A method and system for evaluating the impact of under-forest economy based on carbon sink dynamic monitoring

Abstract

The application relates to evaluation of ecological benefits of under-forest economy, and provides an under-forest economy influence evaluation method and system based on carbon sink dynamic monitoring. The method comprises the following steps: delimiting a treatment group and a control group; collecting multi-source data; pre-processing the multi-source data; calculating net ecosystem productivity by using a CASA model simulation method, and calculating ecological benefit evaluation indexes according to the net ecosystem productivity; calculating economic benefit evaluation indexes according to under-forest planting economic data; calculating social benefit evaluation indexes according to newly added employment numbers and the like; calculating a comprehensive score according to preset weights of the indexes, and performing comprehensive benefit determination according to the comprehensive score. The method realizes accurate quantitative evaluation of an influence direction and an influence degree of under-forest planting activities on carbon sink functions of forest land.

Description

Technical Field

[0001] This application relates to the assessment of the ecological benefits of understory economy, specifically to a method and system for assessing the impact of understory economy based on dynamic carbon sink monitoring. Background Technology

[0002] Understory economy is a diversified management model that relies on forests and their ecological environment to carry out activities such as understory planting, breeding, harvesting and processing, and forest landscape utilization. Its core objective is to efficiently and sustainably transform the ecological product value contained in forests into economic benefits. Since the introduction of ecosystem theory into the field of forestry in the 1960s, forests have no longer been regarded as a single source of timber, but as a treasure trove of biological resources integrating food, medicinal materials, and fiber. Understory economy has evolved from local practices to national strategies, and currently, various practice models have been formed nationwide, including understory planting (medicinal herbs, fungi, etc.), understory breeding, product harvesting and processing, and forest-based health and wellness. According to statistics, the area of ​​large-scale operation and utilization of understory economy in China has reached more than 600 million mu, with an annual output value exceeding 1.2 trillion yuan, employing more than 34 million people, and yielding significant benefits to the people.

[0003] Currently, research on the evaluation of economic benefits under forests has formed a relatively systematic technical methodology. It mainly includes: (1) Fuzzy comprehensive evaluation method based on comprehensive index system, which constructs a three-dimensional index system of economy, society and ecology, uses the analytic hierarchy process to determine the weights, and combines the fuzzy comprehensive evaluation model for quantitative evaluation. However, the determination of index weights is subjective and it is difficult to achieve dynamic and accurate monitoring of carbon sink function; (2) Evaluation method based on emergy analysis, which converts ecological and economic flows into solar energy value units and evaluates sustainability through indicators such as emergy investment rate and environmental load rate. However, the calculation is complicated and it is difficult to intuitively reflect the dynamic changes of carbon sink; (3) Evaluation method based on improved Vague set theory, which processes uncertain information by constructing positive ideal solution and negative ideal solution, but it still belongs to the category of static comprehensive evaluation; (4) Method based on special assessment of ecological service function, which mainly focuses on the value assessment of afforestation carbon sink projects themselves and does not involve comparative analysis of the impact of under-forest management activities on the carbon sink function of existing forest land.

[0004] In summary, existing technologies have the following shortcomings: First, they are mainly based on comprehensive evaluation, focusing on the static assessment of the overall benefits of understory economy, and lack specific dynamic monitoring of individual key ecological functions; second, they mostly use traditional data sources such as questionnaires and statistical yearbooks, which have limited spatiotemporal resolution and are difficult to capture subtle changes and spatial differentiation characteristics of the impact of understory economic activities on forest ecosystems; third, they have not established a spatiotemporal comparative analysis framework between treatment and control groups, making it difficult to separate the pure ecological effects of understory planting activities and accurately answer the core scientific question of whether understory planting activities have a synergistic benefit, a negative interference, or no significant impact on forest carbon sink function. Summary of the Invention

[0005] This invention aims to address the shortcomings of existing technologies, such as the lack of accurate attribution capabilities for the carbon sequestration effects of understory planting activities, the inability to achieve time-series dynamic monitoring and quantitative assessment, and the lack of a unified spatiotemporal comparative analysis framework to quantify the direction and extent of impact. It provides a method and system for assessing the economic impact of understory planting activities based on dynamic carbon sequestration monitoring, enabling accurate quantitative assessment of the direction and extent of the impact of understory planting activities on forest land carbon sequestration functions. The method includes: Vector boundary data of existing understory planting areas within the study region were collected as the treatment group. Unplanted areas with similar area and ecological background to the treatment group were selected as the control group according to pre-defined quantification rules. Meteorological data, remote sensing imagery, land change survey data, DEM data, economic data on understory planting, and data on newly created employment, participating farmers, and average annual income increase for participating farmers were collected. The meteorological data, remote sensing imagery, land change survey data, and DEM data were preprocessed to obtain a normalized vegetation index (NVI), meteorological raster data with uniform spatial resolution, and stable forest land pixels. The meteorological raster data included temperature, precipitation, and solar radiation raster maps. The preprocessed NVI, meteorological raster data, and DEM data were then compared. The CASA model is used to calculate the net primary productivity (NPP) of the processing group and the control group on a pixel-by-pixel basis for each year. The NPP-NEP conversion coefficient is used to convert NPP into NEP to characterize carbon sink capacity. Then, ecological benefit assessment indicators are calculated based on NEP. Economic benefit assessment indicators for the study period are calculated based on the economic data of understory planting. Social benefit assessment indicators for the study period are calculated based on the number of newly employed people, the number of participating farmers, and the average annual income increase of participating farmers. The original values ​​of the ecological benefit assessment indicators, economic benefit assessment indicators, and social benefit assessment indicators are converted into standardized scores. A comprehensive score is calculated based on the pre-set weights of each indicator. The comprehensive benefit is then determined based on the comprehensive score.

[0006] Furthermore, the preprocessing of meteorological data, remote sensing imagery, land change survey data, and DEM data to obtain normalized vegetation index, meteorological raster data with uniform spatial resolution, and stable forest pixels specifically includes: Meteorological data, remote sensing imagery, and DEM data were resampled to a uniform spatial resolution using nearest-neighbor interpolation. Annual land change survey data were overlaid with the vector ranges of the treatment and control groups, and pixels consistently identified as forest land throughout the study period were selected to obtain stable forest land pixels. Annual remote sensing images underwent geometric correction, radiometric calibration, and atmospheric correction sequentially. Multiple images covering the study area were stitched together and cropped to generate complete annual images. The annual Normalized Difference Vegetation Index (NDVI) was calculated based on the remote sensing images. , NIR stands for near-infrared band; Red stands for visible red band.

[0007] Furthermore, the aforementioned assessment indicators based on the ecological benefits of carbon sequestration specifically include: First, the mean and coefficient of variation of carbon sinks in each year for the treatment and control groups were calculated based on the carbon sink volume. , , in, The standard deviation of carbon sequestration. This represents the average amount of carbon sequestration. Then, the ratio of the interannual variation coefficient of carbon sink in the treatment group to the variation coefficient of the control group was set as the carbon sink stability index in the ecological benefit assessment indicators. The annual carbon sequestration change rate is calculated based on the carbon sequestration volume. The carbon sequestration time series of the treatment group and the control group are fitted by linear regression. The average annual change rate is then calculated to obtain the carbon sequestration change index in the ecological benefit assessment index.

[0008] Furthermore, the economic benefit evaluation indicators calculated based on the aforementioned understory planting economic data during the study period specifically include: Obtain data on annual net income, planting area, annual output, annual input, and initial investment from the data on understory economy; Then, calculate the net income per mu, input-output ratio, and investment payback period in the economic benefit evaluation indicators. The specific calculation formula is as follows: Net income per mu = Annual net income / Planting area Input-output ratio = Annual output / Annual input Investment recovery period = Initial investment / Annual net income.

[0009] Furthermore, the aforementioned social benefit evaluation indicators include: employment multiplier, farmer participation rate, and average annual income increase per household, and their calculation formulas are as follows: Job creation multiplier = Number of new jobs / Planting area Farmer participation rate = (Number of participating farmers / Total number of farmers in the region) × 100% Average annual income increase per household = Total average annual income increase per participating farmer.

[0010] Furthermore, the comprehensive score is calculated based on the pre-set comprehensive weights of each indicator, specifically as follows: , in The overall weight of each indicator, Each indicator.

[0011] Furthermore, the determination of comprehensive benefits based on the comprehensive score specifically involves: A comprehensive score of ≥0.8 indicates that the overall benefits are classified as a win-win situation for both ecological and economic benefits. The overall score range is [0.6, 0.8), and the overall benefits are judged to be of a collaborative development type. The overall score range is [0.4, 0.6), and the overall benefit is judged to be of the general benefit type. A comprehensive score in the range of [0.2, 0.4] indicates low overall benefit; a comprehensive score <0.2 indicates poor overall benefit.

[0012] This application also provides a forest economic impact assessment system based on dynamic carbon sink monitoring, which includes at least a microprocessor and a memory. The microprocessor is programmed or configured to perform the steps of the above-described forest economic impact assessment method, or the memory stores a computer program programmed or configured to perform the above-described forest economic impact assessment method.

[0013] This application also provides a computer-readable storage medium storing a computer program programmed or configured to perform the above-described method for assessing the economic impact of forest cover.

[0014] Compared with the prior art, this application has the following beneficial effects: (1) To achieve accurate attribution analysis of the impact of understory planting activities on carbon sequestration function. This invention establishes a rigorous spatiotemporal control experimental framework, selecting unplanted areas with similar climate conditions, topography, soil type, and forest stand type as a control group. Furthermore, it incorporates annual land use change survey data to eliminate interfering pixels related to land use change, effectively removing confounding factors beyond understory planting activities. This technical design overcomes the shortcomings of existing comprehensive evaluation methods that cannot establish a causal relationship between planting activities and ecological effects, achieving precise attribution of the ecological effects of understory planting.

[0015] (2) A dynamic monitoring system based on high-frequency time-series carbon sequestration data was constructed. This invention utilizes continuous, high-frequency carbon sequestration monitoring data year after year, combined with the CASA model to calculate net ecosystem productivity pixel by pixel, to construct a time-series dynamic monitoring system for the impact of understory planting. Compared to existing static assessment models that rely on annual statistics or census data, this invention can capture the short-term fluctuations and subtle impacts of understory planting activities on carbon sequestration function, revealing the spatial differentiation characteristics and temporal evolution patterns of carbon sequestration changes, and providing technical support for early warning and dynamic regulation of understory planting activities.

[0016] (3) A comprehensive evaluation framework for ecological, economic and social benefits was established. This invention overcomes the limitations of existing single-dimensional evaluation technologies. Based on a precise assessment of the ecological effects of carbon sequestration, it simultaneously incorporates statistical analysis of the economic benefits of understory planting and surveys of its social benefits, constructing a comprehensive evaluation framework integrating ecology, economy, and society. Through quantitative judgment of the direction and extent of carbon sequestration changes, combined with qualitative and quantitative analysis of economic increments and social contributions, a scientific criterion for determining the comprehensive feasibility of understory planting activities is established: positive synergy leads to conclusions for promotion and development; negative interference allows for a cost-benefit analysis; and no significant impact allows for management recommendations based on economic and social contributions. This comprehensive evaluation framework provides complete technical support for scientific decision-making in the understory economic industry.

[0017] (4) It provides a quantitative basis for the formulation of under-forest economy policies under the "dual carbon" target. The assessment results from this invention can directly serve the planning and policy formulation of understory economic industries: for carbon sequestration synergistic planting models, they can be prioritized for ecological compensation, carbon trading, and green finance support; for carbon sequestration disturbance planting models, they can provide early warning basis for ecological red line delineation and planting intensity control; for planting models with no significant impact, they can be appropriately developed in conjunction with economic and social contributions. This technical solution effectively responds to the policy needs for the coordinated development of understory economy and forest carbon sequestration under the "dual carbon" target background, and has significant social benefits and application value. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 The flowchart illustrating the understory economic impact assessment method based on dynamic carbon sink monitoring, as described in this application, is shown in the schematic diagram. Detailed Implementation

[0020] To facilitate understanding of this application, the following description will be more comprehensive and detailed in conjunction with the accompanying drawings and preferred embodiments, but the scope of protection of this application is not limited to the following specific embodiments.

[0021] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of this application.

[0022] Please see Figure 1 The implementation steps of the method are as follows: Step 1: Delineation of the treatment and control groups. Vector boundary data of the forest underplanting areas within the study area were collected and designated as the treatment group. Unplanted areas with similar area and ecological background to the treatment group were selected as the control group according to pre-defined quantitative rules.

[0023] The quantization rules described in this step of the embodiment are as follows:

[0024] If there are multiple candidate control groups, the region that is spatially adjacent to the treatment group and has the closest area should be selected first.

[0025] Step 2: Multi-source data collection. Collect meteorological data, remote sensing imagery, land change surveys, DEM data, economic data on understory planting, as well as data on newly created jobs, number of participating farmers, and average annual income increase for participating farmers.

[0026] The time span of the multi-source data must cover at least one year before the implementation of understory planting and at least three years (more than four consecutive years) after implementation. Specific requirements are as follows:

[0027] Step 3: Data Preprocessing. Meteorological data, remote sensing imagery, land change survey data, and DEM data are preprocessed to obtain normalized vegetation index, meteorological raster data with uniform spatial resolution, and stable forest pixels. The meteorological raster data includes temperature, precipitation, and solar radiation raster maps.

[0028] (1) Spatial reference unification. The unified projection coordinate system is CGCS2000 National Geodetic Coordinate System; all raster data (remote sensing, meteorology, DEM) are resampled to a unified spatial resolution of 30m×30m by the nearest neighbor interpolation method.

[0029] (2) Screening of land use stability. The annual land use change survey data were overlaid with the TA and CA vector ranges; pixels that remained forest land throughout the study period were screened out, and the following types were removed: pixels that were converted to non-forest land such as cultivated land, construction land, and water areas; pixels that showed obvious interference such as logging and burning; only stable forest land pixels were retained for subsequent analysis.

[0030] (3) Remote sensing image processing. (a) Perform the following on the annual remote sensing images in sequence: geometric correction (error ≤ 0.5 pixels), radiometric calibration (converted to apparent reflectance), and atmospheric correction (using the 6S or FLAASH model); (b) stitch and crop multiple images covering the study area to generate complete annual images. (c) Calculate the annual Normalized Difference Vegetation Index (NDVI): , In the formula: NIR represents the near-infrared band; Red represents the visible red band.

[0031] (4) Spatialization of meteorological data. Ordinary Kriging interpolation (spherical semi-variogram model) is used to generate annual and monthly raster maps of temperature, precipitation and solar radiation for meteorological station data; the interpolation results are resampled to the same spatial resolution as the remote sensing images.

[0032] Step 4: Carbon sink accounting and ecological benefit assessment index calculation. The preprocessed normalized vegetation index, meteorological raster data, and digital elevation model are input into the CASA model. Net primary productivity is calculated pixel by pixel for each year within the treatment group and the control group. Then, the NPP-NEP conversion coefficient is used to convert net primary productivity into net ecosystem productivity to characterize carbon sink capacity. Finally, ecological benefit assessment indicators are calculated based on net ecosystem productivity.

[0033] In this step, net primary productivity (NPP) is calculated as follows: , in: , Solar radiation (MJ / m²).

[0034] The photosynthetically active radiation absorption ratio, calculated from NDVI: , Light energy utilization rate, calculation formula: , Maximum light energy utilization (gC / MJ), the value varies for different vegetation types (0.692 for forest).

[0035] Temperature stress coefficient.

[0036] : Moisture stress coefficient (calculated from evapotranspiration ratio).

[0037] This step obtains continuous carbon sink raster results for the treatment and control groups year by year, and outputs annual NEP raster data in the TA and CA ranges (unit: gC / m² / yr).

[0038] The aforementioned ecological benefit assessment indicators based on carbon sink volume specifically include: First, the mean and coefficient of variation of carbon sinks in each year for the treatment and control groups were calculated based on the carbon sink volume. We plotted a line graph of annual carbon sequestration changes to preliminarily determine the differences in trends between the two groups.

[0039]

[0040] in, The standard deviation of carbon sequestration. This represents the average amount of carbon sequestration. Then, the ratio of the interannual variation coefficient of carbon sink in the treatment group to the variation coefficient of the control group was set as the carbon sink stability index in the ecological benefit assessment indicators.

[0041] Based on the carbon sequestration volume, the annual carbon sequestration change rate was calculated. Linear regression was used to fit the carbon sequestration time series of the treatment and control groups. The average annual change rate was further calculated to obtain the carbon sequestration change index in the ecological benefit assessment indicators. The annual carbon sequestration change rate is as follows: , Rate of change in total carbon emissions during the study period: , The slope of carbon sink change (Theil-Sen median slope) was calculated pixel by pixel for the treatment group and the control group during the study period.

[0042] Change types are categorized according to the following thresholds:

[0043] Generate a spatial distribution map of carbon sink changes and compare the spatial pattern differences between the treatment group and the control group.

[0044] Finally, attribution judgment was performed. If the carbon sequestration change in the treatment group was significantly better than that in the control group (p < 0.05) and the trend was positive, it was judged as a synergistic gain; if it was significantly worse than that in the control group, it was judged as a negative interference; if there was no significant difference, it was judged as no significant effect.

[0045] Step 5: Calculation of economic benefit evaluation indicators. Based on the economic data of understory planting, the economic benefit evaluation indicators for the study period are calculated.

[0046] (1) Collect the following economic indicators (unit: yuan / mu): Total input: seedlings, fertilizer, labor, management, etc. Annual output: Revenue from sales of medicinal materials Annual net income = Annual output - Annual input (2) Calculation: Net income per mu = Annual net income / Planting area; Input-output ratio = Annual output / Annual input; Investment payback period (years) = Initial investment / Annual net income.

[0047] Step 6: Calculation of social benefit assessment indicators. Based on the number of newly created jobs, the number of participating farmers, and the average annual increase in income for participating farmers, the social benefit assessment indicators for the study period are calculated.

[0048] The following indicators were obtained through questionnaires, interviews with village committee members, and other methods: Employment multiplication factor = Number of new jobs / Planting area (people / 10 mu); Farmer participation rate = (Number of participating farmers / Total number of farmers in the region) × 100%; Average annual income increase per household = Total average annual income increase per participating farmer (yuan / household).

[0049] Step 7: Calculation of comprehensive benefit score. The original values ​​of the ecological benefit assessment indicators, economic benefit assessment indicators, and social benefit assessment indicators are converted into standardized scores. A comprehensive score is calculated based on the pre-set weights of each indicator, and the comprehensive benefit is determined based on the comprehensive score.

[0050] (1) Indicator standardization Convert the raw values ​​of each indicator into standardized scores between 0 and 1: Positive indicators (the higher the better: carbon sink change, carbon sink stability, net income per acre, input-output ratio, employment multiplier, farmer participation, and average annual income increase per household): , Contrarian indicator (the smaller the better: payback period): , (2) Calculation of overall score , in The weighting is the combined weight of each indicator.

[0051] (3) Classification of comprehensive benefit levels

[0052] In this embodiment, the indicator system and calculation methods are shown in the table below:

[0053] In this embodiment, the method for determining the indicator weights is as follows: (1) Hierarchical structure model The assessment questions are divided into three levels: Target Layer (O): Comprehensive Benefit Assessment of Understory Planting Criterion Level (C): Ecological benefits (C1), Economic benefits (C2), Social benefits (C3) Indicator Layer (I): Specific indicators under each criterion (2) Construction of the judgment matrix (1-9 scale method) Using Saaty's 1-9 scale, experts (≥7 people) from the fields of forestry, ecology, and agricultural economics were invited to compare the importance of each indicator in pairs.

[0054] Scale meanings: 1: equally important; 3: slightly important; 5: significantly important; 7: strongly important; 9: extremely important; 2, 4, 6, 8: intermediate values.

[0055] (3) Criterion layer judgment matrix

[0056] Calculate the weights (eigenvector method): (1) Normalize each column: Column 1: 1 / (1+0.5+0.333)=0.545, 0.5 / 1.833=0.273, 0.333 / 1.833=0.182 Column 2: 2 / (2+1+0.5)=0.571, 1 / 3.5=0.286, 0.5 / 3.5=0.143 Column 3: 3 / (3+2+1)=0.5, 2 / 6=0.333, 1 / 6=0.167 (2) Row average: C1 = (0.545+0.571+0.5) / 3 = 0.539 C2 = (0.273+0.286+0.333) / 3 = 0.297 C3 = (0.182+0.143+0.167) / 3 = 0.164 (3) Consistency check: λ_max = (0.539×1 + 0.297×0.5 + 0.164×0.333) / 0.539 + ... = 3.009 CI = (3.009-3) / (3-1) = 0.0045 CR = 0.0045 / 0.58 = 0.0078 < 0.1 (Pass) (4) Weights of indicators under each criterion: (a) Ecological benefits (c1)

[0057] Reasons for weighting: Changes in carbon sequestration directly reflect the net impact of understory planting on carbon sequestration and are a core ecological effect indicator; carbon sequestration stability reflects the ecosystem's resistance to disturbance and serves as a supplementary indicator.

[0058] (b) Economic benefits (c2)

[0059] Reasons for empowerment: Net income per mu is the most direct reflection of farmers' economic benefits; input-output ratio reflects resource utilization efficiency; investment payback period reflects capital turnover risk.

[0060] (c) Social benefits (c3) Reasons for empowerment: Employment creation is the core social contribution of under-forest economy; farmer participation reflects industry acceptance; and average household income increase reflects actual benefits to the people.

[0061] (5) Summary table of comprehensive weights

[0062] In this example, the threshold values ​​for classifying the benefit levels of each indicator are shown in the table below:

[0063] In this embodiment, an evaluation report is also included, which includes: a comparison curve of carbon sink changes between the treatment group and the control group, a spatial distribution map of carbon sink changes, a statistical table of economic and social benefits, a comprehensive judgment conclusion, and policy recommendations.

[0064] This embodiment employs a rigorous spatiotemporal control experimental framework—selecting unplanted areas with largely consistent climate conditions, topography, soil type, and forest stand type as the control group, and combining annual land use change survey data to eliminate interfering pixels related to land use change, effectively removing confounding factors beyond understory planting activities. This technical design overcomes the shortcomings of existing comprehensive evaluation methods in establishing a causal relationship between planting activities and ecological effects. For the first time, it achieves precise attribution of the ecological effects of understory planting, scientifically answering the core question of whether understory planting has a synergistic benefit, a negative interference, or no significant impact on forest carbon sequestration function.

[0065] This embodiment employs high-frequency carbon sequestration monitoring data with a resolution better than 30 meters and continuous annually, combined with the CASA model to calculate net ecosystem productivity pixel by pixel, to construct a time-series dynamic monitoring system for the impact of understory planting. Compared with existing technologies that rely on static assessment models based on annual statistics or census data, this invention can capture the short-term fluctuations and subtle impacts of understory planting activities on carbon sequestration function, revealing the spatial differentiation characteristics and temporal evolution patterns of carbon sequestration changes, and providing technical support for early warning and dynamic regulation of understory planting activities.

[0066] This embodiment breaks through the limitations of existing single-dimensional evaluation technologies. Based on a precise assessment of the ecological effects of carbon sequestration, it simultaneously incorporates statistical analysis of the economic benefits of understory planting and surveys of its social benefits, constructing a comprehensive evaluation framework integrating ecology, economy, and society. Through quantitative judgment of the direction and extent of carbon sequestration changes, combined with qualitative and quantitative analysis of economic increment and social contribution, a scientific criterion for determining the comprehensive feasibility of understory planting activities is formed: positive synergy leads to a conclusion for promotion and development; negative interference allows for a cost-benefit analysis; and no significant impact allows for management recommendations based on economic and social contributions. This comprehensive evaluation framework provides complete technical support for scientific decision-making in the understory economic industry.

[0067] The assessment results generated in this embodiment can directly serve the planning and policy formulation of the understory economy industry: for carbon sink synergistic planting models, they can be prioritized for ecological compensation, carbon trading, and green finance support; for carbon sink disturbance planting models, they can provide early warning basis for ecological red line delineation and planting intensity control; for planting models with no significant impact, they can be appropriately developed in conjunction with economic and social contributions. This technical solution effectively responds to the policy needs for the coordinated development of understory economy and forest carbon sink under the background of "dual carbon" goals, and has significant social benefits and application value.

[0068] This application also provides another embodiment, a forest economic impact assessment system based on dynamic carbon sink monitoring, comprising at least a microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the steps of the forest economic impact assessment method based on dynamic carbon sink monitoring involved in the above embodiments, or the memory stores a computer program programmed or configured to execute the forest economic impact assessment method based on dynamic carbon sink monitoring involved in the above embodiments.

[0069] This application also provides another embodiment, a computer-readable storage medium storing a computer program programmed or configured to perform the understory economic impact assessment method based on dynamic carbon sink monitoring as described in the above embodiments.

[0070] The above are merely preferred embodiments of this application. It should be noted that this application is not limited to the above embodiments. For those skilled in the art, several improvements and modifications can be made without departing from the principles of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should also be considered within the scope of protection of this application.

Claims

1. A method for assessing the economic impact of forest cover based on dynamic carbon sink monitoring, characterized in that, The method includes: Vector boundary data of forest underplanting areas in the study area were collected as the treatment group. Unplanted areas with similar area and ecological background to the treatment group were selected as the control group according to the pre-set quantification rules. Collect meteorological data, remote sensing images, land change survey data, DEM data, economic data on understory planting, as well as the number of newly employed people, the number of participating farmers, and the average annual income increase of participating farmers; Meteorological data, remote sensing images, land change survey data, and DEM data are preprocessed to obtain normalized vegetation index, meteorological raster data with uniform spatial resolution, and stable forest pixels. The meteorological raster data includes temperature, precipitation, and solar radiation raster maps. The preprocessed normalized vegetation index, meteorological raster data, and digital elevation model were input into the CASA model. Net primary productivity was calculated pixel by pixel for each year in the processing group and the control group. Then, the NPP-NEP conversion coefficient was used to convert net primary productivity into net ecosystem productivity to characterize carbon sink capacity. Finally, ecological benefit assessment indicators were calculated based on net ecosystem productivity. Based on the economic data of understory planting, economic benefit evaluation indicators for the study period were calculated. The social benefit evaluation indicators for the study period were calculated based on the number of newly employed people, the number of participating farmers, and the average annual income increase of participating farmers. The original values ​​of ecological benefit assessment indicators, economic benefit assessment indicators and social benefit assessment indicators are converted into standardized scores. A comprehensive score is calculated based on the pre-set weights of each indicator, and the comprehensive benefit is determined based on the comprehensive score.

2. The method for assessing the economic impact of forest cover according to claim 1, characterized in that, The preprocessing of meteorological data, remote sensing imagery, land change survey data, and DEM data to obtain normalized vegetation index, meteorological raster data with uniform spatial resolution, and stable forest pixels specifically includes: Meteorological data, remote sensing images, and DEM data are resampled to a uniform spatial resolution using the nearest neighbor interpolation method. By overlaying the annual land change survey data with the vector ranges of the processing group and the control group, pixels that remained forest land throughout the study period were selected to obtain stable forest land pixels. The annual remote sensing images are sequentially processed with geometric correction, radiometric calibration, and atmospheric correction. Multiple images covering the study area are stitched together and cropped to generate complete annual images. The Normalized Difference Vegetation Index (NDVI) is calculated annually based on remote sensing imagery. ， NIR stands for near-infrared band; Red stands for visible red band.

3. The method for assessing the economic impact of forest cover according to claim 1, characterized in that, The aforementioned ecological benefit assessment indicators based on carbon sink volume specifically include: First, the mean and coefficient of variation of carbon sinks in each year for the treatment and control groups were calculated based on the carbon sink volume. , ， in, The standard deviation of carbon sequestration. This represents the average amount of carbon sequestration. Then, the ratio of the interannual variation coefficient of carbon sink in the treatment group to the variation coefficient of the control group was set as the carbon sink stability index in the ecological benefit assessment indicators. The annual carbon sequestration change rate is calculated based on the carbon sequestration volume. The carbon sequestration time series of the treatment group and the control group are fitted by linear regression. The average annual change rate is then calculated to obtain the carbon sequestration change index in the ecological benefit assessment index.

4. The method for assessing the economic impact of forest cover according to claim 1, characterized in that, The economic benefit evaluation indicators calculated based on the aforementioned understory planting economic data during the study period specifically include: Obtain data on annual net income, planting area, annual output, annual input, and initial investment from the data on understory economy; Then, calculate the net income per mu, input-output ratio, and investment payback period in the economic benefit evaluation indicators. The specific calculation formula is as follows: Net income per mu = Annual net income / Planting area Input-output ratio = Annual output / Annual input Investment recovery period = Initial investment / Annual net income.

5. The method for assessing the economic impact of forest cover according to claim 1, characterized in that, The aforementioned social benefit assessment indicators include: employment multiplier, farmer participation rate, and average annual income increase per household, and their calculation formulas are as follows: Job creation multiplier = Number of new jobs / Planting area Farmer participation rate = (Number of participating farmers / Total number of farmers in the region) × 100% Average annual income increase per household = Total average annual income increase per participating farmer.

6. The method for assessing the economic impact of forest cover according to claim 1, characterized in that, The comprehensive score is calculated based on the pre-set comprehensive weights of each indicator, specifically as follows: ， in The overall weight of each indicator, Each indicator.

7. The method for assessing the economic impact of forest cover according to claim 1, characterized in that, The determination of comprehensive benefits based on the comprehensive score is as follows: A comprehensive score of ≥0.8 indicates that the overall benefits are classified as a win-win situation for both ecological and economic benefits. The overall score range is [0.6, 0.8), and the overall benefits are judged to be of a collaborative development type. The overall score range is [0.4, 0.6), and the overall benefit is judged to be of the general benefit type. A comprehensive score in the range of [0.2, 0.4] indicates low overall benefit; a comprehensive score <0.2 indicates poor overall benefit.

8. A forest understory economic impact assessment system based on dynamic carbon sink monitoring, comprising at least a microprocessor and a memory, characterized in that, The microprocessor is programmed or configured to perform the steps of the understory economic impact assessment method according to any one of claims 1 to 7, or the memory stores a computer program programmed or configured to perform the understory economic impact assessment method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is programmed or configured to perform the understory economic impact assessment method according to any one of claims 1 to 7.

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