Tree planting decision support system as a greenhouse gas sink in urban space

Abstract

The present invention relates to a decision support system for planting trees as greenhouse gas absorbers in urban spaces, comprising: a spatial information storage unit for storing spatial information; a tree information storage unit for storing tree planting information for each of a plurality of tree plantings; an input unit for receiving input of a tree planting purpose and a tree planting area to be planted; and a tree recommendation unit for recommending tree information to be planted in a target area for each planting location within the tree planting area, based on the tree planting purpose, the tree planting area, the spatial information, and the tree planting information.

Description

Technology Field

[0001] The present invention relates to a decision support system for planting trees as greenhouse gas sinks, and more specifically, to a decision support system for planting trees that proposes trees effective for reducing greenhouse gases in an area within an urban space where trees are to be planted. Background Technology

[0002] As concern over global warming spreads worldwide, regulations on atmospheric carbon emissions are being strengthened, and various efforts are being made to address this issue.

[0003] As a series of measures to curb atmospheric carbon emissions, many efforts are being made, such as suppressing reckless development in forest areas, expanding tree-planting campaigns, and using machinery to reduce carbon emissions, but these efforts are still insufficient.

[0004] As Korea is the world's eighth largest emitter of carbon dioxide (CO2) as of 2019, and with the growing international responsibility for reducing greenhouse gases, actively implementing the "carbon neutrality" declared in 2020 has become a national task.

[0005] However, although carbon neutrality requires simultaneous consideration of reducing carbon dioxide emissions and increasing absorption, current domestic policies focus solely on the former, and the development of technologies to increase carbon dioxide absorption remains insufficient; therefore, measures to address this are urgently needed.

[0006] In this regard, expanding forest area, afforestation, reforestation, and forest management are already widely utilized strategies to increase carbon dioxide absorption. In this context, Korean Registered Patent No. 10-0805188 discloses a technology regarding an ecological forest creation method; the core technology aims to further improve the growth of trees by presenting a construction method that prevents the main roots of trees from being severed during forest creation.

[0007] However, due to development conditions and socio-economic limitations, it is difficult to expand forest areas indefinitely. In contrast, urban areas are on the rise.

[0008] Therefore, it is necessary to plant the most efficient trees as effective greenhouse gas absorbers in urban green spaces. In this regard, research on methods for planting trees as greenhouse gas absorbers in urban spaces has not been conducted. The problem to be solved

[0009] To solve the above-mentioned problems, the present invention aims to provide a method for selecting trees to be planted as greenhouse gas absorbers in urban spaces. means of solving the problem

[0010] A decision support system for planting trees as greenhouse gas absorbers in urban space according to an embodiment of the present invention comprises: a spatial information storage unit for storing spatial information; a tree information storage unit for storing tree information for each of a plurality of tree plantings; an input unit for receiving a purpose for planting trees and a tree planting area to be planted; and a tree recommendation unit for recommending tree information to be planted in a target area for each planting location within the tree planting area, based on the purpose for planting trees, the tree planting area, the spatial information, and the tree information.

[0011] In addition, in an embodiment, the tree recommendation unit comprises: a spatial information analysis unit that receives spatial information regarding a tree planting area from the spatial information storage unit and divides the tree planting area into multiple tree planting sub-areas of different environments based on the spatial information of the tree planting area; a tree information analysis unit that analyzes the correlation with carbon absorption amount or vegetation vitality for each of the multiple trees based on tree information provided from the tree information storage unit; and an environmental planting tree proposal unit that proposes at least one tree for each of the multiple tree planting sub-areas of different environments using the correlation for each of the multiple trees.

[0012] In addition, in the embodiment, the spatial information for the tree planting area includes at least one of light environment information, temperature, moisture environment information, soil information, and stress information.

[0013] In addition, in the embodiment, the tree information is tree information for an area larger than the tree planting area.

[0014] In addition, in the embodiment, the tree information analysis unit uses carbon absorption amount or vegetation vitality as a dependent variable and derives a predicted value using existing tree data, such as point locations, tree types, and planting age included in the tree information, as independent variables.

[0015] In addition, in the embodiment, the light environment information includes information regarding memorization and specification according to building height and building location. Effects of the invention

[0016] According to the tree planting decision-making system of the present invention, the tree planting area to be planted is divided into at least one tree planting sub-area according to the spatial environment, and a suitable tree is presented for each tree planting sub-area, thereby enabling the presentation of greenhouse gas trees suitable for urban environments with a structure more complex than that of forests.

[0017] According to the tree planting decision-making system of the present invention, by applying various methodologies such as probability, statistics, machine learning, deep learning, and AI analysis techniques to existing planted trees to derive predicted values, the correlation between greenhouse gas absorption or vegetation vitality according to the environment is analyzed, and based on the correlation resulting from the analysis, trees to be planted in the tree planting area can be suggested more groundedly, and the effects of future tree planting can also be predicted. Brief explanation of the drawing

[0018] FIG. 1 is a drawing illustrating a decision support system for planting trees as a greenhouse gas sink in an urban space according to one embodiment of the present invention. Figure 2 is a drawing for explaining the tree recommendation section illustrated in Figure 1. Figure 3a is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on average precipitation. Figure 3b is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on soil information. Figure 3c is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on the memorization time. Figure 3d is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on the distance from the road. FIG. 3e is an example drawing combining FIG. 3a to FIG. 3d. Specific details for implementing the invention

[0019] The structure and operation according to the present invention will be described in detail below with reference to the attached drawings.

[0020] In the description referring to the attached drawings, identical components are given the same reference regardless of drawing symbols, and redundant descriptions thereof are omitted.

[0021] Terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. Such terms are used solely for the purpose of distinguishing one component from another.

[0022] FIG. 1 is a diagram illustrating a decision support system for planting trees as a greenhouse gas sink in an urban space according to an embodiment of the present invention.

[0023] Referring to FIG. 1, the tree planting decision support system (10) includes a spatial information storage unit (100), a tree information storage unit (200), an input unit (300), and a tree recommendation unit (400).

[0024] The spatial information storage unit (100) stores spatial information for all areas including the tree planting area where trees are to be planted, and analysis information for the tree planting sub-area separated by the spatial information analysis unit (410).

[0025] Spatial information includes location information, temperature information, moisture environment information, soil information, light environment information, and stress information (direct and indirect effects on trees, such as distance from roads and the presence or absence of calcium chloride). Analysis information is explained in the description of the Spatial Information Analysis Department.

[0026] The tree information storage unit (200) stores tree information as information about trees.

[0027] Tree information includes point location information indicating the point location and detailed tree information at the said point location. Trees refer to information about trees that existed in the past or currently exist. Furthermore, tree information may include not only tree information within the tree planting area where trees are to be planted, but also tree information outside the tree planting area.

[0028] Tree details include information on tree species, light environment, planting age, and the corresponding carbon absorption (FLUX) and vegetation vitality (NDVI).

[0029] The input unit (300) receives the purpose of tree planting and the tree planting area where the tree will be planted.

[0030] The tree planting area can be divided into two or more tree planting sub-areas depending on the tree planting location, tree planting purpose, and tree planting environment. If the tree planting sub-areas are not divided and input, the spatial information analysis unit (410) can divide the tree planting area into tree planting sub-areas.

[0031] For example, by the user or the spatial information analysis unit (410), the tree planting area may be divided into two or more tree planting areas according to various purposes within the urban space, such as a tree planting area adjacent to a road, a tree planting area adjacent to a building, a tree planting area located in a resting space or park, or a tree planting area around an outdoor restroom.

[0032] The primary purpose of tree planting is to perform greenhouse gas absorption functions for the purposes of preventing climate change and air pollution. Of course, unlike the above, the purpose of tree planting may also be set for urban biodiversity.

[0033] The input unit (300) can receive the purpose of the tree planting area or the purpose of each of at least one tree planting sub-area within the tree planting area.

[0034] For example, a small area of ​​tree planting adjacent to a road can be set to a sound barrier function or an exhaust smoke blocking function, a small area of ​​tree planting adjacent to a building can be set to a view blocking function, a small area of ​​tree planting located in a resting space can be set to an aesthetic function, and other various functions such as odor blocking, windbreak function, and aesthetic function can be set.

[0035] The tree recommendation unit (400) recommends trees based on spatial information provided from the spatial information storage unit (100), tree information provided from the tree information storage unit (200), the purpose of tree planting provided from the input unit (300), and the tree planting area where the trees are to be planted.

[0036] Figure 2 is a drawing for explaining the tree recommendation section illustrated in Figure 1.

[0037] Referring to FIG. 2, the tree recommendation unit (400) includes a spatial information analysis unit (410), a tree information analysis unit (420), and a planting tree proposal unit (430).

[0038] To select trees that can grow well within the tree planting area, spatial information analysis of the planting area must be performed beforehand.

[0039] The spatial information analysis unit (410) receives spatial information about a tree planting area from the spatial information storage unit (100), analyzes the spatial information about the tree planting area, divides the tree planting area into tree planting sub-areas according to the analysis results, and can store them in the spatial information storage unit (100).

[0040] Spatial information includes location information (including area), building information (height, location, area, etc.), temperature information, moisture environment information, soil information, light environment information, and stress information (direct and indirect effects on trees, such as distance from roads and the presence or absence of calcium chloride).

[0041] The spatial information analysis unit (410) collects temperature-related information such as average temperature information and seasonal temperature information of the tree planting area, collects moisture environment information such as maximum precipitation, average precipitation, and average humidity of the tree planting area, and collects soil information such as soil material and soil environment of the tree planting area.

[0042] Figure 3a is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on average precipitation.

[0043] In FIG. 3a, area A-1 is an area with an average precipitation of 1250 mm or more, area A-2 is an area with an average precipitation of 1200 mm to 1250 mm, and area A-3 is an area with an average precipitation of less than 1200 mm. Here, the detailed precipitation criteria, such as 1250 mm and 1200 mm, may be pre-set, or may be set by the user or automatically.

[0044] Figure 3b is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on soil information.

[0045] In Fig. 3b, the soil in area B-1 consists of brown soil, and the soil in area B-2 consists of reddish-yellow soil. Here, when classifying based on soil information, it can be distinguished according to soil material, soil type, etc., and can be set by the user or automatically.

[0046] The spatial information analysis unit (410) analyzes light environment information for each point location within the tree planting area that is obscured by shadows generated by buildings, based on building information such as building height and building location within the tree planting area. For example, the distance from the building to point location A within the tree planting area is X, and the location of the tree planting area from the building is determined, and the influence is analyzed to analyze the dark period (time of darkness) and light period (time of brightness) of point location A.

[0047] Figure 3c is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on the memorization time.

[0048] Due to Building A, there is a difference in the length of the memorization time between Area C-1 and Area C-2, and Area C-1 can be classified as an area with a memorization time of 15 hours or more, and Area C-2 as an area with a memorization time of less than 15 hours. Here, the memorization time can be set by the user or automatically.

[0049] The spatial information analysis unit (410) determines the possibility of noise or calcium chloride penetration caused by vehicles traveling on the road when the distance to the road from point location B of the tree planting area is Y.

[0050] Figure 3d is an example drawing in which a tree planting area is divided into multiple tree planting sub-areas based on the distance from the road.

[0051] In Fig. 3d, area D-1 is an area with a distance of 10m or more from the road, so it is an area with little stress from the road, and area D-2 is an area with a distance of 10m or less from the road, so it is an area with a large stress from the road. Here, the 10m distance from the road can be set by the user or automatically.

[0052] FIG. 3e is an example drawing combining FIG. 3a to FIG. 3d.

[0053] Referring to FIG. 3e, the spatial information analysis unit (410) divides the tree planting area into multiple tree planting sub-areas based on location information (including area), building information (height, location, area, etc.), temperature information, moisture environment information, soil information, light environment information, and stress information at point locations within the tree planting area. To do this, after dividing the tree planting area for each of the spatial information in FIG. 3a to FIG. 3d, the final tree planting area can be derived by combining them as shown in FIG. 3e.

[0054] Although not depicted in the drawings, the tree planting area may be further subdivided based on buildings of specific uses. For example, the tree planting area can be divided into sub-areas according to various purposes within the urban space, such as sub-areas adjacent to roads, sub-areas adjacent to buildings, sub-areas located in resting areas or parks, and sub-areas around outdoor restrooms.

[0055] The tree information analysis unit (420) uses existing tree data, such as point locations, tree types, and planting ages included in the existing planted tree information provided by the tree information storage unit (200), as independent variables, and uses carbon absorption amount (FLUX) or vegetation vitality (NDVI) per tree as dependent variables to analyze correlations using various methodologies such as probability, statistics, machine learning, deep learning, and AI analysis techniques to derive predicted values ​​and stores them in the tree information storage unit (200). At this time, the tree information analysis unit (420) can also analyze tree information in an area wider than the tree planting area and in an area that does not include the tree planting area.

[0056]

[0057] (y is the dependent variable, x1 to xn are independent variables)

[0058] Taking regression analysis as an example of various methodologies, a regression equation indicates whether a tree’s vegetation vitality or carbon absorption is superior under certain environmental conditions. For instance, within a specific space where a particular tree is planted, light conditions, temperature, moisture, soil, and stress (such as distance from roads or exposure to calcium chloride) serve as independent variables (extracted from spatial data). By analyzing the correlations between these variables, the equation determines which tree exhibits good vegetation vitality or superior carbon absorption under specific environmental conditions. In this process, a regression equation can be derived for each individual tree.

[0059] The tree planting proposal unit (430) predicts the carbon absorption excellence and vegetation vitality of each of the multiple trees for each of the tree planting sub-areas and trees classified according to the spatial environment, based on the regression analysis equation derived for each of the tree planting sub-areas and trees, and based on this, proposes one or more trees for each of the tree planting sub-areas (Z-1 to Z-12) according to the tree planting purpose entered by the user.

[0060] At this time, for each small area for tree planting, two combinations of upper and lower trees that can grow well together can be presented, such as trees, sub-trees, shrubs, and herbaceous plants.

[0061] For example, the tree planting proposal unit (430) suggests a combination of trees or upper trees and lower trees for each tree planting sub-area, such as suggesting Y-1 or Y-1 and Y-11 trees in the Z-1 area, suggesting Y-2 trees or Y-2 and Y-12 in the Z-2 area, and suggesting Y-3 trees or Y-13 in the Z-3 area, and the user can establish a tree planting plan for the corresponding tree planting sub-area based on this.

[0062] Meanwhile, the tree planting proposal unit (430) may further refer to the purpose set for each tree planting sub-area. For example, since the purpose of soundproofing or smoke blocking should take precedence over carbon absorption for tree planting sub-areas close to a road, the tree planting proposal unit (430) recommends trees by taking precedence over the purpose set for each tree planting sub-area, but for tree planting sub-areas where no purpose is set separately, it may recommend trees as described above.

[0063] The tree planting decision support system of the present invention can predict in advance effects such as greenhouse gas absorption by newly planted trees, land cover temperature, air pollution concentration, and reduction rate of environmentally-related diseases by using a regression analysis equation that has already been derived.

[0064] In addition, by utilizing predicted effects and information on future land cover changes, it becomes possible to establish an analysis system for predicting the impacts of greenhouse gas absorption (greenhouse gas flux) and other effects (LST, air quality, environmental diseases) using greenhouse gas sinks, urban land use (land cover), and urban environment data.

[0065] According to the tree planting decision-making system of the present invention, the tree planting area to be planted is divided into at least one tree planting sub-area according to the planting environment, and a suitable tree is presented for each tree planting sub-area, thereby enabling the presentation of greenhouse gas trees suitable for urban environments with a structure more complex than that of forests.

[0066] According to the tree planting decision-making system of the present invention, the correlation between greenhouse gas absorption or vegetation vitality according to the environment is analyzed through regression analysis of existing planted trees, and based on the correlation, trees to be planted in the actual tree planting area can be suggested more groundedly, and the effects of tree planting can also be predicted.

[0067] In the detailed description of the present invention as described above, specific embodiments have been described. However, various modifications are possible within the scope of the present invention. The technical concept of the present invention should not be limited to the embodiments described in the present invention, but should be defined by the claims as well as equivalents thereof. Explanation of the symbols

[0069] 10: Tree Planting Decision Support System 100: Spatial Information Storage Unit 200: Tree Information Storage Unit 300: Input Unit 400: Tree Recommendation Department 410: Spatial Information Analysis Department 420: Tree Information Analysis Department 430: Tree Planting Proposal Department

Claims

Claim 1 A spatial information storage unit that stores spatial information including building information, light environment information, and stress information including distance from the road and the presence or absence of calcium chloride influence; a tree information storage unit that stores tree information including point location information for each of multiple tree plantings and detailed tree information at the corresponding point location; an input unit that receives the purpose of tree planting and the tree planting area where the tree is to be planted; The system is configured to include a tree recommendation unit that recommends tree information to be planted for each planting location within the tree planting area based on the tree planting purpose, the tree planting area, the spatial information, and the tree information. The tree recommendation unit comprises: a spatial information analysis unit that divides the tree planting area into multiple tree planting sub-areas having different environmental conditions by analyzing the spatial information of the tree planting area based on building information, light environment information, and stress information provided by the spatial information storage unit; and a tree information analysis unit that analyzes correlations for each of the multiple trees based on tree planting information provided by the tree information storage unit, using carbon absorption amount or vegetation vitality as a dependent variable and stress information including tree type, planting age, distance from the road, and the presence or absence of calcium chloride influence as an independent variable. A tree planting decision support system as a greenhouse gas sink in urban space, comprising a tree planting proposal unit that predicts the carbon absorption excellence and vegetation vitality of each of a plurality of trees for the spatial environment of each tree planting sub-area using the correlation analysis results by the tree information analysis unit described above, and proposes at least one tree for each of a plurality of tree planting sub-area with different environments based thereon, while proposing the tree prioritizing the tree planting purpose set for each of the plurality of tree planting sub-areas, wherein the spatial information analysis unit analyzes light environment information for each point location within the tree planting area that is obscured by shadows generated by a building based on building information including building height and building location, analyzes dark and light information, and divides the tree planting area into the plurality of sub-areas based on the dark and light information. Claim 2 delete Claim 3 A decision support system for tree planting as a greenhouse gas sink in urban space, wherein, in claim 1, the spatial information regarding the tree planting area comprises at least one of light environment information, temperature, moisture environment information, soil information, and stress information. Claim 4 delete Claim 5 delete

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