single type row tree species model. The more tree data sub-models contained in the row tree species model, the more conducive to the reasonable construction of a stable ecological system, and the more suitable high-quality plants and layout conditions are screened out for the target area.

[0071] It is worth mentioning that the tree data that need to be measured and recorded include tree height, diameter at breast height, branch point height, crown height, leaf growth height, crown width, leaf thickness, leaf area, leaf area index, shade temperature, shade humidity, and shade light intensity. The measurement of each data is shown in Figure 2 , and the instruments used in the measurement of each data are shown in Figure 3 .

[0072] In one embodiment, the measurement methods of the air temperature under shade, the air temperature under full light, the relative humidity under shade, the relative humidity under full light, the light intensity under shade, and the light intensity under full light are all that the handheld instrument is held to 1.5 m above the ground to measure three times and take the average value, and the interval between the three measurements is 20-30 s. 30 mature and non-infested leaves are taken from each direction of the tree crown of each sample plant for the measurement of leaf thickness and leaf area. The obtained leaves are divided into three groups, 10 pieces for one group to overlap, and the thickness of the three groups of leaves is measured three times to take the average value, and then the thickness of a single leaf is calculated through Excel. The measurement of leaf area is the same as above, and the leaves are divided into three groups, 10 pieces for one group, and the leaves are scanned at a uniform speed through the conveying belt of the leaf area meter, and the area of a single leaf is calculated through Excel. The leaf area index is measured through the leaf area index meter and calculated through FV2200 software.

[0073] In detail, each tree data is simulated to obtain simulation results including cooling rate, shading rate, humidification rate, shade quality, and comprehensive shading effect. The specific calculation process is as follows:

[0074] Cooling rate calculation: due to the blocking effect of trees, the solar radiation is weakened, and the most intuitive feeling is that the temperature in the shade is obviously more comfortable than that under the direct sunlight. The percentage of the temperature reduction in the shade to the temperature under the direct sunlight is the cooling rate. The calculation formula of the cooling rate is:

[0075]

[0076] wherein T is the cooling rate, T1 is the temperature under full light, which is measured by holding a thermal line anemometer and hygrometer to a height of 1.5 m above the ground surface, under the condition of direct sunlight and no shelter, at a distance of 2 m from the tree crown edge, T2 is the temperature in the shade, which is measured at a height of 1.5 m above the ground surface in the central part of the street tree shade.

[0077] Shading rate calculation: the tree crown has a certain blocking and absorption to the light intensity. The percentage of the light intensity reduction in the shade to the light intensity under the direct sunlight is the shading rate. The calculation formula of the shading rate is:

[0078]

[0079] wherein L is the shading rate, L1 is the light intensity measured by holding a light intensity meter to a height of 1.5 m above the ground surface, under the condition of direct sunlight and no shelter, at a distance of 2 m from the tree crown edge, L2 is the light intensity measured at a height of 1.5 m above the ground surface in the central part of the street tree shade.

[0080] Humidification rate calculation: the tree shade has a humidification effect in addition to the cooling effect. The percentage of the relative humidity reduction in the shade to the relative humidity under the direct sunlight is the humidification rate. The calculation formula of the humidification rate is:

[0081]

[0082] wherein H is the humidification rate, H1 is the relative humidity measured by holding a thermal line anemometer and hygrometer to a height of 1.5 m above the ground surface, under the condition of direct sunlight and no shelter, at a distance of 2 m from the tree crown edge, H2 is the relative humidity measured at a height of 1.5 m above the ground surface in the central part of the street tree shade.

[0083] Shading quality: Shading quality is the quality of the shadow produced by the sunlight shining on the trees, which is used to compare the quality of the shadow in different sizes, different growth environments, different tree species, etc. The unit of shading quality is n, expressed as "shade degree". The calculation formula of shading quality is:

[0084]

[0085] Wherein, M is the shading quality, L is the light blocking rate, and T is the cooling rate.

[0086] Comprehensive shading effect calculation: The good or bad of the comprehensive shading effect depends on the cooling rate, the light blocking rate and the size of the shading area. The calculation formula of the comprehensive shading effect is:

[0087]

[0088] Wherein, P is the shading effect, M is the shading quality, and S is the shading area.

[0089] The above simulation results are used as independent variables to analyze the correlation or relationship between variables by using multiple linear regression analysis. This analysis is based on the assumption of linear relationship between quantitative variables, similar to the measurement of the correlation between two variables, which measures the "strength" or "degree" of the correlation between variables and its direction. The final result of multiple linear regression analysis is the correlation coefficient, whose value ranges from -1 to +1. The correlation coefficient r is +1, indicating that the two variables are positively completely correlated. The correlation coefficient r is -1, indicating that the two variables are negatively completely correlated. The correlation coefficient is 0, indicating that there is no linear relationship between the two variables studied. When 0.8 < r < 1, it indicates that the two variables are extremely strongly correlated. When 0.6 < r < 0.8, it indicates that the two variables are strongly correlated. When 0.4 < r < 0.6, it indicates that the two variables are moderately correlated. When 0.2 < r < 0.4, it indicates that the two variables are weakly correlated. When 0 < r < 0.2, it indicates that the two variables are extremely weakly correlated or not correlated.

[0090] The effect comparison of street tree forest ecological indexes is a research influenced by many factors, and the good or bad effect is subjective, so it is necessary to know the correlation between the indexes through correlation analysis between multiple indexes, so as to accurately improve the shading effect through human intervention and improve the good experience of pedestrians and vehicles during passing. Therefore, Pearson correlation analysis can be performed to evaluate the relationship between the effects of forest ecological indexes and the relationship between tree structure indexes and the effects of forest ecological indexes, and stepwise multiple linear regression is used to identify and quantify the relationship between the effects of forest ecological indexes and the relationship between tree structure indexes and the effects of forest ecological indexes. Specifically, the step-in and step-out criteria are based on the significance level of F value, which is set to 0.05, and stepwise multiple linear regression can provide an equation that links the characteristics of tree structure and the effects of forest ecological indexes, and a multivariate model of dependent variable Y is constructed based on variables. Based on the highest multiple correlation coefficient (R 2 ), the best equation is selected, which has the following form:

[0091]

[0092] where Y is the dependent variable, representing the effect of forest ecological index, X1, X2...X n are independent variables; is a constant, and when (1≤β i ≤n), it is the standard partial regression coefficient, which represents the change of the response variable Y when the variable changes by 1 unit. All the above statistical analysis is completed by using SPSS Statistics 23.0.

[0093] Based on the above process, the characteristics of each tree data sub-model can be calculated by the following equation:

[0094] (1)

[0095] (2)

[0096] where P is the shading index; T is the cooling rate; L is the shading rate; S is the shading area; C is the heat absorption coefficient; is the light intensity under full light; is the light intensity under shade; T1 is the temperature under full light; T2 is the temperature under shade; K is the correction coefficient, usually 0.5-0.8.

[0097] Step 3) Fuse the regional environment model with the street tree species model to establish a simulation model, train the regional environment model with the tree data sub-model to obtain the characteristics of the simulation model, calculate the heat balance coefficient of the simulation model and the growth characteristic value of the tree species, and make trend curve estimation.

[0098] Specifically, a regional environment model file needs to be loaded in ArcMap, then the required elements are imported into SketchUp using the SketchUp ESRI plug-in in Arc GIS, and the street tree species model is imported into SketchUp to synthesize a simulation model of the regional environment model and the street tree species model. By setting the base height, a three-dimensional model can be quickly established in SketchUp. The corresponding tree data sub-model is selected and copied and / or reasonably laid out according to the terrain to obtain the characteristics of the simulation model, such as the selected plant species, the arrangement of the plants, the number of plants, etc.

[0099] The computer application program Microsoft Excel 2020 is used to calculate the heat balance coefficient of the simulation model and the tree species growth characteristic value. In detail, the heat balance coefficient calculation formula is:

[0100] ;

[0101] wherein, K PI is the heat balance coefficient; R is the net radiation energy on the surface of the tree species (MJ / m 2 d); G is the soil heat flux density (MJ / m 2 d), T2 is the full light temperature 1.5 meters above the ground at a specific time (℃); U is the average wind speed 1.5 meters above the ground (m / s); a is the standard value of solar constant (1353w / m 2 ); △ is the temperature difference value of full light temperature minus shaded temperature; is the full light intensity; is the shaded light intensity;

[0102] The tree species growth characteristic value calculation formula is:

[0103] ;

[0104] In the formula, is the crown width of the tree at time t; is the crown width of the tree at initial time t0; is the growth speed of the tree at initial time t0; is the growth acceleration.

[0105] The trend curve is estimated by using the computer application program Microsoft Office Word 2007. In some embodiments, when the meteorological data is the measured data in a certain time period of a year in the region, the meteorological data in the similar months can be inferred from the measured data in the time period and the meteorological conditions in previous years, so as to be substituted into the heat balance coefficient calculation formula, to calculate the model heat balance coefficient in the similar months, so as to draw the trend curve. For example, by measuring the meteorological data in June of the target region, a plurality of daily meteorological sub-sets are generated, and according to the meteorological conditions in previous years in the region, it can be found that the meteorological data from June to August is similar, so the meteorological data from July to August can be roughly inferred. A plurality of representative daily meteorological sub-sets are selected to calculate the heat balance coefficient, and the trend curve is generated according to the heat balance coefficient. In some embodiments, when the meteorological data is the measured data of the target region in the whole year of the previous year, a representative daily meteorological sub-set is selected to be substituted into the heat balance coefficient calculation formula, to calculate the model heat balance coefficient of the representative meteorological day of each quarter or each month, and the data is collected to draw the trend curve.

[0106] The tree species growth characteristic value is used to calculate the growth trend of the plant. After the plant is planted, it is generally not easily replaced, and therefore, the growth process of the plant such as defoliation, hibernation, and sprouting will become one of the important factors affecting the forest canopy, and will also affect the heat balance in the region. The tree species growth characteristic can be based on the change of the plant in the growth period, so as to calculate the growth trend. Specifically, the tree species growth characteristic value can be calculated by measuring the characteristics of the tree body of different planting years.

[0107] Step 4) Set the target heat balance coefficient and the target growth characteristic value, establish a reference target model, estimate the trend curve, and compare the simulation model characteristics and the corresponding target model characteristics by using the IA method. The calculation formula is:

[0108]

[0109] In the formula, X pi Target value; X mi Simulation value; X p Average value of the target value: X m Average value of the measured value, X pi = X pi -X p , X mi = X mi -X m The value of IA can reflect the degree of coincidence between the target value and the simulation value. In the calculation, the target heat balance coefficient and the target growth characteristic value are substituted into the target value, and the simulation model heat balance coefficient and the tree species growth characteristic value are substituted into the simulation value for calculation.

[0110] When 0.5<IA≤1, the simulation model characteristics and the target model characteristics tend to coincide, and the simulation model meets the screening requirements; when 0≤IA≤0.5, the simulation model characteristics and the target model characteristics differ greatly, and the simulation model does not meet the screening requirements, and the regional environment model is retrained using the tree data sub-model.

[0111] In some embodiments, the step of establishing a reference target model comprises the following steps:

[0112] Step 401: using Sketch-Up software to construct a reference plant model, and setting tree data of the reference plant according to an ideal heat balance coefficient and target growth characteristic values;

[0113] Step 402: fusing the reference plant model to the regional environment model, setting a layout of the reference plant model in the regional environment model, making a heat balance coefficient of the regional environment model reach a target heat balance coefficient, and obtaining a reference target model.

[0114] The construction process of the reference plant model refers to the simulation model construction process, trend curve estimation is performed according to the set target heat balance coefficient and target growth characteristic values, the trend curve constructed by the simulation model is compared with a trend model of the reference plant model, and the higher the coincidence degree is, the more the simulation model meets the demand of the ecological index of the target area.

[0115] The working method of the sidewalk tree screening system based on the ecological index of the shade refers to the method described in the above embodiments, which will not be described here. The sidewalk tree screening system based on the ecological index of the shade comprises a data storage module, a regional environment construction module, a plant construction module, a screening module, and an analysis module. The data storage module is used to store longitude and latitude data, meteorological data, and geological data of a target area, and to perform simulation calculation on a tree data sub-model to obtain a simulation result, and to calculate a characteristic of each tree data sub-model by using stepwise multiple linear regression analysis of the simulation result. The regional environment construction module is used to establish a regional environment model by using DEM technology. The plant construction module is used to establish a plurality of tree data sub-models. The screening module is used to fuse the regional environment model and a sidewalk tree species model, to set a target area in a software database to determine tree arrangement and species, and to customize a tree data sub-model corresponding to each tree in the target area. The analysis module is used to calculate a heat balance coefficient of a simulation model and a tree species growth characteristic value, to perform trend curve estimation of the simulation model, to perform trend curve estimation of a reference target model, and to compare a simulation model characteristic and a corresponding target model characteristic by using an IA method.

[0116] The sidewalk of Jinsui Road in Guangzhou City is taken as an example to further illustrate the present application. First, longitude and latitude data of Jinsui Road and daily meteorological data of the last year are collected, and a plurality of daily meteorological sub-sets are generated by using a computer application program Microsoft Excel 2020, and a regional environment model is established by using DEM technology.

[0117] The common street trees in the main urban area (Haizhu District) of Guangzhou City were investigated in the field, and the opinions of industry experts were consulted. Finally, 7 species of plants were selected to measure and record the tree data. The 7 species of street trees belong to 6 families and 7 genera. The specific characteristics of the sample trees are shown in Table 1. Different sample trees have different specifications and shapes. To reduce the data differences of the shading effect, the sample trees were required to have good growth conditions, with a planting age of five years, a common planting specification in Guangzhou City, a uniform and dense crown layer, and no recent manual pruning. The tree data was recorded by field investigation. The tree structure indexes that need to be measured and recorded include tree height, diameter at breast height, branch point height, crown height, leaf growth height, crown width, leaf thickness, leaf area, leaf area index, shade temperature, shade humidity, and shade light intensity. A wide-angle digital camera was used to take 360° horizontal and 270° vertical pictures. A laser scanner was used to scan synchronously during the shooting process to obtain tree images and three-dimensional information. The tree images were spliced and registered by Easy-Model software, and then a sample tree model was built by Sketch-Up software. The three-dimensional information was corrected according to the tree structure data obtained in the measurement, and a tree data sub-model was constructed.

[0118] Table 1: Characteristics of 7 species of street trees

[0119]

[0120] The tree data of the above plants was obtained by Figure 2 point measurement, and the instruments used for each tree data are shown in Figure 3 Table 2.

[0121] Table 2: Measured tree data of 7 species of street trees

[0122]

[0123] The FV2200 software was used to simulate and calculate each tree data. The simulation results include cooling rate, shading rate, humidification rate, shade quality, and comprehensive shading effect. Pearson correlation analysis was used to evaluate the relationship between the effects of forest shade ecological indicators and the relationship between tree structure indicators and forest shade ecological indicators. The simulation results were substituted into the equation to calculate the stepwise multiple linear regression of the forest shade ecological indicator effects and the tree structure related indicators, as shown in Table 3.

[0124] To compare the contribution of tree structure indexes to the effect of the forest ecological index, stepwise multiple linear regression analysis was conducted based on the above formula, and the insignificant variables (tree height, crown width, branch point height, leaf thickness) were removed. The significant variables included crown height, diameter at breast height, leaf growth height, leaf area, and leaf area index, which had a high correlation with the comprehensive shading effect.

[0125] Table 3 is a stepwise multiple linear regression statistical table of the forest ecological index effect and tree structure related indexes

[0126]

[0127] The effects of the forest ecological index effect were ranked in descending order as cooling rate > shading area > light blocking rate. The cooling rate and shading area had a much greater impact on the forest ecological index effect than the light blocking rate. Crown height and leaf area were extremely significantly positively correlated with the comprehensive shading effect (P < 0.01), and had a linear relationship with the comprehensive shading effect. Diameter at breast height, leaf growth height, and leaf area index were significantly correlated with the forest ecological index effect (0.01 < P < 0.05). The forest ecological index effect was linearly positively correlated with crown height, leaf area, diameter at breast height, and leaf area index, and was linearly negatively correlated with leaf growth height. The order of the absolute correlation coefficients was crown height > leaf growth height > diameter at breast height > leaf area index > leaf area, indicating that crown height had the greatest contribution to the forest ecological index effect. The stepwise multiple linear regression equation between the forest ecological index effect and the tree structure indexes was:

[0128] P = crown height × 0.3 + diameter at breast height × 0.036 - leaf growth height × 0.231 + leaf area × 0.006 + leaf area index × 0.365 - 1.062, where P is the comprehensive shading effect forest ecological index effect. The higher the crown height, the larger the diameter at breast height, the lower the leaf growth height, the larger the leaf area, and the larger the leaf area index, the better the forest ecological index effect.

[0129] The regional environment model is fused with the street tree species model to establish a simulation model, the regional environment model is trained by the tree data sub-model, the corresponding tree data sub-model is copied and / or reasonably arranged according to the terrain to obtain the simulation model characteristics, such as the selected plant species, the arrangement mode of the plants, the number of plants, and the like, the heat balance coefficient of the simulation model and the tree species growth characteristic value are calculated, and the trend curve is estimated. In the simulation model, the regional environment model is trained by the tree data sub-model of the mango tree, in the simulation model, the crown of the tree data sub-model can basically border with the crown of the adjacent tree data sub-model, the simulation distance between the tree data sub-model and the tree data sub-model is 3 m, and the tree data sub-model is arranged at equal intervals along the roadside and on both sides of the road in the regional environment model. According to the heat balance coefficient and the tree species growth characteristic value, the heat balance coefficient trend curve of the simulation model in the regional environment within 10 years and the tree species growth characteristic value trend curve of the simulation model in the regional environment within 10 years are calculated.

[0130] The target heat balance coefficient and the target growth characteristic value are set, the reference target model is established, the heat balance coefficient trend curve of the simulation model in the regional environment within 10 years and the tree species growth characteristic value trend curve of the simulation model in the regional environment within 10 years are compared, the comparison chart is referred to Figure 4 ; the tree species growth characteristic value trend curve of the simulation model is compared with the tree species growth characteristic value trend curve of the reference target model, the comparison chart is referred to Figure 5 , through the IA method comparison, the heat balance coefficient of the simulation model is compared with the heat balance coefficient of the reference target model, 0.58 < IA < 0.79, the tree species growth characteristic value of the simulation model is compared with the tree species growth characteristic value of the reference target model, 0.62 < IA < 0.84, the simulation model characteristics and the target model characteristics tend to coincide, and the simulation model meets the screening requirements.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above functional modules and the division of the modules are exemplified, and in actual application, the above functions can be completed by different functional modules or modules according to needs, that is, the internal structure of the device is divided into different functional modules or modules to complete all or part of the functions described above. Each functional module or module in the embodiment can be integrated in one processing module, or each module can be physically present separately, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or software function module. In addition, the specific name of each functional module or module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the module in the above system can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0132] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0133] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0134] In the embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules or modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0135] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can be physically present separately, or two or more modules can be integrated in one module. The integrated module can be realized in the form of hardware or software function module.

[0136] The integrated module, if realized in the form of a software function module and sold or used as an independent product, can be stored in a computer readable storage medium.

[0137] In one embodiment, the computer storage medium stores a computer program including program instructions, which, when executed by a processor, cause the processor to perform the above method, and the specific process is not repeated.

[0138] The computer readable storage medium can be non-volatile or volatile. Based on this understanding, all or part of the processes in the above-mentioned embodiments can be implemented by a computer program, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer readable storage medium can include any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), electric carrier wave signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer readable storage medium does not include electric carrier wave signal and telecommunication signal.

[0139] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features. These modifications or replacements do not change the essence of the corresponding technical solutions, and should be included in the protection scope of the present application.​

Claims

1. A method for screening street trees based on a canopy ecological index, the method comprising: The method comprises the following steps, ​ Step 1) collecting latitude and longitude data and meteorological data of a target area, and establishing a regional environment model by using a DEM technology; Step 2) measuring and recording tree body data of different kinds of street trees, establishing a plurality of tree body data sub-models, and simulating and calculating the tree body data sub-models one by one to obtain simulation results, and using stepwise multiple linear regression analysis to simulate the simulation results to calculate the characteristics of each tree body data sub-model, and fusing the plurality of tree body data sub-models to obtain a street tree species model; Step 3) fusing the regional environment model and the street tree species model to establish a simulation model, training the regional environment model by using the tree body data sub-model, obtaining simulation model characteristics, calculating a heat balance coefficient of the simulation model and a tree species growth characteristic value, and making trend curve estimation; The heat balance coefficient calculation formula is: wherein K PI is the heat balance coefficient; R is the net radiation energy of the tree species surface, in MJ / m 2 d; G is the soil heat flux density, in MJ / m 2 d; T2 is the full light temperature 1.5 meters above the ground at a specific time, in °C; U is the average wind speed 1.5 meters above the ground, in m / s; and a is the standard value of the solar constant 1353 w / m 2 . △ is the temperature difference value of the temperature under full light minus the temperature under shade; is the light intensity under full light; is the light intensity under shade; The tree species growth characteristic value calculation formula is: wherein is the crown width of the tree at time t; is the crown width of the tree at initial time t0; is the growth speed of the tree at initial time t0; is the growth acceleration; The IA method is used to compare the simulation model characteristics and the corresponding target model characteristics, and the calculation formula is: In the formula, X pi is the target value; X mi is the simulation value; X p is the average of the target value; X m is the average of the measured value; X pi '=X pi -X p , X mi '=X mi -X m ; the value of IA can show the degree of coincidence between the target value and the simulation value; in the calculation, the target heat balance coefficient and the target growth characteristic value are substituted into the target value, and the simulation model heat balance coefficient and the tree species growth characteristic value are substituted into the simulation value for the calculation; Step 4) setting a target heat balance coefficient and a target growth characteristic value, establishing a reference target model, making trend curve estimation, and using the IA method to compare the simulation model characteristics and the corresponding target model characteristics, when 0.5 2. The method for screening street trees based on the ecological index of the forest shelter according to claim 1, characterized in that: In step 1), the meteorological data includes light intensity under full light, temperature under full light, and wind speed; in step 2), the tree body data includes tree height, diameter at breast height, branch point height, crown height, leaf growth height, crown width, leaf thickness, leaf area, leaf area index, shade temperature, shade humidity, and shade light intensity, each tree body data is simulated and calculated, and the simulation results include cooling rate, light shielding rate, humidity increasing rate, shade quality, and comprehensive shade effect.

3. The method of screening street trees based on the index of shelterbelt ecology according to claim 1, characterized in that: In step 1), the establishment of the regional environment model comprises the following steps: Step 101: obtaining DEM data of a target area, performing spatial correction and coordinate registration on the DEM data by using a MapGIS K9 platform to generate DEM data spatial coordinates, cutting and triangulation of the DEM data to form a ground triangular network, and establishing a ground three-dimensional network surface model by using the ground triangular network; Step 102: obtaining an original geological map of the target area, performing coordinate transformation on the geology to make it consistent with the DEM data spatial coordinates; Step 103: selecting and adding or reducing target area geological data by using the MapGIS K9 platform to generate a geological vector map; Step 104: adding the vector geological map generated in step 103 to the three-dimensional network surface model.

4. The method of claim 1, wherein the method is based on a canopy ecological index. In step 2), the establishment of the tree body data sub-model comprises the following steps: Step 201: selecting a sample tree with good growth condition, five years of planting age, relatively uniform crown layer branch and leaf distribution, and no recent artificial intervention pruning, taking pictures by using a wide-angle digital camera at 360° horizontally and 270° vertically, and synchronously scanning the tree by using a laser scanner during the photographing process to obtain tree images and three-dimensional information; Step 202: The tree image is spliced and registered by the easy module software to generate three-dimensional image information and is imported into the software of the laser scanner, and the point cloud data of the sample plant is established and exported, and the exported point cloud data is imported into AutoCAD, and the point cloud software is used to define the coordinate system of the sample plant in the AutoCAD environment, and then the sample plant model is imported into Sketch-Up software, the three-dimensional information is corrected according to the tree structure data obtained in the actual measurement, and the tree data sub-model is constructed.

5. The method of claim 2, wherein the method is based on a canopy ecological index. In step 2), the characteristics of each tree data sub-model are calculated based on the following equation: （1） （2） Wherein, P is the shading index; T is the temperature reduction rate; L is the shading rate; S is the shading area; C is the heat absorption coefficient; is the light intensity under full light; is the light intensity under shade; T1 is the temperature under full light; T2 is the temperature under shade; K is the correction coefficient, usually 0.5-0.

8.

6. The method of claim 1, wherein the method is a method of screening street trees based on a canopy ecological index. In step 4), the reference target model includes the following steps: Step 401: using Sketch-Up software to construct a reference plant model, and setting the tree data of the reference plant according to the ideal heat balance coefficient and the target growth characteristic value; Step 402: fusing the reference plant model to the regional environment model, setting the layout of the reference plant model in the regional environment model, making the heat balance coefficient of the regional environment model reach the target heat balance coefficient, and obtaining the reference target model.

7. A street tree screening system based on the ecological index of the forest, using the method of claim 1, characterized in that: comprise a data storage module for storing the latitude and longitude data, meteorological data and geological data of the target region and the tree data, and performing simulation calculation on the tree data sub-model to obtain simulation results, and using stepwise multiple linear regression analysis on the simulation results to calculate the characteristics of each tree data sub-model; a regional environment construction module for establishing a regional environment model by using DEM technology; a plant construction module for establishing a plurality of tree data sub-models; a screening module for fusing the regional environment model with the street tree species model, freely combining the target region in the software database to determine the arrangement and species of the trees, and defining the corresponding tree data sub-model of each tree in the target region; an analysis module for calculating the heat balance coefficient of the simulation model and the growth characteristic value of the tree species, and estimating the trend curve of the simulation model; estimating the trend curve of the reference target model, extracting the characteristics of the simulation model and the corresponding characteristics of the target model, and comparing them by using the IA method.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program includes program instructions, which, when executed by a processor, cause the processor to execute the method of any one of claims 1-6.

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