A forest carbon sink potential calculation method and system

Abstract

The application discloses a forest carbon sink potential calculation method and system and belongs to the technical field of ecological carbon sink assessment. The present application takes the minimum unit of forest management, i.e. a small plot, as a basic spatial unit, generates a working small plot layer completely matched with the existing management system through standardized preprocessing, superimposes carbon potential quantitative results and a spatial constraint layer, generates four types of functional areas, i.e. a carbon protection and stabilization area, a carbon expansion area through afforestation, a carbon increase area through restoration and a carbon promotion area through management, based on automatic threshold values such as statistical median values, realizes a complete technical process from 'potential assessment' to'spatial layout', and the assessment results can directly guide grass-roots production activities.

Description

Technical Field

[0001] This invention relates to the field of ecological carbon sequestration assessment technology, and more specifically to a method and system for calculating forest carbon sequestration potential. Background Technology

[0002] Forests, as the largest carbon sink in terrestrial ecosystems, play an irreplaceable role in absorbing carbon dioxide and mitigating climate change. Accurately assessing the current status and future potential of forest carbon sinks, and formulating scientifically sound forest management plans accordingly, is a key technological support for achieving the goal of "carbon peaking and carbon neutrality." Currently, scholars both domestically and internationally have conducted extensive research on forest carbon sink potential assessment, primarily employing the following technical approaches:

[0003] One type of method uses continuous forest inventory data or remote sensing data, employing grey prediction models, power function models, or regression models to simulate and predict forest carbon storage and its future trends at the regional scale. While this approach can provide macro-level estimates of total carbon holdings, the assessment units are typically administrative regions (such as counties or cities) or fixed spatial grids. This makes it difficult to reflect the spatial heterogeneity of carbon sink potential caused by differences in land type, tree species, age group, and site conditions. Consequently, the assessment results have limited guiding value for grassroots forestry management decisions.

[0004] Another type is based on stand growth models or process models (such as FORECAST, 3-PG, CENTURY, etc.), combined with climate scenario data, to dynamically simulate the biomass accumulation process of specific tree species. This type of method has high simulation accuracy at the individual tree or stand scale, but when applied to large-scale areas, it faces problems such as difficulty in localizing model parameters, high computational resource consumption, and difficulty in effectively integrating with existing forest resource survey data.

[0005] However, the above methods have problems in assessing forest carbon sink potential, such as mismatch between assessment units and management units, single scenario setting that cannot quantify management additionality, disconnect between potential assessment and layout decision-making, and lack of automated and standardized processing procedures.

[0006] Therefore, there is an urgent need for a method and system for calculating forest carbon sequestration potential that can use small plots as the basic assessment unit, support comparative assessments of multiple natural and management scenarios, and automatically generate spatially defined layout schemes and management measures. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed to provide a method and system for calculating forest carbon sequestration potential that overcomes or at least partially solves the above problems. To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, embodiments of the present invention provide a method for calculating forest carbon sink potential, comprising the following steps: S1. Obtain publicly available multi-source forest resources and geospatial data of the target area and preprocess them to form base data with unified coordinates and format. The base data includes small plot units. S2, based on the base plate data, perform multi-layer spatial overlay analysis of climate, topography and vegetation type, conduct forest growth potential zoning, and associate zoning identification code with each small plot unit; S3. Using the biomass measurement data from the publicly available forest resource inventory fixed sample plot data, a zoning biomass inversion model is established using the random forest regression method, and then a multidimensional biomass benchmark database is constructed. S4, based on the area of ​​the small plot unit and the corresponding multidimensional biomass benchmark database, calculates the current carbon storage, natural scenario carbon potential and operational scenario carbon potential of each small plot unit, and summarizes them to obtain the total carbon potential result of all zoning. S5. Integrate the spatial constraint layer with the total carbon potential results calculated in step S4 to determine the carbon sink functional area to which it belongs.

[0008] Furthermore, in S1, the multi-source forest resources and geospatial data include publicly available forest resource inventory data, land use / land cover data, topographic data, climate raster data, protected area boundary data, administrative division data, forest degradation datasets, and afforestation suitability spatial data.

[0009] Furthermore, the small-class units in the base plate data are obtained through the following steps: S101, Obtain publicly available land use / land cover data and forest resource inventory data for the target area, and generate initial spatial units based on land use patches; S102, Spatial correlation is performed between the initial spatial units and the forest resource inventory data, and each unit is assigned forest stand attributes, including dominant tree species, age group, origin and cover, etc. If the attributes are missing, the attribute estimation method of neighboring sample plots in the same ecological zone is used to complete the attributes. S103, For publicly available forest degradation datasets and afforestation suitability spatial data, relevant information is linked to the attribute table of the corresponding unit through spatial overlay; S104 generates a globally unique identifier for each work unit and establishes a spatial index to obtain the small class unit.

[0010] Furthermore, in S2, forest growth potential zoning is carried out, and the specific zoning identification code associated with each small plot unit includes: S201, based on publicly available climate raster data and topographic data, divides climate zones and landform types, and combines them to generate first-level zoning patches; S202, within the first-level zoning, overlay vegetation functional type, soil texture and hydrological condition data to generate candidate patches for the second-level zoning. S203 cuts and corrects candidate map patches using watershed boundaries or administrative division boundaries, and outputs a polygonal zoning layer. S204: Overlay the polygonal zoning layer with the small class unit, calculate the overlap area between each small class unit and each zoning polygon, and associate the zoning identification code with each small class unit according to the principle of maximizing the overlap area.

[0011] Furthermore, in step S3, establishing a zoned biomass inversion model and constructing a multidimensional biomass benchmark matrix specifically includes the following steps: S301, extract the coordinates, tree species, age group, and measured biomass per unit area of ​​fixed sample plots from publicly available forest resource inventory data as a training sample set. Each sample is also associated with the zoning identification code, land type, age group, or coverage of its respective sub-compartment. S302, construct a random forest regression model with zoning identification code, land type, tree species, age group or coverage, and leaf area index or vegetation coverage extracted from remote sensing data as feature variables, and measured biomass per unit area as target variable. Train the model separately for each secondary zoning or use the zoning variables as input features of the global model. The model parameters are set as follows: number of decision trees is 500, maximum number of features is 3, and minimum sample size of nodes is 5. S303 applies the trained model to each plot unit, predicting the current biomass per unit area based on the plot unit's zoning identifier, land type, tree species, age group or cover, and corresponding remote sensing features. ; S304. Within the same secondary zoning, the current biomass per unit area of ​​all mature and overmature forest sub-compartments is statistically analyzed, and the 85th percentile is taken as the optimal natural biomass per unit area for that tree species within that zoning. ; S305, within the same secondary zoning, selected small plots with successful management demonstration sites or high-quality forest stands, and their current biomass per unit area was statistically analyzed. The 90th percentile is used as the optimal management biomass per unit area for this tree species in this zoning area. If there are no successful demonstration sites, then an empirical formula will be used. λ is the operational improvement coefficient, which ranges from 1.05 to 1.35 and is determined by the hydrothermal conditions of the area. S306, a multidimensional biomass benchmark database was constructed using "zoning identification code-land type-tree species-age group / cover" as a joint index to store the current biomass per unit area corresponding to each plot unit. Optimal natural biomass per unit area Optimal biomass per unit area The carbon content (CF) of tree species is used to form a structured database.

[0012] Further, in step S4, calculating the current carbon storage, natural scenario carbon potential, and operational scenario carbon potential on a patch-by-patch basis specifically includes: Calculate the current carbon storage of the i-th sub-unit: ; Calculate the natural carbon capacity of the i-th small class unit: ; Calculate the operating carbon capacity of the i-th small class unit:

[0013] Calculate the natural scenario carbon potential of the i-th class unit:

[0014] Calculate the operating scenario carbon potential of the i-th small class unit:

[0015] in, Let be the carbon content of the tree species in the i-th sub-class unit. , , These represent the current biomass per unit area, the optimal natural biomass per unit area, and the optimal operational biomass per unit area for the i-th sub-unit, respectively. Let be the area of ​​the i-th small class unit.

[0016] Furthermore, the total carbon potential for each zone is summarized, specifically including the following formula:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024] in, This represents the carbon storage of the x-th primary zoning zone. This represents the set of secondary districts within the x-th primary district. This represents the set of small class units within the k-th secondary zone; m is the total number of primary zones within the target area; This represents the total carbon storage within the target area; This represents the natural carbon capacity of the x-th primary zoning area; This represents the total natural carbon capacity within the target area; This represents the operational carbon capacity of the x-th primary zone; This indicates the total operating carbon capacity within the target area; This represents the total natural scenario carbon potential of the target area. This represents the total operating scenario carbon potential of the target area.

[0025] Furthermore, step S4, the assessment of the carbon potential of the operating scenario, further includes a dynamic simulation sub-step: Using the logistic growth model, calculate the predicted biomass for each year or planning period during the period from the current state to the optimal operating state:

[0026] in, To determine the optimal carrying capacity biomass, k represents the intrinsic growth rate specific to the tree species in each zone, with values ​​ranging from 0.08 to 0.12 for coniferous forests, 0.10 to 0.15 for broadleaf forests, and 0.12 to 0.18 for shrub forests. This is the inflection point of growth; Based on the predicted biomass, calculate the dynamic carbon sink increment for each year or planning period during the period of achieving optimal operating conditions;

[0027] in, For dynamic carbon sequestration increments, Let be the area of ​​the i-th small class unit.

[0028] Furthermore, step S5 specifically includes the following steps: S501, Obtain the spatial constraint layer of the target area. The spatial constraint layer includes the boundary layer of nature reserves, the ecological protection red line layer, the planned afforestation spatial layer, and the degraded forest distribution layer. Convert all layers into the same coordinate system and vector format as the data base plate. S502, Perform spatial overlay analysis on each small class unit and the spatial constraint layer, and calculate derived indices for each small class unit: The ecological red line marker Flag_protected is 1 if the small plot unit is located in the core area of ​​the nature reserve or the ecological red line restricted area, and 0 otherwise; The afforestation planning flag, Flag_plant, is 1 if the sub-compartment is located in the planned afforestation space area, and 0 otherwise. The degraded forest flag is Flag_degraded. If the sub-compartment is located in a degraded forest distribution area, Flag_degraded is 1; otherwise, it is 0. The management potential per unit area, V_managed, is 1 if the management potential per unit area of ​​a small plot is greater than the median management potential per unit area of ​​all degraded forest plots within the secondary zoning to which the small plot belongs; otherwise, it is 0. S503, for each small class, the carbon sink functional area type is automatically determined according to the following priority order based on the derived indicators: a. If R_protected == 1, then the region is determined to be a protected carbon stable region, and the determination is terminated; b. If a does not satisfy the condition and Flag_plant == 1, then it is determined to be an afforestation and carbon expansion zone; c. If a and b do not satisfy the condition and Flag_degraded == 1 while V_managed == 1, then it is determined to repair the carbon-added area; d. If none of a, b, and c are satisfied, and the land parcel is classified as arbor forest or shrub forest, then it is determined to be a carbon-promoting management zone; e. If none of a, b, c, and e are satisfied, it is determined to be a general operating area that does not take any special carbon sequestration measures. S504. For sub-compartments identified as carbon-increasing zones for restoration or carbon-promoting zones for management, further matching combinations of forest management measures are retrieved from a pre-set measures knowledge base based on the sub-compartment's zoning identification code, dominant tree species, age group / coverage, and degradation type. S505: Write the functional area type and measure code of each small class into the corresponding field of the small class attribute table to generate scientific layout thematic data.

[0029] Secondly, embodiments of the present invention provide a forest carbon sink potential calculation system, comprising: The forest growth potential zoning module is used for spatial overlay analysis based on climate, topography and vegetation type to divide the target area into multiple secondary zoning with different growth potentials and assign a zoning identification code to each spatial unit. The carbon storage and carbon capacity benchmark modeling module is used to construct and store a multidimensional biomass benchmark matrix indexed by "zoning identification code-land type-tree species-age group / coverage". The matrix includes at least the current biomass per unit area, the optimal natural biomass per unit area, and the optimal managed biomass per unit area. The carbon potential assessment calculation module, connected to the modeling module, is used to calculate the current carbon storage, natural scenario carbon potential, operational scenario carbon potential and operational net carbon sink on a unit-by-unit basis according to the biomass benchmark matrix and unit area. The operational net carbon sink is equal to the operational scenario carbon potential minus the natural scenario carbon potential. The layout optimization module connects the zoning module and the carbon potential assessment module. It internally stores a multi-level decision rule set based on spatial constraint layers and carbon potential assessment results. It is used to automatically divide each spatial unit into carbon protection and stabilization zones, afforestation and carbon expansion zones, restoration and carbon increase zones, or management and carbon promotion zones, and generate specific forest management measures suggestions for the latter three types of zones.

[0030] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for calculating forest carbon sequestration potential, which has the following beneficial effects: This invention uses the smallest unit of forest management, the "compartment," as the basic spatial unit. Through standardized preprocessing, it generates a working compartment layer that is fully compatible with the existing management system. The carbon potential quantification results are overlaid with the spatial constraint layer, and four functional zones are generated based on automatic thresholds such as the statistical median: "protection and carbon stabilization, afforestation and carbon expansion, restoration and carbon increase, and management and carbon promotion." This invention realizes a complete technology from "potential assessment to spatial layout," and the assessment results can directly guide grassroots production activities.

[0031] This invention creatively couples macro-level ecological zoning and geomorphological analysis with micro-level forest stand factors (tree species, age group) to construct a four-dimensional biomass benchmark database of "zoning-land type-tree species-age group", which solves the problem of accuracy loss caused by "one-size-fits-all" parameters in large-area assessment. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0033] Figure 1 This is an overall flowchart of the forest carbon sequestration potential calculation method provided in the embodiments of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] This invention discloses a method for calculating forest carbon sequestration potential, such as... Figure 1 As shown, it includes the following steps: S1. Obtain publicly available multi-source forest resources and geospatial data of the target area and preprocess them to form base data with unified coordinates and format. The base data includes small plot units. S2, based on the base plate data, perform multi-layer spatial overlay analysis of climate, topography and vegetation type, conduct forest growth potential zoning, and associate zoning identification code with each small plot unit; S3. Using the biomass measurement data from the publicly available forest resource inventory fixed sample plot data, a zoning biomass inversion model is established using the random forest regression method, and then a multidimensional biomass benchmark database is constructed. S4, based on the area of ​​the small plot unit and the corresponding multidimensional biomass benchmark database, calculates the current carbon storage, natural scenario carbon potential and operational scenario carbon potential of each small plot unit, and summarizes them to obtain the total carbon potential result of all zoning. S5. Integrate the spatial constraint layer with the total carbon potential results calculated in step S4 to determine the carbon sink functional area to which it belongs.

[0036] The steps of the present invention will be described in detail below to illustrate the specific implementation of the present invention.

[0037] In step S1, the multi-source forest resources and geospatial data include publicly available forest resource inventory data, land use / land cover data, topographic data, climate raster data, protected area boundary data, administrative division data, forest degradation datasets, and afforestation suitability spatial data.

[0038] These data need to be standardized, specifically including: Standardized format: All spatial data is uniformly converted to Shapefile format.

[0039] Coordinate System 1: The "2000 National Geodetic Coordinate System" (CGCS2000) and the "1985 National Elevation Datum" are adopted uniformly.

[0040] Projection uniformity: Gauss-Kruger projection is used, and the projection is divided into 3° zones.

[0041] Topology check and repair: Checks patch boundaries for topology errors such as overlaps and gaps, and automatically repairs them to ensure data quality.

[0042] The preprocessed base plate data serves as an important small unit, which is obtained through the following steps: S101, Obtain publicly available land use / land cover data and forest resource inventory data for the target area, and generate initial spatial units based on land use patches; S102, Spatial correlation is performed between the initial spatial units and the forest resource inventory data, and each unit is assigned forest stand attributes, including dominant tree species, age group, origin and cover, etc. If the attributes are missing, the attribute estimation method of neighboring sample plots in the same ecological zone is used to complete the attributes. S103, For publicly available forest degradation datasets and afforestation suitability spatial data, relevant information is linked to the attribute table of the corresponding unit through spatial overlay; S104 generates a globally unique identifier for each work unit and establishes a spatial index to obtain the small class unit.

[0043] After data preprocessing, step S2, forest growth potential zoning, is performed, and a zoning identification code is associated with each sub-compartment.

[0044] Step S2 specifically includes: S201, based on publicly available climate raster data and topographic data, divides climate zones and landform types, and combines them to generate first-level zoning patches; S202, within the first-level zoning, overlay vegetation functional type, soil texture and hydrological condition data to generate candidate patches for the second-level zoning. S203 cuts and corrects candidate map patches using watershed boundaries or administrative division boundaries, and outputs a polygonal zoning layer. S204: Overlay the polygonal zoning layer with the small class unit, calculate the overlap area between each small class unit and each zoning polygon, and associate the zoning identification code with each small class unit according to the principle of maximizing the overlap area.

[0045] The division of zoning patches can be executed through a multi-level zoning rule engine stored internally. This engine is built on a knowledge base of the following three levels: Primary regionalization rule base: rules based on macro-climate zones, such as cold temperate zone, temperate zone, warm temperate zone, and dominant landform types (including mountains, hills, and high plains).

[0046] Secondary zoning rule base: Within the primary zoning, further combine zonal vegetation types (coniferous forest, broad-leaved forest, mixed coniferous and broad-leaved forest, shrub forest), soil types (black soil, black calcareous soil, chestnut calcareous soil, etc.) and hydrological conditions (endorheic area / exorheic area).

[0047] Boundary correction rules: Use administrative boundaries, watershed boundaries, or nature reserve boundaries to fine-tune the above-mentioned zoning boundaries to ensure the integrity of management units.

[0048] The multi-level zoning rule engine is configured to automatically execute the following steps based on the spatial raster data (climate, DEM, NDVI) of the input target area, using spatial overlay analysis and region growing algorithms: Input climate zoning raster and geomorphic zoning raster, perform spatial intersection operation to generate preliminary first-level zoning patches.

[0049] Within the primary zoning map patches, vegetation type distribution maps and soil maps are overlaid, and multi-layer fusion clustering is performed to generate candidate secondary zoning map patches.

[0050] Apply boundary correction rules to fit the candidate patches of the secondary zoning with the administrative or watershed boundaries, output the final polygon zoning layer, and generate a unique identifier Zone_ID for each secondary zone.

[0051] In one specific embodiment, for a certain arid and semi-arid region in the north, three primary regions (mountainous region, hilly region, and high plain region) and 23 secondary regions can be generated according to the above steps.

[0052] In the above steps, the original plot units in the integrated forestry and grassland ecological monitoring data are spatially overlaid and their boundaries aligned with the land survey change data. The original plot boundaries are then cut and reconstructed based on the boundaries of the land survey map patches, and a land use consistency determination is performed to generate working plot units that are fully compatible with the current forest resource management system. Each plot unit has a globally unique identifier, which can be seamlessly integrated with the grassroots forestry management information system.

[0053] The purpose of step S3 is to construct a multidimensional biomass benchmark database. This step can be achieved through the following steps: S301, extract the coordinates, tree species, age group, and measured biomass per unit area of ​​fixed sample plots from publicly available forest resource inventory data as a training sample set. Each sample is also associated with the zoning identification code, land type, age group, or coverage of its respective sub-compartment. S302, construct a random forest regression model with zoning identification code, land type, tree species, age group or coverage, and leaf area index or vegetation coverage extracted from remote sensing data as feature variables, and measured biomass per unit area as target variable. Train the model separately for each secondary zoning or use the zoning variables as input features of the global model. The model parameters are set as follows: number of decision trees is 500, maximum number of features is 3, and minimum sample size of nodes is 5. S303 applies the trained model to each plot unit, predicting the current biomass per unit area based on the plot unit's zoning identifier, land type, tree species, age group or cover, and corresponding remote sensing features. ; S304. Within the same secondary zoning, the current biomass per unit area of ​​all mature and overmature forest sub-compartments is statistically analyzed, and the 85th percentile is taken as the optimal natural biomass per unit area for that tree species within that zoning. ; S305, within the same secondary zoning, selected small plots with successful management demonstration sites or high-quality forest stands, and their current biomass per unit area was statistically analyzed. The 90th percentile is used as the optimal management biomass per unit area for this tree species in this zoning area. If there are no successful demonstration sites, then an empirical formula will be used. λ is the operational improvement coefficient, which ranges from 1.05 to 1.35 and is determined by the hydrothermal conditions of the area. S306, a multidimensional biomass benchmark database was constructed using "zoning identification code-land type-tree species-age group / cover" as a joint index to store the current biomass per unit area corresponding to each plot unit. Optimal natural biomass per unit area Optimal biomass per unit area The carbon content (CF) of tree species is used to form a structured database.

[0054] A multidimensional biomass benchmark database was constructed, with the following structure:

[0055] S4. After obtaining the multidimensional biomass benchmark database, based on the area of ​​the small plot unit and the corresponding multidimensional biomass benchmark database, calculate the current carbon storage, natural scenario carbon potential and operational scenario carbon potential of each small plot unit, and summarize them to obtain the total carbon potential result of all zoning. First, calculate the current carbon storage, natural scenario carbon potential, and operational scenario carbon potential for each small unit: Calculate the current carbon storage of the i-th sub-unit: ; Calculate the natural carbon capacity of the i-th small class unit: ; Calculate the operating carbon capacity of the i-th small class unit:

[0056] Calculate the natural scenario carbon potential of the i-th class unit:

[0057] Calculate the operating scenario carbon potential of the i-th small class unit:

[0058] in, Let be the carbon content of the tree species in the i-th sub-class unit. , , These represent the current biomass per unit area, the optimal natural biomass per unit area, and the optimal operational biomass per unit area for the i-th sub-unit, respectively. Let be the area of ​​the i-th small class unit.

[0059] Furthermore, based on the carbon potential calculation results of each small unit, these results are summarized to obtain the total carbon potential result for each zoning, specifically including the following steps: Calculate the carbon storage of each primary zoning area based on the current carbon storage of each sub-unit: Carbon storage based on primary zoning Calculate total carbon storage : Calculate the natural carbon capacity of each primary zoning area based on the natural carbon capacity of each small class unit: Calculate total natural carbon capacity based on carbon storage in primary zoning. : Calculate the operating carbon capacity of each primary zone based on the operating carbon capacity of each small unit: Calculate the total operating carbon capacity based on the carbon storage capacity of the primary zoning. : Based on total natural carbon capacity and total carbon storage Calculate the total natural scenario carbon potential of the target region: Based on total operating carbon capacity and total carbon storage The total operating scenario carbon potential of the target area is calculated using the following formula:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] in, This represents the carbon storage of the x-th primary zoning zone. This represents the set of secondary districts within the x-th primary district. This represents the set of small class units within the k-th secondary zone; m is the total number of primary zones within the target area; This represents the total carbon storage within the target area; This represents the natural carbon capacity of the x-th primary zoning area; This represents the total natural carbon capacity within the target area; This represents the operational carbon capacity of the x-th primary zone; This indicates the total operating carbon capacity within the target area; This represents the total natural scenario carbon potential of the target area. This represents the total operating scenario carbon potential of the target area.

[0068] The assessment of the carbon potential of operating scenarios further includes a dynamic simulation sub-step: Using the logistic growth model, calculate the predicted biomass for each year or planning period during the period from the current state to the optimal operating state:

[0069] in, To determine the optimal carrying capacity biomass, k represents the intrinsic growth rate specific to the tree species in each zone, with values ​​ranging from 0.08 to 0.12 for coniferous forests, 0.10 to 0.15 for broadleaf forests, and 0.12 to 0.18 for shrub forests. This is the inflection point of growth; Based on the predicted biomass, calculate the dynamic carbon sink increment for each year or planning period during the period of optimal operation:

[0070] in, For dynamic carbon sequestration increments, Let be the area of ​​the i-th small class unit.

[0071] S5 integrates the spatial constraint layer with the total carbon potential calculated in step S4 to determine its corresponding carbon sink functional area. The specific steps are as follows: S501, Obtain the spatial constraint layer of the target area. The spatial constraint layer includes the boundary layer of nature reserves, the ecological protection red line layer, the planned afforestation spatial layer, and the degraded forest distribution layer. Convert all layers into the same coordinate system and vector format as the data base plate. S502, Perform spatial overlay analysis on each small class unit and the spatial constraint layer, and calculate derived indices for each small class unit: The ecological red line marker Flag_protected is 1 if the small plot unit is located in the core area of ​​the nature reserve or the ecological red line restricted area, and 0 otherwise; The afforestation planning flag, Flag_plant, is 1 if the sub-compartment is located in the planned afforestation space area, and 0 otherwise. The degraded forest flag is Flag_degraded. If the sub-compartment is located in a degraded forest distribution area, Flag_degraded is 1; otherwise, it is 0. The management potential per unit area, V_managed, is 1 if the management potential per unit area of ​​a small plot is greater than the median management potential per unit area of ​​all degraded forest plots within the secondary zoning to which the small plot belongs; otherwise, it is 0. S503, for each small class, the carbon sink functional area type is automatically determined according to the following priority order based on the derived indicators: a. If R_protected == 1, then the region is determined to be a protected carbon stable region, and the determination is terminated; b. If a does not satisfy the condition and Flag_plant == 1, then it is determined to be an afforestation and carbon expansion zone; c. If a and b do not satisfy the condition and Flag_degraded == 1 while V_managed == 1, then it is determined to repair the carbon-added area; d. If none of a, b, and c are satisfied and the plot is classified as arbor forest or shrub forest, then it is determined to be a carbon-promoting management zone; This invention encodes arbor forests and shrub forests by age group as N1, N2, or N3 (young forest, middle-aged forest, near-mature forest) and coverage as F1, F2, or F3 (low, medium, medium-high coverage). e. If none of a, b, c, and e are satisfied, it is determined to be a general operating area (no special carbon sequestration measures are taken). S504. For sub-compartments identified as carbon-increasing zones for restoration or carbon-promoting zones for management, further search for matching combinations of forest management measures from the measures knowledge base based on the sub-compartment's zoning identification code, dominant tree species, age group / coverage, and degradation type (if any). The measures knowledge base in this invention is a predefined decision table. Each record contains attribute conditions (zoning type, tree species, age group range, degradation type) and a corresponding set of measures codes (including one or more of the following: low-quality and inefficient forest transformation, artificial afforestation, artificial regeneration, tending and management, and forest closure for natural regeneration). S505: Write the functional area type and measure code of each small class into the corresponding field of the small class attribute table to generate scientific layout thematic data.

[0072] This invention also provides a forest carbon sink potential calculation system to realize a method for calculating forest carbon sink potential. The system includes: The forest growth potential zoning module is used for spatial overlay analysis based on climate, topography and vegetation type to divide the target area into multiple secondary zoning with different growth potentials and assign a zoning identification code to each spatial unit. The carbon storage and carbon capacity benchmark modeling module is used to construct and store a multidimensional biomass benchmark matrix indexed by "zoning identification code-land type-tree species-age group / coverage". The matrix includes at least the current biomass per unit area, the optimal natural biomass, and the optimal managed biomass per unit area. The carbon potential assessment calculation module, connected to the modeling module, is used to calculate the current carbon storage, natural scenario carbon potential, operational scenario carbon potential and operational net carbon sink on a unit-by-unit basis according to the biomass benchmark matrix and unit area. The operational net carbon sink is equal to the operational scenario carbon potential minus the natural scenario carbon potential. The layout optimization module connects the zoning module and the carbon potential assessment module. It internally stores a multi-level decision rule set based on spatial constraint layers and carbon potential assessment results. It is used to automatically divide each spatial unit into carbon protection and stabilization zones, afforestation and carbon expansion zones, restoration and carbon increase zones, or management and carbon promotion zones, and generate specific forest management measures suggestions for the latter three types of zones.

[0073] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0074] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating forest carbon sequestration potential, characterized in that, Includes the following steps: S1. Obtain publicly available multi-source forest resources and geospatial data of the target area and preprocess them to form base data with unified coordinates and format. The base data includes small plot units. S2, based on the base plate data, perform multi-layer spatial overlay analysis of climate, topography and vegetation type, conduct forest growth potential zoning, and associate zoning identification code with each small plot unit; S3. Using the biomass measurement data from the publicly available forest resource inventory fixed sample plot data, a zoning biomass inversion model is established using the random forest regression method, and then a multidimensional biomass benchmark database is constructed. S4, based on the area of ​​the small plot unit and the corresponding multidimensional biomass benchmark database, calculates the current carbon storage, natural scenario carbon potential and operational scenario carbon potential of each small plot unit, and summarizes them to obtain the total carbon potential result of all zoning. S5. Integrate the spatial constraint layer with the total carbon potential results calculated in step S4 to determine the carbon sink functional area to which it belongs.

2. The method according to claim 1, characterized in that, In S1, the multi-source forest resources and geospatial data include publicly available forest resource inventory data, land use / land cover data, topographic data, climate raster data, protected area boundary data, administrative division data, forest degradation datasets, and afforestation suitability spatial data.

3. The method according to claim 2, characterized in that, The small-class units in the base plate data are obtained through the following steps: S101, Obtain publicly available land use / land cover data and forest resource inventory data for the target area, and generate initial spatial units based on land use patches; S102, Spatial correlation is performed between the initial spatial units and the forest resource inventory data, and each unit is assigned forest stand attributes, including dominant tree species, age group, origin and cover, etc. If the attributes are missing, the attribute estimation method of neighboring sample plots in the same ecological zone is used to complete the attributes. S103, For publicly available forest degradation datasets and afforestation suitability spatial data, relevant information is linked to the attribute table of the corresponding unit through spatial overlay; S104 generates a globally unique identifier for each work unit and establishes a spatial index to obtain the small class unit.

4. The method according to claim 1, characterized in that, In S2, forest growth potential zoning is carried out, and the specific zoning identification code associated with each small plot unit includes: S201, based on publicly available climate raster data and topographic data, divides climate zones and landform types, and combines them to generate first-level zoning patches; S202, within the first-level zoning, overlay vegetation functional type, soil texture and hydrological condition data to generate candidate patches for the second-level zoning. S203 cuts and corrects candidate map patches using watershed boundaries or administrative division boundaries, and outputs a polygonal zoning layer. S204: Overlay the polygonal zoning layer with the small class unit, calculate the overlap area between each small class unit and each zoning polygon, and associate the zoning identification code with each small class unit according to the principle of maximizing the overlap area.

5. The method as described in claim 1, characterized in that, In step S3, establishing a zoned biomass inversion model and constructing a multidimensional biomass benchmark matrix specifically includes the following steps: S301, extract the coordinates, tree species, age group, and measured biomass per unit area of ​​fixed sample plots from publicly available forest resource inventory data as a training sample set. Each sample is also associated with the zoning identification code, land type, age group, or coverage of its respective sub-compartment. S302, construct a random forest regression model with zoning identification code, land type, tree species, age group or coverage, and leaf area index or vegetation coverage extracted from remote sensing data as feature variables, and measured biomass per unit area as target variable. Train the model separately for each secondary zoning or use the zoning variables as input features of the global model. The model parameters are set as follows: number of decision trees is 500, maximum number of features is 3, and minimum sample size of nodes is 5. S303 applies the trained model to each plot unit, predicting the current biomass per unit area based on the plot unit's zoning identifier code, land type, tree species, age group or cover, and corresponding remote sensing features. ; S304. Within the same secondary zoning, the current biomass per unit area of ​​all mature and overmature forest sub-compartments is statistically analyzed, and the 85th percentile is taken as the optimal natural biomass per unit area for that tree species within that zoning. ; S305, within the same secondary zoning, selected small plots with successful management demonstration sites or high-quality forest stands, and their current biomass per unit area was statistically analyzed. The 90th percentile is used as the optimal management biomass per unit area for this tree species in this zoning area. If there are no successful demonstration sites, then an empirical formula will be used. λ is the operational improvement coefficient, which ranges from 1.05 to 1.35 and is determined by the hydrothermal conditions of the area. S306, a multidimensional biomass benchmark database was constructed using "zoning identification code-land type-tree species-age group / cover" as a joint index to store the current biomass per unit area corresponding to each plot unit. Optimal natural biomass per unit area Optimal biomass per unit area The carbon content (CF) of tree species is used to form a structured database.

6. The method according to claim 1, characterized in that, In step S4, the current carbon storage, natural scenario carbon potential, and operational scenario carbon potential are calculated patch by patch, specifically including: Calculate the current carbon storage of the i-th sub-unit: ; Calculate the natural carbon capacity of the i-th small class unit: ; Calculate the operating carbon capacity of the i-th small class unit: Calculate the natural scenario carbon potential of the i-th class unit: Calculate the operating scenario carbon potential of the i-th small class unit: in, Let be the carbon content of the tree species in the i-th sub-class unit. , , These represent the current biomass per unit area, the optimal natural biomass per unit area, and the optimal operational biomass per unit area for the i-th sub-unit, respectively. Let be the area of ​​the i-th small class unit.

7. The method according to claim 6, characterized in that, The total carbon potential for each zone is obtained by summing the results, specifically including the following formula: in, This represents the carbon storage of the x-th primary zoning zone. This represents the set of secondary districts within the x-th primary district. This represents the set of small class units within the k-th secondary zone; m is the total number of primary zones within the target area; This represents the total carbon storage within the target area; This represents the natural carbon capacity of the x-th primary zoning area; This represents the total natural carbon capacity within the target area; This represents the operational carbon capacity of the x-th primary zone; This indicates the total operating carbon capacity within the target area; This represents the total natural scenario carbon potential of the target area. This represents the total operating scenario carbon potential of the target area.

8. The method according to claim 6, characterized in that, In step S4, the assessment of the carbon potential of the operating scenario further includes a dynamic simulation sub-step: Using the logistic growth model, calculate the predicted biomass for each year or planning period during the period from the current state to the optimal operating state: in, To determine the optimal carrying capacity biomass, k represents the intrinsic growth rate specific to the tree species in each zone, with values ​​ranging from 0.08 to 0.12 for coniferous forests, 0.10 to 0.15 for broadleaf forests, and 0.12 to 0.18 for shrub forests. This is the inflection point of growth; Based on the predicted biomass, calculate the dynamic carbon sink increment for each year or planning period during the period of achieving optimal operating conditions; in, For dynamic carbon sequestration increments, Let be the area of ​​the i-th small class unit.

9. The method according to claim 7, characterized in that, Step S5 specifically includes the following steps. S501, Obtain the spatial constraint layer of the target area. The spatial constraint layer includes the boundary layer of nature reserves, the ecological protection red line layer, the planned afforestation spatial layer, and the degraded forest distribution layer. Convert all layers into the same coordinate system and vector format as the data base plate. S502, Perform spatial overlay analysis on each small class unit and the spatial constraint layer, and calculate derived indices for each small class unit: The ecological red line marker Flag_protected is 1 if the small plot unit is located in the core area of ​​the nature reserve or the ecological red line restricted area, and 0 otherwise; The afforestation planning flag, Flag_plant, is 1 if the sub-compartment is located in the planned afforestation space area, and 0 otherwise. The degraded forest flag is Flag_degraded. If the sub-compartment is located in a degraded forest distribution area, Flag_degraded is 1; otherwise, it is 0. The management potential per unit area, V_managed, is 1 if the management potential per unit area of ​​a small plot is greater than the median management potential per unit area of ​​all degraded forest plots within the secondary zoning to which the small plot belongs; otherwise, it is 0. S503, for each small class, the carbon sink functional area type is automatically determined according to the following priority order based on the derived indicators: a. If R_protected == 1, then the region is determined to be a protected carbon stable region, and the determination is terminated; b. If a does not satisfy the condition and Flag_plant == 1, then it is determined to be an afforestation and carbon expansion zone; c. If a and b do not satisfy the condition and Flag_degraded == 1 while V_managed == 1, then it is determined to repair the carbon-added area; d. If none of a, b, and c are satisfied, and the land parcel is classified as arbor forest or shrub forest, then it is determined to be a carbon-promoting management zone; e. If none of a, b, c, and e are satisfied, it is determined to be a general operating area that does not take any special carbon sequestration measures. S504. For sub-compartments identified as carbon-increasing zones for restoration or carbon-promoting zones for management, further matching combinations of forest management measures are retrieved from a pre-set measures knowledge base based on the sub-compartment's zoning identification code, dominant tree species, age group / coverage, and degradation type. S505: Write the functional area type and measure code of each small class into the corresponding field of the small class attribute table to generate scientific layout thematic data.

10. A forest carbon sequestration potential calculation system, characterized in that, include: The forest growth potential zoning module is used for spatial overlay analysis based on climate, topography and vegetation type to divide the target area into multiple secondary zoning with different growth potentials and assign a zoning identification code to each spatial unit. The carbon storage and carbon capacity benchmark modeling module is used to construct and store a multidimensional biomass benchmark matrix indexed by "zoning identification code-land type-tree species-age group / coverage". The matrix includes at least the current biomass per unit area, the optimal natural biomass per unit area, and the optimal managed biomass per unit area. The carbon potential assessment calculation module, connected to the modeling module, is used to calculate the current carbon storage, natural scenario carbon potential, operational scenario carbon potential and operational net carbon sink on a unit-by-unit basis according to the biomass benchmark matrix and unit area. The operational net carbon sink is equal to the operational scenario carbon potential minus the natural scenario carbon potential. The layout optimization module connects the zoning module and the carbon potential assessment module. It internally stores a multi-level decision rule set based on spatial constraint layers and carbon potential assessment results. It is used to automatically divide each spatial unit into carbon protection and stabilization zones, afforestation and carbon expansion zones, restoration and carbon increase zones, or management and carbon promotion zones, and generate specific forest management measures suggestions for the latter three types of zones.

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