Green land carbon sink quantity estimation method for ecological restoration planning

Abstract

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological restoration, and in particular to a method for estimating green space carbon sinks for ecological restoration planning. Background Art

[0002] As the global climate change problem becomes increasingly serious, reducing greenhouse gas emissions has become a common concern of the international community. As an important part of natural carbon sinks, urban green spaces, nature reserves, wetlands and other ecological areas play a key role in improving carbon sink capacity and reducing atmospheric carbon dioxide concentrations. Therefore, how to scientifically and accurately estimate the carbon sink of different ecosystems and provide a basis for planning and decision-making has become an important research topic in the current field of ecological protection and ecological restoration.

[0003] At present, the estimation methods of ecosystem carbon sinks are mainly divided into two categories: one is a simple estimation method based on fixed coefficients, and the other is a multi-factor model based on plant biomass and soil carbon storage. The traditional fixed coefficient estimation method often relies on the average carbon sink coefficient to simply quantify the carbon sink capacity of vegetation, but ignores the impact of different plant species, soil types and growth environments on carbon sinks, resulting in low accuracy of the results and difficulty in adapting to the diversity needs of different ecological environments. The multi-factor model based on plant biomass and soil carbon storage is more scientific and estimates carbon sinks by considering multidimensional factors such as vegetation type, age, density, and soil characteristics. However, this type of model still faces certain challenges in practical applications, such as difficulty in data collection and insufficient consideration of dynamic changes.

[0004] With the rapid development of remote sensing technology, drone monitoring technology, and geographic information system (GIS) technology, more accurate data sources and processing methods have been provided for carbon sink estimation, especially in complex ecological environments such as urban green spaces and wetlands. These technologies can obtain more detailed important information such as vegetation coverage, terrain characteristics, and climate conditions. In addition, machine learning algorithms and statistical analysis methods in recent years can optimize carbon sink estimation models through historical data and field sampling data to improve the accuracy and dynamic adaptability of estimates. However, in existing technical applications, how to effectively integrate multiple data sources, use environmental factors to make real-time corrections to carbon sink models, and establish differentiated models for different ecosystems are still difficult problems that need to be solved.

[0005] In view of the above problems, the present invention proposes a method for estimating green space carbon sinks for ecological restoration planning. Summary of the invention

[0006] The purpose of the present invention is to provide a method for estimating green space carbon sinks for ecological restoration planning in view of the shortcomings of the prior art, so as to solve the problems raised in the background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for estimating green space carbon sinks for ecological restoration planning, the method comprising the following steps:

[0009] Data collection steps: Obtain geographic information, vegetation coverage, species distribution, soil type and other data of the target green space through remote sensing images, drone monitoring or on-site sampling;

[0010] Carbon sink model construction steps: Based on the collected data, a carbon sink calculation model including a vegetation carbon storage model and a soil carbon storage model is constructed;

[0011] Environmental factor correction step: Use meteorological data and environmental conditions to correct the carbon sink model to obtain a carbon sink that is more in line with actual ecological conditions;

[0012] Dynamic carbon sink estimation steps: by considering factors such as vegetation growth cycle and seasonal changes, estimate the carbon sink of the target green space at different times;

[0013] Data visualization and decision support step: visualize the estimation results and generate carbon sequestration analysis results for different restoration options.

[0014] As a preferred technical solution of the present invention, the vegetation carbon storage model is constructed based on the Allometric model, and its specific steps include:

[0015] Classify trees, shrubs and lawns in green space and establish biomass calculation models for each;

[0016] The biomass of individual trees or shrubs was calculated using the following formula:

[0017] B=a·D b

[0018] Among them, B is biomass, D is diameter at breast height, and a and b are empirical coefficients determined according to plant species;

[0019] Based on the biomass estimation results, calculate the vegetation carbon storage:

[0020] C veg =B·f

[0021] Among them, C veg is the vegetation carbon storage, and f is the carbon content coefficient.

[0022] As a preferred technical solution of the present invention, the soil carbon storage model adopts a multi-layer carbon storage estimation method, and its specific steps include:

[0023] The soil profile was layered, with each layer being 20 cm deep;

[0024] The carbon storage in each soil layer was estimated using the following formula:

[0025] C soil,i =ρ i ·OC i ·d

[0026] Among them, C soil,i is the carbon storage of the i-th layer of soil, ρ i is the soil density of the i-th layer, OC i is the organic carbon content of the i-th layer, and d is the layer depth;

[0027] Calculate the sum of the carbon storage in the entire soil profile to obtain the total soil carbon storage:

[0028]

[0029] Among them, C soil is the total soil carbon stock, and n is the number of soil layers.

[0030] As a preferred technical solution of the present invention, the total carbon storage C total is the vegetation carbon storage C veg Soil carbon storage C soil The sum is calculated as follows:

[0031] C total =C veg +C soil

[0032] As a preferred technical solution of the present invention, the environmental factor correction step corrects the total carbon storage based on temperature, precipitation, sunshine time, slope and altitude, and is characterized in that:

[0033] The total carbon stock is adjusted using the following correction formula:

[0034] C adjusted =C total ·F temp ·F precip ·F solar ·F slope ·F altitude

[0035] Among them, C a Djusted is the corrected carbon storage, F t emp、F precip 、F solar 、F slope and F altitude are the correction factors for temperature, precipitation, sunshine hours, slope and altitude respectively;

[0036] The correction coefficients are obtained based on statistical regression analysis or machine learning models, which are consistent with the environmental characteristics of the target area.

[0037] As a preferred technical solution of the present invention, the dynamic carbon sink estimation step constructs a time series model to estimate the changes in carbon sinks at different time scales, which is characterized by:

[0038] The change in carbon sequestration for each time period was calculated using the following formula:

[0039] ΔC=C adjusted,t+1 -C adjusted,t

[0040] Among them, ΔC is the change value of carbon sink, C adjusted,t+1 and C adjusted,t are the revised carbon stocks for the next time period and the current time period, respectively;

[0041] The growth cycle and seasonal changes are introduced into the carbon sink model, and time series analysis is used to predict the long-term trend of carbon sink.

[0042] As a preferred technical solution of the present invention, the data visualization and decision support steps are implemented through a geographic information system:

[0043] (1) Generate two-dimensional and three-dimensional carbon sink distribution maps based on the carbon sink estimation results;

[0044] (2) The carbon storage contribution of different green space types is displayed through the “carbon sink-area” relationship curve, supporting decision makers to intuitively analyze the carbon sink distribution and restoration effect.

[0045] As a preferred technical solution of the present invention, the green space types include different ecological types such as trees, shrubs, lawns, and water areas:

[0046] (1) Establish carbon sink estimation models according to different ecological types;

[0047] (2) The carbon sinks of different ecological types are weighted according to their characteristics and functions to obtain an estimate of the overall carbon sink of green spaces.

[0048] As a preferred technical solution of the present invention, the parameters of vegetation biomass coefficient and soil organic carbon content in the method are derived from field sampling and experimental determination, and are characterized in that:

[0049] (1) Obtain plant and soil samples through on-site sampling and measure biomass and organic carbon content;

[0050] (2) The measurement results are used as input parameters of the carbon sink model to improve the accuracy of the estimation results.

[0051] As a preferred technical solution of the present invention, the dynamic carbon sink estimation step in the method is combined with a machine learning algorithm to optimize the model parameters:

[0052] (1) Use random forest or neural network algorithms to train carbon sequestration models, with input parameters including environmental factors, vegetation characteristics, and soil properties;

[0053] (2) The model is optimized through algorithm iteration to obtain the optimal estimation parameters, thereby improving the accuracy and applicability of carbon sink estimation.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The carbon sink estimation method of the present invention significantly improves the accuracy and applicability of the estimation. By introducing vegetation carbon storage, soil carbon storage and environmental factor correction modules, this method can comprehensively consider the diversity characteristics of different ecosystems, and use multi-factor models and field data to dynamically adjust carbon sink estimation to adapt to changes at different time and spatial scales. This method avoids the limitations of traditional fixed coefficient estimation and can more accurately reflect the actual carbon sink capacity of green space.

[0056] 2. The present invention collects real-time data through remote sensing, UAV and GIS technology, and introduces vegetation growth cycle and seasonal changes in combination with time series analysis, providing dynamic carbon sink monitoring capabilities for ecological restoration planning. Through data visualization and carbon sink comparison functions of multiple restoration schemes, it helps decision makers to intuitively understand the carbon sink contribution of different ecological restoration schemes, and support scientific decision-making and optimization of ecological restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The present invention is a flowchart of a method for estimating green space carbon sinks for ecological restoration planning. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] The following is combined with Figure 1 With reference to the accompanying drawings and a number of embodiments, specific embodiments of the present invention are described in detail.

[0060] Example 1: Estimation of carbon sequestration in mixed green spaces of trees, shrubs and grasses in urban parks

[0061] In a city park in a certain city, the green land coverage rate is 80%, including different types of vegetation such as trees, shrubs and lawns. The area of ​​the park is 50 hectares. This embodiment demonstrates the specific steps of estimating the annual carbon sink of the park using the method of the present invention.

[0062] Data collection: Collect geographic information through remote sensing images and drones to obtain vegetation coverage and distribution information.

[0063] Field surveys were used to determine the specific species and density of trees, shrubs, and lawns in the park, and to measure the DBH and tree height of sample trees.

[0064] Construction of carbon sink model: The biomass of trees was calculated using the Allometric model, assuming that the main tree species was camphor, with empirical coefficients a = 0.15 and b = 2.5.

[0065] The calculation formula of tree biomass B is:

[0066] B=0.15·D 2.5

[0067] Corresponding biomass calculation formulas are established for shrubs and lawns, assuming that the shrub biomass is B = 0.1·D 2.2 ,The lawn biomass is estimated based on the biomass per unit area.

[0068] Calculate vegetation carbon storage C veg =B·f, where f=0.45 represents the carbon content coefficient.

[0069] Soil carbon storage model: The soil profile was layered, with each layer being 20 cm deep, and field sampling was performed to determine the organic carbon content OC and soil density ρ.

[0070] Use the soil carbon storage calculation formula:

[0071] C soil,i =ρ i ·OC i ·d

[0072] Calculate the total soil carbon storage C by summing up the carbon storage in each layer of soil. soil =∑ i n ＝1 C soil,i .

[0073] Total carbon storage calculation: by formula C total =C veg +C soil Calculate the total carbon stock of the park.

[0074] Environmental factor correction: According to local meteorological data, determine the correction coefficient F of environmental factors such as temperature, precipitation, and sunshine time temp 、F pecip 、F solar wait.

[0075] Use the correction formula: C adjusted =C total ·F temp ·F precip ·F solar Obtain the corrected annual carbon sink.

[0076] Result visualization: GIS technology is used to display the spatial distribution of carbon sinks, providing scientific reference for ecological restoration and green space management.

[0077] Example 2: Estimation of carbon sink in wetland ecological restoration area

[0078] In a wetland ecological restoration area along a river, the main vegetation types are aquatic plants and wetland trees. The area is 100 hectares, and it is planned to increase the wetland carbon sink by increasing the vegetation density. This example shows how to use this method to estimate the wetland carbon sink and evaluate the carbon sink effect of different restoration plans.

[0079] Data collection: Use drone imagery and field surveys to obtain distribution and density data of wetland trees and aquatic plants.

[0080] Soil samples were collected from the wetland area and analyzed for soil organic carbon content and soil density.

[0081] Carbon sink model construction: The Allometric model was used to calculate the biomass of wetland trees, assuming that the wetland tree species was willow, with empirical coefficients a = 0.12, b = 2.3.

[0082] The carbon storage of aquatic plants is estimated based on biomass per unit area, assuming that the average biomass of aquatic plants is 0.3 kg per square meter.

[0083] Calculate vegetation carbon storage C veg , where the carbon content coefficients f of willow and aquatic plants are 0.45 and 0.4 respectively.

[0084] Soil carbon storage model: A multi-layer soil carbon storage model is used to calculate the soil carbon storage in each layer.

[0085] C soil,i =ρ i ·OC i ·d

[0086] And sum them up to get the total soil carbon storage C in the wetland area. soil .

[0087] Total carbon stock calculation:

[0088] By formula C total =C veg +C soil Calculate the total carbon stock in the wetland area;

[0089] Environmental factor correction: Determine the correction factor based on the climatic conditions of the wetland (humidity, temperature, precipitation). Correction formula:

[0090] C adjusted =C total ·F temp ·F humidity ·F precip

[0091] The corrected wetland carbon sequestration was obtained.

[0092] Scheme comparison and decision support: For restoration schemes with different vegetation densities, the carbon sink amount of each scheme is estimated, and the "carbon sink-area" relationship curve is output to support the selection of the best restoration scheme.

[0093] Example 3: Estimation of carbon sinks in mountain ecological restoration areas

[0094] An ecological restoration project was carried out in a certain mountainous area, where the green space types are mainly shrubs and grasslands. The ecological conditions in mountainous areas are complex, the temperature and precipitation vary greatly, and the slope has a significant impact. This embodiment shows the application steps in ecological restoration in mountainous areas.

[0095] The terrain and vegetation coverage information of the mountains were obtained through remote sensing images, and the distribution of shrubs and grasslands was determined. Soil samples were collected on site to measure soil density ρ and organic carbon content OC.

[0096] Carbon sink model construction:

[0097] Data collection: Shrub biomass is estimated based on DBH and empirical coefficients. Assuming the empirical coefficients are a = 0.08 and b = 2.1, the shrub biomass is calculated as B = 0.08·D 2.1 .

[0098] The grassland biomass is estimated per unit area, assuming that the average grassland biomass is 0.2 kg per square meter.

[0099] Vegetation carbon storage C veg =B·f, where the carbon content coefficients f of shrubs and grasslands are 0.42 and 0.38 respectively.

[0100] Soil carbon storage model: The soil carbon storage is calculated layer by layer using the layered calculation method to obtain the total soil carbon storage C soil =∑ i n ＝1 ρ i·OC i ·d.

[0101] Total carbon stock calculation: Calculate the total carbon stock C total =C veg +C soil .

[0102] Environmental factor correction: Consider the special environmental factors in mountainous areas, such as slope and altitude, and set the corresponding correction coefficient F slope and F altitude And the revised carbon storage calculation formula:

[0103] C adjusted =C total ·F temp ·F precip ·F slope ·F altitude

[0104] The corrected mountain carbon sink was calculated.

[0105] Result output and analysis: GIS technology is used to visualize the spatial distribution of carbon sinks in mountainous areas, and the distribution characteristics of carbon sinks under different slopes and altitudes are analyzed to provide data support for subsequent ecological restoration.

[0106] Example 4: Long-term dynamic carbon sink monitoring

[0107] Data collection and model construction: For green spaces in a certain urban area, the carbon sequestration trend is monitored over a long period of time to evaluate the restoration effect of the green spaces.

[0108] Geographic information and vegetation growth conditions of urban green spaces are collected regularly, and a carbon sink model is established using the method in Example 1.

[0109] Dynamic carbon sink estimation: Establish a time series model to record carbon sink data for each quarter.

[0110] Use the formula ΔC = C adjusted,t+1 -C adjusted,t Calculate the change in carbon sink in each time period.

[0111] Long-term trend analysis: Through time series analysis, we draw a curve of changes in green space carbon sinks, analyze the fluctuations of carbon sinks with seasonal and climate changes, and provide a scientific basis for long-term ecological management.

[0112] 1. Plant biomass model coefficient (Allometric model coefficient)

[0113] Empirical coefficient a: Usually the value range is between 0.01-0.3 depending on the plant species.

[0114] Exponential coefficient b: usually between 1.5-3.0, depending on the size and shape of the plant species.

[0115] Different types of plants, such as trees, shrubs, and herbs, require different a and b coefficients. The specific values ​​are usually determined by field sampling or reference to relevant literature.

[0116] 2. Carbon content coefficient f

[0117] The carbon content coefficient f is the coefficient of plant biomass conversion into carbon storage, which usually depends on the type of plant and the proportion of carbon components it contains.

[0118] For most plants, the value range is 0.45·0.5.

[0119] The commonly used value for trees is 0.45-0.48, while that for shrubs and lawns is generally between 0.4 and 0.45.

[0120] 3. Soil carbon storage model coefficients

[0121] Soil density ρ: ±Soil density depends on the soil type and usually ranges from 1.0-1.6 g / cm°.

[0122] Organic carbon content OC: The organic carbon content in the soil varies depending on the region and vegetation type, and is generally 1%-10% (i.e. a ratio of 0.01-0.1).

[0123] 4. Environmental factor correction factor

[0124] Temperature correction factor F temp : The value range is between 0.9-1.1, and the carbon sink is fine-tuned according to temperature changes.

[0125] Precipitation correction factor F precip : Generally between 0.85-1.15, the value depends on whether the precipitation is high or low.

[0126] Sunshine time correction factor F solar :Usually between 0.9-1.05, and adjusted appropriately according to the impact of sunshine duration on carbon sequestration.

[0127] Slope correction factor F slope : Slope affects the carbon sequestration capacity of vegetation, and the value range is usually 0.8-1.

[0128] Altitude correction factor F altitude :The carbon sequestration capacity of high-altitude areas may be low, usually ranging from 0.85-1.0.

[0129] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating green space carbon sinks for ecological restoration planning, characterized in that: The method comprises the following steps: Data collection steps: Obtain the geographic information, vegetation coverage, species distribution, and soil type data of the target green space through remote sensing images, drone monitoring, or on-site sampling; Carbon sink model construction steps: Based on the collected data, a carbon sink calculation model including a vegetation carbon storage model and a soil carbon storage model is constructed; Environmental factor correction step: Use meteorological data and environmental conditions to correct the carbon sink model to obtain a carbon sink that is more in line with actual ecological conditions; Dynamic carbon sink estimation steps: by considering the vegetation growth cycle and seasonal changes, estimate the carbon sink of the target green space at different times; Data visualization and decision support step: visualize the estimation results and generate carbon sequestration analysis results for different restoration options.

2. A method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The vegetation carbon storage model is constructed based on the Allometric model, and its specific steps include: Classify trees, shrubs and lawns in green space and establish biomass calculation models for each; The biomass of individual trees or shrubs was calculated using the following formula: B=a·D b Among them, B is biomass, D is diameter at breast height, and a and b are empirical coefficients determined according to plant species; Based on the biomass estimation results, calculate the vegetation carbon storage: C veg =B·f Among them, C veg is the vegetation carbon storage, and f is the carbon content coefficient.

3. The method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The soil carbon storage model adopts a multi-layer carbon storage estimation method, and its specific steps include: The soil profile was layered, with each layer being 20 cm deep; The carbon storage in each soil layer was estimated using the following formula: C soil,i =ρ i ·OC i ·d Among them, C soil,i is the carbon storage of the i-th layer of soil, ρ i is the soil density of the i-th layer, OC i is the organic carbon content of the i-th layer, and d is the layer depth; Calculate the sum of the carbon storage in the entire soil profile to obtain the total soil carbon storage: Among them, C soil is the total soil carbon stock, and n is the number of soil layers.

4. The method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The total carbon stock C total is the vegetation carbon storage C veg Soil carbon storage C soil The sum is calculated as follows: C total =C veg +C soil 。 5. The method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The environmental factor correction step corrects the total carbon storage based on temperature, precipitation, sunshine time, slope and altitude, and is characterized by: The total carbon stock is adjusted using the following correction formula: C adjusted =C total ·F temp ·F precip ·F solar ·F slope ·F altitude Among them, C a Djusted is the corrected carbon storage, F t emp、F precip 、F solar 、F slope and F altitude are the correction factors for temperature, precipitation, sunshine hours, slope and altitude respectively; The correction coefficients are obtained based on statistical regression analysis or machine learning models, which are consistent with the environmental characteristics of the target area.

6. A method for estimating green space carbon sinks for ecological restoration planning according to claim 1, wherein: The dynamic carbon sink estimation step constructs a time series model to estimate the changes in carbon sinks at different time scales, and is characterized by: The change in carbon sequestration for each time period was calculated using the following formula: ΔC=C adjusted,t+1 -C adjusted,t Among them, ΔC is the change value of carbon sink, C adjusted,t+1 and C adjusted,t are the revised carbon stocks for the next time period and the current time period, respectively; The growth cycle and seasonal changes are introduced into the carbon sink model, and time series analysis is used to predict the long-term trend of carbon sink.

7. The method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The data visualization and decision support steps are implemented by a geographic information system, which is characterized by: (1) Generate two-dimensional and three-dimensional carbon sink distribution maps based on the carbon sink estimation results; (2) The carbon storage contribution of different green space types is displayed through the "carbon sink-area" relationship curve, supporting decision makers to intuitively analyze the carbon sink distribution and restoration effect.

8. The method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The green space types include different ecological types such as trees, shrubs, lawns, and water areas, and are characterized by: (1) Establish carbon sink estimation models according to different ecological types; (2) The carbon sinks of different ecological types are weighted according to their characteristics and functions to obtain an estimate of the overall carbon sink of green spaces.

9. The method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The parameters of vegetation biomass coefficient and soil organic carbon content in the method are derived from field sampling and experimental determination, and are characterized in that: (1) Obtain plant and soil samples through on-site sampling and measure biomass and organic carbon content; (2) The measurement results are used as input parameters of the carbon sink model to improve the accuracy of the estimation results.

10. The method for estimating green space carbon sinks for ecological restoration planning according to claim 1, characterized in that: The dynamic carbon sink estimation step in the method is combined with a machine learning algorithm to optimize model parameters, characterized in that: (1) Use random forest or neural network algorithms to train carbon sequestration models, with input parameters including environmental factors, vegetation characteristics, and soil properties; (2) The model is optimized through algorithm iteration to obtain the optimal estimation parameters, thereby improving the accuracy and applicability of carbon sink estimation.

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