Fine estimation method and device for carbon sink capacity of border tree

Abstract

The invention relates to the technical field of street tree carbon reserve estimation, and provides a street tree carbon sink capability fine estimation method and device, and the method comprises the steps: obtaining first-stage street tree data and second-stage street tree data; estimating the border tree biomass of the target area according to human management and protection factors, the first-stage border tree data and the second-stage border tree data on the basis of a target diameter at breast height-tree height model to obtain carbon reserves of the first-stage border trees and the second-stage border trees; calculating street tree carbon sink parameters based on the carbon reserves of the first-stage street tree and the second-stage street tree and the road characteristics of the target area so as to evaluate the street tree carbon sink capability of the target area; wherein the road features comprise at least one of the greening length of each street in the target area, the plant spacing between adjacent trees, the total greening length of all streets and the total road area of all streets; the border tree carbon sink parameters comprise at least one of carbon sequestration amount, carbon sink, carbon sequestration density and carbon sink density. According to the method, the prediction accuracy of the carbon sink capability of the street tree is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon storage estimation of roadside trees, and in particular to a method and device for finely estimating the carbon sequestration capacity of roadside trees. Background Art

[0003] Tree height and breast diameter are the most basic and important survey factors in forestry production surveys. As elements of standing timber volume, they are important indicators for calculating forest stock, biomass and carbon reserves, as well as measuring forest stand growth potential and forest site quality. Urban street trees are not only affected by common factors such as the natural environment and genetic characteristics during their growth, but are also closely related to human management factors. These factors can affect the growth of street trees and thus affect their carbon sequestration function.

[0004] At present, most tree height growth models usually use the breast diameter as the only independent variable to directly predict the tree height. The carbon storage data of street trees calculated according to this model cannot reflect the differences in breast diameter-tree height growth under different environments, resulting in inaccurate statistical results on the carbon sequestration capacity of street trees, making it difficult to evaluate the environmental benefits of urban trees. Summary of the invention

[0005] The present invention provides a method and device for finely estimating the carbon sequestration capacity of street trees, which is used to solve the defect in the prior art that the carbon storage data of street trees calculated by using a tree height growth model cannot reflect the growth difference of breast diameter-tree height under different environments, resulting in inaccurate statistical results of the carbon sequestration capacity of street trees, thereby improving the accuracy of predicting the carbon sequestration capacity of street trees.

[0006] The present invention provides a method for finely estimating the carbon sequestration capacity of roadside trees, comprising: Acquire the first phase of roadside tree data and the second phase of roadside tree data; the first phase of roadside tree data and the second phase of roadside tree data are collected at different time points in the target area; Based on the target DBH-tree height model, the biomass of the street trees in the target area is estimated according to the human management factors, the first phase street tree data and the second phase street tree data, to obtain the carbon storage of the first phase street trees and the second phase street trees; wherein the target DBH-tree height model is determined based on the biomathematical model; the human management factors include at least two of the street tree spacing, tree pit side length, neighboring tree species, trunk whitewashing, tree pit anti-trampling protection, road building shading and vertical structure; The carbon sink parameters of the street trees are calculated based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area to evaluate the carbon sink capacity of the street trees in the target area; wherein the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the carbon sink parameters of the street trees include at least one of the carbon fixation amount, carbon sink, carbon fixation density and carbon sink density.

[0007] According to a method for finely estimating carbon sequestration capacity of roadside trees provided by the present invention, obtaining the first-phase roadside tree data and the second-phase roadside tree data comprises: Randomly sampling streets within the target area to obtain sampling streets; Each street tree on each sampled street is measured to obtain the first phase of street tree data, wherein each tree measurement includes measuring the tree species name, tree height, diameter at breast height, and the spacing between adjacent trees; When the target area passes through the target time interval, each roadside tree on the sampled street is measured to obtain the second-phase roadside tree data.

[0008] According to a method for fine estimation of carbon sequestration capacity of roadside trees provided by the present invention, the target area includes a city; The random sampling of streets in the target area to obtain the sampled streets includes: Divide the roads in the target area according to the urban road grade classification conditions to obtain urban expressways, urban trunk roads, urban secondary trunk roads and urban branch roads; Dividing the urban branch roads according to the road network density and road width to obtain new urban branch roads; The sampled streets are obtained by random sampling on the urban expressways, urban trunk roads, urban secondary trunk roads and urban branch roads.

[0009] According to a method for fine estimation of carbon sequestration capacity of roadside trees provided by the present invention, the biomathematical model includes a linear model, a logarithmic model and an allometric growth model; The target DBH-tree height model is constructed by the following steps: For each biomathematical model, the target evaluation index is calculated based on the biomathematical model according to the sample tree height data and the sample breast diameter data to obtain the index calculation result; the target evaluation index includes the determination coefficient R 2 , at least one of Akaike information criterion AIC, root mean square error RMSE and mean percentage error MEP; The target DBH-tree height model is determined based on the maximum value among the index calculation results.

[0010] According to a method for finely estimating the carbon sequestration capacity of roadside trees provided by the present invention, the roadside tree biomass in the target area is estimated based on the target DBH-tree height model according to human management factors, the first-phase roadside tree data, and the second-phase roadside tree data, and the carbon storage of the first-phase roadside trees and the second-phase roadside trees is obtained, including: A dummy variable DBH-tree height model is constructed by taking the human management factor as the dummy variable of the target DBH-tree height model; Calculate the total plant biomass of each roadside tree based on the dummy variable DBH-tree height model according to the first phase roadside tree data and the second phase roadside tree data; The carbon storage of the first phase of street trees is obtained according to the total plant biomass and the preset carbon content.

[0011] According to a method for finely estimating the carbon sequestration capacity of roadside trees provided by the present invention, the human management factors include the spacing of roadside trees, the side length of tree pits, adjacent tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; Before constructing a dummy variable DBH-tree height model using the human management factor as a dummy variable of the target DBH-tree height model, the method further includes: A significant correlation analysis is performed on the human management and maintenance factors to obtain analysis results, and the analysis results are screened according to a significant impact threshold to obtain human management and maintenance factors with significant results.

[0012] According to a method for finely estimating carbon sequestration capacity of roadside trees provided by the present invention, after obtaining the carbon storage of roadside trees in the first period, the method further includes: The allometric growth equation constructed based on the traditional forest survey is used to estimate the biomass of the street trees in the target area according to the first phase street tree data and the second phase street tree data to obtain a new street tree carbon stock; Based on the carbon stocks of the first-phase roadside trees, the second-phase roadside trees and the new roadside trees, an evaluation result of the carbon sequestration capacity of the roadside trees is obtained.

[0013] The present invention also provides a device for finely estimating the carbon sink capacity of roadside trees, comprising: A data acquisition module is used to acquire the first phase of roadside tree data and the second phase of roadside tree data; the first phase of roadside tree data and the second phase of roadside tree data are collected at different time points in the target area; A biomass estimation module is used to estimate the biomass of street trees in the target area based on the target DBH-tree height model according to human management factors, the first phase street tree data and the second phase street tree data, so as to obtain the carbon storage of the first phase street trees and the second phase street trees; wherein the target DBH-tree height model is determined based on a biomathematical model; the human management factors include at least two of street tree spacing, tree pit side length, adjacent tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; A carbon sink capacity estimation module is used to calculate the carbon sink parameters of street trees based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area, so as to evaluate the carbon sink capacity of street trees in the target area; wherein the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the street tree carbon sink parameters include at least one of carbon fixation amount, carbon sink, carbon fixation density and carbon sink density.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a method for finely estimating the carbon sequestration capacity of roadside trees as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for finely estimating the carbon sequestration capacity of roadside trees.

[0016] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for finely estimating the carbon sequestration capacity of roadside trees.

[0017] The method and device for fine estimation of carbon sink capacity of street trees provided by the present invention estimate the biomass of street trees in a target area according to human management factors, first-phase street tree data and second-phase street tree data through a target diameter-tree height model, obtain the carbon storage of street trees in the first phase and the second phase, and calculate the carbon sink parameters of street trees according to the carbon storage of street trees and road characteristics of the target area to evaluate the carbon sink capacity of street trees in the target area, thereby improving the prediction accuracy of carbon sink capacity of street trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is one of the flow charts of the detailed estimation method of carbon sequestration capacity of street trees provided by the present invention.

[0020] Figure 2 This is a scatter plot of DBH-tree height of street trees provided by the present invention.

[0021] Figure 3 This is the second flow chart of the detailed estimation method of the carbon sequestration capacity of street trees provided by the present invention.

[0022] Figure 4 It is a structural schematic diagram of the device for finely estimating the carbon sequestration capacity of street trees provided by the present invention.

[0023] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Combine the following Figure 1-Figure 4 The present invention describes a method and device for finely estimating the carbon sequestration capacity of roadside trees.

[0026] Figure 1 This is one of the flow charts of the method for fine estimation of carbon sequestration capacity of roadside trees provided by the present invention, such as Figure 1 As shown, the method comprises the following steps: Step 110, obtaining the first phase of roadside tree data and the second phase of roadside tree data; the first phase of roadside tree data and the second phase of roadside tree data are collected at different time points in the target area.

[0027] In this step, the target area includes cities, towns, or other artificially planned areas with diverse road systems and roadside trees.

[0028] In this step, the first phase roadside tree data and the second phase roadside tree data include but are not limited to data such as the name of the roadside tree, tree height, diameter at breast height, and distance to neighboring trees.

[0029] In this embodiment, the same time point may be a time range measured in days, months, seasons or years. For example, a period of time may be ten years.

[0030] In this embodiment, the above-mentioned roadside tree data is obtained by measuring each sampled roadside tree.

[0031] The following takes City A as an example to illustrate that the sampling streets are determined by random sampling or uniform sampling of the streets in the target area of ​​City A. Then, random sampling is performed on the roadside trees planted near the sampling streets, and each sampled street tree is measured and inspected to obtain the first phase of street tree data. Ten years later, nearby sampling is performed on the sampling streets or areas near the sampling streets, and each sampled street tree is measured and inspected to obtain the second phase of street tree data.

[0032] Step 120, based on the target DBH-tree height model, estimate the biomass of street trees in the target area according to human management factors, the first-phase street tree data and the second-phase street tree data, and obtain the carbon storage of the first-phase street trees and the second-phase street trees; wherein, the target DBH-tree height model is determined based on a biomathematical model; and the human management factors include at least two of street tree spacing, tree pit side length, neighboring tree species, trunk whitewashing, tree pit anti-trampling protection, road building shading and vertical structure.

[0033] In this step, the biomathematical model includes but is not limited to linear models, logarithmic models, allometric growth models and other commonly used basic models with biological significance. The biomathematical model is used as a reference model for the dummy variable model to study the impact of city A's management measures on the relationship between the height and breast diameter of street trees.

[0034] In this step, a DBH-tree height model of street trees in the main urban area of ​​City A was constructed with human management factors as dummy variables. The model was used to estimate the carbon storage of street trees in the main urban area of ​​City A, and the impact of human management factors in the urban environment on the DBH and tree height of street trees was analyzed.

[0035] Specifically, the biomathematical model includes a linear model, a logarithmic model, and an allometric model; the target DBH-tree height model is constructed through the following steps: (1) For each biomathematical model, the target evaluation index is calculated based on the biomathematical model according to the sample tree height data and the sample DBH data to obtain the index calculation results; the target evaluation index includes the determination coefficient R 2, at least one of Akaike information criterion AIC, root mean square error RMSE and mean percentage error MEP; determine the target breast diameter-tree height model target based on the maximum value of the indicator calculation results.

[0036] It should be noted that the tree height growth model can reflect the law that tree height changes with breast diameter, and is an important component of the forest growth and harvesting model system. It is also an important method for estimating tree height, stock volume and biomass. Introducing human management factors as dummy variables into the currently known breast diameter-tree height growth model can construct a growth model that is more suitable for urban street trees, providing a more refined estimation method for evaluating the carbon sequestration function of street trees.

[0037] In this example, the coefficient of determination (R 2 ), Akaike information criterion (AIC), root mean square error (RMSE) and mean percentage error (MPE) are used to evaluate the accuracy of the selected common models; SSE is the residual sum of squares; SEE is the standard deviation of the estimate; Adj-R 2 is the adjusted determination coefficient; R 2 , RMSE is the most commonly used indicator of regression model, among which the coefficient of determination R 2 The closer the value is to 1, the better the model fitting accuracy is, the higher the proportion of dependent variables that can be explained is, the smaller the RMSE value is, the smaller the difference between the predicted value and the observed value is, and the higher the model prediction accuracy is; MPE is an accuracy index reflecting the average tree height estimate. The smaller the absolute value, the more reliable the prediction result; AIC is an indicator that comprehensively reflects the model fitting accuracy and complexity. The smaller the value, the higher the model quality.

[0038] Figure 2 is the scatter plot of DBH-tree height of roadside trees provided by the present invention. Figure 2 In the embodiment shown, a scatter plot of the DBH-tree height of street trees is drawn in combination with field survey data, and it can be found that the tree heights of the street trees in City A are concentrated in the range of 10-15m, and the DBH is in the range of 20-35cm; the overall tree height increases with the increase of the DBH, and the growth rate shows a trend of first increasing and then decreasing; there is a nonlinear correlation between the two, and the data of the DBH and tree height are approximately normally distributed.

[0039] (2) Determine the target DBH-tree height model based on the maximum value of the indicator calculation results.

[0040] In this embodiment, the model with the highest fitting accuracy is selected from the selected accuracy models as the basic model of tree height and breast diameter of street trees, that is, the target breast diameter-tree height model, which improves the prediction performance of the breast diameter-tree height model.

[0041] In this embodiment, the tree species, breast diameter, tree height and trunk biomass of the roadside trees in the two surveys are estimated by the tree species allometric equation method and then converted into carbon storage.

[0042] This embodiment introduces the factor of human maintenance into the growth process of urban street trees, and adopts the radial growth detection method to monitor the growth change process of street trees in City A over a period of ten years, thereby establishing a more accurate and more targeted growth model that is different from traditional forest types, providing a scientific reference for the scientific planning, planting, maintenance, and carbon sequestration function evaluation of urban street trees.

[0043] Step 130, calculating the carbon sink parameters of the street trees based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area to evaluate the carbon sink capacity of the street trees in the target area; wherein the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the carbon sink parameters of the street trees include at least one of the carbon fixation amount, carbon sink, carbon fixation density and carbon sink density.

[0044] In this step, the spatial heterogeneity of the carbon storage and carbon sequestration capacity of street trees in City A can be analyzed from different scales according to the characteristics of the urban structure.

[0045] In this example, the carbon storage in the target area is calculated by the following formula: ; ; in, C entire is the carbon stock in the target area, C j is the carbon stock of a sampled street, C i is the carbon storage of individual trees measured; l green is the green length of a single sampled street; L entire is the total green length of all sampled streets; l sample is the distance between adjacent trees; In this embodiment, the carbon sink in the target area is calculated by the following formula: ; in, Csink It is a carbon sink in the target area; C x It is the second survey of carbon density in the target area after a ten-year interval; C y It is the first time that the carbon density in the target area has been investigated.

[0046] In this embodiment, the carbon sink density of the target area is calculated by the following formula: ; in, C density is the carbon sink density of the target area; S area is the total road area of ​​all streets.

[0047] Taking city A as the target area as an example, the carbon storage, carbon sink, carbon storage density and carbon sink density of the study area will be calculated and spatial heterogeneity analysis will be carried out according to the administrative regions of city A.

[0048] The method for fine estimation of the carbon sequestration capacity of street trees provided in an embodiment of the present invention estimates the biomass of street trees in a target area according to human management factors, first-phase street tree data and second-phase street tree data through a target breast diameter-tree height model, obtains the carbon storage of first-phase street trees and second-phase street trees, and calculates the carbon sequestration parameters of street trees according to the carbon storage of street trees and road characteristics of the target area to evaluate the carbon sequestration capacity of street trees in the target area, thereby improving the prediction accuracy of the carbon sequestration capacity of street trees.

[0049] In some embodiments, obtaining the first phase of roadside tree data and the second phase of roadside tree data includes: (1) Randomly sample streets within the target area to obtain sampling streets.

[0050] In this embodiment, the sampling roads can be selected in the study area according to the stratified sampling principle.

[0051] Taking City A as the target area, streets in multiple ring road systems of City A were selected for sampling based on the history, culture, and economic development factors of City A; the selected major urban areas should meet the above characteristics, and 6 representative urban areas of City A were mainly selected as key research areas; sampling streets were selected within the five ring road systems in accordance with the principle of random sampling: sampling points were evenly selected at intervals of 2 kilometers, and streets were selected near the sampling points for sampling. More than 200 sampled streets were counted among the nearly 1,000 roads in the 6 urban areas of City A.

[0052] (2) Each street tree on each sampled street was measured to obtain the first phase of street tree data. The measurement of each tree included measuring the tree species name, tree height, breast diameter, and the spacing between adjacent trees.

[0053] Specifically, according to field surveys, most roads have only one tree species and two rows of trees. In the rare case where there are only two tree species on a street, 5 trees of each species are selected for measurement. If there are multiple tree species near the street, 10 street trees can be randomly selected for each sampled street. For each tree, the species name is recorded, and the tree height, breast diameter and distance from neighboring trees are measured to obtain the first phase of street tree data.

[0054] (3) When the target area passes through the target time interval, each street tree on the sampled street is measured to obtain the second phase of street tree data.

[0055] In this embodiment, according to the inventory cycle of the second type of forest resource monitoring survey, each tree is measured again on the sampling streets determined in the first survey after an interval of ten years to obtain the second phase of street tree data.

[0056] It should be noted that due to changes in roads and roadside trees caused by urban planning and construction during this period, the method of measuring other streets nearby was adopted.

[0057] In this embodiment, the time interval for the second field survey of City A is set to 10 years, and each tree is measured again on the sampled streets in the initial survey year. During this period, due to the changes in roads and street trees caused by urban planning and construction, other streets are measured nearby; at the same time, the second field survey includes an investigation on human management factors of street trees; for example, seven human management factors that can be investigated and judged on site, including whitewashing of tree trunks, anti-trampling protection devices in tree pits, building shading, vertical structure of trees, plant spacing, side length of tree pits, and adjacent tree species, are selected for statistics, and the latitude and longitude of the survey sampling points are recorded with a handheld GPS (Garmin631csx). A total of more than 200 sampled roads were surveyed in this survey, and nearly 700 valid data were obtained; GPS data can be displayed on the corresponding images of the sampled streets or sampled street trees to mark the image sampling time and geographical location.

[0058] The method for fine estimation of the carbon sequestration capacity of street trees provided in an embodiment of the present invention obtains sampled streets by randomly sampling from streets in a target area; measures each street tree on each sampled street to obtain the first phase of street tree data; and measures each street tree on the sampled street when the target area passes through a target time interval to obtain the second phase of street tree data, thereby providing reliable data support for the subsequent acquisition of the carbon storage of street trees in the target area.

[0059] In some embodiments, the target area includes a city; randomly sampling streets in the target area to obtain sampled streets includes: (1) According to the urban road grade classification criteria, the roads in the target area are divided into urban expressways, urban trunk roads, urban secondary trunk roads, and urban branch roads.

[0060] In this embodiment, based on the urban road grade classification regulations, ArcGISpro (19.1.0) software is used to divide the road grades within the urban area of ​​the central urban area of ​​City A into different grades, where Road 1 is a first-class road, i.e., an urban expressway including the second to sixth ring roads; Road 2 is a second-class road, i.e., an urban trunk road; Road 3 is a third-class road, i.e., an urban secondary road; and Roads 4-6 are fourth-class roads, i.e., urban branch roads.

[0061] (2) The urban branches are divided according to the road network density and road width to obtain new urban branches.

[0062] According to the road network density and road width, urban branch roads are divided into three levels: Road 4, Road 5 and Road 6.

[0063] (3) Random sampling is carried out on urban expressways, urban trunk roads, urban secondary trunk roads and urban branch roads to obtain sampling streets.

[0064] In this embodiment, following the principle of random sampling, sampling streets are selected in different road grade systems of the five ring roads of City A: sampling points are evenly selected at intervals of 2 kilometers, and streets are selected near the sampling points for sampling. A total of more than 200 sampled streets are counted on roads of all levels in the central urban area of ​​City A.

[0065] The method for fine estimation of carbon sink capacity of roadside trees provided in the embodiment of the present invention divides the roads in the target area according to the urban road grade classification conditions to obtain urban expressways, urban trunk roads, urban secondary trunk roads and urban branch roads. The urban branch roads are then divided according to the road network density and road width to obtain new urban branch roads. Finally, random sampling is performed on the urban expressways, urban trunk roads, urban secondary trunk roads and urban branches to obtain the sampled streets. The objective variables such as urban thermal environment and human factors are comprehensively considered to achieve effective sampling of urban streets, and high-quality roadside tree data can be obtained.

[0066] In some embodiments, based on the target DBH-tree height model, the biomass of street trees in the target area is estimated according to human management factors, the first-phase street tree data, and the second-phase street tree data, and the carbon storage of the first-phase street trees and the second-phase street trees is obtained, including: (1) A dummy variable DBH-tree height model was constructed with human management factors as the dummy variable of the target DBH-tree height model.

[0067] It should be noted that the tree height growth model can reflect the law that the height of trees changes with the diameter at breast height. It is an important component of the forest growth and harvesting model system, and it is also an important method for estimating tree height, stock volume and biomass. This embodiment introduces human management factors as dummy variables into the currently known diameter at breast height-tree height growth model, which can construct a growth model that is more suitable for urban street trees, and provides a more refined estimation method for evaluating the carbon sequestration function of street trees.

[0068] For example, human maintenance factors can be road building shading, tree pit anti-trampling protection, tree trunk whitewashing and tree pit side length; the target DBH-tree height model is a logarithmic model; the above four factors are incorporated into the DBH-tree height growth model, and their impact on the tree growth relationship is further analyzed.

[0069] Specifically, the basic model is selected as a logarithmic model. Taking the introduction of a trunk whitening dummy variable at parameter a as an example, the model form is as follows: ; in, H For the tree to be tall; D is the breast diameter; a , b is the general parameter of the model; t To distinguish the dummy variable of whitening the tree trunks, T Introduce parameters for whitewashing. When the tree trunk is whitewashed, T =1, when the trunk is not painted white, T =0.

[0070] In this embodiment, the measurement data of street trees are classified according to whitewashing of trunks, ground anti-trampling devices, building shading, and tree pit side length, and a dummy variable breast diameter-tree height model is established. After introducing corresponding dummy variables at appropriate positions in the model, the accuracy of the breast diameter-tree height growth model of street trees is significantly improved.

[0071] (2) The total biomass of each street tree was calculated based on the dummy variable DBH-tree height model according to the street tree data of the first period and the street tree data of the second period.

[0072] For example, the distribution characteristics of carbon sequestration, carbon sink, carbon sequestration density, and carbon sink density of roadside trees in the central urban area of ​​City A can be quantitatively analyzed from three different angles: urban-rural gradient division, road grade division, and administrative area division.

[0073] In this embodiment, the selected sampling street trees include ash, Sophora japonica, Ginkgo biloba, Populus and Sycamore. After the DBH data and tree height data of each tree species are determined by the target DBH-tree height model, the tree species allometric growth equation is used to calculate the biomass of individual tree species; the biomass of each tree species is calculated according to the following allometric growth equation formula: B 梣属=2.1893+3.2949×10 -2 D 2 H; B 国槐 =0.714+0.029D 2 H; B 银杏 =-0.684+0.090 D 2 H; B 杨属 =0.015 (D 2 H) 1.032; lgB 梧桐 =-1.161443+0.913291lg(D 2 H)); Among them, B is the total plant biomass, D is the diameter at breast height (trunk diameter at 1.3 meters), and H is the tree height.

[0074] In this embodiment, if a tree species does not have an allometric growth equation for a single species, the allometric growth equation of the species of the same genus or family is used; if the allometric growth equation of the species of the same genus or family is still lacking, the following generalized growth equation is used: B=0.11 D 2.47 ; In this embodiment, the generalized equation is applicable to tree species such as Koelreuteria paniculata, Pinus bungeana, Salix matsudana, Ailanthus altissima, Ulmus pumila and Acer truncatum.

[0075] (3) The carbon storage of the first-phase and second-phase street trees was obtained based on the total plant biomass and the preset carbon content.

[0076] In this embodiment, the carbon storage of each sampled road is calculated by measuring each tree at the sampling location, and the carbon storage of the roadside trees in the selected urban area is the sum of the carbon storage of individual tree species at the sampling point.

[0077] Specifically, the individual carbon storage within the sampling point is calculated by the following formula: Individual carbon storage = carbon content × individual biomass; The carbon content can be set according to user needs. For example, the reference value of the carbon content is 0.5.

[0078] The method for fine estimation of the carbon sequestration capacity of street trees provided in the embodiment of the present invention, by combining field investigation with model estimation, constructs a breast diameter-tree height model for multiple urban street trees in City A and introduces human maintenance factors, thereby improving the estimation accuracy of the breast diameter-tree height model.

[0079] In some embodiments, human maintenance factors include street tree spacing, tree pit side length, neighboring tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; before constructing a dummy variable DBH-tree height model with human maintenance factors as the dummy variables of the target DBH-tree height model, the method also includes: performing significant correlation analysis on the human maintenance factors to obtain analysis results, and screening the analysis results according to the significant impact threshold to obtain new human maintenance factors.

[0080] In this embodiment, after determining the target DBH-tree height model, the dummy variable of human management factors is incorporated into the DBH-tree height growth model. First, SPSS (Statistical Package for the Social Sciences) is used to verify whether the seven management measures have a significant correlation with the distribution of tree height and DBH of street trees.

[0081] In this embodiment, the Mann-Whitney test was used to analyze the data, confirming that the installation of ground anti-trampling protection nets and building shading had a significant effect on the growth distribution of the DBH of street trees (P<0.01).

[0082] In this embodiment, the kw test was used to analyze the data, confirming that different tree pit side lengths had a significant effect on the DBH distribution of street trees (P<0.05).

[0083] Research has found that human interference in urban ecosystems has caused an increase in soil bulk density, a decrease in porosity, changes in soil texture, a decrease in soil organic matter content, a decrease in effective water content, obstruction of plant root extension, and poor breathing, which reduces plant growth potential. Setting up a tree pit anti-trampling protection net can effectively reduce the damage to the root environment of street trees caused by human interference, protect the growth environment, promote the absorption of root nutrients, and thereby affect the growth changes of street trees' diameter at breast height and tree height.

[0084] Street trees in urban habitats are often disturbed by diseases and insect pests at night. Whitewashing the surface of the trunks can fill the cracks in the bark, destroy the breeding ground for diseases and insect pests, kill mites and other forest pests, and reduce the incidence of diseases and insect pests. At the same time, due to the influence of the urban heat island effect in the central urban area, the climate is hot in summer and cold in winter. Whitewashing can effectively prevent tree trunks from being burned or frozen. Therefore, whitewashing the trunks can reduce the interference of diseases and insect pests or physical damage to street trees, which is more conducive to the growth of breast diameter and tree height.

[0085] Moreover, plant photosynthesis is inseparable from light conditions. Good light conditions can promote photosynthesis, stimulate cell division and growth, and thus affect the tree height and diameter at breast height. When designing tree pits for street trees, it is also necessary to reserve sufficient tree pit growth space for street trees based on the different tree species, so as to store more water and nutrients, so that the root system can fully grow and develop. Therefore, the side length of the tree pit also has an impact on the growth of street trees, which in turn affects the growth of the street trees' diameter at breast height and tree height.

[0086] In addition, street trees of the same species are often planted on the roads in the central urban area of ​​City A, and it is extremely rare that adjacent trees are of different species. In addition, the spacing between plants and the vertical structure do not significantly affect the height and breast diameter of street trees. In summary, this embodiment incorporates four factors, namely, road building shading, tree pit anti-trampling protection, trunk whitewashing, and tree pit side length, into the breast diameter-tree height growth model, and further analyzes their impact on the tree growth relationship.

[0087] The method for fine estimation of the carbon sequestration capacity of street trees provided in an embodiment of the present invention performs significant correlation analysis on human management factors and screens the analysis results according to significant impact thresholds to obtain new human management factors, thereby further improving the estimation accuracy of the DBH-tree height model.

[0088] In some embodiments, after obtaining the first phase of roadside tree carbon storage, the roadside tree carbon sequestration capacity fine estimation method further includes: (1) The biomass of street trees in the target area was estimated based on the allometric growth equation constructed based on the traditional forest survey data of street trees in the first period and the second period, and the carbon storage of street trees in the second period was obtained.

[0089] The carbon sink estimation results were compared by using the allometric equation based on traditional forest surveys and the carbon sink estimation results calculated using the DBH-tree height optimization model established based on the growth relationship of street trees in City A.

[0090] The DBH-tree height model was fitted and tested by using the data of 112 roadside trees. The results of the fitted model were good, with high estimation accuracy. The R value of the DBH-tree height model with dummy variables was 2 , MPE, AIC, and RMSE model accuracy parameter values ​​are better than the traditional DBH-tree height allometric growth model, and the results of estimating the carbon sequestration of street trees in City A are more accurate.

[0091] (2) The carbon sequestration capacity of street trees was evaluated based on the carbon storage of the first and second phase street trees and the new carbon storage of street trees.

[0092] It should be noted that the analysis and discussion of the traditional DBH-tree height model often focuses on the analysis of environmental factors and its own genetic characteristics. The existing common growth models also find it difficult to establish a unique connection between human management factors and the growth of street trees. However, as an important part of urban forests, human management and urban habitats play a vital role in the growth process of street trees. Ben Fangming conducted statistics on the human management measures of street trees surveyed on the spot, and after analyzing their impact on the growth of DBH and tree height respectively, he selected trunk whitewashing, ground anti-trampling devices, building shading, and tree pool side length as dummy variables to introduce into the corresponding parameter positions of the model, constructed a dummy variable model of human management factors, and merged street trees of different management types, thereby improving the universality of the DBH-tree height growth model of street trees.

[0093] In addition, this embodiment combines the road greening length with the allometric growth equation, and based on the characteristics of the urban structure, calculates the carbon storage and carbon sink distribution of the street trees in City A from three different angles, and analyzes its spatial heterogeneity; this embodiment can also determine the growth rate of trees through the growth detection method of street trees, and combine objective variables such as urban thermal environment and human factors to establish a more accurate and targeted growth model, which provides a reference for the scientific selection, planning and planting, maintenance and management of urban street trees in the future, and the evaluation of carbon sink functions.

[0094] The method for fine estimation of the carbon sequestration capacity of street trees provided in an embodiment of the present invention estimates the carbon sequestration of street trees in a target area through the allometric growth equation constructed through traditional forest surveys, and compares it with the dummy variable DBH-tree height model that introduces human management factors, thereby realizing comparative analysis between the traditional DBH-tree height model and the target DBH-tree height model.

[0095] Figure 3 This is the second flow chart of the method for fine estimation of carbon sequestration capacity of roadside trees provided by the present invention. Figure 3In the embodiment shown, a sampling survey method is used to measure each street tree in the main urban area of ​​City A to obtain the measurement data of DBH, tree height, and adjacent plant spacing (first-phase street tree data), and after an interval of ten years, each street tree is measured again to obtain the second-phase street tree data; an investigation is conducted on the human management factors of street trees in the main urban area of ​​City A, and the DBH-tree height model with the highest accuracy is selected from multiple biological mathematical models as the basic biological model, and the DBH-tree height allometric growth equation model is used to calculate the carbon storage of street trees in the first and second phases, thereby obtaining To obtain the carbon sink of street trees in City A under the traditional model; introduce human management factors as dummy variables of the basic biological model, construct a dummy variable breast diameter-tree height model, calculate the carbon storage of street trees in the first and second periods, and then obtain the carbon sink of street trees in City A under the target model; at the same time, classify street trees from three angles: urban-rural gradient, road grade, and administrative division, and analyze the spatial heterogeneity of street trees; combine the carbon sink parameters of urban street trees under the traditional model and the carbon sink parameters of urban street trees under the target model, and obtain a more accurate estimation result of street tree carbon sink in City A through model accuracy comparison, so as to evaluate the carbon sink capacity of urban street trees.

[0096] The device for finely estimating the carbon sink capacity of roadside trees provided by the present invention is described below. The device for finely estimating the carbon sink capacity of roadside trees described below and the method for finely estimating the carbon sink capacity of roadside trees described above can be referred to each other.

[0097] Figure 4 : is a schematic diagram of the structure of the device for finely estimating the carbon sink capacity of roadside trees provided by the present invention, such as Figure 4 As shown, the device for finely estimating the carbon sequestration capacity of roadside trees includes: a data acquisition module 410 , a biomass estimation module 420 and a carbon sequestration capacity estimation module 430 .

[0098] The data acquisition module 410 is used to acquire the first-phase roadside tree data and the second-phase roadside tree data; the first-phase roadside tree data and the second-phase roadside tree data are collected at different time points in the target area; The biomass estimation module 420 is used to estimate the biomass of street trees in the target area based on the target DBH-tree height model according to human management factors, the first-phase street tree data and the second-phase street tree data, so as to obtain the carbon storage of the first-phase street trees and the second-phase street trees; wherein the target DBH-tree height model is determined based on the biological mathematical model; the human management factors include at least two of the street tree spacing, tree pit side length, adjacent tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; The carbon sink capacity estimation module 430 is used to calculate the carbon sink parameters of street trees based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area, so as to evaluate the carbon sink capacity of street trees in the target area; wherein the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the carbon sink parameters of street trees include at least one of carbon fixation amount, carbon sink, carbon fixation density and carbon sink density.

[0099] The device for finely estimating the carbon sink capacity of street trees provided in an embodiment of the present invention estimates the biomass of street trees in a target area according to human management factors, first-phase street tree data and second-phase street tree data through a target breast diameter-tree height model to obtain the carbon stock of street trees, and calculates the carbon sink parameters of street trees according to the carbon stock of street trees and the road characteristics of the target area to evaluate the carbon sink capacity of street trees in the target area, thereby improving the prediction accuracy of the carbon sink capacity of street trees.

[0100] Figure 5 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530 and a communications bus 540, wherein the processor 510, the communications interface 520 and the memory 530 communicate with each other via the communications bus 540. The processor 510 may call the logic instructions in the memory 530 to execute a method for fine estimation of the carbon sequestration capacity of roadside trees, the method comprising: obtaining the first-period roadside tree data and the second-period roadside tree data; the first-period roadside tree data and the second-period roadside tree data are collected at different time points in the target area; based on the target DBH-tree height model, the biomass of the roadside trees in the target area is estimated according to the human management factors, the first-period roadside tree data and the second-period roadside tree data, to obtain the carbon storage of the first-period roadside trees and the second-period roadside trees; wherein the target DBH-tree height model is determined based on a biological mathematical model; human The maintenance factors include at least two of the spacing between street trees, the side length of tree pits, neighboring tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; the carbon sink parameters of street trees are calculated based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area to evaluate the carbon sink capacity of street trees in the target area; wherein the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the carbon sink parameters of street trees include at least one of carbon sequestration amount, carbon sink, carbon sequestration density and carbon sink density.

[0101] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0102] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for fine estimation of the carbon sequestration capacity of street trees provided by the above methods. The method includes: obtaining the first-phase street tree data and the second-phase street tree data; collecting the first-phase street tree data and the second-phase street tree data at different time points in the target area; estimating the biomass of street trees in the target area based on the target diameter-tree height model according to human management factors, the first-phase street tree data and the second-phase street tree data, and obtaining the first-phase street tree data and the second-phase street tree data. The carbon storage of street trees in the second phase; wherein, the target DBH-tree height model is determined based on a biomathematical model; human maintenance factors include at least two of street tree spacing, tree pit side length, neighboring tree species, trunk whitewashing, tree pit anti-trampling protection, road building shading and vertical structure; street tree carbon sink parameters are calculated based on the carbon storage of street trees in the first phase and the second phase and the road characteristics of the target area to evaluate the carbon sink capacity of street trees in the target area; wherein, road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; street tree carbon sink parameters include at least one of carbon fixation amount, carbon sink, carbon fixation density and carbon sink density.

[0103] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the method for finely estimating the carbon sequestration capacity of street trees provided by the above-mentioned methods, the method comprising: obtaining the first-period street tree data and the second-period street tree data; the first-period street tree data and the second-period street tree data are collected at different time points in the target area; based on the target DBH-tree height model, the biomass of street trees in the target area is estimated according to human management factors, the first-period street tree data and the second-period street tree data, to obtain the carbon storage of the first-period street trees and the second-period street trees; wherein, the target The DBH-tree height model is determined based on a biomathematical model; human management factors include at least two of the spacing between street trees, the side length of tree pits, neighboring tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; the street tree carbon sink parameters are calculated based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area to evaluate the carbon sink capacity of street trees in the target area; wherein, the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the street tree carbon sink parameters include at least one of carbon sequestration amount, carbon sink, carbon sequestration density and carbon sink density.

[0104] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0105] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for fine estimation of carbon sequestration capacity of roadside trees, characterized in that: include: Obtain the first phase of roadside tree data and the second phase of roadside tree data; The first phase of roadside tree data and the second phase of roadside tree data are collected at different time points in the target area; Based on the target DBH-tree height model, the biomass of the street trees in the target area is estimated according to the human management factors, the first phase street tree data and the second phase street tree data, to obtain the carbon storage of the first phase street trees and the second phase street trees; wherein the target DBH-tree height model is determined based on the biomathematical model; the human management factors include at least two of the street tree spacing, tree pit side length, neighboring tree species, trunk whitewashing, tree pit anti-trampling protection, road building shading and vertical structure; The carbon sink parameters of the street trees are calculated based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area to evaluate the carbon sink capacity of the street trees in the target area; wherein the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the carbon sink parameters of the street trees include at least one of the carbon fixation amount, carbon sink, carbon fixation density and carbon sink density.

2. The method for fine estimation of carbon sequestration capacity of roadside trees according to claim 1 is characterized in that: The obtaining of the first phase roadside tree data and the second phase roadside tree data comprises: Randomly sampling streets within the target area to obtain sampling streets; Each street tree on each sampled street is measured to obtain the first phase of street tree data, wherein each tree measurement includes measuring the tree species name, tree height, diameter at breast height, and the spacing between adjacent trees; When the target area passes through the target time interval, each roadside tree on the sampled street is measured to obtain the second-phase roadside tree data.

3. The method for fine estimation of carbon sequestration capacity of roadside trees according to claim 2 is characterized in that: The target area includes cities; The random sampling of streets in the target area to obtain the sampled streets includes: Divide the roads in the target area according to the urban road grade classification conditions to obtain urban expressways, urban trunk roads, urban secondary trunk roads and urban branch roads; Dividing the urban branch roads according to the road network density and road width to obtain new urban branch roads; The sampled streets are obtained by random sampling on the urban expressways, urban trunk roads, urban secondary trunk roads and urban branch roads.

4. The method for fine estimation of carbon sequestration capacity of roadside trees according to claim 1 is characterized in that: Biomathematical models include linear models, logarithmic models, and allometric models; The target DBH-tree height model is constructed by the following steps: For each biomathematical model, the target evaluation index is calculated based on the biomathematical model according to the sample tree height data and the sample breast diameter data to obtain the index calculation result; the target evaluation index includes the determination coefficient R 2 , at least one of Akaike information criterion AIC, root mean square error RMSE and mean percentage error MEP; The target DBH-tree height model is determined based on the maximum value among the index calculation results.

5. The method for fine estimation of carbon sequestration capacity of roadside trees according to claim 1 is characterized in that: The target DBH-tree height model is used to estimate the biomass of the street trees in the target area according to human management factors, the first phase street tree data, and the second phase street tree data, and the carbon storage of the first phase street trees and the second phase street trees is obtained, including: A dummy variable DBH-tree height model is constructed by taking the human management factor as the dummy variable of the target DBH-tree height model; Calculate the total biomass of each roadside tree based on the dummy variable DBH-tree height model according to the first phase of roadside tree data; The carbon storage of the first phase of street trees is obtained according to the total plant biomass and the preset carbon content.

6. The method for fine estimation of carbon sequestration capacity of roadside trees according to claim 5 is characterized in that: The human management factors include the spacing of street trees, the length of tree pits, adjacent tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; Before constructing a dummy variable DBH-tree height model using the human management factor as a dummy variable of the target DBH-tree height model, the method further includes: A significant correlation analysis is performed on the human management and protection factors to obtain analysis results, and the analysis results are screened according to a significant impact threshold to obtain new human management and protection factors.

7. The method for fine estimation of carbon sequestration capacity of roadside trees according to claim 1 is characterized in that: After obtaining the first phase of roadside tree carbon storage, the method further includes: The allometric growth equation constructed based on the traditional forest survey is used to estimate the biomass of the street trees in the target area according to the first phase street tree data and the second phase street tree data to obtain a new street tree carbon stock; Based on the carbon stocks of the first-phase roadside trees, the second-phase roadside trees and the new roadside trees, an evaluation result of the carbon sequestration capacity of the roadside trees is obtained.

8. A device for finely estimating the carbon sink capacity of roadside trees, characterized in that: include: A data acquisition module, used to acquire the first phase roadside tree data and the second phase roadside tree data; The first phase of roadside tree data and the second phase of roadside tree data are collected at different time points in the target area; A biomass estimation module is used to estimate the biomass of street trees in the target area based on the target DBH-tree height model according to human management factors, the first phase street tree data and the second phase street tree data, so as to obtain the carbon storage of the first phase street trees and the second phase street trees; wherein the target DBH-tree height model is determined based on a biomathematical model; the human management factors include at least two of street tree spacing, tree pit side length, adjacent tree species, whitewashing of tree trunks, anti-trampling protection of tree pits, road building shading and vertical structure; A carbon sink capacity estimation module is used to calculate the carbon sink parameters of street trees based on the carbon storage of the first-phase street trees and the second-phase street trees and the road characteristics of the target area, so as to evaluate the carbon sink capacity of street trees in the target area; wherein the road characteristics include at least one of the greening length of each street in the target area, the spacing between adjacent trees, the total greening length of all streets and the total road area of ​​all streets; the street tree carbon sink parameters include at least one of carbon fixation amount, carbon sink, carbon fixation density and carbon sink density.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for fine estimation of carbon sequestration capacity of roadside trees as claimed in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for fine estimation of carbon sequestration capacity of roadside trees as claimed in any one of claims 1 to 7 is implemented.

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