Landscaping coverage rate data statistical method

Abstract

The invention relates to a landscaping coverage rate data statistical method. Comprising the steps of generating independent greening coverage rate data of each layer based on vegetation multi-dimensional stratified analysis of ecological characteristics, and obtaining an overall greening coverage rate through a superposition algorithm; calculating a dynamic coverage rate based on plant spectral characteristics, then calculating a green coverage rate actually participating in an ecological function based on an evaluation result, and adjusting coverage rate data in real time according to a plant health state to obtain the dynamic coverage rate; correcting coverage rate data by analyzing soil humidity and air temperature environmental factors in different time periods based on ecological influence weights of spatial and temporal changes; the ecological functional indexes are associated with the greening coverage rate; according to the method, the vegetation coverage rate and real-time environment data are coupled to construct a statistical model, and then adjusted data are combined with multispectral remote sensing data to form coverage rate evaluation; by introducing an ecological compensation mechanism, the dynamic process of landscaping change is simulated.

Description

Technical Field

[0001] The present invention relates to the field of gardening and greening, and in particular to a gardening and greening coverage rate data statistics method. Background Art

[0002] At present, methods for statistical data on garden greening coverage, such as the Chinese patent number 2024109389697, and the patent name "A method and system for intelligent garden data management", do provide new technical means for garden management and maintenance. This patent mainly uses image acquisition, grayscale anomaly index analysis, plant pruning cycle prediction and other means to identify the growth status of garden plants and reasonably arrange pruning and maintenance plans. Although this method has shown good results in predicting plant pruning and abnormal warning, it still has some obvious deficiencies and disadvantages in the statistics of garden greening coverage and ecological functional evaluation.

[0003] First of all, the patent mainly relies on image acquisition and grayscale anomaly index for data analysis, but this image-based analysis method has certain limitations. The image grayscale anomaly index can identify changes in the appearance of plants, such as the health status or growth abnormalities of plants, but this method cannot deeply reflect the actual functional contribution of plants in the ecosystem. For example, ecological functions such as the carbon absorption capacity, soil and water conservation efficiency, and transpiration of plants cannot be accurately evaluated through simple image analysis. This recognition method based on appearance features can only provide a physical area judgment of the appearance of vegetation when evaluating the coverage rate of garden greening, ignoring the important contribution of plants to ecological functions such as carbon cycle, climate regulation, and soil and water conservation, resulting in the statistics of green coverage rate remaining only on the surface.

[0004] Secondly, although the patent can provide abnormal warnings through image difference analysis, it lacks in-depth functional analysis of plant types. The ecological functions of different plant types in gardens are significantly different. For example, the tree layer usually plays an important role in carbon absorption and cooling effects, while the shrub layer may be more suitable for soil and water conservation. In the existing technical solutions, although plant types can be identified through image differences, the functional differences of plants are not included in the evaluation. This means that the system cannot make an effective quantitative assessment of the functional contributions of different vegetation layers in the ecosystem, which limits the scientificity and accuracy of the green coverage statistics.

[0005] Third, the image analysis method of this patent is greatly affected by external factors such as lighting conditions and weather changes. Although the patent mentions that it can eliminate some of the effects of seasonal changes and lighting conditions, in actual operation, light intensity, shadows, seasonal vegetation color changes, etc. may still interfere with image analysis and lead to recognition errors. In high humidity or rainy weather, the quality of image acquisition will also be affected, which in turn affects the accuracy of the grayscale anomaly index. This method of green coverage statistics that relies on image acquisition lacks reliability, especially when the environment changes drastically, it cannot provide high-precision data support. Moreover, it cannot continuously monitor and quantify the functional changes of plants, especially when responding to dynamic environmental factors such as climate change and seasonal changes, and cannot achieve real-time and dynamic green coverage assessment. Summary of the invention

[0006] The purpose of the present invention is to provide a method for calculating the landscaping coverage rate data, thereby solving some of the drawbacks and deficiencies pointed out in the background technology.

[0007] The present invention solves the above-mentioned technical problems by adopting the following technical solutions: a statistical method for landscaping coverage data, comprising: S1, multi-dimensional hierarchical analysis of vegetation based on ecological characteristics:

[0008] S1.1. First, classify the topography, soil type and climate conditions of the garden, and then conduct a comprehensive analysis of the ecological characteristics of the plants; use vegetation height sensor LiDAR technology and multispectral image data to identify the distribution of different vegetation layers;

[0009] S1.2, then based on the photosynthesis efficiency, canopy density, and transpiration coefficient ecological parameters of different vegetation layers, spatial stratification is performed, and corresponding ecological weights are assigned to each layer;

[0010] S1.3, generate independent green coverage data for each layer, and obtain the overall green coverage through superposition algorithm;

[0011] S2. Dynamic coverage calculation based on plant spectral characteristics:

[0012] S2.1. Use multispectral images in remote sensing data, combined with plant health indices including NDVI and EVI, to establish a dynamic model of vegetation health status through spectral analysis; extract the spectral characteristics of each pixel point and compare them with the established vegetation health database to evaluate the health status of the point;

[0013] S2.2, then calculate the green coverage rate that actually participates in ecological functions based on the evaluation results, and adjust the coverage rate data in real time according to the health status of the plants to obtain a dynamic coverage rate;

[0014] S3. Ecological impact weights based on temporal and spatial changes:

[0015] S3.1. Use spatiotemporal data analysis to establish a seasonal weight model by overlaying and analyzing remote sensing images of different seasons; and assign different ecological weights to different seasons and time points through historical data correction;

[0016] S3.2. Correct the coverage data by analyzing soil moisture and temperature environmental factors in different time periods;

[0017] S4. Correlation between ecological functional indicators and green coverage rate:

[0018] S4.1. By introducing ecological functional indicators, including carbon absorption, soil and water conservation capacity, and cooling effect, the green coverage rate is associated with ecological functional parameters; the corresponding ecological functional contribution value of each green area is calculated according to the plant type and distribution characteristics;

[0019] S5. Build a statistical model by coupling vegetation coverage with real-time environmental data:

[0020] S5.1. First, install environmental sensors to collect climate and soil data in real time to establish a response function between coverage and environmental conditions;

[0021] S5.2, then combining the adjusted data with multispectral remote sensing data to form a coverage assessment;

[0022] S6. By introducing an ecological compensation mechanism, the dynamic process of landscaping changes can be simulated:

[0023] S6.1. First, construct a model of the impact of reduced greening on the ecosystem, including increased carbon emissions and soil erosion risks;

[0024] S6.2. Then introduce ecological compensation measures, including calculating the restoration effect on green coverage by planting new plants and increasing vegetation density.

[0025] Furthermore, the multi-dimensional hierarchical analysis method of vegetation based on ecological characteristics includes:

[0026] By combining remote sensing data with ground sensors including LiDAR and multispectral data, the spatial distribution of each layer of vegetation is identified and a three-dimensional model is generated. After identifying each vegetation layer, ecological parameters are extracted. The ecological parameters are integrated into a comprehensive hierarchical function, defined as S(l, t), which represents the overall ecological performance of the lth vegetation layer at time t. The specific formula is:

[0027]

[0028] Among them, S(l, t) is a hierarchical function, which represents the ecological efficiency of the lth layer of vegetation at time t; Ωl represents the spatial area of ​​the lth vegetation layer; α l , β l , γ l The adjustment coefficients are used to weight the photosynthesis efficiency φ(x, y, z, t), canopy density ρ(x, y, z, t) and transpiration coefficient τ(x, y, z, t); φ(x, y, z, t) is the three-dimensional distribution function of photosynthesis efficiency, defined in three-dimensional space (x, y, z) and time t, and represents the photosynthesis efficiency of vegetation in this space and time; ρ(x, y, z, t) is the canopy density distribution function, which represents the leaf area index of vegetation in three-dimensional space (x, y, z); τ(x, y, z, t) is the transpiration coefficient distribution function, which represents the contribution of vegetation to water evaporation.

[0029] Furthermore, the multi-dimensional hierarchical analysis method of vegetation based on ecological characteristics includes:

[0030] Assign ecological weights to each vegetation layer; the setting of the ecological weights integrates the contribution of each layer of vegetation to the entire ecosystem; and quantifies the actual ecological function of the vegetation; the weights are calculated by weighted calculation of photosynthesis efficiency, canopy density and transpiration coefficient; define the layered ecological weight function W(l, t), the formula of which is:

[0031]

[0032] in,

[0033] W(l, t) is the hierarchical ecological weight function, which represents the ecological weight of the lth vegetation layer at time t; λ l , μ l 、v l The weighting coefficients for photosynthesis, canopy density and transpiration coefficient respectively represent the contribution of each ecological function to the weight; η1, η2, η3 are nonlinear indexes that adjust the relative contribution of photosynthesis efficiency φ(x, y, z, t), canopy density ρ(x, y, z, t) and transpiration coefficient τ(x, y, z, t); ψ(x, y, z, t) is a comprehensive biomass distribution function used for normalization in weight calculation to reflect the overall productivity of vegetation; Ω l Represents the spatial area of ​​the lth layer of vegetation.

[0034] Furthermore, the multi-dimensional hierarchical analysis method of vegetation based on ecological characteristics includes:

[0035] When the hierarchical functions S(l, t) and ecological weights W(l, t) of all levels are determined, the overall green coverage rate is calculated by weighted superposition; the weighted superposition integrates the physical coverage areas of different levels and the ecological contribution of each layer of vegetation; the overall garden green coverage rate R(t) is defined, and its formula is:

[0036]

[0037] Among them, R(t) is the overall green coverage rate, which means the total coverage rate of the entire garden at time t; γ l The weighting factor of level l reflects the comprehensive contribution of each vegetation level to the overall green coverage rate; W(l, t) hierarchical ecological weight function represents the ecological weight of the lth vegetation level; S(l, t) hierarchical function represents the ecological efficiency of the lth vegetation level; n is the total number of vegetation levels.

[0038] Furthermore, the method for associating the ecological functional index with the green coverage rate includes:

[0039] By real-time monitoring of the photosynthesis efficiency of plants, a carbon absorption function C(x, y, t) is defined, where x and y represent the spatial position of the greening area, t represents the time, and the function C(x, y, t) represents the amount of carbon absorbed by the plants in the area at a specific time; it is defined as P(x, y, t), which represents the contribution of plants to soil and water conservation at time t and spatial position (x, y); the cooling effect is the ability of plants to reduce the ambient temperature through transpiration and shading effect, which is defined as T(x, y, t), and the function represents the cooling effect of the area (x, y) within time t;

[0040] Then, the comprehensive functional weight function W(x, y, t) was introduced, and different weight coefficients were assigned to each indicator to reflect its contribution to the overall ecological benefits of the garden; the specific formula is as follows:

[0041]

[0042] Among them, W(x, y, t) represents the comprehensive functional weight function, which represents the comprehensive ecological functional contribution of plants in the area (x, y) at time t; C(x, y, t) represents the carbon absorption function, which represents the ability of plants to absorb carbon dioxide at the spatial position (x, y) and time t; P(x, y, t) represents the soil and water conservation capacity function, which represents the ability of plants to prevent soil erosion and retain water at the spatial position (x, y) and time t; T(x, y, t) represents the cooling effect function, which represents the ability of plants to cool down through transpiration and shading at the spatial position (x, y) and time t; α, β, and γ represent the weight coefficients of carbon absorption, soil and water conservation capacity, and cooling effect, respectively; δ represents the area correction coefficient, which is used to balance the relationship between ecological function and physical area; A(x, y) is the physical area function, which represents the actual green area of ​​the area (x, y); Ω represents the spatial range of the entire green area, which is used to calculate the ecological functional contribution within this range.

[0043] Furthermore, the method for associating the ecological functional index with the green coverage rate includes:

[0044] A hierarchical model was introduced to conduct a functional hierarchical assessment of each green area based on the type and distribution characteristics of plants; the plant functions of each layer were quantified through real-time monitoring and sensor data collection; the carbon absorption capacity, soil and water conservation function and cooling effect of each plant layer including trees, shrubs and herbs were calculated separately;

[0045] To generate a dynamic ecological functional contribution value R (x, y, t), the calculation formula is:

[0046] R(x,y,t)=W(x,y,t)·A(x,y)

[0047] Among them, R(x, y, t) represents the functional coverage rate, which means the green coverage rate after comprehensively considering the ecological functional weight at time t and spatial position (x, y); W(x, y, t) represents the comprehensive functional weight function, which reflects the ecological benefits of plants, combining the functional contributions of carbon absorption, soil and water conservation and cooling; A(x, y) represents the physical area function, which represents the actual green area of ​​the area.

[0048] Furthermore, the method for associating the ecological functional index with the green coverage rate includes:

[0049] By combining the functional weight model with the physical coverage rate, the statistics of the green coverage rate can reflect the area distribution of vegetation and its actual contribution to the ecological environment. The functional coverage rate R(x, y, t) of each area is calculated and summarized to generate the total green coverage rate of the entire garden. The specific formula is:

[0050] R total (t) = ∫ Ω R(x,y,t)dΩ

[0051] Among them, R total (t) is the total green coverage rate at time t, which indicates the comprehensive functional coverage rate of the entire garden at that moment, combined with the ecological functional contribution of each vegetation in the area; R(x, y, t) is the functional coverage rate, which indicates the comprehensive contribution of the green area at each location after combining the ecological functionality; Ω indicates the spatial range of the entire garden.

[0052] Beneficial effects of the present invention:

[0053] 1. Comprehensive ecological function assessment: Compared with the traditional green coverage rate calculation method based solely on physical area, the present invention introduces ecological functional indicators (such as carbon absorption, soil and water conservation capacity, and cooling effect) so that the green coverage rate not only reflects the actual coverage area of ​​vegetation, but also reflects the specific contribution of plants to the ecological environment.

[0054] 2. Multi-level vegetation stratification analysis: This method conducts stratified analysis on different vegetation layers (such as tree layer, shrub layer, herb layer), identifies the specific role of each layer in the ecosystem, and ensures that the function of each vegetation is scientifically evaluated and quantified. This stratified model can improve the accuracy of green coverage statistics and make the management decision of the park more refined.

[0055] 3. Dynamic data monitoring and real-time adjustment: This invention can dynamically reflect the health status of plants and environmental changes by real-time monitoring of ecological parameters such as plant photosynthesis efficiency, transpiration, and canopy density, combined with sensor data and multispectral imaging technology. After acquiring this data, the system can adjust the coverage data in real time according to changes in time, season, and environmental conditions, thus realizing dynamic garden ecological monitoring and management.

[0056] 4. Weight calculation of quantified ecological benefits: By introducing a functional weight model, the present invention quantifies the contribution of each vegetation layer to the overall ecological function of the garden, and provides scientific weight distribution for plants in each region in combination with actual ecological benefits (such as carbon absorption, soil and water conservation, etc.), making the calculation of garden greening coverage more objective and reflecting the complexity of the ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The present invention is a flow chart of a method for statistically analyzing landscaping coverage data.

[0058] Figure 2 This is a flow chart of the multi-dimensional hierarchical analysis method of vegetation based on ecological characteristics of the present invention.

[0059] Figure 3 This is a flow chart of the method for associating ecological functional indicators with green coverage rate of the present invention. DETAILED DESCRIPTION

[0060] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0061] Combined with Figure 1 Flowchart, a garden greening coverage data statistics method of the present invention includes: S1, realizing detailed evaluation and coverage statistics of different vegetation layers through multi-dimensional layered analysis of vegetation based on ecological characteristics.

[0062] S1.1. First, the terrain, soil type and climate conditions of the garden are comprehensively classified to determine the impact of various environmental variables in the garden on plant growth. Then, the height and spatial distribution of plants are identified using LiDAR technology and multispectral image data. In particular, the three-dimensional height information of vegetation is obtained through LiDAR sensors, so that the spatial position and distribution range of different vegetation layers such as tree layer, shrub layer and herb layer can be accurately distinguished. At the same time, multispectral image data is used to analyze plant health status, leaf area index and other information to further refine the functional characteristics of vegetation at different levels.

[0063] S1.2. After identifying the vegetation layers, spatial stratification is performed based on the ecological characteristics of each layer, such as photosynthesis efficiency, canopy density, and transpiration coefficient. These ecological parameters are key indicators that affect the contribution of plants to the environment. Photosynthesis efficiency indicates the ability of vegetation to absorb carbon dioxide and release oxygen, canopy density reflects the ability of vegetation to capture light energy, and transpiration coefficient affects the humidity regulation of the environment by plants. Through these parameters, the system assigns corresponding ecological weights to each vegetation layer, so that plants at different levels are reasonably reflected in statistics according to their ecological benefits.

[0064] S1.3. After completing the spatial stratification and ecological weight allocation of each layer, the system generates independent green coverage data for each vegetation layer. By calculating the physical coverage area of ​​each layer and combining its ecological weight, the system can obtain the green coverage of the tree layer, shrub layer and herb layer respectively. Finally, the coverage data of each layer is comprehensively processed using the overlay algorithm to obtain the overall green coverage.

[0065] S2. Through dynamic coverage calculation based on plant spectral characteristics, real-time assessment of vegetation health and dynamic adjustment of green coverage can be achieved.

[0066] S2.1. First, we use multispectral image data in remote sensing technology, combined with commonly used vegetation health indices such as NDVI (normalized vegetation index) and EVI (enhanced vegetation index), to build a dynamic vegetation health status model through spectral analysis. Specifically, the spectral feature data of each pixel is compared with the established vegetation health database, which contains the spectral reflectance characteristics of vegetation in different health states in different bands. By analyzing the spectral information of each pixel, the system can evaluate the health status of plants in different areas in real time. Spectral characteristics mainly reflect key parameters such as chlorophyll content and leaf area index of vegetation. By comparing the health database, the health of vegetation in a certain area can be determined, such as whether the plants are in the best growth state or are under some kind of stress (such as drought or pests and diseases).

[0067] S2.2. After evaluating the health status of plants in each area, the system calculates the green coverage rate that actually participates in ecological functions based on these evaluation results. This means that not only the physical coverage area of ​​the plants is calculated, but also corrections need to be made according to the health status of the vegetation. Only vegetation in good health can effectively participate in ecological functions, such as carbon absorption and photosynthesis. Therefore, the system dynamically adjusts the green coverage rate of plants in different regions according to their health status. For example, vegetation areas with poor health will be given lower weights, thereby reducing their contribution to the overall green coverage rate. Through this process, the system can adjust the coverage data in real time when the health status of vegetation changes (such as deterioration of vegetation health due to environmental changes, pests and diseases, etc.), ensuring that the final green coverage rate can dynamically reflect the actual ecological function contribution of each vegetation area in the current garden, and realize accurate and real-time green coverage rate evaluation.

[0068] S3. Dynamically adjust the green coverage assessment by using ecological impact weights based on spatiotemporal changes to more accurately reflect the ecological functions of vegetation in different time periods.

[0069] S3.1. First, using spatiotemporal data analysis, the system establishes a seasonal weight model by superimposing and analyzing remote sensing images of different seasons. Specifically, climatic conditions in different seasons (such as light intensity, rainfall, temperature, etc.) will affect the growth status and ecological function contribution of plants, so the coverage rate needs to be corrected according to seasonal changes. To achieve this, the system superimposes and compares remote sensing image data of multiple seasons, and combines the historical data of each season to calculate the different contributions of each season to the green coverage rate. Through this seasonal analysis, different ecological weights are given to different seasons and time points. For example, in the growing season (such as spring and summer), the photosynthesis and carbon absorption capacity of plants are strong, and they will get higher weights in the coverage assessment, while in winter or non-growing seasons, the ecological functions of plants are weakened, and their weights are reduced accordingly.

[0070] S3.2. The system further corrects the coverage data by analyzing the impact of environmental factors (such as soil moisture, temperature, etc.) on vegetation health and coverage in different time periods. Soil moisture and temperature are important factors affecting plant growth. Changes in soil moisture in different time periods will affect the transpiration and water absorption of plants, while changes in temperature will affect the photosynthesis efficiency of plants. Therefore, the system further adjusts the green coverage rate by monitoring these environmental data in real time and combining the results of spatiotemporal analysis. For example, in drought or high temperature periods, the ecological functions of plants will be suppressed, and the coverage data will be adjusted downward. In humid climates and suitable temperatures, the ecological functions of plants are enhanced, and the coverage data will be adjusted accordingly. Through this spatiotemporal dynamic correction method, the system ensures that the evaluation results of the green coverage rate can more accurately reflect the actual ecological contribution of plants in the garden under different times and climatic conditions.

[0071] S4. By introducing ecological functional indicators, the correlation between green coverage rate and actual ecological benefits can be achieved, thereby evaluating the ecological contribution of garden greening in a more comprehensive and scientific manner.

[0072] S4.1. First, three key ecological functional indicators were introduced, namely carbon absorption, soil and water conservation capacity, and cooling effect. These indicators directly reflect the functional contribution of plants in the ecosystem. For example, carbon absorption measures the ability of plants to absorb carbon dioxide and reduce carbon emissions through photosynthesis. The carbon absorption capacity of different plant types (such as trees, shrubs, and herbs) varies significantly; soil and water conservation capacity reflects the role of plants in fixing soil and preventing soil erosion through their roots. This ability is particularly important for plants in slopes or wetland environments; the cooling effect is the ability of plants to reduce ambient temperature through transpiration and shading effects, especially in areas where the urban heat island effect is more obvious, and the effect of vegetation on local climate regulation is significant. Through these ecological functional indicators, the system can quantify the actual benefits of each green area in the ecosystem, not just based on its physical coverage area.

[0073] After collecting and analyzing these ecological functional parameters, the system conducts a detailed analysis of the plant types and distribution characteristics of each green area. For example, the tree layer usually contributes more to carbon absorption and cooling, while the herbaceous layer performs more prominently in soil and water conservation. Based on the ecological functional contributions of different plant types, the system calculates the ecological functional contribution value of each area. In this way, different types of plants are no longer evaluated solely based on their physical coverage area, but are calculated based on their actual ecological benefits. Specifically, the system classifies the plants in each green area, identifies different types of trees, shrubs, herbs, etc., and combines their spatial distribution characteristics (such as plant density, coverage) to calculate the ecological functional contribution of each type of plant in the area one by one. Subsequently, these functional contribution values ​​are combined to form an overall ecological contribution score for the area, which is combined with the physical green coverage rate.

[0074] Through this association method, the green coverage rate not only reflects the physical existence of vegetation, but also comprehensively reflects the actual contribution of plants to the ecosystem. Therefore, even if the physical green area of ​​two regions is the same, if the plants in one of the regions perform better in carbon absorption, soil and water conservation, or cooling effects, their ecological function contribution value will be higher, and ultimately their comprehensive green coverage rate will increase accordingly. By associating ecological functional indicators with green coverage rates, the system can more accurately evaluate the actual ecological benefits of gardens, thereby providing more scientific data support for ecological planning and management. This method breaks through the limitations of traditional reliance on area statistics and realizes the transition from "area-oriented" to "function-oriented".

[0075] S5. By coupling vegetation coverage with real-time environmental data, a dynamic statistical model is constructed to achieve more accurate coverage assessment.

[0076] S5.1. First, install environmental sensors in the garden area. These sensors can collect climate and soil data closely related to plant growth in real time, including air temperature, humidity, soil moisture, soil nutrients and other information. Through the real-time collection of these environmental data, the system can monitor the changes in key external conditions that affect plant health and growth, and establish a response function between coverage and environmental conditions based on this data. This response function is used to quantify the response of plants to environmental changes. For example, when the temperature rises or the soil moisture decreases, the growth rate of plants slows down and the photosynthesis efficiency decreases, which in turn affects the overall ecological function. Through this functional association, the system can dynamically adjust the plant coverage data so that it can be updated in real time as the environment changes. This method ensures real-time monitoring of plant health and ecological functions.

[0077] S5.2. The system combines the data adjusted by the environmental sensors with the multispectral remote sensing data to further refine the coverage assessment. Multispectral remote sensing data can provide the spectral reflectance characteristics of plants, reflecting the health status and growth of plants. By integrating the data collected by environmental sensors with these remote sensing data, the system can form a more comprehensive plant health and coverage assessment model. Specifically, the spectral data in the remote sensing image can be used to calculate the health index of vegetation (such as NDVI or EVI), while the environmental data of the sensor is used to correct and supplement the impact of environmental changes on plant growth that cannot be reflected in real time by remote sensing data. Through this fusion of data, the coverage assessment model can not only capture the physical state of the plant, but also reflect the dynamic impact of environmental changes on the ecological functions of the plant. Finally, the system generates real-time and dynamically adjusted coverage assessment results based on these two types of data, which can more accurately reflect the coverage level of plants in the garden under specific environmental conditions.

[0078] S6. By introducing an ecological compensation mechanism, the dynamic process of changes in garden greening can be simulated to cope with the negative impact of the reduction in green coverage in the garden on the ecosystem.

[0079] S6.1. First, a model is constructed to evaluate the direct impact of reduced greening on the ecosystem. Specifically, when vegetation decreases, it will lead to a series of ecological problems, such as increased carbon emissions and increased risk of soil erosion. The model evaluates the extent of the harm of vegetation reduction to the environment by quantifying these impacts. For example, when the vegetation cover in a certain area decreases, the system will calculate the reduced carbon absorption capacity due to reduced photosynthesis based on the reduced area, and the resulting increase in carbon emissions. In addition, the reduction in vegetation will also weaken the soil's retention capacity and increase the risk of soil erosion, especially on slopes or in areas with sufficient rainfall. After the vegetation is reduced, the soil is susceptible to erosion, further affecting ecological stability. Through a comprehensive analysis of these factors, the system can derive the global impact of vegetation reduction on the ecosystem and generate a dynamically changing ecological impact model.

[0080] S6.2. The system introduces ecological compensation measures, which aim to restore or increase the green coverage rate through human intervention to reduce or offset the negative impact of vegetation reduction on the ecosystem. Common compensation measures include planting new plants and increasing the density of existing vegetation. The compensation method of planting new plants is to replant suitable plant species in the area where the green coverage is reduced, thereby increasing the ecological function recovery rate of the area; while the method of increasing vegetation density is to increase the planting density of existing vegetation, improve its photosynthesis efficiency and soil retention capacity, thereby enhancing the ecological function of the area. Based on these compensation measures, the system will simulate the ecological restoration process after compensation and calculate the restoration effect of ecological compensation on green coverage. For example, by planting new plants, the system will calculate the growth cycle, photosynthesis efficiency and contribution of new vegetation to carbon absorption, so as to evaluate the offsetting effect of new vegetation on increased carbon emissions. Similarly, by increasing vegetation density, the system will calculate the improvement of soil and water conservation and cooling effects based on the increase in vegetation density, and finally derive the restoration effect value of these ecological compensation measures on green coverage.

[0081] Embodiment 1:

[0082] See also Figure 2 Flowchart,In this embodiment, in a certain urban ecological park, the manager hopes to conduct a comprehensive assessment of its landscaping in a scientific way to understand the contribution of different vegetation layers to the park's ecosystem and use these data to optimize the park's greening planning and management. In order to achieve this goal, a statistical method for landscaping coverage data is adopted. This method combines LiDAR remote sensing technology, multispectral data and ground sensors to identify the spatial distribution of different vegetation layers in the park, and conducts detailed analysis and calculation of the ecological parameters of each layer.

[0083] First, a three-dimensional model of the park was generated using LiDAR technology. The LiDAR sensor scans the park with a laser beam to obtain height information of different vegetation layers. With this data, the system identifies the spatial distribution of the tree layer, shrub layer, and herb layer, and builds a three-dimensional model. In addition, multispectral data is used to identify the spectral characteristics of each type of vegetation, mainly used to determine the health status and chlorophyll content of the vegetation. These remote sensing data are combined with environmental parameters such as soil moisture and temperature collected in real time by ground sensors to further refine the evaluation of the ecological effectiveness of vegetation.

[0084] After completing the spatial distribution identification, the system starts to extract ecological parameters. The photosynthesis efficiency, canopy density and transpiration coefficient of the tree layer are set to φ(x, y, z, t), ρ(x, y, z, t) and τ(x, y, z, t) respectively. In the formula, α l , β l and γ lRepresent the adjustment coefficients of photosynthesis, canopy density and transpiration coefficient respectively. These parameters will affect the contribution of vegetation to the ecosystem. The adjustment coefficient of the tree layer is set to range from α1 = 0.8 (high photosynthesis efficiency), β1 = 0.6 (high canopy density), and γ1 = 0.4 (medium transpiration).

[0085] The hierarchical function of the system is defined as:

[0086]

[0087] Specifically for the tree layer, the spatial area Ω1 is set to represent a 10-hectare tree coverage area. Substituting each ecological parameter into the formula, the photosynthesis efficiency of the tree layer is set to φ(x, y, z, t) = 0.9, the canopy density ρ(x, y, z, t) = 0.7, and the transpiration coefficient τ(x, y, z, t) = 0.5. The ecological efficiency of the tree layer is calculated as:

[0088]

[0089] After calculation, we get:

[0090]

[0091] The ecological efficiency of the tree layer is 1.34 times the coverage area of ​​10 hectares, which is 13.4 units of ecological efficiency.

[0092] Next, similar calculations were performed for the shrub layer and the herb layer, with the parameters of the shrub layer set to α2 = 0.6, β2 = 0.5, γ2 = 0.7, and its photosynthesis, canopy density, and transpiration coefficient, respectively, φ(x, y, z, t) = 0.7, ρ(x, y, z, t) = 0.6, τ(x, y, z, t) = 0.8. The shrub layer covers an area of ​​5 hectares, and the formula is:

[0093]

[0094] The ecological efficiency of the shrub layer is 1.28 times 5 hectares, which gives an ecological efficiency of 6.4 units.

[0095] The parameters of the herbaceous layer are set to α3 = 0.4, β3 = 0.3, γ3 = 0.5, and its photosynthesis, canopy density and transpiration coefficient are φ(x, y, z, t) = 0.5, ρ(x, y, z, t) = 0.4, τ(x, y, z, t) = 0.6, respectively. The herbaceous layer covers an area of ​​8 hectares. Substituting into the formula:

[0096]

[0097] The ecological efficiency of the herbaceous layer is 0.62 times 8 hectares, which gives an ecological efficiency of 4.96 units.

[0098] Finally, by adding up the ecological benefits of the tree layer, shrub layer, and herb layer, the system derives the comprehensive ecological benefits of the entire garden area:

[0099] S total (t) = 13.4 + 6.4 + 4.96 = 24.76

[0100] This value reflects the overall ecological efficiency of vegetation at all levels in the park at a specific time. Combined with the actual coverage data, this result can provide park managers with a detailed ecological assessment, thereby helping to optimize the park's greening strategy, such as increasing a certain level of vegetation or improving the health of certain areas to enhance the overall ecological function.

[0101] In the process of managing the ecological park, managers hope to further optimize the statistics of the park's green coverage rate and evaluate the functional contribution of different vegetation layers to the ecosystem. To achieve this goal, it is necessary to assign ecological weights to each vegetation layer to quantify the actual ecological function of each layer. This scheme obtains the ecological weight of each vegetation layer through weighted calculation of photosynthesis efficiency, canopy density and transpiration coefficient.

[0102] First, the system needs to calculate the ecological weight of the tree layer. Set the photosynthesis efficiency of the tree layer to φ(x, y, z, t) = 0.9, the canopy density to ρ(x, y, z, t) = 0.7, and the transpiration coefficient to τ(x, y, z, t) = 0.5, and set the weighting coefficients to λ1 = 0.8 (photosynthesis contributes more to the ecosystem), μ1 = 0.6 (canopy density contributes moderately to ecological functions), and v1 = 0.5 (transpiration coefficient contributes less). In order to more accurately adjust the contribution of each ecological parameter, nonlinear indexes η1, η2, and η3 are also introduced, and these indexes are set to η1 = 1.2, η2 = 1.1, and η3 = 1.0, respectively, so as to adjust for different ecological parameters.

[0103] The ecological weight W(1, t) of the tree layer is calculated by the following formula:

[0104]

[0105] Among them, ψ(x, y, z, t) is the comprehensive biomass distribution function, which represents the total biomass of the tree layer at time t, and is set to ψ(x, y, z, t) = 1.5 units. Substituting each parameter into the formula, we can get:

[0106]

[0107] After calculation, the ecological weight function of the tree layer is:

[0108]

[0109] Assuming the spatial area Ω1 of the tree layer is 10 hectares, the calculation result of the ecological weight of the tree layer is:

[0110]

[0111] Next, calculate the ecological weight of the shrub layer. Set the photosynthesis efficiency of the shrub layer to φ(x, y, z, t) = 0.7, the canopy density to ρ(x, y, z, t) = 0.6, and the transpiration coefficient to τ(x, y, z, t) = 0.8. The weighting coefficients are λ2 = 0.6, μ2 = 0.5, v2 = 0.7, and the nonlinear indexes are η1 = 1.0, η2 = 1.1, and η3 = 1.2. The calculation formula for the ecological weight W(2, t) of the shrub layer is:

[0112]

[0113] Assuming the comprehensive biomass distribution function of the shrub layer ψ(x, y, z, t) = 1.2 units, and the coverage area of ​​the shrub layer is 5 hectares, the numerical calculation results are:

[0114]

[0115] The ecological weight results of the shrub layer are:

[0116]

[0117] Finally, the ecological weight of the herbaceous layer is calculated. The photosynthetic efficiency of the herbaceous layer is set to φ(x, y, z, t) = 0.5, the canopy density ρ(x, y, z, t) = 0.4, and the transpiration coefficient τ(x, y, z, t) = 0.6. The weighting coefficients are λ3 = 0.4, μ3 = 0.3, and v3 = 0.5, respectively. The nonlinear indexes are η1 = 1.0, η2 = 1.0, and η3 = 1.1. The formula for the ecological weight W(3, t) of the herbaceous layer is:

[0118]

[0119] Assuming the comprehensive biomass distribution function of the herbaceous layer ψ(x, y, z, t) = 1.0 unit, and the coverage area of ​​the herbaceous layer is 8 hectares, the calculation results are:

[0120]

[0121] The ecological weight results of the herbaceous layer are:

[0122]

[0123] In summary, the ecological weights of the tree layer, shrub layer, and herb layer are 0.9687, 1.2167, and 0.969, respectively. By calculating these weights, the system can reasonably allocate ecological contributions to vegetation at different levels in the park, making the overall green coverage assessment of the garden more scientific and ecologically beneficial. At the same time, these weights provide a basis for future garden greening optimization. Managers can improve the coverage or health of certain vegetation layers in a targeted manner based on the actual ecological function contribution of each layer of vegetation to achieve the best ecological benefits.

[0124] Managers have evaluated the ecological contribution of each layer of vegetation to the entire park by calculating the stratification function S(l, t) and ecological weight W(l, t) of each vegetation layer. Now, the system further uses this data to calculate the overall green coverage rate through a weighted superposition formula. The overall green coverage rate not only takes into account the physical area, but also integrates the ecological function contribution of each layer of vegetation.

[0125] The system will first calculate the overall green coverage of the garden. The overall green coverage formula is defined as:

[0126]

[0127] Among them, R(t) is the overall landscaping coverage rate at time t, reflecting the ecological function efficiency of the entire garden. l It is the weighting factor of level l, which indicates the relative contribution of each layer of vegetation to the overall green coverage rate. The system sets weighting factors for the tree layer, shrub layer and herb layer respectively, and sets the weighting factor of the tree layer γ1 = 0.5 (larger contribution), the weighting factor of the shrub layer γ2 = 0.3, and the weighting factor of the herb layer γ3 = 0.2. The selection of these factors is based on the actual performance of each vegetation layer in ecological function.

[0128] Arbor layer contribution calculation:

[0129] Assume that the ecological weight of the tree layer is W(1, t) = 0.9687 and its stratification function is S(1, t) = 13.4, and substitute into the formula:

[0130] R1(t)=0.5·0.9687·13.4=6.487

[0131] Calculation of shrub layer contribution:

[0132] Assume that the ecological weight of the shrub layer is W(2, t) = 1.2167 and its stratification function is S(2, t) = 6.4, and substitute into the formula:

[0133] R2(t)=0.3·1.2167·6.4=2.336

[0134] Calculation of herb layer contribution:

[0135] Assuming the ecological weight of the herbaceous layer W(3, t) = 0.969 and its stratification function S(3, t) = 4.96, substitute into the formula:

[0136] R3(t)=0.2·0.969·4.96=0.961

[0137] Calculate the total green coverage:

[0138] The final overall landscaping coverage is obtained by adding the contribution of each vegetation layer:

[0139] R(t)=R1(t)+R2(t)+R3(t)=6.487+2.336+0.961=9.784

[0140] Therefore, at this specific time t, the green coverage rate of the entire park is 9.784 units. This result not only reflects the physical coverage area of ​​vegetation, but also includes the actual functional contribution of each layer of vegetation to the ecosystem, such as the comprehensive effect of ecological efficiency such as photosynthesis, transpiration and canopy density.

[0141] This approach allows park managers to take improvement measures to address deficiencies in certain layers, such as by increasing the planting of trees or increasing the density of the shrub layer, thereby further improving the overall ecological benefits.

[0142] Embodiment 2:

[0143] Combination Figure 3 In this embodiment, in an ecological park in a certain city, managers hope to quantify the green coverage rate of the park through more accurate ecological assessment methods, and evaluate the actual ecological function contribution of different plants. To this end, a statistical method of garden greening coverage data is adopted, which is not only based on the physical area, but also combines the ecological functional indicators of plants, including carbon absorption, soil and water conservation capacity and cooling effect, to comprehensively evaluate the greening efficiency of each area. In order to achieve this goal, the carbon absorption function C(x, y, t) is first defined by real-time monitoring of the photosynthesis efficiency of plants, where x and y represent the spatial position in the park, and t represents a specific time. The carbon absorption function C(x, y, t) represents the amount of carbon dioxide absorbed by plants in different areas and times. According to the data, the carbon absorption efficiency of the tree layer in a certain area of ​​the park is set to C(x, y, t) = 0.8 units per square meter.

[0144] Next, the system defines the soil and water conservation capacity function P(x, y, t), which is used to measure the protective effect of plants on the soil through their roots to prevent soil erosion. It is assumed that the shrub layer in this area has a relatively dense root system and contributes more to soil and water conservation. Its soil and water conservation capacity is P(x, y, t) = 0.6 units per square meter. The cooling effect function T(x, y, t) represents the ability of plants to reduce the ambient temperature through transpiration and shading effects, which is especially important in hot summers. For example, the transpiration of the herbaceous layer and the shading of vegetation on the area are set to have a cooling effect of T(x, y, t) = 0.5 units per square meter.

[0145] In order to calculate the comprehensive ecological functional contribution of each area, the system introduced a comprehensive functional weight function W (x, y, t), which integrates the three major ecological indicators of carbon absorption, soil and water conservation, and cooling effect. The system assigns different weight coefficients to each indicator, setting the weight of carbon absorption α = 0.7, the weight of soil and water conservation β = 0.5, and the weight of cooling effect γ = 0.4. These weights are set based on the actual needs and environmental conditions of the park.

[0146] The calculation formula of the comprehensive functional weight function is as follows:

[0147]

[0148] Among them, δ is the area correction coefficient, which is used to balance the relationship between ecological functional contribution and physical coverage area. Setting the correction coefficient δ = 1.2 means that the system will slightly tend to the contribution of ecological functional indicators rather than relying solely on area. A(x, y) is the physical area function, which represents the actual green area of ​​the region. The green area of ​​the region is set to 10 hectares.

[0149] Substituting the above data into the comprehensive functional weight function for calculation, the data for carbon absorption, soil and water conservation, and cooling effects are C(x, y, t) = 0.8, P(x, y, t) = 0.6, and T(x, y, t) = 0.5, respectively. After substituting the weight coefficient and area data, the calculation is as follows:

[0150]

[0151] First, calculate the contribution of each ecological functional indicator:

[0152] 0.7·0.8=0.56,0.5·0.6=0.30,0.4·0.5=0.20

[0153] The total ecological function contribution is:

[0154]

[0155] Therefore, the comprehensive ecological functional weight of the area is 0.0883, which indicates the comprehensive contribution of plants to the overall ecosystem at that specific moment and location, including carbon absorption, soil and water conservation, and cooling effects. Through this calculation, the system can organically combine the physical area with the actual ecological functional contribution, making the assessment of landscape greening coverage more ecologically effective, rather than relying solely on the physical area. This method not only provides a more comprehensive assessment of park greening, but also helps managers develop more refined park planning strategies, such as planting more efficient photosynthetic trees in areas with high carbon absorption needs, or increasing the density of the shrub layer in areas prone to soil erosion, thereby improving the ecosystem function of the entire park.

[0156] In order to further improve the management of the park, managers decided to introduce a hierarchical model to conduct functional hierarchical evaluation of each green area based on the type of plants (trees, shrubs, herbs) and their distribution characteristics. Through real-time monitoring by sensors, the system continuously collects data to quantify the ecological functions of plants at each level, such as carbon absorption, soil and water conservation, and cooling. These data are then used to generate the dynamic ecological functional contribution value R(x, y, t) for each area.

[0157] First, the system defines different ecological functional parameters for each plant layer. For example, in a certain area, the carbon absorption capacity of the tree layer is C(x, y, t) = 0.9 units per square meter, the soil and water conservation capacity is P(x, y, t) = 0.7 units per square meter, and the cooling effect is T(x, y, t) = 0.6 units per square meter. The carbon absorption capacity of the shrub layer is relatively low, set to C(x, y, t) = 0.6, the soil and water conservation capacity is relatively strong, set to P(x, y, t) = 0.8, and the cooling effect is medium, set to T(x, y, t) = 0.5. The carbon absorption and soil and water conservation capacity of the herb layer are relatively low, set to C(x, y, t) = 0.4, P(x, y, t) = 0.5, but due to its wide coverage area, the cooling effect is set to T(x, y, t) = 0.7.

[0158] Then, the system introduces weight coefficients for these functional parameters. The weight coefficient for carbon absorption is set to α = 0.7, the weight coefficient for soil and water conservation is set to β = 0.6, and the weight coefficient for cooling effect is set to γ ​​= 0.5. These weights are set based on the actual needs of managers, such as the desire to enhance the carbon absorption function of plants in areas with high carbon emissions, while strengthening soil and water conservation to prevent soil erosion.

[0159] The system calculates the comprehensive functional weight W(x, y, t) according to the following formula:

[0160]

[0161] Wherein, δ is the area correction coefficient, and δ=1.1 is set to balance the relationship between physical area and ecological functional contribution. The green area A(x, y) of the region is set to 10 hectares.

[0162] Calculation of comprehensive ecological function of tree layer:

[0163] For the tree layer, carbon absorption, soil and water conservation, and cooling effects are substituted into the weight formula:

[0164]

[0165] Calculate the weighted value of each ecological function:

[0166] 0.7 0.9 = 0.63, 0.6 0.7 = 0.42, 0.5 0.6 = 0.30

[0167] The total weighted value is:

[0168]

[0169] Calculation of comprehensive ecological functions of the shrub layer:

[0170] For the shrub layer, substitute the weight formula:

[0171]

[0172] Calculate the weighted value of each ecological function:

[0173] 0.7·0.6=0.42,0.6·0.8=0.48,0.5·0.5=0.25

[0174] The total weighted value is:

[0175]

[0176] Calculation of comprehensive ecological function of herbaceous layer:

[0177] For the herb layer, substitute the weight formula:

[0178]

[0179] Calculate the weighted value of each ecological function:

[0180] 0.7·0.4=0.28,0.6·0.5=0.30,0.5·0.7=0.35

[0181] The total weighted value is:

[0182]

[0183] Calculation of dynamic ecological functional contribution value:

[0184] Next, the system calculates the dynamic ecological functional contribution value R(x, y, t) of each layer of plants, and the formula is:

[0185] R(x,y,t)=W(x,y,t)·A(x,y)

[0186] The actual coverage area of ​​each layer of plants was set to 10 hectares, and the comprehensive functional weight of each layer was substituted for calculation.

[0187] Arbor layer:

[0188] R 乔木 (x, y, t) = 1 . 227 · 10 = 12 . 27

[0189] Shrub layer:

[0190] R 灌木 (x, y, t) = 1 . 045 · 10 = 10 . 45

[0191] Herbaceous layer:

[0192] R 草本 (x, y, t) = 0 . 845 · 10 = 8 . 45

[0193] Total dynamic ecological functional contribution value:

[0194] Finally, the system adds up the ecological contribution values ​​of each layer of vegetation to obtain the total dynamic ecological functional contribution value of the entire area:

[0195] R 总 (x, y, t) = R 乔木 (x,y,t)+ R 灌木 (x,y,t)+ R 草本 (x, y, t)

[0196] R 总 (x, y, t)=12.27+10.45+8.45=31-17

[0197] Therefore, in this area, combined with the dynamic evaluation results of ecological functions such as carbon absorption, soil and water conservation, and cooling effects, the system obtained a total ecological functional contribution value of 31.17 units. This result will provide managers with a comprehensive ecological benefit evaluation and help take targeted measures in planning and decision-making, such as strengthening areas with weak soil and water conservation capabilities or further improving the ecological functions of the park by increasing plant coverage.

[0198] In the aforementioned ecological park management process, managers further hope to combine the functional weight model with the physical coverage rate to more comprehensively count the green coverage rate of the park and reflect the actual contribution of vegetation to the ecological environment. In this way, the system can calculate the comprehensive functional coverage rate of the park not only based on the physical area, but also based on the ecological efficiency of vegetation in each area. At this time, the manager needs to calculate the functional coverage rate R(x, y, t) of each area to generate the total green coverage rate R of the entire garden. total (t). The calculation formula is as follows:

[0199] R total (t) = ∫ Ω R(x,y,t)dΩ

[0200] Among them, R total (t) represents the total green coverage rate of the garden at time t, reflecting the comprehensive functional coverage rate of the entire garden and covering the ecological functional contribution of each area. R(x, y, t) is the functional coverage rate of each area, which represents the green coverage rate after combining the ecological functional index at time t and spatial position (x, y). Ω represents the spatial range of the entire garden.

[0201] Calculation of functional coverage of tree layer:

[0202] In the above scenario, the system has calculated the comprehensive functional weight W of the tree layer. 乔木 (x, y, t) = 1.227, and set the actual coverage area of ​​the tree layer A(x, y) = 10 hectares. At this time, the system calculates the functional coverage rate R of the tree layer 乔木 (x, y, t):

[0203] R 乔木 (x, y, t) = W 乔木 (x,y,t)·A(x,y)=1.227·10=12.27

[0204] Calculation of functional coverage of shrub layer:

[0205] For the shrub layer, the system has calculated its comprehensive functional weight W 灌木(x, y, t) = 1.045, assuming that the actual coverage area of ​​the shrub layer is also 10 hectares. Therefore, the functional coverage rate of the shrub layer R 灌木 (x, y, t) is calculated as:

[0206] R 灌木 (x, y, t) = W 灌木 (x,y,t)·A(x,y)=1.045·10=10.45

[0207] Calculation of functional coverage of herb layer:

[0208] Similarly, the comprehensive functional weight of the herb layer W 草本 (x, y, t) has been calculated to be 0.845, and the coverage area of ​​the herb layer is 10 hectares, so the functional coverage rate of the herb layer is R 草本 (x, y, t) is:

[0209] R 草本 (x, y, t) = W 草本 (x,y,t)·A(x,y)=0.845·10=8.45

[0210] Calculation of total green coverage:

[0211] Now, the system adds up the functional coverage of the tree layer, shrub layer, and herb layer to calculate the total green coverage of the entire garden:

[0212] R total (t) = R 乔木 (x,y,t)+R 灌木 (x,y,t)+R 草本 (x, y, t)

[0213] R total (t)=12.27+10.45+8.45=31.17

[0214] Therefore, the total green coverage rate of the entire garden is 31.17 units. This result not only reflects the physical coverage area of ​​different vegetation layers in the park, but also combines the ecological functional contributions of each layer of vegetation, such as carbon absorption, soil and water conservation, and cooling effects. This green coverage rate assessment method based on ecological functions enables managers to understand the ecological benefits of the park more scientifically and take targeted measures, such as increasing tree planting in areas with weaker carbon absorption efficiency or increasing the coverage area of ​​shrubs in areas with poor soil and water conservation functions.

[0215] Through this method, managers can make dynamic adjustments in ecological planning based on real-time data to ensure that the ecological functions of the park are in the optimal state. At the same time, they have more scientific data support in management to promote the sustainable development of the park.

[0216] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A statistical method for landscaping coverage data, characterized in that The steps include: S1. Multi-dimensional hierarchical analysis of vegetation based on ecological characteristics: S1.

1. First, classify the topography, soil type and climate conditions of the garden, and then conduct a comprehensive analysis of the ecological characteristics of the plants; use vegetation height sensor LiDAR technology and multispectral image data to identify the distribution of different vegetation layers; S1.2, then based on the photosynthesis efficiency, canopy density, and transpiration coefficient ecological parameters of different vegetation layers, spatial stratification is performed, and corresponding ecological weights are assigned to each layer; S1.3, generate independent green coverage data for each layer, and obtain the overall green coverage through superposition algorithm; S2. Dynamic coverage calculation based on plant spectral characteristics: S2.

1. Use multispectral images in remote sensing data, combined with plant health indices including NDVI and EVI, to establish a dynamic model of vegetation health status through spectral analysis; extract the spectral characteristics of each pixel point and compare them with the established vegetation health database to evaluate the health status of the point; S2.2, then calculate the green coverage rate that actually participates in ecological functions based on the evaluation results, and adjust the coverage rate data in real time according to the health status of the plants to obtain a dynamic coverage rate; S3. Ecological impact weights based on temporal and spatial changes: S3.

1. Use spatiotemporal data analysis to establish a seasonal weight model by overlaying and analyzing remote sensing images of different seasons; and assign different ecological weights to different seasons and time points through historical data correction; S3.

2. Correct the coverage data by analyzing soil moisture and temperature environmental factors in different time periods; S4. Correlation between ecological functional indicators and green coverage rate: S4.

1. By introducing ecological functional indicators, including carbon absorption, soil and water conservation capacity, and cooling effect, the green coverage rate is associated with ecological functional parameters; the corresponding ecological functional contribution value of each green area is calculated according to the plant type and distribution characteristics; S5. Build a statistical model by coupling vegetation coverage with real-time environmental data: S5.

1. First, install environmental sensors to collect climate and soil data in real time to establish a response function between coverage and environmental conditions; S5.2, then combining the adjusted data with multispectral remote sensing data to form a coverage assessment; S6. By introducing an ecological compensation mechanism, the dynamic process of landscaping changes can be simulated: S6.

1. First, construct a model of the impact of reduced greening on the ecosystem, including increased carbon emissions and soil erosion risks; S6.

2. Then introduce ecological compensation measures, including calculating the restoration effect on green coverage by planting new plants and increasing vegetation density.

2. A method for calculating landscaping coverage data according to claim 1, characterized in that The multi-dimensional hierarchical analysis method of vegetation based on ecological characteristics includes: By combining remote sensing data with ground sensors including LiDAR and multispectral data, the spatial distribution of each layer of vegetation is identified and a three-dimensional model is generated; after identifying each vegetation layer, ecological parameters are extracted.

3. A method for calculating landscaping coverage data according to claim 2, characterized in that The multi-dimensional hierarchical analysis method of vegetation based on ecological characteristics includes: Assign ecological weights to each vegetation layer; the setting of the ecological weights integrates the contribution of each layer of vegetation to the entire ecosystem; and quantifies the actual ecological function of the vegetation; the weights are calculated by weighting the photosynthesis efficiency, canopy density and transpiration coefficient; define the layered ecological weight function W(l,t), the formula of which is: in, W(l,t) is the hierarchical ecological weight function, which represents the ecological weight of the lth vegetation layer at time t; λ l , μ l , ν l The weighting coefficients for photosynthesis, canopy density and transpiration coefficient respectively represent the contribution of each ecological function to the weight; η1, η2, η3 are nonlinear indexes that adjust the relative contribution of photosynthesis efficiency φ(x, y, z, t), canopy density ρ(x, y, z, t) and transpiration coefficient τ(x, y, z, t); ψ(x, y, z, t) is a comprehensive biomass distribution function, which is used for normalization in weight calculation and reflects the overall productivity of vegetation; Ω l Represents the spatial area of ​​the lth layer of vegetation.

4. A method for calculating landscaping coverage data according to claim 3, characterized in that The multi-dimensional hierarchical analysis method of vegetation based on ecological characteristics includes: When the hierarchical functions S(l, t) and ecological weights W(l, t) of all levels are determined, the overall green coverage rate is calculated by weighted superposition; the weighted superposition integrates the physical coverage areas of different levels and the ecological contribution of each layer of vegetation; the overall garden green coverage rate R(t) is defined, and its formula is: Among them, R(t) is the overall green coverage rate, which means the total coverage rate of the entire garden at time t; γ l The weighting factor of level l reflects the comprehensive contribution of each vegetation level to the overall green coverage rate; W(l,t) hierarchical ecological weight function represents the ecological weight of the lth layer of vegetation; S(l,t) hierarchical function represents the ecological efficiency of the lth layer of vegetation; n is the total number of vegetation layers.

5. The method for calculating the landscaping coverage rate according to claim 1 is characterized in that The method for associating the ecological functional index with the green coverage rate includes: By real-time monitoring of the photosynthesis efficiency of plants, a carbon absorption function C(x,y,t) is defined, where z and y represent the spatial position of the greening area, t represents time, and the function C(x,y,t) represents the amount of carbon absorbed by plants in the area at a specific time; it is defined as P(x,y,t), which represents the contribution of plants to soil and water conservation at time t and spatial position (x,y); the cooling effect is the ability of plants to reduce the ambient temperature through transpiration and shading effect, which is defined as T(x,y,t), and the function represents the cooling effect of the area (x,y) within time t; Then, the comprehensive functional weight function W(x, y, t) was introduced, and different weight coefficients were assigned to each indicator to reflect its contribution to the overall ecological benefits of the garden; the specific formula is as follows: Wherein, W(x,y,t) represents the comprehensive functional weight function, which indicates the comprehensive ecological functional contribution of plants in region (x,y) at time t; C(x,y,t) represents the carbon absorption function, which indicates the ability of plants to absorb carbon dioxide at spatial position (x,y) and time t; P(x,y,t) represents the soil and water conservation capacity function, which indicates the ability of plants to prevent soil erosion and retain water at spatial position (x,y) and time t; T(x,y,t) represents the cooling effect function, which indicates the ability of plants to cool down through transpiration and shading at spatial position (x,y) and time y; α, β, and γ represent the weight coefficients of carbon absorption, soil and water conservation capacity, and cooling effect, respectively; δ represents the area correction coefficient, which is used to balance the relationship between ecological function and physical area; A(x,y) is the physical area function, which indicates the actual greening area of ​​region (x,y); Ω represents the spatial range of the entire greening area, which is used to calculate the ecological functional contribution within this range.

6. A method for calculating landscaping coverage data according to claim 5, characterized in that The method for associating the ecological functional index with the green coverage rate includes: A hierarchical model was introduced to conduct a functional hierarchical assessment of each green area based on the type and distribution characteristics of plants; the plant functions of each level were quantified through real-time monitoring and sensor data collection; the carbon absorption capacity, soil and water conservation function and cooling effect of each plant level including trees, shrubs and herbs were calculated separately.

7. A method for calculating landscaping coverage data according to claim 6, characterized in that The method for associating the ecological functional index with the green coverage rate includes: By combining the functional weight model with the physical coverage rate, the statistics of green coverage rate can reflect the area distribution of vegetation and its actual contribution to the ecological environment; the functional coverage rate of each area is summarized and calculated to generate the total green coverage rate of the entire garden.

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