Mine area vegetation carbon sink improvement planning method based on ecological survey data

Abstract

The application provides a mine area vegetation carbon sink improvement planning method based on ecological survey data, vegetation characteristic variables are obtained by ecological survey on the mine area, and a vegetation characteristic variable distribution map is established; stepwise regression analysis is performed on vegetation above-ground carbon storage and vegetation characteristic variables, and a vegetation above-ground carbon storage distribution map is established through the vegetation characteristic variable distribution map; stepwise regression analysis is performed on vegetation growth stress factors and vegetation characteristic variables, and a vegetation growth stress factor distribution map is established through the vegetation characteristic variable distribution map; a mine area vegetation carbon sink improvement spatial distribution map is established through the vegetation above-ground carbon storage distribution map and the vegetation growth stress factor distribution map; a dominant parameter distribution map of the vegetation growth stress factor is established, a mine area vegetation carbon sink improvement parameter distribution map is established based on the vegetation carbon sink improvement spatial distribution map; vegetation carbon sink improvement measures are marked on the vegetation carbon sink improvement parameter distribution map, a mine area vegetation carbon sink improvement planning map is established, and vegetation carbon sink improvement planning is completed.

Description

Technical Field

[0001] This invention relates to the field of land reclamation and ecological restoration technology, specifically to a planning method for enhancing carbon sequestration in mining areas based on ecological mapping data. Background Technology

[0002] As a crucial pathway for carbon sequestration and enhancement, vegetation carbon sinks in mining areas have received increasing attention from governments and mining enterprises following the introduction of the national dual-carbon strategy. Vegetation restoration in mining areas is a core component of this strategy. While vegetation restoration can increase carbon sequestration, the rugged terrain and poor soil and water conditions in mining areas make vegetation growth susceptible to water deficits and nutrient stress, resulting in insufficient carbon sequestration capacity. Therefore, rational planning for enhancing vegetation carbon sequestration in mining areas is necessary.

[0003] Currently, technologies related to vegetation carbon sequestration in mining areas mainly focus on carbon sequestration assessment techniques, such as those based on light energy utilization models and those based on ground ecological surveys. Technologies specifically for planning and enhancing vegetation carbon sequestration in mining areas are still scarce. Traditional methods for enhancing vegetation carbon sequestration rely primarily on ground surveys and empirical judgment to diagnose the health of plant communities and propose carbon sequestration enhancement strategies. However, these methods suffer from drawbacks such as high degree of haphazardness, large workload, and a lack of spatial planning, making it difficult to meet the overall layout and planning requirements for enhancing vegetation carbon sequestration in mining areas. Therefore, a new method is needed to address the planning issues related to enhancing vegetation carbon sequestration in mining areas, and to guide land reclamation and ecological restoration in these areas. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for planning the enhancement of carbon sequestration in mining areas based on ecological mapping data.

[0005] The technical solution provided by this invention is as follows:

[0006] A planning method for enhancing carbon sequestration in mining areas based on ecological mapping data includes the following steps:

[0007] S1: Conduct ecological mapping of the mining area, obtain vegetation characteristic variables from the ecological mapping data, and establish a distribution map of vegetation characteristic variables;

[0008] S2: Perform stepwise regression analysis on the aboveground carbon storage of vegetation with the vegetation characteristic variables, and establish a distribution map of aboveground carbon storage of vegetation through the distribution map of the vegetation characteristic variables;

[0009] S3: Perform stepwise regression analysis on vegetation growth stress factors and the vegetation characteristic variables, and establish a distribution map of vegetation growth stress factors through the distribution map of the vegetation characteristic variables;

[0010] S4: Establish a spatial distribution map of vegetation carbon sink enhancement in the mining area using the aboveground carbon storage distribution map of the vegetation and the distribution map of vegetation growth stress factors;

[0011] S5: Establish a distribution map of dominant parameters of vegetation growth stress factors in the mining area using the distribution map of vegetation growth stress factors and the distribution map of vegetation characteristic variables;

[0012] S6: Mark vegetation carbon sequestration enhancement measures on the distribution map of vegetation carbon sequestration enhancement parameters, establish a vegetation carbon sequestration enhancement planning map for the mining area, and complete the vegetation carbon sequestration enhancement plan.

[0013] Furthermore, in step S1, LiDAR point cloud data and hyperspectral images of the mining area are established by UAV mapping; the vegetation characteristic variables include LiDAR characteristic variables and hyperspectral characteristic variables; the vegetation characteristic variable distribution map includes LiDAR characteristic variable distribution map and hyperspectral characteristic variable distribution map.

[0014] Furthermore, the LiDAR feature variables include plant height, vegetation canopy structure, and topographic factors, while the hyperspectral feature variables include vegetation index, texture factor, vegetation type, and species diversity.

[0015] Furthermore, in step S2, the stepwise regression analysis function formula for the aboveground carbon storage of vegetation and the vegetation characteristic variables is as follows:

[0016]

[0017] In the formula: - Logarithmic function symbol; - Aboveground carbon storage of vegetation; -The i-th LiDAR feature variable; - The j-th hyperspectral characteristic variable; c - a constant; - The regression coefficient of the i-th LiDAR feature variable; - The regression coefficient of the j-th hyperspectral characteristic variable; n - the number of LiDAR characteristic variables; m - the number of hyperspectral characteristic variables.

[0018] Furthermore, in step S3, the stepwise regression analysis function formula for the vegetation growth stress factors and the vegetation characteristic variables is as follows:

[0019]

[0020] In the formula: - The kth vegetation growth stress factor, where the maximum value of k is 3; -The i-th LiDAR feature variable; - The j-th hyperspectral characteristic variable; f - a constant; - The regression coefficient of the i-th LiDAR feature variable; - The regression coefficient of the j-th hyperspectral characteristic variable; n - the number of LiDAR characteristic variables; m - the number of hyperspectral characteristic variables.

[0021] Furthermore, in step S4, the spatial distribution map of carbon sink enhancement in the mining area is composed of carbon sink enhancement regions.

[0022] Furthermore, the method for identifying the carbon sink enhancement area includes the following steps:

[0023] a. Using the aboveground carbon storage distribution map of the vegetation as the dependent variable and the distribution map of vegetation growth stress factors as the independent variable, based on geographically weighted regression analysis, the regression coefficient map of vegetation growth stress factors and the distribution map of the significance p-values ​​of vegetation growth stress factors are obtained respectively.

[0024] b. Overlay the regression coefficient map of the vegetation growth stress factors and the distribution map of the significance p-value of the vegetation growth stress factors, and mark the patches with the largest regression coefficient and the significance p-value less than 0.05. The area of ​​the patch is the vegetation carbon sink enhancement area.

[0025] Furthermore, in step S5, the method for establishing the distribution map of dominant parameters of vegetation growth stress factors is as follows: using the distribution map of vegetation growth stress factors as the dependent variable and the distribution map of vegetation characteristic variables as the independent variable, the dominant parameters of vegetation carbon sequestration stress factors for each patch in the spatial distribution map of vegetation carbon sequestration enhancement are identified based on the geographic detector, and the distribution map of dominant parameters of vegetation growth stress factors is established.

[0026] Furthermore, the vegetation growth stress factors include vegetation moisture, vegetation nitrogen-phosphorus ratio and vegetation density, and the vegetation carbon sequestration enhancement measures include thinning, replanting, thinning, topdressing and terrain reshaping.

[0027] The beneficial effects of this invention are:

[0028] 1. The method proposed in this invention uses drones to collect ecological mapping data in mining areas, avoiding extensive field surveys of vegetation in mining areas and saving a lot of manpower and resources.

[0029] 2. Based on the mining of UAV ecological mapping data, effective planning for vegetation carbon sequestration enhancement in mining areas can be carried out, providing technical guidance for land reclamation and ecological restoration, vegetation management, and carbon sequestration monitoring and evaluation. Specific vegetation carbon sequestration enhancement planning maps can also be generated. These maps include information on carbon sequestration enhancement space, enhancement parameters, and enhancement measures, facilitating precise guidance for vegetation carbon sequestration enhancement projects in mining areas and avoiding blind construction.

[0030] 3. The method proposed in this invention takes into account the spatial relationship between carbon reserves and the growth of vegetation in the mining area, water content, nitrogen-phosphorus ratio, and density stress factors. It can accurately identify areas where vegetation carbon sequestration needs to be improved, and is highly targeted. Attached Figure Description

[0031] Appendix Figure 1 : A flowchart illustrating the method described in this invention;

[0032] Appendix Figure 2 Schematic diagram of the distribution of topographic factor characteristic variables;

[0033] Appendix Figure 3 Schematic diagram of the distribution of species diversity characteristic variables;

[0034] Appendix Figure 4 Schematic diagram of the distribution of vegetation type characteristic variables;

[0035] Appendix Figure 5 Schematic diagram of aboveground carbon storage distribution in vegetation;

[0036] Appendix Figure 6 Schematic diagram of the distribution of vegetation water stress factors;

[0037] Appendix Figure 7 Schematic diagram of the distribution of vegetation carbon-phosphorus ratio stress factors;

[0038] Appendix Figure 8 Schematic diagram of the distribution of vegetation density stress factors;

[0039] Appendix Figure 9 Schematic diagram of spatial distribution for enhancing vegetation carbon sequestration;

[0040] Appendix Figure 10 Schematic diagram of vegetation carbon sequestration enhancement parameters;

[0041] Appendix Figure 11 Schematic diagram of vegetation carbon sequestration enhancement plan. Detailed Implementation

[0042] The present invention will now be described in detail with reference to the accompanying drawings and by way of embodiments.

[0043] Please see the appendix Figure 1 This application provides a method for planning the enhancement of vegetation carbon sequestration in mining areas based on ecological mapping data, including the following steps:

[0044] S1: Use UAVs to obtain ecological mapping parameters for the mining area, and establish LiDAR point cloud data and hyperspectral images of the mining area respectively.

[0045] S11: Vegetation characteristics in the mining area include LiDAR and hyperspectral variables. LiDAR variables include plant height, canopy structure, and topographic factors; hyperspectral variables include vegetation indices, texture factors, vegetation type, and species diversity.

[0046] S12: Extract vegetation feature variable distribution maps from LiDAR point cloud data and hyperspectral images. The LiDAR feature variable distribution maps include plant height distribution maps, vegetation canopy structure distribution maps, and topographic factor distribution maps; the hyperspectral feature variable distribution maps include vegetation index distribution maps, texture factor distribution maps, vegetation type distribution maps, and species diversity distribution maps.

[0047] S13: Define topographic factors, vegetation type, and species diversity as the dominant variables for vegetation characteristics. (Appendix) Figure 2 - Appendix Figure 4 The diagrams showing the distribution of the dominant variables of vegetation characteristics are shown respectively.

[0048] S2: Establish a map of aboveground carbon storage distribution in vegetation.

[0049] S21: Based on the vegetation aboveground carbon storage data obtained from the field quadrat survey, stepwise regression analysis was adopted to establish the functional relationship between vegetation aboveground carbon storage and LiDAR characteristic variables and hyperspectral characteristic variables through the following formula (1):

[0050] (1)

[0051] In the formula: - Logarithmic function symbol;

[0052] - Aboveground carbon storage of vegetation;

[0053] -The i-th LiDAR feature variable;

[0054] -The j-th hyperspectral feature variable;

[0055] c - constant;

[0056] - The regression coefficient of the i-th LiDAR feature variable;

[0057] - The regression coefficient of the j-th hyperspectral characteristic variable;

[0058] The number of n-LiDAR feature variables;

[0059] m - The number of hyperspectral characteristic variables.

[0060] S22: Substitute the LiDAR characteristic variable distribution map and the hyperspectral characteristic variable distribution map into formula (1) to establish the aboveground carbon storage distribution map of the mining area vegetation, denoted as C-map. Figure 5 A schematic diagram showing the distribution of aboveground carbon storage in vegetation is presented.

[0061] S3: In the ecological environment of mining areas, vegetation moisture, vegetation nitrogen-phosphorus ratio and vegetation density are stress factors affecting vegetation growth. Establish a distribution map of stress factors affecting vegetation growth.

[0062] S31: Based on the vegetation moisture, vegetation nitrogen-phosphorus ratio and vegetation density data obtained from the field quadrat survey, stepwise regression analysis was adopted, and the functional relationship between vegetation growth stress factors and LiDAR characteristic variables and hyperspectral characteristic variables was established through the following formula (2):

[0063] (2)

[0064] In the formula: - The kth vegetation growth stress factor, where the maximum value of k is 3;

[0065] -The i-th LiDAR feature variable;

[0066] -The j-th hyperspectral feature variable;

[0067] f - constant;

[0068] - The regression coefficient of the i-th LiDAR feature variable;

[0069] - The regression coefficient of the j-th hyperspectral characteristic variable;

[0070] The number of n-LiDAR feature variables;

[0071] m - The number of hyperspectral characteristic variables.

[0072] S32: Substitute the LiDAR characteristic variable distribution map and the hyperspectral characteristic variable distribution map into formula (2) to establish a vegetation growth stress factor distribution map, specifically including: a vegetation water stress distribution map, denoted as W-map; a vegetation nitrogen-phosphorus ratio stress distribution map, denoted as NP-map; and a vegetation density stress distribution map, denoted as D-map. (Appendix) Figure 6 - Appendix Figure 8 The diagrams show the distribution of stress factors on vegetation growth.

[0073] S4: Establish a spatial distribution map of carbon sequestration enhancement in the mining area and obtain the correspondence between carbon sequestration enhancement space and vegetation growth stress factors.

[0074] S41: Using the distribution map of aboveground carbon storage in vegetation (C-map) as the dependent variable and the distribution maps of vegetation growth stress factors (W-map, NP-map, D-map) as independent variables, based on geographically weighted regression analysis, the regression coefficient map of vegetation growth stress factors and the distribution map of the significance p-value of vegetation growth stress factors were obtained respectively.

[0075] Specifically,

[0076] The regression coefficient maps of vegetation growth stress factors include: vegetation water regression coefficient map, denoted as W-Co-map; vegetation nitrogen-phosphorus ratio regression coefficient map, denoted as NP-Co-map; and vegetation density regression coefficient map, denoted as D-Co-map.

[0077] The distribution maps of significant p-values ​​of vegetation growth stress factors include: the distribution map of significant p-values ​​of vegetation water, denoted as Wp-map; the distribution map of significant p-values ​​of vegetation nitrogen-phosphorus ratio, denoted as NP-p-map; and the distribution map of significant p-values ​​of vegetation density, denoted as Dp-map.

[0078] S42: Overlay the regression coefficient map of vegetation growth stress factors with the significance p-value distribution map, and mark the patches with the largest regression coefficient and a significance p-value less than 0.05 to obtain the spatial distribution map of vegetation carbon sequestration enhancement in the mining area, denoted as CI-map. The patch areas in the map are the vegetation carbon sequestration enhancement areas. (Appendix) Figure 9 The diagram shows the spatial distribution of vegetation carbon sequestration enhancement. In the diagram, "moisture" refers to vegetation moisture, "nutrients" refers to the nitrogen-phosphorus ratio of vegetation, and "density" refers to vegetation density.

[0079] S5: Obtain the dominant parameters of vegetation growth stress factors in the vegetation carbon sequestration enhancement area and form a distribution map of vegetation carbon sequestration enhancement parameters in the mining area.

[0080] S51: Using the distribution map of vegetation growth stress factors in the mining area as the dependent variable and LiDAR and hyperspectral characteristic variables as independent variables, a geographic detector was used to analyze and obtain the distribution maps of the dominant parameters of vegetation growth stress factors. Specifically, this includes: the distribution map of the dominant parameter of vegetation moisture, denoted as W-Parameter-map; the distribution map of the dominant parameter of vegetation nitrogen-phosphorus ratio, denoted as NP-Parameter-map; and the distribution map of the dominant parameter of vegetation density, denoted as D-Parameter-map.

[0081] S52: By overlaying the spatial distribution map of vegetation carbon sequestration enhancement (CI-map) with the distribution map of dominant parameters of vegetation growth stress factors, the dominant parameters of vegetation growth stress factors in the space for vegetation carbon sequestration enhancement are identified, resulting in a distribution map of vegetation carbon sequestration enhancement parameters in the mining area, denoted as CI-Parameter-map. (Appendix) Figure 10A schematic diagram showing the distribution of parameters for enhancing vegetation carbon sequestration is presented.

[0082] S6: Match the vegetation carbon sequestration enhancement parameter distribution map with the vegetation carbon sequestration enhancement measures to establish a vegetation carbon sequestration enhancement planning map, thereby completing the layout of vegetation carbon sequestration enhancement measures.

[0083] S61: Match vegetation carbon sequestration enhancement measures based on the CI-Parameter-map of the mining area. Vegetation carbon sequestration enhancement measures include: thinning, replanting, pruning, topdressing, and terrain reshaping.

[0084] The matching rules are shown in the table below:

[0085]

[0086] The table above is described in detail below.

[0087] A: If the vegetation growth stress factor for improving vegetation carbon sequestration space is vegetation moisture:

[0088] When the dominant parameter of vegetation growth stress factors is vegetation type, water should be replenished, that is, water should be replenished for vegetation types that are short of water using drip irrigation, sprinkler irrigation and other methods.

[0089] When the dominant parameter of vegetation growth stress factors is plant diversity, thinning should be carried out, that is, removing plants with high water consumption and replanting plants with low water consumption.

[0090] When the dominant parameter of vegetation growth stress factors is topographic factors, topographic reshaping should be carried out, that is, using water-retaining methods such as horizontal ditches, horizontal steps, and fish-scale pits to modify the micro-topography.

[0091] B: If the vegetation growth stress factor for improving vegetation carbon sequestration space is the vegetation nitrogen-phosphorus ratio:

[0092] When the dominant parameter of vegetation growth stress factors is vegetation type, topdressing should be applied, namely nitrogen fertilizer and phosphorus fertilizer.

[0093] When the dominant parameter of vegetation growth stress factors is plant diversity, replanting should be carried out, that is, replanting nitrogen-fixing plants.

[0094] When the dominant parameter of vegetation growth stress is topographic factor, replanting should be carried out, that is, replanting soil and water conservation plants.

[0095] C: If the vegetation growth stress factor for improving vegetation carbon sequestration space is vegetation density:

[0096] When the dominant parameter of vegetation growth stress factors is vegetation type, thinning should be carried out, that is, thinning should be used to reduce plant density.

[0097] When the dominant parameter of vegetation growth stress factors is plant diversity, replanting should be carried out to enrich the horizontal and vertical structure of the community.

[0098] When the dominant parameter of vegetation growth stress is topographic factor, replanting should be carried out, that is, replanting soil and water conservation plants.

[0099] S62: Mark vegetation carbon sequestration enhancement measures in the corresponding areas on the vegetation carbon sequestration enhancement parameter distribution map (CI-Parameter-map) to obtain the vegetation carbon sequestration enhancement planning map of the mining area, denoted as CI-Planning-map. (Attached) Figure 11 A schematic diagram of the vegetation carbon sequestration enhancement plan is shown.

[0100] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for planning the enhancement of vegetation carbon sequestration in mining areas based on ecological mapping data, characterized in that, Includes the following steps: S1: Conduct ecological mapping of the mining area, obtain vegetation characteristic variables from the ecological mapping data, and establish a distribution map of vegetation characteristic variables; S2: Perform stepwise regression analysis on the aboveground carbon storage of vegetation with the vegetation characteristic variables, and establish a distribution map of aboveground carbon storage of vegetation through the distribution map of the vegetation characteristic variables; S3: Perform stepwise regression analysis on vegetation growth stress factors and the vegetation characteristic variables, and establish a distribution map of vegetation growth stress factors through the distribution map of the vegetation characteristic variables; S4: Using the aboveground carbon storage distribution map of the vegetation and the distribution map of vegetation growth stress factors, establish a spatial distribution map of vegetation carbon sink enhancement in the mining area, wherein the spatial distribution map of vegetation carbon sink enhancement in the mining area is composed of carbon sink enhancement areas; The method for identifying the carbon sink enhancement area includes the following steps: a. Using the aboveground carbon storage distribution map of the vegetation as the dependent variable and the distribution map of vegetation growth stress factors as the independent variable, based on geographically weighted regression analysis, the regression coefficient map of vegetation growth stress factors and the distribution map of the significance p-values ​​of vegetation growth stress factors are obtained respectively. b. Overlay the regression coefficient map of the vegetation growth stress factors and the distribution map of the significance p-value of the vegetation growth stress factors, and mark the patches with the largest regression coefficient and the significance p-value less than 0.

05. The area of ​​the patch is the vegetation carbon sink enhancement area. S5: By using the distribution map of vegetation growth stress factors and the distribution map of vegetation characteristic variables, establish a distribution map of dominant parameters of vegetation growth stress factors. By overlaying the spatial distribution map of vegetation carbon sequestration enhancement and the distribution map of dominant parameters of vegetation growth stress factors, establish a distribution map of parameters for vegetation carbon sequestration enhancement. The method for establishing the distribution map of dominant parameters of vegetation growth stress factors is as follows: using the distribution map of vegetation growth stress factors as the dependent variable and the distribution map of vegetation characteristic variables as the independent variable, the dominant parameters of vegetation carbon sequestration stress factors in each patch of the spatial distribution map of vegetation carbon sequestration enhancement are identified based on the geographic detector, and the distribution map of dominant parameters of vegetation growth stress factors is established. S6: Mark vegetation carbon sequestration enhancement measures on the distribution map of vegetation carbon sequestration enhancement parameters, establish a vegetation carbon sequestration enhancement planning map for the mining area, and complete the vegetation carbon sequestration enhancement plan.

2. The method for planning and enhancing carbon sequestration in mining areas based on ecological mapping data according to claim 1, characterized in that, In step S1, LiDAR point cloud data and hyperspectral images of the mining area are established by UAV mapping; the vegetation characteristic variables include LiDAR characteristic variables and hyperspectral characteristic variables; the vegetation characteristic variable distribution map includes LiDAR characteristic variable distribution map and hyperspectral characteristic variable distribution map.

3. The method for planning and enhancing carbon sequestration in mining areas based on ecological mapping data according to claim 2, characterized in that, The LiDAR feature variables include plant height, vegetation canopy structure, and topographic factors, while the hyperspectral feature variables include vegetation index, texture factor, vegetation type, and species diversity.

4. The method for planning and enhancing carbon sequestration in mining areas based on ecological mapping data according to claim 1, characterized in that, In step S2, the stepwise regression analysis function formula for the aboveground carbon storage of vegetation and the vegetation characteristic variables is as follows: , In the formula: - Logarithmic function symbol; - Aboveground carbon storage of vegetation; -The i-th LiDAR feature variable; - The j-th hyperspectral characteristic variable; c - a constant; - The regression coefficient of the i-th LiDAR feature variable; - The regression coefficient of the j-th hyperspectral characteristic variable; n - the number of LiDAR characteristic variables; m - the number of hyperspectral characteristic variables.

5. The method for planning and enhancing carbon sequestration in mining areas based on ecological mapping data according to claim 1, characterized in that, In step S3, the stepwise regression analysis function formula for the vegetation growth stress factors and the vegetation characteristic variables is as follows: , In the formula: - The kth vegetation growth stress factor, where the maximum value of k is 3; -The i-th LiDAR feature variable; - The j-th hyperspectral characteristic variable; f - a constant; - The regression coefficient of the i-th LiDAR feature variable; - The regression coefficient of the j-th hyperspectral characteristic variable; n - the number of LiDAR characteristic variables; m - the number of hyperspectral characteristic variables.

6. The method for planning the enhancement of vegetation carbon sequestration in mining areas based on ecological mapping data according to claim 1, characterized in that, The vegetation growth stress factors include vegetation moisture, vegetation nitrogen-phosphorus ratio and vegetation density, and the vegetation carbon sequestration enhancement measures include thinning, replanting, pruning, topdressing and terrain reshaping.

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