A rapid calculation method for carbon sequestration in urban landscapes

Abstract

The present invention discloses a method for rapidly calculating the carbon sequestration of an urban landscape, and relates to the technical field of carbon sequestration measurement. The method comprises the following steps: A, establishing an urban landscape plant database. The plant carbon sequestration test data in existing literature is used as the basis of the plant database. Step B, establishing a plant image recognition system. The convolutional neural network model in deep learning is used to perform class recognition on plant images. Step C, using an unmanned aerial vehicle to obtain landscape images. Step D, establishing an urban landscape GIS model. Step E, calculating the total green volume and distribution of the urban landscape. Step F, calculating the total daily average carbon sequestration and distribution of the urban landscape. Compared with the prior art, the method can save more manpower, time, and instrument costs, is easy to operate, and has greater popularity and promotion value. In addition, the method of the present invention records the measurement results on a map, which facilitates the visualization analysis of the data, and is conducive to the management personnel to more targetedly manage, transform and improve the landscape, thereby improving the carbon sequestration effect of urban landscape greening.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon sequestration measurement, and in particular to a method for rapidly measuring carbon sequestration in urban landscapes. Background Art

[0002] With the rapid development of cities, greenhouse gases, mainly CO2, have caused serious damage to the ecological environment of cities due to the increase in industrial emissions, affecting the smooth operation of cities. In order to improve the urban environment and improve the health of citizens, there are two main countermeasures: energy conservation and emission reduction, and carbon sink. The combination of controlling emission sources and carbon sinks has become a powerful means for many countries to control the greenhouse effect. Urban carbon sink refers to the ability of green plants within the city to absorb and fix CO2 in the atmosphere in vegetation and soil through photosynthesis. Conducting research on urban landscape carbon sinks and improving the potential of urban landscape carbon sinks have important theoretical and practical significance for alleviating the pressure of urban carbon emission reduction and mitigating global climate change.

[0003] Although there are many studies on carbon sinks in forest ecosystems, forestry scientists have not conducted and it is difficult for them to conduct research on the carbon sink function of urban vegetation from the perspective of microscopic scale and urban vegetation types. Landscape scientists pay more attention to the research on urban landscape, urban landscape greening coverage, plant individuals, species characteristics, etc., but lack basic data and research related to the ecological functions of plants such as vegetation biomass and growth. Therefore, there is still little research on carbon sinks in urban landscape greening.

[0004] Of course, judging from the current number of cities and urban green space in my country, the carbon sink function of cities is indeed very small compared to forests and other ecosystems. However, with the development of urbanization and the improvement of urban landscape greening levels, this part of the carbon sink potential is very large. The size of the carbon sink function of urban landscape greening directly evaluates and reflects the ecological value of the urban landscape, and is directly related to the carbon balance of the city itself.

[0005] The acquisition of carbon sink information of urban landscape greening is not a simple technology as everyone thinks. Accurately obtaining greening carbon sink information, that is, carbon fixation, requires a lot of manpower to count the carbon fixation and greenness of each plant leaf one by one. The conventional plant carbon fixation calculation method currently used in forests and farmlands is not suitable for the calculation of urban landscape greening, such as: biomass method, assimilation box method, eddy correlation method, etc. The biomass method is to calculate the biomass of greening, multiply it by the biomass-carbon storage conversion factor, and obtain the carbon storage. The biomass-carbon storage conversion factor is generally between 0.45 and 0.55, but there are large differences in plant species and morphology in urban landscape greening. There will inevitably be large errors in the measurement using this empirical method. The assimilation box method is to seal the measured plant and its required growth environment in a box, and measure the carbon sink of the plant by measuring the change in gas concentration in the box per unit time. It is generally suitable for farmland and grassland in flat areas. Eddy covariance flux observation achieves direct observation of carbon flux from a meteorological perspective by measuring the pulsation of vertical wind speed and CO2 density. It can obtain a large amount of high-temporal resolution CO2 flux and environmental change data in a short time. However, its application is easily limited by terrain and meteorological conditions. The test requires the underlying surface to be flat and uniform, so this test method is not suitable for urban environments.

[0006] From the perspective of the factors affecting carbon sequestration in urban landscape greening, it mainly includes the carbon sequestration capacity of individual plants, unit area and other factors. In order to understand the carbon sequestration benefits of urban landscape greening, we can use drones to conduct urban landscape greening surveys, GIS technology to establish a management platform and plant database for rapid calculations.

[0007] The full name of "UAV" is "Unmanned Aerial Vehicle", which is an unmanned aircraft controlled by remote control equipment and software and hardware adapted to the aircraft. UAV flight operation is simple, take-off and landing are flexible, and the impact of environmental conditions is small. It is suitable for tasks at different regional scales. At present, due to the open source flight control system of UAV and the maturity of hardware technology, UAV has made great breakthroughs in technology, and its application scenarios are gradually expanding.

[0008] GIS (Geographic Information Systems) is a product of the intersection of multiple disciplines. It is based on geographic space, uses geographic model analysis methods, and provides multiple spatial and dynamic geographic information in real time. It is a computer technology system that serves geographic research and geographic decision-making. Its basic function is to convert tabular data (whether it comes from a database, a spreadsheet file, or directly entered into the program) into a geographic graphic display, and then browse, operate, and analyze the display results. Its display range can range from intercontinental maps to very detailed street maps.

[0009] At the same time, related greening carbon sequestration measurement techniques, such as: A method for detecting forest carbon sinks (application number: 201410348570.X) This patented method samples each tree species, measures the cell wall rate of each tree species in the laboratory, and obtains the storage capacity of each tree species at the survey point, that is, the average breast diameter and tree height of each tree species at the survey point. Multifunctional small carbon sink measurement assimilation chamber device (application number: 201410640610.8) This patented measurement method requires the plants to be placed in a closed assimilation chamber facility and is only suitable for measuring small plants, potted plants, etc. A method for measuring the carbon sink of a single urban tree (application number: 201510178889.7) This method requires measuring the crown light energy utilization rate G, crown width D, root respiration ratio B, leaf shading rate f and instantaneous photosynthetic rate P i .

[0010] The above methods for calculating carbon sequestration in greening require sampling and survey of plants, which consumes a lot of manpower and time...

[0011] In summary, the current calculation methods of carbon sequestration in greening require professionals with certain forestry expertise to conduct on-site surveys and tests on plants, and a survey requires a lot of manpower and time. Therefore, at the urban landscape level, due to the large demand for professionals and the time-consuming and labor-intensive field surveys, urban landscapes lack carbon sequestration measurement and data statistics, so the carbon sequestration situation in urban landscapes is blank data. Summary of the invention

[0012] The purpose of the present invention is to overcome the deficiencies and shortcomings of the prior art and to propose a method for rapidly calculating the carbon sequestration in urban landscapes.

[0013] The present invention provides a method for rapidly calculating carbon sequestration in urban landscapes. The method is based on the existing literature to record the carbon sequestration per unit leaf area of ​​different plants. The method uses artificial intelligence image recognition technology to establish a plant image recognition system. The method uses drone image acquisition technology to perform field scanning of landscape greening through drones. Based on the geographic information technology GIS model, the method then rapidly calculates and intuitively displays the carbon sequestration of urban landscape greening, including the size and distribution of carbon sequestration in different regions. The visual display of the calculation results can provide a scientific evaluation of the ecological benefits of the current urban landscape, and also helps to more reasonably plan urban landscape greening and build an ecological and low-carbon urban landscape.

[0014] This method includes the following steps

[0015] Step 1: Establish an urban landscape plant database. Urban landscape plant data includes plant leaf area index information and plant daily average carbon fixation per unit area information. The leaf area index parameter of plants is an important parameter reflecting plant morphology. This parameter can reflect the lushness of plant leaves. The lushness of plant leaves is closely related to the type of plant. The daily average carbon fixation per unit leaf area is an important parameter that can reflect the carbon fixation capacity of the plant leaves. It means the average amount of CO2 fixed per day under the total leaf area of ​​1 square meter of the plant, usually the average of spring days, autumn days and summer days. The plant carbon fixation test data in the existing literature is used as the basis of the plant database. Considering the existence of extreme samples and test conditions in the test data, the statistical truncation mean method is used to obtain the central trend data, discard the 10% of the largest data and the 10% of the lowest data in the data, take the average and standard deviation of the remaining data, and establish a plant database containing the leaf area index and daily average carbon fixation per unit leaf area of ​​different plant species.

[0016] Step 2: Establish a plant image recognition system. Use the convolutional neural network model in deep learning to classify and identify plant images. Collect photos of urban landscape plants. The more samples collected, the higher the accuracy. The accuracy of the recognition system is related to the number of samples collected. The number of photos collected should meet the requirement of more than 90% recognition rate of the system. In the process of extracting plant image recognition features, the leaf shape, petal shape and crown shape of the plant are used as recognition feature elements. Use the convolutional neural network model in deep learning to train and test the samples.

[0017] Step 3: Use a drone to obtain landscape images. Let the drone follow the preset flight path to obtain landscape images. This step requires a drone device, flight navigation and control system, ground monitoring system, high-definition digital camera, and data transmission system.

[0018] Step 4: Establish an urban landscape GIS model. Use CAD to make landscape plants of the same plant type into closed polylines, and use the plant image recognition system to set the identified plant species in different layers. Import each layer into ArcGIS according to the surface element (Polygon), and first establish a GIS model of the distribution of urban landscape plant species.

[0019] Step 5: Calculate the total green volume and distribution of the urban landscape. Calculate the green volume of plants in each surface feature layer using formula (1-4), and assign the green volume to the table attributes of different surface feature layers. Use the Merge tool to merge the surface feature layers to obtain the total green volume of the landscape.

[0020] The calculation method of tree green volume is to calculate the tree green volume based on the identified tree species using the tree category crown diameter-crown height equation and the crown shape-green volume equation. The method of obtaining the green volume of shrubs and herbs is to calculate it based on the leaf area index and coverage area of ​​the plant species.

[0021] The crown diameter and crown height of trees can be calculated using formula (1): (1)

[0023] y=1 / (a+be -cx )

[0024] In the formula: x is the crown diameter of the tree, m; y is the crown height of the tree, m; a, b, c are coefficients, which are determined by the type of tree, for example: weeping willow a=0.832, b=0.042, c=0.758.

[0025] The green volume of trees can be calculated based on the crown shape, crown diameter and crown height of the species: (2)

[0027] LA tree =f(x,y)

[0028] Serial number Crown shape Calculation formula of tree green volume 1 Oval, spherical <![CDATA[LA tree =πx 2 y / 6]]> 2 Conical <![CDATA[LA tree =πx 2 y / 12]]> 3 ball fan <![CDATA[LA tree =π(2y 3 -y 2 ) / 3]]> 4 Spherical <![CDATA[LA tree =π(3xy 2- 2y 3 ) / 6]]> 5 Cylindrical <![CDATA[LA tree =πx 2 y / 4]]>

[0029] Where: LA tree is the green volume of this type of tree, m 2 ; x is the crown diameter of the tree, m; y is the crown height of the tree, m.

[0030] The green volume of shrubs and herbaceous plants can be calculated by formula (3) and formula (4): (3)

[0032] LA shurb =LAI shurb S shurb

[0033] LA grass =LAI grass S grass (4)

[0034] Where: LA shurb is the green volume of this type of shrub, m 2 LA grass is the green amount of this type of grass, m 2 ;LAI shurb LAI is the leaf area index of this type of shrub; grass is the leaf area index of the shrub species; S shurb is the coverage area of ​​this type of shrub, m 2 ; S grass is the coverage area of ​​this type of grass, m 2.

[0035] Add a column of green volume per unit area in the table attributes of each polygon feature layer. In ArcGIS, use the Polygon to Raster tool to convert the green volume per unit area in each map layer feature into raster data information. Then use the Raster Calculator to add the data of each plant raster layer to display the green volume distribution of landscape greening on the map.

[0036] Step 6: Calculate the total daily average carbon sequestration and distribution of urban landscapes. According to formula (5), the daily average carbon sequestration per unit leaf area of ​​each plant in the urban landscape plant database and the green volume of each surface element calculated in step 5 are used to calculate the daily average carbon sequestration value of each surface element and assign it to the table attributes of different plant surface element layers. The surface element layers are merged through the merge tool (Merge) to obtain the total daily average carbon sequestration data of urban landscape greening.

[0037] W CO2 =w CO2，i ×LA i (5)

[0038] Where: W CO2 is the average daily carbon sequestration in urban landscape, g / d; w CO2，i is the daily average carbon fixation per unit leaf area of ​​plant species i, g / m 2 ·d;LA i is the greenness of plant species i, m 2 .

[0039] Add a column of daily average carbon fixation per unit area in the table attributes of each polygon feature layer. Use the Polygon to Raster tool in ArcGIS to convert the daily average carbon fixation per unit area in each map layer feature into raster data information. Then use the Raster Calculator to add the data of each plant raster layer. The distribution of daily average carbon fixation of landscape greening can be displayed on the map.

[0040] As mentioned above, the present invention has the following advantages compared with the prior art:

[0041] This method uses drone image acquisition technology, artificial intelligence image recognition technology, and geographic information technology to quickly calculate the amount of carbon sequestration in urban landscapes. Compared with existing on-site measurement methods, this method can save more manpower, time, and instrument costs, is simpler and more convenient to operate, and can be completed without the need for professionals in the fields of landscape and forestry, and is more popular and has greater promotion value. In addition, compared with the existing technology where the measurement result is only a data value, the present invention records the measurement results on a map, which facilitates the visual analysis of data, is conducive to the management of the landscape in a more targeted manner, and improves the carbon sequestration effect of urban landscape greening. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flowchart of a method for rapidly calculating carbon sequestration in urban landscapes according to the present invention;

[0043] Figure 2 A landscape greening plant distribution model established in GIS according to an embodiment of the present invention;

[0044] Figure 3 A landscape greening green quantity distribution map established in GIS according to an embodiment of the present invention;

[0045] Figure 4 This is a distribution map of the average daily carbon sequestration of landscape greening established in GIS according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0047] The present invention provides a method for rapidly calculating carbon sequestration in urban landscapes (see attached) Figure 1 shown).

[0048] Step A: Establish an urban landscape plant database. The plant carbon fixation test data in the existing literature is used as the basis of the plant database. Taking into account the existence of extreme samples and test conditions in the test data, the statistical truncation mean method is used to obtain the central trend data, discarding the 10% largest data and the 10% lowest data in the data, and taking the mean and standard deviation of the remaining data. For example: There are 28 groups of daily average carbon fixation per unit area data for camphor trees. It is necessary to remove the first three groups of maximum data and the first three groups of minimum data, and calculate the mean and standard deviation of the remaining 22 groups of data. According to this method, a database of leaf area index and daily average carbon fixation per unit leaf area for different plant species is established, as shown in the following table.

[0049] Table 1 Urban landscape plant database

[0050]

[0051] Step B: Establish a plant image recognition system. Use the convolutional neural network model in deep learning to identify plant images. In the process of extracting plant image recognition features, the leaf shape, petal shape, and crown shape of the plant are used as recognition feature elements. According to the type of landscape plants, use drones to take pictures of urban landscape plants to obtain overhead photos of common landscape plants, and collect at least 500 photos of each plant type. Process the plant images taken by drones and convert them into pictures of uniform size format to establish a common urban landscape plant image library. Randomly select the first 80% of each plant category as training data set samples, and the last 20% as test data set samples to construct a convolutional neural image recognition classification.

[0052] Step C: Use a drone to obtain landscape images. Choose a time with good weather conditions and no clouds, and use a drone equipped with a high-resolution digital camera to take aerial photos of the survey area according to a pre-set flight route. In order to ensure the consistency of the scale of each part of the later image and the clarity of the image, the lens should be set vertically downward. According to the test environment, the drone flight altitude can be fixed at 50m, the image ground resolution is 2cm, and the heading overlap is 80%.

[0053] Step D: Establish an urban landscape GIS model. Use the plant image recognition system to identify the plant categories in the landscape images taken by the drone. Use CAD software to draw the plants in the landscape image into closed polyline graphics, and place them in different plant species layers according to the identified plant species. Export the CAD files of each plant species according to the plant species. Import the CAD files of different plant species into ArcGIS according to the surface elements (Polygon), and first establish a GIS model of the distribution of urban landscape plant species, as shown in the attached figure. Figure 2 shown.

[0054] Step E: Calculate the total green volume and distribution of the urban landscape. Calculate the green volume of plants in each surface element, and assign the green volume to the table attributes of different surface element layers. Use the Merge tool to merge the surface element layers to obtain the total green volume of the landscape.

[0055] The methods for calculating the green amount of plants in each surface element can be divided into obtaining the green amount of trees and obtaining the green amount of shrubs and herbs.

[0056] The method for obtaining the green volume of trees is to calculate the green volume of trees based on the identified tree species and crown diameter.

[0057] Crown diameter-crown height equation for tree category:

[0058] (I)

[0059] y=1 / (a+be -cx )

[0060] In the formula: x is the crown diameter of the tree, m; y is the crown height of the tree, m; a, b, c are coefficients, which are determined by the tree species.

[0061] Crown shape-green volume equation:

[0062] (II)

[0063] LA tree =f(x,y)

[0064] Serial number Crown shape Calculation formula of tree green volume 1 Oval, spherical <![CDATA[LA tree =πx 2 y / 6]]> 2 Conical <![CDATA[LA tree =πx 2 y / 12<!-- 5 --> ]]> 3 ball fan <![CDATA[LA tree =π(2y 3 -y 2 ) / 3]]> 4 Spherical <![CDATA[LA tree =π(3xy 2 -2y 3 ) / 6]]> 5 Cylindrical <![CDATA[LA tree =πx 2 y / 4]]>

[0065] Where: LA tree is the green volume of this type of tree, m 2 ; x is the crown diameter of the tree, m; y is the crown height of the tree, m.

[0066] The green volume of shrubs and herbaceous plants can be calculated by the leaf area index and plant coverage area in the plant database, as shown in formula (III) and formula (IV):

[0067] (III)

[0068] LA shurb =LAI shurb S shurb

[0069] LA grass =LAI grass S grass (IV)

[0070] Where: LA shurb is the green volume of this type of shrub, m 2 LA grass is the green amount of this type of grass, m 2 ;LAI shurb LAI is the leaf area index of this type of shrub; grass is the leaf area index of the shrub species; S shurb is the coverage area of ​​this type of shrub, m 2 ; S grass is the coverage area of ​​this type of grass, m 2 .

[0071] For example, there are 5 locust trees in the locust tree layer, and the crown diameter of the locust tree is 3.5m. According to the crown diameter-crown height equation, the crown height of the locust tree is y=1 / (0.654+0.311e -0.634x )=1.45m, the total green volume of the locust tree is 70m 2 For example, the leaf area index of azalea is 2.19, and the coverage area is 10m 2 , then the green volume of azalea is 21.9m 2 .

[0072] Add a column of green volume per unit area in the table properties of each layer, and convert the green volume per unit area in each map layer element into raster data information by using the polygon feature to raster tool in ArcGIS. Use the raster calculator to convert each layer according to the formula 'Con(IsNull("Dragon_PolygonToRaster_Green Volume"), 0, "Dragon_PolygonToRaster_Green Volume") + Con(IsNull("Purple Leaf Peach_PolygonToRaster_Green Volume"), 0, "Purple Leaf Peach_PolygonToRaster_Green Volume") + Con(IsNull("Dragon_PolygonToRaster_Green Volume"), 0, "Dragon_PolygonToRaster_Green Volume") + Con(IsNull("Purple Leaf Peach_PolygonToRaster_Green Volume"), 0, "Purple Leaf Peach_PolygonToRaster_Green Volume") + Con(IsNull( ″Acer serrata_PolygonToRaster_Green amount″), 0,″Acer serrata_PolygonToRaster_Green amount″)+Con(IsNull(″Daylily_PolygonToRaster_Green amount″), 0,″Daylily_PolygonToRaster_Green amount″)+Con(IsNull(″Palm_PolygonToRaster_Green amount″), 0,″Palm_PolygonToRaster_Green amount″)+Con(IsNull(″Ginkgo_PolygonToRaster_Green amount″), 0, ″Ginkgo_PolygonToRaster_GreenQuantity″)+Con(IsNull(″Purple-Leafed Peach_PolygonToRaster_GreenQuantity″),0,″Purple-Leafed Peach_PolygonToRaster_GreenQuantity″)+Con(IsNull(″Crape Myrtle_PolygonToRaster_GreenQuantity″),0,″Crape Myrtle_PolygonToRaster_GreenQuantity″)+Con(IsNull(″Gardenia_PolygonToRaster_GreenQuantity″),0,″Gardenia_PolygonToRaster_GreenQuantity″)+Con( IsNull("Poa annua_PolygonToRaster_GreenQuantity"), 0,"Poa annua_PolygonToRaster_GreenQuantity")+Con(IsNull("Iris_PolygonToRaster_GreenQuantity"), 0,"Iris_PolygonToRaster_GreenQuantity")+Con(IsNull("Cherry Blossom_PolygonToRaster_GreenQuantity"), 0,"Cherry Blossom_PolygonToRaster_GreenQuantity")+Con(IsNull("Luan Tree_PolygonToRaster_GreenQuantity"), 0,”Luanshu_PolygonToRaster_Green amount”)+Con(IsNull(”Cinnamomum camphora_PolygonToRaster_Green amount”), 0, “Cinnamomum camphora_PolygonToRaster_Green amount”)+Con(IsNull(”Coral_PolygonToRaster_Green amount”), 0, “Coral_PolygonToRaster_Green amount”)+Con(IsNull(”Holly_PolygonToRaster_Green amount”), 0, “Holly_PolygonToRaster_Green amount”)+Con(IsNull(”Golden Ligustrum lucidum_PolygonToRaster_Green amount”), 0, “Golden Ligustrum lucidum_PolygonToRaster_Green amount”)+Con(IsNull(”Osmanthus fragrans_PolygonToRaster_Green amount”), 0, “Osmanthus fragrans_PolygonToRaster_Green amount”)+ Add Con(IsNull(″Golden Leaf Forsythia_PolygonToRaster_Green Quantity″), 0, “Golden Leaf Forsythia_PolygonToRaster_Green Quantity″)+Con(IsNull(″Red Maple_PolygonToRaster_Green Quantity″), 0, “Red Maple_PolygonToRaster_Green Quantity″)+Con(IsNull(″Lucky Grass_PolygonToRaster_Green Quantity″), 0, “Lucky Grass_PolygonToRaster_Green Quantity″)+Con(IsNull(″Boxwood_PolygonToRaster_Green Quantity″), 0, “Boxwood_PolygonToRaster_Green Quantity″)+Con(IsNull(″Red Leaf Plum_PolygonToRaster_Green Quantity″), 0, “Red Leaf Plum_PolygonToRaster_Green Quantity″)′, and set the calculation range in the environment to the model layer range. After the calculation is completed, the distribution of landscape greening can be obtained on the map, as shown in the attached figure. Figure 3 shown.

[0073] Step F: Calculate the total daily average carbon fixation and distribution of urban landscape. According to formula (V), the daily average carbon fixation per unit leaf area of ​​each plant in the urban landscape plant database and the green volume of each surface element calculated in step 5 are used to calculate the daily average carbon fixation value of each surface element and assign it to the table attributes of different plant surface element layers. The surface element layers are merged through the merge tool (Merge) to obtain the total daily average carbon fixation data of urban landscape greening.

[0074] W CO2 =w CO2，i ×LA i (V)

[0075] Where: W CO2 is the average daily carbon sequestration in urban landscape, g / d; w CO2，i is the daily average carbon fixation per unit leaf area of ​​plant species i, g / m 2 ·d;LA i is the greenness of plant species i, m 2 .

[0076] For example: The average daily carbon fixation value of Ginkgo is 6.9g / m 2 , the total green area shown in the attribute list is 45m 2 , the average daily carbon fixation value of the ginkgo tree is 310.5g.

[0077] Add a column of daily average carbon fixation per unit area in the table properties of each layer, and convert the green volume per unit area in each map layer element into raster data information by using the polygon feature to raster tool in ArcGIS. Use the raster calculator to convert each layer according to the formula 'Con(IsNull("Dragon Bamboo_PolygonToRaster_Carbon Fixation"), 0, "Dragon Bamboo_PolygonToRaster_Carbon Fixation") + Con(IsNull("Azalea_PolygonToRaster_Carbon Fixation"), 0, "Azalea_PolygonToRaster_Carbon Fixation") + Con(IsNull("Purple Leaf Peach_PolygonToRaster_Carbon Fixation"), 0, "Purple Leaf Peach_PolygonToRaster_Carbon Fixation") Carbon content") + Con(IsNull("Aglaonema plate_PolygonToRaster_Carbon content"), 0,"Aglaonema plate_PolygonToRaster_Carbon content") + Con(IsNull("Daylily_PolygonToRaster_Carbon content"), 0,"Daylily_PolygonToRaster_Carbon content") + Con(IsNull("Palm_PolygonToRaster_Carbon content"), 0,"Palm_PolygonToRaster_Carbon content") + Con(IsNull("Palm_PolygonToRaster_Carbon content"), 0,"Palm_PolygonToRaster_Carbon content") + Con(IsNull("Palm_PolygonToRaster_Carbon content"), 0,"Palm_PolygonToRaster_Carbon content") + Con(IsNull("Palm_PolygonToRaster_Carbon content"), 0,"Palm_PolygonToRaster_Carbon content") IsNull("Ginkgo_PolygonToRaster_Carbon fixation"), 0,"Ginkgo_PolygonToRaster_Carbon fixation") + Con(IsNull("Purple-leafed Peach_PolygonToRaster_Carbon fixation"), 0,"Purple-leafed Peach_PolygonToRaster_Carbon fixation") + Con(IsNull("Crape Myrtle_PolygonToRaster_Carbon fixation"), 0,"Crape Myrtle_PolygonToRaster_Carbon fixation") + Con(IsNull("Gardenia_Polygon =ToRaster_Carbon fixation"), 0, "Gardenia_PolygonToRaster_Carbon fixation") + Con(IsNull("Poa annua_PolygonToRaster_Carbon fixation"), 0, "Poa annua_PolygonToRaster_Carbon fixation") + Con(IsNull("Iris_PolygonToRaster_Carbon fixation"), 0, "Iris_PolygonToRaster_Carbon fixation") + Con(IsNull("Sakura_PolygonToRaster_Carbon fixation"), 0,″Cherry Blossom_PolygonToRaster_Carbon Fixation″)+Con(IsNull(″Koelreuteria_PolygonToRaster_Carbon Fixation″),0,″Koelreuteria_PolygonToRaster_Carbon Fixation″)+Con(IsNull(″Cinnamomum camphora_PolygonToRaster_Carbon Fixation″),0,″Cinnamomum camphora_PolygonToRaster_Carbon Fixation″)+Con(IsNull(″Coral_PolygonToRaster_Carbon Fixation″), 0, "Coral_PolygonToRaster_Carbon fixation") + Con(IsNull("Holly_PolygonToRaster_Carbon fixation"), 0, "Holly_PolygonToRaster_Carbon fixation") + Con(IsNull("Golden Ligustrum_PolygonToRaster_Carbon fixation"), 0, "Golden Ligustrum_PolygonToRaster_Carbon fixation") + Con(IsNull("Osmanthus_PolygonToRaster_Carbon fixation") Carbon content"), 0,"Osmanthus_PolygonToRaster_Carbon fixation") + Con(IsNull("Forsythia suspensa_PolygonToRaster_Carbon fixation"), 0,"Forsythia suspensa_PolygonToRaster_Carbon fixation") + Con(IsNull("Red maple_PolygonToRaster_Carbon fixation"), 0,"Red maple_PolygonToRaster_Carbon fixation") + Con(IsNull("Lucky grass_PolygonToRaster_Carbon fixation") aster_Carbon fixation"), 0, "Lucky grass_PolygonToRaster_Carbon fixation") + Con(IsNull("Boxwood_PolygonToRaster_Carbon fixation"), 0, "Boxwood_PolygonToRaster_Carbon fixation") + Con(IsNull("Red leaf plum_PolygonToRaster_Carbon fixation"), 0, "Red leaf plum_PolygonToRaster_Carbon fixation")' are added together, and the calculation range is set to the model layer range in the environment. After the calculation is completed, the distribution of the average daily carbon fixation of landscape greening can be obtained in the map, as shown in the attached. Figure 4 shown.

[0078] In summary, the present invention has the following advantages:

[0079] 1) As is known to all, plants can absorb carbon dioxide in the atmosphere through photosynthesis, thus alleviating the greenhouse effect. This method can achieve the ecological benefits of urban landscape, facilitate the improvement and benefit of urban environment.

[0080] 2) Using drone technology and existing empirical models and data, the carbon sequestration value of urban landscapes can be quickly calculated, saving a lot of manpower, equipment and time costs in field surveys and improving work efficiency.

[0081] 3) Artificial intelligence big data recognition is used in the process of plant species identification, which can help operators who are not in the landscape or forestry professional fields to complete their work. The operation is simple and there are no professional threshold restrictions, which is conducive to the popularization and rapid promotion of this method.

[0082] 4) The carbon sequestration results can be displayed on a map, so that city managers can intuitively see the total amount and distribution of carbon sequestration in the urban landscape. This visual ecological benefit information can be more conducive to urban landscape planning and management.

Claims

1. A method for rapidly calculating carbon sequestration in urban landscapes, characterized in that: The steps include: Step A. Establishing an urban landscape plant database; Step B. Establishing a plant image recognition system; Step C. Using a drone to obtain landscape images; Step D. Establishing the urban landscape GIS model includes the following steps: D.1 Use CAD to make landscape plants into closed polylines; D.2 Using the plant image recognition system, the identified plant species are set on different layers; D.3 Import different plant types into ArcGIS according to polygon elements to establish a GIS model of urban landscape plant species distribution; Step E. Calculate the total green volume and distribution of urban landscape, including the following steps: E.1 Calculate the greenness of each surface element, including trees, shrubs, and herbaceous plants; E.2 Assign the green volume to the table attributes of different surface feature layers, merge the surface feature layers through the merge tool (Merge) to obtain the total green volume value of the landscape; E.3 Use the Polygon to Raster tool to convert each polygon layer into a raster according to the amount of green space per unit area, and then use the Raster Calculator to add the raster layers together to obtain the GIS model of landscape green space distribution; Step F. Calculate the total daily average carbon sequestration and distribution in the urban landscape.

2. A rapid calculation method for urban landscape carbon sequestration as claimed in claim 1, characterized in that: The step A. further comprises the following steps: A.1 The measured data in the literature on plant carbon fixation were processed using the statistical truncation mean method, the largest 10% of the data and the lowest 10% of the data were discarded, the mean and standard deviation of the remaining data were taken, and a plant database containing leaf area index and daily average carbon fixation per unit leaf area of ​​different plant species was established.

3. A rapid calculation method for urban landscape carbon sequestration as claimed in claim 1, characterized in that: The step B. also includes the following steps: B.1 Collect photos of urban landscape plants, the number of which meets the requirement that the system recognition rate reaches more than 90%; B.2 In the process of extracting plant image recognition features, the leaf shape, petal shape, and crown shape of the plant are used as recognition feature elements; B.3 Use deep learning convolutional neural network model to train and test samples.

4. A rapid calculation method for urban landscape carbon sequestration as claimed in claim 1, characterized in that: The step C. also includes the following steps: Step C.

1. Take aerial photos of the survey area according to the pre-set flight route; Step C.

2. To ensure the consistency of scale of each part of the post-production image and the clarity of the image, set the lens to vertically downward, and fix the flight altitude of the drone at 50m according to the test environment, with an image ground resolution of 2cm and a heading overlap of 80%.

5. A rapid calculation method for urban landscape carbon sequestration as claimed in claim 1, characterized in that: The step F. further comprises the following steps: F.1 Using the urban landscape plant database and the calculated plant green volume, calculate the daily average carbon sequestration of plants in each surface element, including trees, shrubs, and herbaceous plants; F.2 Assign the daily average carbon sequestration value to the surface element table attributes on different layers, merge the surface element layers using the Merge tool (Merge) to obtain the total daily average carbon sequestration value of the landscape; F.3 Use the Polygon to Raster tool to convert each polygon layer into a raster according to the daily average carbon sequestration per unit area, and then use the Raster Calculator to add the rasters of each layer to obtain the GIS model of the daily average carbon sequestration distribution in the landscape.

6. A method for rapidly calculating carbon sequestration in urban landscapes as claimed in claim 4, characterized in that: The step C.

1. takes aerial photos of the survey area according to a pre-set flight route using a high-definition digital camera.

7. A method for rapidly calculating carbon sequestration in urban landscapes as claimed in claim 1, characterized in that: The green volume of the tree in step E.1 is calculated according to the tree category crown diameter-crown height equation and the crown shape-green volume equation; The crown diameter and crown height of trees are calculated by formula (I): y=1 / (a+be -cx ) (I) Where: x is the crown diameter of the tree, m; y is the crown height of the tree, m; a, b, c are coefficients, which are determined by the tree species, among which: for weeping willow, a=0.832, b=0.042, c=0.758; The green volume of trees is calculated based on the crown shape, crown diameter and crown height of the species: THERE tree =f(x,y) (II) Where: LA tree is the green volume of this type of tree, m 2 ; x is the crown diameter of the tree, m; y is the crown height of the tree, m; The green volume of shrubs and herbaceous plants is calculated by formula (III) and formula (IV): DAY shurb =LAI shurb S shurb (III) GET grass =LAI grass S grass (IV) Where: LA shurb is the green volume of this type of shrub, m 2 LA grass is the green amount of this type of grass, m 2 ;LAI shurb LAI is the leaf area index of this type of shrub; grass is the leaf area index of the shrub species; S shurb is the coverage area of ​​this type of shrub, m 2 ; S grass is the coverage area of ​​this type of grass, m 2 .

8. A method for rapidly calculating carbon sequestration in urban landscapes as claimed in claim 5, characterized in that: In step F.1, according to formula V, the carbon sequestration value and the green value in the layer are multiplied to obtain the carbon sequestration amount of the urban landscape, and the distribution and size of the carbon sequestration amount of the landscape are displayed on the map; W CO2 =w CO2，i ×LA i (V) Where: W CO2 is the average daily carbon sequestration in urban landscape, g / d; w CO2，i is the daily average carbon fixation per unit leaf area of ​​plant species i, g / m 2 ·d;LA i is the green mass of plant species i, m 2 .

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