Carbon sequestration tree species and site condition matching method applied to mangrove forest

Abstract

The invention discloses a carbon sequestration tree species and site condition matching method applied to a mangrove forest, and relates to the technical field of geographic data processing, and the method comprises the steps: dividing a to-be-analyzed region according to a stable site index, obtaining a plurality of sub-regions, accurately dividing the region according to the stable site index of the region, and guaranteeing the reliability of region classification. And analyzing an unstable site index in each sub-region to calculate an ecological vulnerability index of each sub-region, and evaluating the ecological vulnerability index of the region to describe the ecological vulnerability degree of the region. According to the method, growth information of tree species is analyzed to screen relative carbon sequestration tree species in a to-be-analyzed region, and two ecological niche suitability evaluation models are provided for a region with data support and a region without data support, so that the optimal carbon sequestration tree species are accurately screened for each sub-region, the screening precision of the carbon sequestration tree species is improved, and the screening efficiency of the carbon sequestration tree species is improved. The matching degree of carbon sequestration tree species and site conditions is improved, so that the method adapts to a complex and changeable ecological environment, and the carbon sequestration capacity of the whole habitat is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic data processing and forest carbon sequestration capacity analysis, and in particular to a method for matching carbon sequestration tree species and site conditions applied to mangroves. Background Art

[0002] Mangroves, as important wetland plants, possess remarkable carbon sequestration capacity and serve as a crucial carbon sink for addressing global climate change. When selecting mangrove carbon-sequestering tree species, it's important to consider their adaptability and carbon sequestration efficiency. For example, Sonneratia apetala, with its rapid growth and strong adaptability, is widely used in mangrove establishment and restoration. Native tree species such as Kandelia candel and Avicennia marina are also favored for their excellent growth characteristics and carbon sequestration capacity.

[0003] Site conditions are also crucial when selecting carbon-sequestering tree species. Mangroves typically grow on intertidal mudflats and must adapt to harsh environments such as high salinity, flooding, and high temperatures. Therefore, suitable habitat conditions, such as hydrological conditions and climatic factors, are key factors influencing the carbon sequestration effectiveness of mangroves. Through scientific site selection and planting practices, we can ensure the survival rate and carbon sequestration capacity of mangroves, contributing to addressing global climate change.

[0004] In the existing technology, when classifying site conditions, only geographical or topographical features are considered for regional classification, resulting in poor screening accuracy of carbon-fixing tree species and poor matching between carbon-fixing tree species and site conditions, and inability to adapt to complex and changing ecological environments.

[0005] Therefore, how to improve the screening accuracy of tree species and the matching of tree species with site conditions is a technical problem that needs to be solved. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems in the prior art of poor screening accuracy of carbon sequestration tree species, poor matching between carbon sequestration tree species and site conditions, and inability to adapt to complex and changing ecological environments. A method for matching carbon sequestration tree species with site conditions applied to mangroves is proposed, which includes:

[0007] Obtaining mangrove tree species information, classifying the mangrove tree species according to the mangrove tree species information, and establishing a tree species classification information database;

[0008] Obtaining geographic data related to the area to be analyzed, screening multiple site indicators from the geographic data, dividing the site indicators of the area to be analyzed into stable site indicators and unstable site indicators, and dividing the area to be analyzed according to the stable site indicators to obtain multiple sub-areas;

[0009] Analyze the unstable site index in each sub-region to calculate the ecological vulnerability index of each sub-region and build a regional classification information database for the analyzed regions;

[0010] Obtain the growth information of each tree species in the tree species classification information database, analyze the growth information of the tree species to screen the relatively carbon-sequestering tree species in the area to be analyzed, and plan the mangrove carbon-sequestering tree species planting plan based on the relatively carbon-sequestering tree species.

[0011] In some embodiments of the present application, tree species are classified according to mangrove tree species information, and a tree species classification information database is established, including:

[0012] The tree species information of mangroves includes tree species category, basic carbon sequestration value and habit description. The corresponding basic carbon sequestration value and habit description are differentiated according to the tree species category to establish a tree species classification information database.

[0013] In some embodiments of the present application, multiple site indicators are screened from geographically relevant data, and the site indicators of the area to be analyzed are divided into stable site indicators and unstable site indicators, including:

[0014] The geographical feature relevance of geographically relevant data is calculated based on the basic carbon sequestration value and habit description of each tree species in the tree species classification information database, thereby screening multiple site indicators from the geographically relevant data;

[0015] Site indicators include soil type, topographic characteristics, soil-related indicators, water-related indicators, and climate-related indicators;

[0016] Among them, soil type, terrain characteristics, and soil-related indicators are stable site indicators, while moisture-related indicators and climate-related indicators are unstable site indicators.

[0017] In some embodiments of the present application, the area to be analyzed is divided according to the stable site index to obtain multiple sub-areas, including:

[0018] The area to be analyzed is divided into different terrain units according to the terrain characteristics of the area to be analyzed, and each terrain unit is then divided into soil units under different types of soil according to the soil type;

[0019] Integrate all soil units in the area to be analyzed and establish a soil property distribution map. Mark soil-related indicators at the corresponding positions on the soil property distribution map. Split the soil unit into multiple small soil units according to the size of the soil unit. Normalize multiple soil-related indicators. Generate a soil property grade for each small soil unit through soil units, small soil units, and soil-related indicators.

[0020]

[0021] in, is the j2th small soil unit under the j1th soil unit, is the conversion coefficient under the j1th soil unit, n1 is the number of soil-related indicators of the j2th small soil unit under the j1th soil unit, α i is the combined weight of the i-th soil-related index of the j2-th small soil unit under the j1-th soil unit, is the size of the i-th soil-related index of the j2-th small soil unit under the j1-th soil unit, n2 is the number of soil units in the soil property distribution map, τ is the soil constant, n2→τ is the soil constant obtained by mapping the number of soil units, and [] is the rounding symbol;

[0022] According to the soil property grade of each small soil unit under the same soil unit, the small soil units with the same soil property grade and adjacent positions are divided into the same sub-region, thereby obtaining multiple sub-regions.

[0023] In some embodiments of the present application, the unstable site index in each sub-region is analyzed to calculate the ecological vulnerability index of each sub-region, and a regional classification information database of the region to be analyzed is constructed, including:

[0024] Obtaining unstable site indicators over a period of time, and establishing an unstable site indicator curve for each unstable site indicator changing over time, calculating the coefficient of variation of each unstable site indicator, splitting the unstable site indicator curve into several partial curves based on the coefficient of variation, calculating the slope change average value and average value of each partial curve, and summarizing the slope change average value and average value of all partial curves to obtain the overall slope change average value and average value of the unstable site indicator curve;

[0025] Collect disaster events that have occurred in each sub-region, analyze the disaster events and give the disaster risk coefficient of each sub-region;

[0026] The ecological vulnerability index of the subregion was calculated based on the disaster risk coefficient, the average value of the overall slope change and the average value of each unstable site indicator;

[0027]

[0028] Among them, E is the ecological vulnerability index of the sub-region, ρ is the disaster risk coefficient of the sub-region, m is the number of unstable site indicators, β k is the conversion coefficient of the kth unstable site index, W1 k is the average value of the kth unstable site index, W2 k is the average value of the overall slope change of the kth unstable site index, and a is a preset constant;

[0029] Each sub-region of the area to be analyzed is marked with its own ecological vulnerability index, and a regional classification information database of the area to be analyzed is constructed according to the category of each sub-region.

[0030] In some embodiments of the present application, analyzing the growth information of tree species to screen relatively carbon-fixing tree species in the area to be analyzed includes:

[0031] The growth information of tree species includes growth geographical information, growth and development information, and environmental improvement information;

[0032] According to the similarity between the growth geographic information and each sub-region in the area to be analyzed, the sub-regions are divided into sub-regions with data support and sub-regions without data support;

[0033] For sub-regions with data support, the first niche suitability evaluation model is trained based on growth and development information and environmental improvement information;

[0034] For the sub-regions without data support, the second ecological niche suitability evaluation model is trained based on the sub-regions with data support;

[0035] The relative carbon sequestration tree species in each sub-region were screened using the first niche suitability evaluation model and the second niche suitability evaluation model.

[0036] In some embodiments of the present application, for a sub-region with data support, a first niche suitability evaluation model is trained based on growth and development information and environmental improvement information, including:

[0037] Obtaining a first carbon sequestration value and a second carbon sequestration value based on the growth and development information and the environmental improvement information, respectively, and adjusting the basic carbon sequestration value based on the first carbon sequestration value and the second carbon sequestration value to obtain a target carbon sequestration value;

[0038] The target carbon sequestration value of the tree species, the geographical characteristics of the sub-region, and the ecological vulnerability index of the sub-region are used as model inputs to train the first niche suitability evaluation model, which outputs the first niche suitability between the tree species and the sub-region.

[0039] In some embodiments of the present application, for a sub-region without data support, a second niche suitability evaluation model is trained based on a sub-region with data support, including:

[0040] Relying on the data on the sub-region with data support, the sub-region without data support is interpolated and analogized, the data on the sub-region without data support is filled, and the second ecological niche suitability evaluation model is trained. The second ecological niche suitability evaluation model outputs the second ecological niche suitability between tree species and sub-regions.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The analysis area was divided into multiple sub-regions based on site stability indicators. Accurately dividing the regions according to site stability indicators ensured the reliability of regional classification. The unstable site indicators within each sub-region were analyzed to calculate the ecological vulnerability index for each sub-region. The regional ecological vulnerability index was then assessed to describe the ecological vulnerability of the region and lay the foundation for subsequent niche suitability evaluation.

[0043] 2. Analyze tree species growth information to screen relatively carbon-sequestering tree species within the analyzed area. Provide two niche suitability evaluation models for areas with and without data support, so as to accurately select the best carbon-sequestering tree species for each sub-area. This improves the accuracy of carbon-sequestering tree species screening and enhances the matching degree between carbon-sequestering tree species and site conditions, so that they can adapt to complex and changing ecological environments, improve the carbon sequestration capacity of the entire habitat, and ensure the stable evolution of the ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a method for matching carbon sequestration tree species and site conditions applied to mangroves, as proposed in the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0046] Reference Figure 1 A method for matching carbon sequestration tree species with site conditions in mangroves includes the following steps:

[0047] Step S101: obtaining tree species information of mangroves, classifying the tree species according to the tree species information of mangroves, and establishing a tree species classification information database.

[0048] In the present embodiment, mangroves are a type of woody plant community that grows in the intertidal zone of tropical and subtropical coasts. They have unique ecological characteristics and growth habits. Although mangrove plants can be broadly divided into two major categories: true mangrove plants and semi-mangrove plants, in fact, the number and variety of mangrove species are far more than this. Globally, the number and variety of mangrove species are quite rich. According to authoritative organizations and research data, the number of mangrove plant species in the world can reach 28 families, 38 genera and 98 species (some say there are 16 families, 120 species, or more), showing its extremely high biodiversity. In China, mangrove species also show diversity, with a total of 19 families, 21 genera and 29 species (some say there are 37 species, including 25 true mangroves and 12 semi-mangroves).

[0049] In this example, mangroves, as unique wetland ecosystems, have a close and complex relationship between their tree species and site conditions. As a core component of this ecosystem, the growth, reproduction, and carbon sequestration capacity of mangrove species are profoundly influenced by site conditions. Site conditions, including soil properties, moisture levels, climate, topography, and other factors, provide the fundamental environment for the survival and development of mangrove species.

[0050] The adaptability of mangrove species to site conditions is key to their survival and ecological function in specific environments. Different mangrove species have varying tolerances and requirements for environmental factors such as soil salinity, moisture, and temperature. For example, some species may be more adapted to saline-alkali environments, able to tolerate high-salt soil and seawater immersion; while others may be more drought-tolerant or shade-tolerant, with specific water and light requirements.

[0051] In some embodiments of the present application, tree species are classified according to mangrove tree species information, and a tree species classification information database is established, including:

[0052] The tree species information of mangroves includes tree species category, basic carbon sequestration value and habit description. The corresponding basic carbon sequestration value and habit description are differentiated according to the tree species category to establish a tree species classification information database.

[0053] In this embodiment, the basic carbon sequestration value is a parameter that is quantified to describe the carbon sequestration capacity of a tree species, and the habit description includes growth habit, salt and alkali tolerance, drought tolerance, etc.

[0054] Step S102: obtain geographic data related to the area to be analyzed, select multiple site indicators from the geographic data, divide the site indicators of the area to be analyzed into stable site indicators and unstable site indicators, and divide the area to be analyzed according to the stable site indicators to obtain multiple sub-areas.

[0055] In this embodiment, key site indicators affecting mangrove growth are screened from geographic data, including topographic features, soil type, soil-related indicators (such as soil pH, organic matter content, and nutrient content, which directly affect tree growth and root development), moisture-related indicators (such as annual precipitation, flooding frequency, and soil moisture content), and climate-related indicators (such as average annual temperature, extreme temperatures, wind speed, and evaporation, which reflect regional climate characteristics and have a significant impact on tree growth and distribution). Regions are initially divided according to topographic features and soil type, and then soil regions are secondary divided according to specific soil conditions (soil-related indicators).

[0056] In some embodiments of the present application, multiple site indicators are screened from geographically relevant data, and the site indicators of the area to be analyzed are divided into stable site indicators and unstable site indicators, including:

[0057] The geographical feature relevance of geographically relevant data is calculated based on the basic carbon sequestration value and habit description of each tree species in the tree species classification information database, thereby screening multiple site indicators from the geographically relevant data;

[0058] Site indicators include soil type, topographic characteristics, soil-related indicators, water-related indicators, and climate-related indicators;

[0059] Among them, soil type, terrain characteristics, and soil-related indicators are stable site indicators, while moisture-related indicators and climate-related indicators are unstable site indicators.

[0060] In this embodiment, the terrain type and geomorphic characteristics, these indicators are mainly formed by geological structures and long-term natural processes, and the changes are relatively small. Soil type, on a longer time scale (such as geological age), the changes in soil type are relatively small unless large-scale geological activities or artificial transformations occur. Soil-related indicators are mainly affected by geological background and seawater, and change less in a relatively stable environment. These indicators are relatively stable and can be used as a basis for dividing regions. Water-related indicators and climate-related indicators, because these indicators are easily affected by external factors and change, their classification of site conditions has greater uncertainty, and they are used as the basis for calculating the subsequent ecological vulnerability index.

[0061] In some embodiments of the present application, the area to be analyzed is divided according to the stable site index to obtain multiple sub-areas, including:

[0062] The area to be analyzed is divided into different terrain units according to the terrain characteristics of the area to be analyzed, and each terrain unit is then divided into soil units under different types of soil according to the soil type;

[0063] Integrate all soil units in the area to be analyzed and establish a soil property distribution map. Mark soil-related indicators at the corresponding positions on the soil property distribution map. Split the soil unit into multiple small soil units according to the size of the soil unit. Normalize multiple soil-related indicators. Generate a soil property grade for each small soil unit through soil units, small soil units, and soil-related indicators.

[0064]

[0065] in, is the j2th small soil unit under the j1th soil unit, is the conversion coefficient under the j1th soil unit, n1 is the number of soil-related indicators of the j2th small soil unit under the j1th soil unit, α i is the combined weight of the i-th soil-related index of the j2-th small soil unit under the j1-th soil unit, is the size of the i-th soil-related index of the j2-th small soil unit under the j1-th soil unit, n2 is the number of soil units in the soil property distribution map, τ is the soil constant, n2→τ is the soil constant obtained by mapping the number of soil units, and [] is the rounding symbol;

[0066] According to the soil property grade of each small soil unit under the same soil unit, the small soil units with the same soil property grade and adjacent positions are divided into the same sub-region, thereby obtaining multiple sub-regions.

[0067] In this embodiment, each soil unit represents one or similar soil type, and soil-related indicators are normalized and combined to generate a soil property grade. represents the conversion factor determined by soil type, represents the quality of the soil determined by the class average, and (n2→τ) represents a constant determined by the number of soil units (the number of soil types) to help determine the grade.

[0068] Step S103 , analyzing the unstable site index in each sub-region to calculate the ecological vulnerability index of each sub-region, and constructing a regional classification information database of the region to be analyzed.

[0069] In this embodiment, for each sub-region, the variation range and trend of its unstable site index are analyzed, and an appropriate mathematical model or evaluation system is used to calculate the ecological vulnerability index of each sub-region.

[0070] In some embodiments of the present application, the unstable site index in each sub-region is analyzed to calculate the ecological vulnerability index of each sub-region, and a regional classification information database of the region to be analyzed is constructed, including:

[0071] Obtaining unstable site indicators over a period of time, and establishing an unstable site indicator curve for each unstable site indicator changing over time, calculating the coefficient of variation of each unstable site indicator, splitting the unstable site indicator curve into several partial curves based on the coefficient of variation, calculating the slope change average value and average value of each partial curve, and summarizing the slope change average value and average value of all partial curves to obtain the overall slope change average value and average value of the unstable site indicator curve;

[0072] Collect disaster events that have occurred in each sub-region, analyze the disaster events and give the disaster risk coefficient of each sub-region;

[0073] The ecological vulnerability index of the subregion was calculated based on the disaster risk coefficient, the average value of the overall slope change and the average value of each unstable site indicator;

[0074]

[0075] Among them, E is the ecological vulnerability index of the sub-region, ρ is the disaster risk coefficient of the sub-region, m is the number of unstable site indicators, β k is the conversion coefficient of the kth unstable site index, W1 k is the average value of the kth unstable site index, W2 k is the average value of the overall slope change of the kth unstable site index, and a is a preset constant;

[0076] Each sub-region of the area to be analyzed is marked with its own ecological vulnerability index, and a regional classification information database of the area to be analyzed is constructed according to the category of each sub-region.

[0077] In this embodiment, the coefficient of variation represents the generally fuzzy stability of a certain unstable site indicator. The number of splits is set according to the stability, and the unstable site indicator curve is adaptively split to provide a method for capturing changes in the unstable site indicator.

[0078] In this embodiment, a comprehensive collection of historically recorded disaster events within each subregion, such as floods, droughts, landslides, and mudslides, is performed. Information such as the time, location, type, intensity, impact range, and resulting losses are recorded. Based on this collected disaster event data, the frequency and intensity of the risk of different disasters in each subregion are analyzed. Appropriate statistical methods or models (such as probability analysis or risk assessment matrices) are used to calculate the disaster risk coefficient for each subregion. Indicates the effect of the average value of the overall slope change (degree of change) on β k W1 k Correction of ecological vulnerability.

[0079] The analysis area was divided into multiple subregions based on site stability indicators. Accurately dividing the regions according to site stability indicators ensured the reliability of regional classification. The unstable site indicators within each subregion were analyzed to calculate the ecological vulnerability index for each subregion. The regional ecological vulnerability index was then assessed to describe the region's ecological vulnerability and lay the foundation for subsequent niche suitability assessments.

[0080] Step S104: acquiring growth information of each tree species in the tree species classification information library, analyzing the growth information of the tree species to screen relatively carbon-fixing tree species in the area to be analyzed, and planning a mangrove carbon-fixing tree species planting plan based on the relatively carbon-fixing tree species.

[0081] In this embodiment, the growth information of each tree species is the relevant growth information in the previous data, the growth geographical information is the geographical conditions of the region, the growth and development information is the growth and development conditions of the tree species (growth rate, health status, tree height, diameter at breast height, etc. of the tree species), and the environmental improvement information (after the tree species are sown, the improvement effect of the tree species on the ecosystem, such as soil improvement, water quality improvement, air purification, etc.). By analyzing the relevant growth information in the past, the relative carbon sequestration tree species in the area to be analyzed are screened, that is, tree species are selected and screened for each sub-area in the area to be analyzed, and the best tree species after screening are the relative carbon sequestration tree species. Plan the planting plan, and formulate reasonable planting density, configuration pattern and planting sequence according to the ecological fragility level of the sub-area and the characteristics of the relative carbon sequestration tree species to ensure the maximization of the restoration and carbon sequestration effect of the mangroves, while taking into account ecological diversity and sustainability.

[0082] Enhanced carbon sequestration: As an important carbon sink, mangroves' carbon sequestration capacity is influenced by both tree species and site conditions. By matching the most suitable tree species to specific site conditions, we can fully realize the carbon sequestration potential of mangroves and make a greater contribution to addressing global climate change.

[0083] Promoting ecosystem stability: The stability of mangrove ecosystems depends on the diversity and adaptability of their constituent species. By precisely matching tree species with site conditions, we can promote the diversity and stability of mangrove ecosystems, enhancing their resilience to natural disasters and human disturbances.

[0084] In some embodiments of the present application, analyzing the growth information of tree species to screen relatively carbon-fixing tree species in the area to be analyzed includes:

[0085] The growth information of tree species includes growth geographical information, growth and development information, and environmental improvement information;

[0086] According to the similarity between the growth geographic information and each sub-region in the area to be analyzed, the sub-regions are divided into sub-regions with data support and sub-regions without data support;

[0087] For sub-regions with data support, the first niche suitability evaluation model is trained based on growth and development information and environmental improvement information;

[0088] For the sub-regions without data support, the second ecological niche suitability evaluation model is trained based on the sub-regions with data support;

[0089] The relative carbon sequestration tree species in each sub-region were screened using the first niche suitability evaluation model and the second niche suitability evaluation model.

[0090] In this example, there are sub-regions with data support (regions with terrain and other features that are relatively similar to those in previous records) and sub-regions without data support (regions with terrain and other features that are less similar to those in previous records). For each type of region, an ecological niche suitability assessment model is established to describe the matching between tree species and sub-regions in each region. The base carbon sequestration value of a tree species is adjusted based on growth and development information and environmental improvement information, thereby training the first ecological niche suitability assessment model.

[0091] Improving restoration success rates: Mangrove restoration and reestablishment is a complex and time-consuming process. By precisely matching tree species to site conditions, we can select species with strong adaptability, rapid growth, and high carbon sequestration capacity, thereby increasing the success rate of restoration projects. This not only saves time and resources, but also avoids unnecessary ecological risks.

[0092] In some embodiments of the present application, for a sub-region with data support, a first niche suitability evaluation model is trained based on growth and development information and environmental improvement information, including:

[0093] Obtaining a first carbon sequestration value and a second carbon sequestration value based on the growth and development information and the environmental improvement information, respectively, and adjusting the basic carbon sequestration value based on the first carbon sequestration value and the second carbon sequestration value to obtain a target carbon sequestration value;

[0094] The target carbon sequestration value of the tree species, the geographical characteristics of the sub-region, and the ecological vulnerability index of the sub-region are used as model inputs to train the first niche suitability evaluation model, which outputs the first niche suitability between the tree species and the sub-region.

[0095] In this embodiment, growth and development information and environmental improvement information each represent relative carbon sequestration capacity from two dimensions: one is the growth and development of the tree species (indirectly, the better the growth and development of the tree species, the greater the carbon sequestration capacity), and the other is the environmental improvement (directly, reflected in the ecosystem surrounding or in which the tree species resides). Therefore, the first and second carbon sequestration values ​​are determined based on these two dimensions. The basic carbon sequestration value is adjusted by combining the first and second carbon sequestration values. Specifically, the first and second carbon sequestration values ​​are weighted and summed to obtain a total carbon sequestration value. The average of the total carbon sequestration value and the basic carbon sequestration value is then taken as the target carbon sequestration value.

[0096] It should be noted that the basic carbon sequestration value (i.e. the amount of carbon sequestered by a tree species per unit time) varies for the same tree species under different site conditions (geographical climate and other conditions). Therefore, the basic carbon sequestration value needs to be adjusted. Tree species information, target carbon sequestration value of tree species, geographical characteristics of the sub-region (such as climate, soil type, etc.), and ecological vulnerability index of the sub-region are used as model inputs. Machine learning algorithms (such as random forests, support vector machines, etc.) or niche models are used to train the first niche suitability evaluation model. The model outputs the first niche suitability between tree species and sub-regions, that is, a comprehensive assessment of the survival and carbon sequestration capacity of tree species in a specific sub-region.

[0097] By analyzing the growth information of tree species, we can screen relatively carbon-sequestering tree species in the area to be analyzed. We provide two niche suitability evaluation models for areas with data support and areas without data support, so as to accurately screen the best carbon-sequestering tree species for each sub-area. This improves the accuracy of carbon-sequestering tree species screening and enhances the matching degree between carbon-sequestering tree species and site conditions, so that they can adapt to complex and changing ecological environments, ensure the stable evolution of ecosystems, and improve the carbon sequestration capacity of the entire habitat.

[0098] In some embodiments of the present application, for a sub-region without data support, a second niche suitability evaluation model is trained based on a sub-region with data support, including:

[0099] Relying on the data on the sub-region with data support, the sub-region without data support is interpolated and analogized, the data on the sub-region without data support is filled, and the second ecological niche suitability evaluation model is trained. The second ecological niche suitability evaluation model outputs the second ecological niche suitability between tree species and sub-regions.

[0100] In this embodiment, interpolation and analogy processing are performed on the sub-region without data support based on the data of the sub-region with data support. The interpolation method can be spatial interpolation (such as Kriging interpolation, inverse distance weighted interpolation, etc.), and the analogy processing is based on the principle of similarity, applying the data of similar sub-regions (sub-regions with data support) to the region without data. When training the model, you can first use the data of the sub-region with data support for pre-training to obtain a preliminary model. Then, use the transfer learning method to transfer the knowledge of the preliminary model to the sub-region without data support. At the same time, the fill data is input into the model as auxiliary information to help the model better understand the characteristics of the sub-region without data support.

[0101] Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented through hardware or by using software plus the necessary general hardware platform. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) and includes a number of instructions for enabling a computer device (such as a personal computer, a server, or a network device) to execute the methods described in various implementation scenarios of the present invention.

[0102] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0103] Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices in the implementation scenario according to the implementation scenario description, or can be modified accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module or further split into multiple submodules.

[0104] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for matching carbon sequestration tree species and site conditions for mangroves, characterized in that: include, Obtaining tree species information of mangroves, classifying tree species according to the tree species information of mangroves, and establishing a tree species classification information database; Obtaining geographic related data of the area to be analyzed, screening out multiple site indicators from the geographic related data, dividing the site indicators of the area to be analyzed into stable site indicators and unstable site indicators, and dividing the area to be analyzed according to the stable site indicators to obtain multiple sub-areas; The unstable site index in each sub-region is analyzed to calculate the ecological vulnerability index of each sub-region and to build a regional classification information database for the region to be analyzed; The growth information of each tree species in the tree species classification information database is obtained, and the growth information of the tree species is analyzed to screen the relatively carbon-fixing tree species in the area to be analyzed, and the carbon-fixing tree species planting plan of mangroves is planned through the relatively carbon-fixing tree species.

2. The method for matching carbon sequestration tree species and site conditions for mangroves according to claim 1, characterized in that: Classify the tree species according to the tree species information of mangroves, and establish a tree species classification information database, including: The tree species information of mangroves includes tree species category, basic carbon fixation value and habit description. The corresponding basic carbon fixation value and habit description are differentiated according to the tree species category, and a tree species classification information database is established.

3. The method for matching carbon sequestration tree species and site conditions for mangroves according to claim 2, characterized in that: Multiple site indicators are screened out from geographically relevant data, and the site indicators of the area to be analyzed are divided into stable site indicators and unstable site indicators, including: The geographical feature relevance of geographically relevant data is calculated based on the basic carbon sequestration value and habit description of each tree species in the tree species classification information database, thereby screening out multiple site indicators from the geographically relevant data; Site indicators include soil type, topographic characteristics, soil-related indicators, water-related indicators, and climate-related indicators; Among them, soil type, terrain characteristics, and soil-related indicators are stable site indicators, while water-related indicators and climate-related indicators are unstable site indicators.

4. The method for matching carbon sequestration tree species and site conditions for mangroves according to claim 3, characterized in that: The analysis area is divided according to the stable site index to obtain multiple sub-areas, including: The area to be analyzed is divided into different terrain units according to the terrain characteristics of the area to be analyzed, and each terrain unit is then divided into soil units under different types of soil according to the soil type; Integrate all soil units in the area to be analyzed, establish a soil property distribution map, mark soil-related indicators at corresponding positions on the soil property distribution map, split the soil unit into multiple small soil units according to the size of the soil unit, normalize multiple soil-related indicators, and generate the soil property grade of each small soil unit through soil units, small soil units and soil-related indicators; in, is the j2th small soil unit under the j1th soil unit, is the conversion coefficient under the j1th soil unit, n1 is the number of soil-related indicators of the j2th small soil unit under the j1th soil unit, α i is the combined weight of the i-th soil-related index of the j2-th small soil unit under the j1-th soil unit, is the size of the i-th soil-related index of the j2-th small soil unit under the j1-th soil unit, n2 is the number of soil units in the soil property distribution map, τ is the soil constant, n2→τ is the soil constant obtained by mapping the number of soil units, and [] is the rounding symbol; According to the soil property grade of each small soil unit under the same soil unit, the small soil units with the same soil property grade and adjacent positions are divided into the same sub-region, thereby obtaining multiple sub-regions.

5. The method for matching carbon sequestration tree species and site conditions for mangroves according to claim 3, characterized in that: The unstable site index in each sub-region is analyzed to calculate the ecological vulnerability index of each sub-region, and a regional classification information database of the area to be analyzed is constructed, including: Obtain unstable site indicators within a period of time, and establish an unstable site indicator curve of each unstable site indicator changing with time, calculate the coefficient of variation of each unstable site indicator, split the unstable site indicator curve into several partial curves based on the coefficient of variation, calculate the slope change average value and average value of each partial curve, and calculate the slope change average value and average value of all partial curves to obtain the overall slope change average value and average value of the unstable site indicator curve; Collect disaster events that have occurred in each sub-region, analyze the disaster events and give the disaster risk coefficient of each sub-region; The ecological vulnerability index of the sub-region was calculated based on the disaster risk coefficient, the average value of the overall slope change and the average value of each unstable site indicator; Among them, E is the ecological vulnerability index of the sub-region, ρ is the disaster risk coefficient of the sub-region, m is the number of unstable site indicators, β k is the conversion coefficient of the kth unstable site index, W1 k is the average value of the kth unstable site index, W2 k is the average value of the overall slope change of the kth unstable site index, and a is a preset constant; Each sub-region of the area to be analyzed is marked with its own ecological vulnerability index, and a regional classification information database of the area to be analyzed is constructed according to the category of each sub-region.

6. The method for matching carbon sequestration tree species and site conditions for mangroves according to claim 2, characterized in that: Analyze the growth information of tree species to screen the relatively carbon-fixing tree species in the area to be analyzed, including: The growth information of tree species includes growth geographical information, growth and development information, and environmental improvement information; According to the similarity between the growth geographic information and each sub-region in the area to be analyzed, the sub-regions are divided into sub-regions with data support and sub-regions without data support; For sub-regions with data support, the first niche fitness evaluation model is trained based on growth and development information and environmental improvement information; For the sub-regions without data support, the second ecological niche suitability evaluation model is trained based on the sub-regions with data support; The relative carbon-fixing tree species in each sub-region were screened using the first niche fitness evaluation model and the second niche fitness evaluation model.

7. The method for matching carbon sequestration tree species and site conditions for mangroves according to claim 6, characterized in that: For sub-regions with data support, the first niche fitness evaluation model is trained based on growth and development information and environmental improvement information, including: Obtaining a first carbon fixation value and a second carbon fixation value according to the growth and development information and the environmental improvement information respectively, and adjusting the basic carbon fixation value in combination with the first carbon fixation value and the second carbon fixation value to obtain a target carbon fixation value; The target carbon fixation value of the tree species, the geographical characteristics of the sub-region, and the ecological vulnerability index of the sub-region are used as model inputs to train the first niche suitability evaluation model, which outputs the first niche suitability between the tree species and the sub-region.

8. The method for matching carbon sequestration tree species and site conditions for mangroves according to claim 6, characterized in that: For the sub-regions without data support, the second niche suitability evaluation model is trained based on the sub-regions with data support, including: Relying on the data on the sub-region with data support, the sub-region without data support is interpolated and analogized, the data on the sub-region without data support is filled, and the second ecological niche fitness evaluation model is trained. The second ecological niche fitness evaluation model outputs the second ecological niche fitness between tree species and sub-regions.

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