Afforestation optimization method based on degradation risk avoidance and ecosystem service improvement

Abstract

The application discloses a forestation optimization method based on forest degradation risk avoidance and ecosystem service improvement, comprising a forest degradation risk area type and distribution identification process, a potential forestation area identification process based on forest degradation risk avoidance, a typical ecosystem service evaluation process, and a forestation pattern and tree species optimization process.The application has the beneficial effect that the technical scheme is based on the ideas of forest degradation risk avoidance and ecosystem service improvement, potential forestation areas are identified on the basis of analyzing forest degradation risks, and forestation pattern and forestation tree species optimization are carried out in the target partition in the light of various ecosystem service improvement, so that a set of forestation optimization technical system is finally formed, which provides guidance for forestation planning and management in northern China, can avoid forestation degradation risks to a certain extent, and realizes regional ecosystem service improvement based on the improvement of soil and water conservation and wind prevention and sand fixation services, and the all-in-one of carbon sequestration and water lifting.

Description

Technical Field

[0001] This invention relates to the field of land space optimization methods, and in particular to afforestation optimization methods based on degradation risk avoidance and ecosystem service enhancement. Background Technology

[0002] Large-scale afforestation projects have been carried out in northern China, effectively enhancing various ecosystem services. However, unreasonable afforestation areas and tree species selection have led to widespread forest degradation in major afforestation areas of northern China (e.g., Figure 1 Forest degradation in forest areas is characterized by low afforestation survival rates, slow growth and development, overexploitation of regional water resources, and soil carbon loss, severely limiting the ecological and economic benefits of afforestation. Therefore, conducting suitability assessments for large-scale afforestation is a widely concerned issue. However, the identification of suitable afforestation areas often only considers their ecological suitability, neglecting the risk of forest degradation. This is particularly important for the major afforestation areas in northern China, where the risk of forest degradation is high. On the other hand, optimizing afforestation patterns through multi-objective optimization can effectively reduce investment costs and synergistically improve various ecosystem services. Therefore, in the future, under the continued large-scale afforestation efforts, it is necessary to rationally optimize afforestation patterns and tree species to reduce the risk of afforestation degradation and improve various ecosystem services. This has certain practical reference value for future afforestation work in northern China. Summary of the Invention

[0003] The purpose of this invention is to solve the above-mentioned problems by designing an optimized afforestation method based on degradation risk avoidance and ecosystem service enhancement, so as to reduce the risk of afforestation degradation and achieve regional ecosystem service enhancement based on soil and water conservation and windbreak and sand fixation services, carbon sequestration and water lifting in a multi-dimensional manner, providing guidance for the implementation of afforestation planning in major afforestation areas in northern China.

[0004] To achieve the above objectives, the technical solution of this invention is an afforestation optimization method based on degradation risk avoidance and ecosystem service enhancement, including a process for identifying the types and distribution of forest degradation risk areas, a process for identifying potential afforestation areas based on forest degradation risk avoidance, a process for assessing typical ecosystem services, and a process for optimizing afforestation patterns and tree species.

[0005] As a further description of this technical solution, in the process of identifying the types and distribution of forest degradation risk areas, the types, levels and distribution of forest degradation risk areas are identified from three aspects: changes in forest leaf area index, sensitivity to climate fluctuations and changes in soil moisture.

[0006] As a further description of this technical solution, in the process of identifying the types and distribution of forest degradation risk areas, based on the forest leaf area index (LAI) of the afforested area, a univariate linear regression trend analysis method is used to identify the changing trend of forest LAI over time. After data preprocessing, the trend analysis and significance test of LAI are completed using MATLAB software. The main calculation formula is as follows:

[0007]

[0008] In the formula, X represents the rate of change of forest LAI; n is the number of years; X i Let LAI be the value for year i. A positive value indicates an overall upward trend. A negative value indicates an overall downward trend;

[0009] Furthermore, residual analysis was used to identify the driving types of forest degradation and the risk areas of forest degradation caused by natural factors. By performing multiple linear regression fitting on forest LAI with temperature and precipitation, the predicted value of forest LAI was obtained. The predicted value is the forest LAI value that the region should have under the condition that it is only affected by changes in temperature and precipitation. Subtracting the predicted value from the actual value of forest LAI yields the impact of human factors on changes in forest LAI, calculated using the following formula:

[0010] ε = LAI 真实值 - LAI 预测值

[0011] In the formula, ε represents the residual value, which is the impact of human activities on LAI. When ε > 0, human activities promote an increase in LAI; when ε < 0, human activities lead to a decrease in LAI.

[0012] As a further description of this technical solution, in the process of identifying the types and distribution of forest degradation risk zones, a multiple linear relationship between the regional forest normalized vegetation index (NDVI) and the monthly average fluctuations of climate factors over the past 5 years is used. These climate factors include temperature and drought index. The sensitivity of forests to climate fluctuations is quantified, and the calculation results are obtained after running the calculation in R software. The results are then visualized in MATLAB. The main calculation formulas are as follows:

[0013]

[0014] In the formula, , , The first standardized The monthly fluctuations in NDVI, temperature, and g drought index; NDVI t-1denoted as the standardized NDVI fluctuation value for month t-1; α, β, and δ are the regression coefficients for temperature, drought index, and NDVI fluctuation value for month t-1, respectively. Represents the residual.

[0015] As a further description of this technical solution, in the process of identifying the types and distribution of forest degradation risk areas, based on annual soil moisture data, univariate linear regression trend analysis is used to identify the evolution characteristics of soil moisture content after afforestation. The trend analysis and significance test of soil moisture are completed based on MATLAB software, and then the areas with significantly reduced soil moisture content caused by afforestation are identified spatially based on residual analysis.

[0016] As a further description of this technical solution, in the process of identifying potential afforestation areas based on forest degradation risk avoidance, the distribution data of forests and shrubs in typical forest degradation and potential degradation risk areas caused by climate factors are extracted respectively. Combined with regional climate, topography, soil and hydrological information, GeoSOS-FLUS software is used to simulate and identify the distribution of different types of afforestation risk areas.

[0017] As a further description of this technical solution, in the process of identifying potential afforestation areas based on forest degradation risk avoidance, based on the distribution data of non-degraded forests and shrubs, combined with regional climate, topography, soil and hydrological information, the GeoSOS-FLUS software is used to conduct an afforestation suitability assessment and identify the spatial distribution of suitable afforestation areas and suitable irrigation areas.

[0018] As a further description of this technical solution, in the process of identifying potential afforestation areas based on forest degradation risk avoidance, a decision framework for identifying potential afforestation areas is constructed. The potential afforestation areas include suitable afforestation areas and suitable irrigation areas. Unsuitable afforestation areas are eliminated, and the types and distribution of potential afforestation areas are finally determined.

[0019] As a further description of this technical solution, the typical ecosystem service assessment process evaluates four typical ecosystem services in the afforestation area after afforestation: water conservation, carbon sequestration, soil and water conservation, and windbreak and sand fixation, and analyzes their spatiotemporal evolution characteristics.

[0020] As a further description of this technical solution, in the typical ecosystem service assessment process, water conservation services are assessed using the InVEST model's water production module, soil and water conservation services are assessed using the general soil loss equation (RUSLE), windbreak and sand fixation services are assessed using the modified wind erosion equation (RWEQ), and carbon sequestration services are assessed using net primary productivity as a proxy indicator and the Carnegie-Ames-Stanford Approach (CASA) model.

[0021] As a further description of this technical solution, in the typical ecosystem service assessment process, the multi-year average values ​​of climate factors are input into the InVEST, RULSE, RWEQ and CASA models to estimate forest water conservation, soil and water conservation, windbreak and sand fixation, and carbon sequestration under average climate conditions. Furthermore, the changes in ecosystem services under average climate and actual climate conditions are compared to calculate the impact pattern and contribution of climate change and afforestation on ecosystem service changes, and then the afforestation-driven ecosystem service change zone is spatially identified.

[0022] As a further description of this technical solution, in the process of optimizing the afforestation pattern and tree species, the annual soil wind erosion and water erosion are evaluated based on the RUSLE model and the RWEQ model, and their annual average values ​​are calculated. Then, the region is divided into a soil and water loss-dominated area, a wind and sand erosion-dominated area, a combined area dominated by both, and other areas.

[0023] As a further description of this technical solution, in the process of optimizing the afforestation pattern and tree species, based on the improvement of soil and water conservation and windbreak and sand fixation services, and with the goal of maximizing carbon sequestration and minimizing water consumption, optimization functions are constructed for different types of afforestation areas, and different weights and multiple ecosystem service enhancement scenarios are set.

[0024] Major areas of soil and water loss:

[0025]

[0026] Windbreak and sand fixation main areas:

[0027]

[0028] Both dominate the area:

[0029]

[0030] Non-soil erosion zone:

[0031]

[0032] In the formula, SC i SF i and CS i The potential for enhancing soil and water conservation, windbreak and sand fixation, and carbon sequestration services represents the planning unit i. i ω1 represents the actual evapotranspiration of planning unit i; ω1-ω4 represent the weights of various ecosystem services. ω1 is assigned four values ​​in the interval of 0.4-1 with an interval of 0.2, and ω2 is assigned the value of 1-ω1. ω3 is assigned four values ​​in the interval of 0.2-0.8 with an interval of 0.2, and ω4 is assigned the value of 1-ω3.

[0033] As a further description of this technical solution, in the process of optimizing the afforestation pattern and tree species, the area optimization module in GeoSOS software is used to identify priority afforestation areas. 10%, 20%, and 30% of the potential afforestation area are selected as the area to be optimized in different types of areas, thereby identifying the top 10%-30% of key areas for priority afforestation. The afforestation pattern optimization is first carried out from the entire potential afforestation area, and then carried out from the suitable afforestation area and the suitable irrigation area respectively.

[0034] As a further description of this technical solution, in the process of optimizing the afforestation pattern and tree species, the main afforestation tree species in the dominant areas of each ecological risk type are identified, and the main afforestation tree species are evaluated from three aspects: degradation risk, ecosystem service supply capacity, and economic benefits. Different weights are set for the three dimensions according to regional characteristics, and the optimal combination of afforestation tree species in each region is selected to construct mixed forests.

[0035] Its beneficial effects are that, based on the idea of ​​forest degradation risk avoidance and ecosystem service enhancement, this technical solution can avoid the risk of afforestation degradation to a certain extent, and achieve regional ecosystem service enhancement in a multi-dimensional way, including carbon sequestration and water lifting, based on the improvement of soil and water conservation and windbreak and sand fixation services. Attached Figure Description

[0036] Figure 1 This is the technical roadmap of this technical solution;

[0037] Figure 2 This is a decision-making framework diagram for identifying afforestation risk zones in this technical solution;

[0038] Figure 3 This is a decision-making framework diagram for identifying potential afforestation areas in this technical solution;

[0039] Figure 4 This is a schematic diagram of the afforestation pattern optimization scenario setting for this technical solution;

[0040] Figure 5 This is a flowchart outlining the afforestation tree species optimization process of this technical solution. Detailed Implementation

[0041] First, let me explain the original intention of this invention. Large-scale afforestation projects have been carried out in northern China, which have effectively improved various ecosystem services. However, unreasonable selection of afforestation areas and tree species has led to large-scale forest degradation in the main afforestation areas of northern China. The main manifestations of forest degradation are low survival rate of afforestation, slow growth and development, over-exploitation of regional water resources and loss of soil carbon, which seriously limit the ecological and economic benefits of afforestation. Therefore, this invention designs an afforestation optimization method based on degradation risk avoidance and ecosystem service enhancement.

[0042] The present invention will now be described in detail with reference to the accompanying drawings, such as... Figures 1-5As shown, the afforestation optimization method based on degradation risk avoidance and ecosystem service enhancement includes the identification process of forest degradation risk zone types and distribution, the identification process of potential afforestation areas based on forest degradation risk avoidance, the assessment process of typical ecosystem services, and the optimization process of afforestation patterns and tree species.

[0043] Step 1: In the process of identifying the types and distribution of forest degradation risk zones, the types, levels, and distribution of forest degradation risk zones are identified from three aspects: changes in forest leaf area index, sensitivity to climate fluctuations, and changes in soil moisture. Figure 2 As shown;

[0044] The forest leaf area index (LAI) change analysis was conducted based on the LAI of the afforested area. A univariate linear regression trend analysis method was used to identify the changing trend of forest LAI over time. After data preprocessing, the trend analysis and significance test of LAI were performed using MATLAB software. The main calculation formulas are as follows:

[0045]

[0046] In the formula, X represents the rate of change of forest LAI; n is the number of years; X i Let LAI be the value for year i. A positive value indicates an overall upward trend. A negative value indicates an overall downward trend;

[0047] Furthermore, residual analysis is employed as a method to quantify the impact of human activities. Its principle involves performing multiple linear regression fitting on forest LAI (Lower Altitude Area) and temperature and precipitation to obtain predicted values ​​for forest LAI. The predicted value represents the forest LAI value that the region should have under conditions where only temperature and precipitation changes affect it. Subtracting the predicted value from the actual forest LAI value yields the impact of human factors on forest LAI changes, calculated using the following formula:

[0048] ε = LAI 真实值 - LAI 预测值

[0049] In the formula, ε represents the residual value, which is the impact of human activities on LAI. When ε > 0, human activities promote an increase in LAI; when ε < 0, human activities lead to a decrease in LAI.

[0050] In ArcGIS software, areas of significant LAI reduction are extracted, and those areas of significant LAI reduction driven by human activities are removed, thus obtaining areas of significant LAI reduction driven by climate factors.

[0051] The climate fluctuation sensitivity assessment is based on the multiple linear relationship between the regional forest normalized vegetation index (NDVI) and the monthly mean fluctuations of climate factors over the past five years. These climate factors include temperature and drought index. The sensitivity index calculation and visualization code was provided by Seddon (Seddon, AWR, Macias-Fauria, M., Long, PR, Benz, D., Willis, KJ, 2016. Sensitivity of global terrestrial ecosystems to climate variability. Nature. 531, 229-232). The calculation results were obtained after running in R software, and the results were visualized in MATLAB. The main calculation formulas are as follows:

[0052]

[0053] In the formula, , , The first standardized Monthly fluctuations in NDVI, temperature, and drought index; NDVI t-1 denoted as the standardized NDVI fluctuation value for month t-1; α, β, and δ are the regression coefficients for temperature, drought index, and NDVI fluctuation value for month t-1, respectively. Represents residuals;

[0054] The analysis of soil moisture content changes is based on annual soil moisture data. Univariate linear regression trend analysis is used to identify the evolution characteristics of soil moisture content after afforestation. The trend analysis and significance test of soil moisture are completed using MATLAB software. Then, residual analysis is used to spatially identify areas where soil moisture content has decreased significantly due to afforestation.

[0055] Step 2: In the process of identifying potential afforestation areas based on forest degradation risk avoidance, GeoSOS-FLUS software is used to identify afforestation risk areas and assess afforestation suitability. First, the spatial distribution of typical forest degradation risk areas caused by climate factors is extracted. This is combined with data on temperature, precipitation, drought index, slope, aspect, soil texture, and farmland distribution as the basic data for running neural network analysis. Further, after inputting the basic data into the FLUS model, the sampling mode is set to Random Sampling in the ANN Training box, meaning the number of sampling points for each forest degradation risk type is related to its area proportion. Simultaneously, the sampling parameter is set to 20, representing 2% of the total area, and the number of hidden layers in the neural network is set to 12. After running the model, the spatial distribution probability of forest degradation risk is obtained. Similarly, the spatial distribution of non-degraded forests and shrubs is extracted. Based on the FLUS model, an afforestation suitability assessment is conducted to identify the spatial distribution of suitable afforestation and irrigation areas, constructing a decision-making framework for identifying potential afforestation areas (suitable afforestation and irrigation areas). Figure 3 Areas unsuitable for afforestation were excluded, as shown in Table 1. The suitability and risk of afforestation were analyzed to ultimately determine the types and distribution of potential afforestation areas.

[0056] Table 1 Land Use Types Where Afforestation Is Not Possible

[0057]

[0058] Step 3: In the process of assessing the typical ecosystem services, evaluate the four typical ecosystem services of water conservation, carbon sequestration, soil and water conservation and windbreak and sand fixation in the afforestation area after afforestation, and analyze their spatiotemporal evolution characteristics.

[0059] 1) The InVEST model's water production module is used to evaluate water conservation services. This module calculates the water production of each pixel based on the difference between precipitation and actual evapotranspiration. The input parameters for this module include potential evapotranspiration, precipitation, land use type, available water content for vegetation, maximum root depth, and a biophysical parameter table. The main formulas are as follows:

[0060]

[0061] In the formula, Given a raster x for land use type j, the annual water conservation capacity (mm). The annual actual evapotranspiration (mm) for a given grid x under land use type j; This represents the annual precipitation (mm) for a given raster x.

[0062]

[0063] In the formula, The Budyko coefficient for a given raster under a land use type is calculated using the following formula:

[0064]

[0065] In the formula, Land use types Given grid evapotranspiration coefficient; It is the relative evaporation of grid x (mm); Given the effective water coefficient of grid x, which characterizes the response of vegetation to soil moisture use, the calculation formula is as follows:

[0066]

[0067] In the formula, This is a seasonal or climate pattern coefficient (ranging from 1 to 10), representing the amount and distribution of precipitation. Given the effective water content of a grid under a specific land use type;

[0068] 2) The RUSLE (Reduced Soil Loss Equation) quantifies potential and actual soil erosion to assess soil and water conservation services. The data required for this model primarily includes daily precipitation, DEM (Digital Emission Model), land use type, soil type, and management factor data. First, the monthly soil conservation amount is calculated, and then these are summed to obtain the annual soil conservation amount. The calculation formula is as follows:

[0069]

[0070] In the formula, WAEP represents the annual soil and water conservation volume (t). hm 2 / a); R is the precipitation erosion factor (MJ·mm / (hm) 2 ·h)); K is the soil erodibility factor (t·hm). 2 ·h / (hm 2 ·MJ·mm)); LS is the slope length and slope factor (dimensionless); C and P are the vegetation cover factor and management factor (dimensionless), respectively.

[0071] The formula for calculating the R factor is as follows:

[0072]

[0073]

[0074] In the formula, is the factor for the i-th month (MJ·mm·hm) -2 ·h -1); k is the number of days in a month; Pj is the effective precipitation on the j-th day of the i-th month, that is, the daily precipitation ≥ 12 mm, otherwise it is recorded as 0 mm;

[0075] parameter and The definition is as follows:

[0076]

[0077]

[0078] In the formula, p d12 Daily average precipitation ≥12 mm; p y12 Annual average precipitation with a daily average precipitation ≥ 12 mm;

[0079] The formula for calculating the K-factor is as follows:

[0080]

[0081] In the formula, SA, SI, CL, and SOC represent the contents of soil sand, silt, clay, and organic carbon, respectively.

[0082] The LS factor represents the impact of slope length and slope gradient on soil erosion, and the calculation formula is as follows:

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] In the formula, Slope length factor; Slope length factor; The slope length index; The horizontal projected distance of each grid along the slope of the runoff direction; It is the slope of each grid cell; It is the slope factor; θ Slope;

[0089] The formula for calculating the vegetation cover factor C is as follows:

[0090]

[0091] In the formula, Vegetation cover is calculated using the normalized vegetation index.

[0092] 3) The windbreak and sand fixation service uses the Revised Wind Erosion Equation (RWEQ) to assess the windbreak and sand fixation service that reduces soil wind erosion due to vegetation cover. This model considers multiple factors (such as climate, soil properties, snow cover, and topography) and can predict wind erosion relatively accurately. First, the actual wind erosion modulus considering vegetation and the potential wind erosion modulus not considering vegetation are calculated. Then, the difference between the two is calculated as the windbreak and sand fixation amount. First, the wind erosion amount is calculated on a monthly scale, and then the values ​​are accumulated to obtain the annual wind erosion amount. The main calculation formula is as follows:

[0093]

[0094]

[0095] .

[0096]

[0097]

[0098] In the formula, WIEP represents the amount of windbreak and sand fixation; WE P and WE A These represent potential and actual wind erosion (kg / m²), respectively. 2 ); Q pmax and Q amax These represent the potential and actual maximum sediment transport capacity (kg / m); S p and S a , respectively, represent the potential critical length (m) and the actual critical length (m); z represents the maximum downwind erosion distance (m); WF represents the meteorological factor (kg / m); EF, SCF, K', and COG represent the soil erodibility factor, soil crust factor, surface roughness factor, and vegetation factor (dimensionless), respectively.

[0099] The formula for calculating the WF factor is as follows:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106] In the formula, W f Wind force factor (m3 s -3 ); ρ is the air density (kg) m -3 g is the acceleration due to gravity (m). s -2 ); SW is the soil moisture factor (dimensionless); SD is the snow cover factor (dimensionless); u1 is the critical wind speed at a height of 2m (m). s -1 ), set to 6 m s -1 u2 is the actual wind speed (m) at a height of 2m. s -1 ); N d This represents the number of days in each month when wind speeds exceed the critical wind speed; EL is the altitude (km); T is the absolute temperature (K); ET p R represents potential evapotranspiration (mm); R represents precipitation (mm); R d It refers to the number of rainy days per month; SR solar radiation (cal cm -2 DT represents the average temperature (°C); P represents the probability of snowfall depth ≥ 25.4 mm.

[0107] The formulas for calculating EF and SCF are as follows:

[0108]

[0109]

[0110] In the formula, SA, SI, CL, SOM and CC represent the contents (%) of sand, silt, clay, soil organic matter and calcium carbonate, respectively, while EF and SCF do not change over time;

[0111] The formula for calculating the vegetation factor (C) is as follows:

[0112]

[0113]

[0114] In the formula, VC represents vegetation coverage (%); NDVI soil Normalized Difference Vegetation Index (NDVI) for soil veg The maximum normalized vegetation index (NDVI) for vegetation. veg and NDVI soil These represent NDVI values ​​at cumulative frequencies of 95% and 5%, respectively.

[0115] The formula for calculating K′ is as follows:

[0116]

[0117] In the formula, α represents the slope, which is calculated from DEM data.

[0118] 4) Carbon sequestration service assessment uses net primary productivity (NPP) as a proxy indicator for assessing carbon sequestration services. NPP is estimated using the Carnegie-Ames-Stanford Approach (CASA) model, which estimates it by measuring the photosynthetically active radiation absorbed by vegetation (APAR) and the efficiency of converting radiation into organic matter. The main calculation formula is as follows:

[0119] NPP(x,t)=APRA(x,t)×ε(x,t)

[0120] APAR(x,t)=SOL(x,t)×FPAR(x,t)×0.5

[0121] In the formula, SOL(x,t) represents the total solar radiation at grid x in month t (MJ / m²). 2 / month), FPAR represents the proportion of effective radiation absorbed by vegetation, and the constant 0.5 represents the proportion of solar radiation utilized by vegetation to the total solar radiation;

[0122] 5) In the analysis of the contribution of ecosystem service changes, in order to clarify the contribution of climate change and afforestation activities to ecosystem service changes, the multi-year average values ​​of climate factors are input into the InVEST, RULSE, RWEQ and CASA models to estimate forest water conservation, soil and water conservation, windbreak and sand fixation and carbon sequestration under average climate conditions. Furthermore, by comparing the changes in ecosystem services under average climate and actual climate conditions, the impact pattern and contribution of climate change and afforestation on ecosystem service changes are calculated, and then the ecosystem service change areas driven by afforestation are identified spatially.

[0123] Step 4: In the process of optimizing the afforestation pattern, the primary goal of afforestation in northern China is to prevent soil erosion and desertification. Therefore, the goal of optimizing the afforestation pattern is to focus on improving the corresponding ecosystem services in the areas dominated by ecological risks of soil erosion and wind erosion, and to optimize and improve carbon sequestration services. At the same time, water resources are scarce in the main afforestation areas of northern China, so it is necessary to consider reducing water consumption in afforestation and improving water conservation services.

[0124] 1) First, based on the RUSLE and RWEQ models, the multi-year soil wind erosion and water erosion were assessed, and their multi-year averages were calculated. The region was divided into areas dominated by soil and water loss, areas dominated by wind and sand erosion, areas jointly dominated by both, and other areas. Optimization functions were constructed for each region. Furthermore, different weights were assigned to various ecosystem services within the optimization functions to form multiple afforestation pattern optimization scenarios (such as...). Figure 4Finally, simulation analysis was conducted on each scenario to calculate its ability to enhance various ecosystem services after implementation, and the optimal scenario was identified.

[0125] Optimization functions are constructed separately for different regions:

[0126] Major areas of soil and water loss:

[0127]

[0128] Key areas for windbreak and sand fixation:

[0129]

[0130] Both dominate the area:

[0131]

[0132] Non-soil erosion zone:

[0133]

[0134] In the formula, SC i SF i and CS i The potential for enhancing soil and water conservation, windbreak and sand fixation, and carbon sequestration services represents the planning unit i. i ω1 represents the actual evapotranspiration of planning unit i; ω1-ω4 represent the weights of various ecosystem services. ω1 is assigned four values ​​in the interval of 0.4-1 with an interval of 0.2, and ω2 is assigned the value of 1-ω1. ω3 is assigned four values ​​in the interval of 0.2-0.8 with an interval of 0.2, and ω4 is assigned the value of 1-ω3.

[0135] 2) Further, the surface optimization module in GeoSOS software is used to identify priority afforestation areas. This module employs an ant colony algorithm, using suitability and compactness as evaluation functions to optimize the selection of afforestation land of a specific area. During the operation of the surface optimization module, a suitability layer is first constructed based on the enhancement capacity and weights of various ecosystem services. Then, by introducing ant colony agents, the accumulation or volatilization of pheromones guides the agents to identify the optimal afforestation location. A compactness function is then used to spatially aggregate these agents, forming a surface optimization layer that is both compact and located in areas with high afforestation demand. First, it is assumed that all potential afforestation areas are covered by forest to obtain the enhancement capacity of afforestation for various ecosystem services. Furthermore, the planning unit area is set to 1 km². 2Based on the area optimization module, afforestation pattern optimization is carried out. 10%, 20% and 30% of the potential afforestation area are selected as the area to be optimized in different types of areas, and then the top 10%-30% key areas for priority afforestation are identified. Afforestation pattern optimization is first carried out from the entire potential afforestation area, and then carried out from the suitable afforestation area and the suitable irrigation area respectively.

[0136] 3) In the process of optimizing afforestation tree species, forest degradation is closely related to the selection of afforestation tree species, and different tree species have different effects on improving ecosystem services. Therefore, based on the optimization of afforestation patterns, the selection of afforestation tree species can be optimized to reduce the risk of forest degradation and further improve the supply of ecosystem services. First, the main afforestation tree species in the dominant areas of each ecological risk type are identified, and the main afforestation tree species are evaluated from three aspects: degradation risk, ecosystem service supply capacity, and economic benefits. Different weights are set for the three dimensions according to regional characteristics, and the optimal combination of afforestation tree species in each region is selected to construct mixed forests, such as... Figure 5 As shown.

[0137] The above technical solutions only embody the preferred technical solutions of the present invention. Any modifications that may be made by those skilled in the art to certain parts thereof embody the principles of the present invention and fall within the protection scope of the present invention.

Claims

1. An optimized afforestation method based on degradation risk avoidance and ecosystem service enhancement, characterized in that, This includes the process of identifying the types and distribution of forest degradation risk zones, the process of identifying potential afforestation areas based on forest degradation risk avoidance, the process of assessing typical ecosystem services, and the process of optimizing afforestation patterns and tree species; In the process of identifying the types and distribution of forest degradation risk areas, the types, levels and distribution of forest degradation risk areas are identified from three aspects: changes in forest leaf area index, sensitivity to climate fluctuations and changes in soil moisture. In the process of identifying potential afforestation areas based on forest degradation risk avoidance, GeoSOS-FLUS software is used to identify afforestation risk areas and assess afforestation suitability, and a decision framework for identifying potential afforestation areas is constructed. The potential afforestation areas include suitable afforestation areas and suitable irrigation areas. Unsuitable afforestation areas are eliminated, and the types and distribution of potential afforestation areas are finally determined. In the typical ecosystem service assessment process, water conservation services were assessed using the InVEST model's water production module, soil and water conservation services were assessed using the general soil loss equation (RUSLE), windbreak and sand fixation services were assessed using the modified wind erosion equation (RWEQ), and carbon sequestration services were assessed using net primary productivity as a proxy indicator and the Carnegie-Ames-Stanford Approach (CASA) model. During the optimization of afforestation patterns and tree species, the amount of soil wind erosion and water erosion over many years is evaluated based on the RUSLE model and RWEQ model, and their multi-year average value is calculated. The region is then divided into areas dominated by soil and water loss, areas dominated by wind and sand erosion, areas jointly dominated by both, and other areas. In the process of optimizing the afforestation pattern and tree species, based on the improvement of soil and water conservation and windbreak and sand fixation services, and with the goal of maximizing carbon sequestration and minimizing water consumption, optimization functions are constructed for different types of afforestation areas, and different weights and multiple ecosystem service enhancement scenarios are set. In the process of optimizing the afforestation pattern and tree species, the area optimization module in GeoSOS software is used to identify priority afforestation areas. 10%, 20%, and 30% of the potential afforestation area are selected as the proposed optimization area in different types of areas, thereby identifying the top 10%-30% of key areas for priority afforestation. The afforestation pattern optimization is first carried out from the entire potential afforestation area, and then carried out from the suitable afforestation area and the suitable irrigation area respectively. In the process of optimizing the afforestation pattern and tree species, the main afforestation tree species in the dominant areas of each ecological risk type are identified, and the main afforestation tree species are evaluated from three aspects: degradation risk, ecosystem service supply capacity, and economic benefits. Different weights are set for the three dimensions according to regional characteristics, and the optimal combination of afforestation tree species in each region is selected to build mixed forests.

2. In the afforestation optimization method based on degradation risk avoidance and ecosystem service enhancement as described in claim 1, the leaf area index change analysis adopts univariate linear regression trend analysis to identify the spatiotemporal evolution trend of forest leaf area index after afforestation, and completes the trend analysis and significance test of leaf area index through MATLAB software. The residual analysis is used to identify the driving type of forest degradation and the forest degradation risk area caused by natural factors.

3. In the afforestation optimization method based on degradation risk avoidance and ecosystem service enhancement as described in claim 1, the climate fluctuation sensitivity analysis is based on the multiple linear relationship between the monthly average fluctuation of the forest normalized vegetation index (NDVI) and the monthly average fluctuation of climate factors in the afforestation area over the past 5 years, wherein the climate factors include temperature and drought index, to assess the sensitivity of forests to climate fluctuations and identify the spatial distribution of highly sensitive areas.

4. In the afforestation optimization method based on degradation risk avoidance and ecosystem service enhancement as described in claim 1, in the soil moisture change analysis, based on soil moisture data, a univariate linear regression trend analysis is used to identify the evolution characteristics of soil moisture content after afforestation, and the trend analysis and significance test of soil moisture are completed based on MATLAB software. Then, based on residual analysis, areas with significant decrease in soil moisture content caused by afforestation are identified spatially.

5. The afforestation optimization method based on degradation risk avoidance and ecosystem service enhancement according to claim 1, characterized in that, In the identification of afforestation risk areas, data on the distribution of forests and shrubs in typical and potential forest degradation risk areas caused by climate factors are extracted. Combined with regional climate, topography, soil and hydrological information, GeoSOS-FLUS software is used to simulate and identify the distribution of different types of afforestation risk areas. In the assessment of afforestation suitability, the spatial distribution of non-degraded forests and shrubs is extracted. Combined with regional climate, topography, soil and hydrological information, GeoSOS-FLUS software is used to conduct an assessment of afforestation suitability and identify the spatial distribution of suitable afforestation and irrigation areas.

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