Ecosystem service optimization assessment method based on mountain micro-geomorphological characteristics analysis

Abstract

The present invention provides an ecosystem service optimization assessment method based on mountain micro-geomorphological feature analysis, comprising: performing topographic and geomorphological division on a mountainous area, performing micro-geomorphological type division on the mountainous area based on a Geomorphons method, and obtaining mountain location information; obtaining a mountain forest type based on the mountain location information; based on the mountain forest type, obtaining a coefficient of variation of a vegetation index of a ratio value and a tree layer biomass of each mountain forest type area according to multispectral unmanned aerial vehicle image acquisition and processing and tree layer investigation; using the coefficient of variation of the vegetation index of a ratio value and the tree layer biomass of each mountain forest type area to respectively correct an InVEST model habitat suitability parameter and a carbon density value, and obtaining an optimal InVEST model; and using the optimal InVEST model to obtain an optimization assessment result of an ecosystem service. The present invention solves the problem of low accuracy of ecosystem service assessment in mountainous areas in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of ecosystem service assessment, and in particular to an ecosystem service optimization assessment method based on mountain micro-geomorphological feature analysis. Background Art

[0002] The unique three-dimensional ecological environment characteristics and complex and diverse habitat types in mountainous areas enable them to provide diverse ecosystem services. Affected by the instability of endogenous mountain types and human interference, mountain ecosystems are fragile.

[0003] At present, human activities such as global industrialization and urbanization are accelerating and advancing continuously. Due to the irrational development of natural resources, the loss of habitats in mountainous areas has accelerated, habitat fragmentation and ecosystem service degradation have intensified, seriously threatening the sustainable development of mountainous areas. Ecosystem services in mountainous areas have become a hot issue that has attracted much attention. Existing studies on ecosystem services in mountainous areas are mostly based on InVEST model simulation and evaluation and focus on the analysis of the spatiotemporal pattern of ecosystem services. Aiming at the terrain gradient characteristics of ecosystem services, elevation, slope, surface undulation and terrain index are gradually used to reveal the terrain differentiation characteristics of ecosystem services. Existing studies often discuss the spatial heterogeneity of ecosystem services in mountainous areas based on a single terrain factor, but the simulation and evaluation process of ecosystem services has not fully considered the three-dimensional ecological and environmental characteristics such as mountain topography and landforms. How to effectively integrate multiple terrain factors to identify micro-topography and landform types that reflect the characteristics of mountainous areas and optimize the evaluation of ecosystem services in mountainous areas has become a key issue that needs to be solved urgently.

[0004] Therefore, it is necessary to optimize and improve the accuracy of ecosystem service assessment in mountainous areas through analysis of mountain micro-geomorphological characteristics. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide an ecosystem service optimization assessment method based on mountain micro-geomorphological feature analysis. The present invention solves the problem of low accuracy of ecosystem service assessment in mountainous areas in the prior art.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for optimizing and evaluating ecosystem services based on the analysis of mountain micro-geomorphological characteristics, including:

[0008] Divide the mountainous area into topographic and geomorphic categories to obtain the topographic and geomorphic types of the mountainous area;

[0009] According to the topographic and geomorphic types of the mountainous area, the micro-topographic types of the mountainous area are divided based on the Geomorphons method to obtain the mountainous location information;

[0010] According to the mountain location information, the mountain forest type is obtained;

[0011] Based on the mountain forest type, the coefficient of variation of the regional ratio vegetation index and the biomass of the tree layer of each mountain forest type were obtained according to the multispectral UAV image acquisition and processing and the tree layer investigation;

[0012] The coefficient of variation of the regional ratio vegetation index and the biomass of the tree layer of each mountain forest type were used to modify the habitat suitability parameters and carbon density values ​​of the InVEST model, and the optimal InVEST model was obtained.

[0013] The optimal InVEST model is used to obtain the optimized evaluation results of ecosystem services, wherein the optimized evaluation results include: carbon fixation and habitat quality.

[0014] Preferably, the mountainous area is divided into topographic and geomorphic types to obtain the topographic and geomorphic types of the mountainous area, including:

[0015] According to SimDTA software, the terrain data of the mountainous area was obtained;

[0016] The rectangular moving window method and mean change point method are used to determine the optimal analysis window;

[0017] Based on the optimal analysis window and the terrain data, determining the principal component factors by using the dimensionality reduction technique according to the principal component analysis;

[0018] Combining the principal component factors by bands based on a band synthesis tool to obtain multi-band raster data;

[0019] The multi-band raster data are unsupervisedly classified using the ISODATA iterative self-organizing data analysis method to obtain the topographic and geomorphic types of the mountainous area.

[0020] Preferably, the method of dividing the mountain area into micro-topography types based on the Geomorphons method according to the topography and landform types of the mountain area to obtain the mountain location information includes:

[0021] Based on the Geomorphons method, the elevation relationship between the central grid and the neighboring grids in the other eight directions is determined by the terrain openness index to obtain the spatial structure information of the landform in the mountainous area.

[0022] The mountain location information is obtained by overlaying and analyzing the topographic and geomorphic types of mountainous areas and the spatial structure information of the geomorphic morphology of mountainous areas.

[0023] Preferably, the mountain forest types include:

[0024] Forest land on the top of the slope, forest land in the middle of the slope, forest land at the bottom of the slope and forest land in the valley.

[0025] Preferably, based on the mountain forest type and according to the tree layer survey, the tree layer biomass of each mountain forest type area is obtained, including:

[0026] Based on the mountain forest type, all trees in the forest sample areas of different mountainous parts in the mountainous area were measured to obtain the average breast diameter and number of each tree species in the forests of different mountainous parts;

[0027] Based on the biomass equation, the biomass of the tree layer in each mountain forest type area was obtained according to the average breast diameter and number of each tree species in the forests in different mountainous areas.

[0028] Preferably, based on the mountain forest type, according to the multispectral UAV image acquisition and processing, the coefficient of variation of the regional ratio vegetation index of each mountain forest type is obtained, including:

[0029] Based on the types of mountain forests, multispectral UAV image collection and processing of forests in different mountainous areas were carried out to obtain UAV image data;

[0030] Based on the spectral variation hypothesis, the coefficient of variation of the regional ratio vegetation index of each mountain forest type was calculated according to the UAV image data.

[0031] Preferably, the expression of the biomass equation is:

[0032] W=aD b ;

[0033] Among them, W is the biomass of a certain organ of the tree, in kg; D is the tree's diameter at breast height; a is a coefficient related to tree density; and b is a coefficient related to the growth environment.

[0034] Preferably, the calculation expression of the habitat quality is:

[0035]

[0036]

[0037] Among them, Q xj represents the habitat quality of grid x in land use type j; H j D is habitat suitability; xj is the degree of habitat degradation; k is the half-saturation constant, which is generally set to 1 / 2 of the maximum degree of habitat degradation; z is the default parameter of the model, which is usually set to 2.5. R represents the number of threat factors; Y r is the total number of grids with threat factor r; w ris the weight; r y is the number of threat factors on grid y; i rxy is the influence degree of threat factor r in grid y on grid x; β x is the degree of habitat protection; S jr is the sensitivity of land use type j to threat factor r.

[0038] Preferably, the calculation expression of carbon fixation is:

[0039] C=C above +C below +C soil +C dead ;

[0040] Where C is the total carbon fixation; C above is the carbon stock of all living vegetation above ground; C below is the carbon storage of underground vegetation roots; C soil is the soil organic carbon storage; C dead It is the carbon storage of dead organic matter such as dead standing trees, fallen trees and litter.

[0041] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0042] The present invention provides an ecosystem service optimization assessment method based on the analysis of mountain micro-geomorphological characteristics, comprising: performing landform division on mountainous areas to obtain landform types of mountainous areas; performing micro-geomorphological division on mountainous areas based on the Geomorphons method according to the landform types of mountainous areas to obtain mountain location information; obtaining mountain forest types according to the mountain location information; based on the mountain forest types, obtaining the coefficient of variation of the regional ratio vegetation index and the tree layer biomass of each mountain forest type according to multi-spectral unmanned aerial vehicle image acquisition and processing and tree layer investigation; using the coefficient of variation of the regional ratio vegetation index and the tree layer biomass of each mountain forest type to respectively correct the habitat suitability parameters and carbon density values ​​of the InVEST model to obtain the optimal InVEST model; using the optimal InVEST model to obtain the optimization assessment results of ecosystem services, wherein the optimization assessment results include: carbon fixation and habitat quality. The present invention can subdivide forest land according to different mountainous areas through the classification of topographic and geomorphic types and the analysis of mountain micro-geomorphic characteristics; by conducting field surveys of forest land in different mountainous areas and combining the survey data, the parameter settings of the InVEST model can be corrected, thereby improving the accuracy of the ecosystem service assessment in mountainous areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 A flow chart of an ecosystem service optimization assessment method based on mountain micro-geomorphological feature analysis provided by an embodiment of the present invention;

[0045] Figure 2 A detailed flow chart of an ecosystem service optimization assessment method based on mountain micro-geomorphological feature analysis provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of an optimization evaluation of a biodiversity maintenance service provided by an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the optimization evaluation results of the carbon sequestration service provided in an embodiment of the present invention;

[0048] Figure 5 A comparison chart of the biodiversity maintenance service results of the original method provided in the embodiment of the present invention and the method of the present invention, wherein: Figure 5 (a) is the result diagram of biodiversity maintenance service of the original method, Figure 5 (b) is a diagram showing the biodiversity maintenance service results of the method of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide an ecosystem service optimization assessment method based on mountain micro-geomorphological feature analysis. The present invention solves the problem of low accuracy of ecosystem service assessment in mountainous areas in the prior art.

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] like Figure 1 As shown, the present invention provides an ecosystem service optimization assessment method based on mountain micro-geomorphological feature analysis, comprising:

[0053] Step 100: Divide the mountainous area into topographical and geomorphic types to obtain the topographical and geomorphic types of the mountainous area;

[0054] Step 200: According to the topographical type of the mountainous area, based on the Geomorphons method, the micro-topographical type of the mountainous area is divided to obtain the mountainous location information;

[0055] Step 300: Obtaining the mountain forest type according to the mountain location information;

[0056] Step 400: Based on the mountain forest type, according to the multispectral UAV image acquisition and processing and the tree layer investigation, the coefficient of variation of the regional ratio vegetation index and the tree layer biomass of each mountain forest type are obtained respectively;

[0057] Step 500: using the coefficient of variation of the regional ratio vegetation index of each mountain forest type and the biomass of the tree layer to respectively modify the habitat suitability parameters and carbon density values ​​of the InVEST model (an integrated assessment model of ecosystem services and trade-offs) to obtain the optimal InVEST model;

[0058] Step 600: Obtaining an optimized evaluation result of ecosystem services using the optimal InVEST model, wherein the optimized evaluation result includes: carbon sequestration and habitat quality.

[0059] Specifically, Figure 2 As shown, this embodiment also provides another way of expressing an ecosystem service optimization evaluation method based on mountain micro-geomorphological feature analysis:

[0060] Step 1: Based on the DEM elevation data, with the help of SimDTA software, the topography and geomorphology types of the study area are divided to obtain the topography and geomorphology types of the mountainous area.

[0061] Step 2: Based on the Geomorphons method, the mountainous area in step 1 is divided into micro-landform types to extract the mountain location information.

[0062] Step 3: Based on the above mountain location information, the mountain forest land is subdivided into multiple types.

[0063] Step 4, taking biodiversity maintenance service and carbon sequestration service as two key ecosystem services as examples, through field multispectral UAV image collection and processing and tree layer survey of forests in different mountainous areas, the coefficient of variation of the regional ratio vegetation index and the biomass of the tree layer in forests in different mountainous areas were obtained, and the habitat suitability parameters and carbon density values ​​of the InVEST model were corrected.

[0064] Step 5: Based on the habitat suitability parameters and carbon density values ​​of the InVEST model modified in step 4, the optimized evaluation results of two key ecosystem services, namely biodiversity maintenance services and carbon sequestration services in mountainous areas, are finally obtained based on the InVEST model.

[0065] Specifically, Figure 2 Remote sensing data refers to Landsat remote sensing images.

[0066] Furthermore, the mountainous area is divided into topographic and geomorphic types to obtain the topographic and geomorphic types of the mountainous area, including:

[0067] According to SimDTA software, the terrain data of the mountainous area was obtained;

[0068] The rectangular moving window method and mean change point method are used to determine the optimal analysis window;

[0069] Based on the optimal analysis window and the terrain data, determining the principal component factors by using the dimensionality reduction technique according to the principal component analysis;

[0070] Combining the principal component factors by bands based on a band synthesis tool to obtain multi-band raster data;

[0071] The multi-band raster data are unsupervisedly classified using the ISODATA iterative self-organizing data analysis method to obtain the topographic and geomorphic types of the mountainous area.

[0072] Specifically, terrain data such as slope, surface relief, surface roughness, terrain site index, terrain position index, mountain index, valley index and surface curvature were calculated with the help of SimDTA software. When determining the optimal analysis window for terrain factors, the rectangular moving window method and mean change point method were used to determine the optimal analysis window;

[0073] Through principal component analysis and dimensionality reduction technology, a few comprehensive indicators containing a large amount of terrain information are determined to replace the original terrain factor indicators, and the identified principal component factors are combined into bands based on the band synthesis tool. Finally, the ISODATA iterative self-organizing data analysis method is used to perform unsupervised classification on the multi-band rasters to obtain the terrain and landform types of mountainous areas, including mountains, hills, troughs, plains, rivers, etc.

[0074] Furthermore, according to the topographical type of the mountainous area, based on the Geomorphons method, the micro-topographical type of the mountainous area is divided to obtain the mountainous location information, including:

[0075] Based on the Geomorphons method, the elevation relationship between the central grid and the neighboring grids in the other eight directions is determined by the terrain openness index to obtain the spatial structure information of the landform in the mountainous area.

[0076] The mountain location information is obtained by overlaying and analyzing the topographic and geomorphic types of mountainous areas and the spatial structure information of the geomorphic morphology of mountainous areas.

[0077] Specifically, based on the r.geomorphon method in GRASSGIS, the elevation relationship between the central grid and the neighboring grids in the other eight directions is judged by the terrain openness index, and a series of geometric morphological patterns are combined. They are then simplified and merged into 10 terrain elements according to their geological meanings, namely flat land, peaks, ridges, shoulders, convex back slopes, straight back slopes, concave back slopes, foot of the mountain, valleys and depressions, to realize the extraction of spatial structural information of landform morphology.

[0078] The mountain type in the topography and geomorphology types was extracted and superimposed with the micro-topography types to obtain the spatial distribution data of the mountain micro-topography. Among them, the peaks, ridges and shoulders together constitute the skeleton of the mountain terrain, and all belong to the top area of ​​the mountain; the convex back slope, straight back slope and concave back slope are staggered and all belong to the middle part of the slope, forming the main body of the mountain; the foot of the mountain and the valley have the lowest elevation and all belong to the bottom area of ​​the mountain.

[0079] Furthermore, the mountain forest types include:

[0080] Forest land on the top of the slope, forest land in the middle of the slope, forest land at the bottom of the slope and forest land in the valley.

[0081] Specifically, the land use data of the study area (land use data obtained by interpreting Landsat remote sensing images) and the mountain micro-topography data were superimposed and analyzed to obtain five types of forest land divided according to information on different mountain locations, including forest land on top of slopes (peaks, ridges and shoulders), forest land in the middle of slopes (convex back slopes, straight back slopes and concave back slopes), forest land at the bottom of slopes (foot of the mountain), valley forest land and other forest land. More detailed forest land divisions can also be carried out according to the actual conditions of the study area.

[0082] Furthermore, based on the mountain forest type and the tree layer survey, the tree layer biomass of each mountain forest type area was obtained, including:

[0083] Based on the mountain forest type, all trees in the forest sample areas of different mountainous parts in the mountainous area were measured to obtain the average breast diameter and number of each tree species in the forests of different mountainous parts;

[0084] Based on the biomass equation, the biomass of the tree layer in each mountain forest type area was obtained according to the average breast diameter and number of each tree species in the forests in different mountainous areas.

[0085] Furthermore, based on the mountain forest type and the multispectral UAV image acquisition and processing, the coefficient of variation of the regional ratio vegetation index of each mountain forest type was obtained, including:

[0086] Based on the types of mountain forests, multispectral UAV image collection and processing of forests in different mountainous areas were carried out to obtain UAV image data;

[0087] Based on the spectral variation hypothesis, the coefficient of variation of the regional ratio vegetation index of each mountain forest type was calculated according to the UAV image data.

[0088] Specifically, for biodiversity maintenance services, multispectral UAV image collection and processing of forests in different mountainous areas were carried out. Based on the spectral variation hypothesis, the variation characteristics of vegetation index are often used to monitor tree species diversity. Therefore, the habitat suitability parameter settings are corrected by calculating the ratio vegetation index variation coefficient, and the habitat suitability parameters of forests in different mountainous areas are obtained; taking Jinyun Mountain in Chongqing as an example, the DJI Phantom 4 multispectral UAV was used to collect regional image data of forests in different mountainous areas. Based on the spectral variation hypothesis, the variation characteristics of vegetation index are often used to monitor tree species diversity. Therefore, the habitat suitability parameter settings are corrected by calculating the ratio vegetation index variation coefficient, and the RVI variation coefficient value of the forest at the top of the slope is the highest (0.370), and the habitat suitability is set to 1; the valley forest (0.339) and the middle slope forest (0.338) are second, and the habitat suitability is set to 0.95; the forest at the bottom of the slope (0.241) is the lowest, and its habitat suitability is set to 0.9 with other non-mountainous forests.

[0089] In order to provide carbon sequestration services, we conducted surveys on the tree layer in forests in different mountainous areas. Studies have shown that the biomass of the tree layer is absolutely dominant in the total biomass of the forest vegetation layer. Therefore, the carbon sequestration service of forests in mountainous areas mainly depends on the carbon storage of the tree layer. Based on the theory of tree allometric growth, a univariate relative growth model with breast diameter as the independent variable was used to estimate the biomass of the tree layer. Through field tree surveys and biomass estimation in different mountainous areas, the carbon density values ​​were corrected. The specific methods are as follows:

[0090] Field survey of tree layer. Set up 30m×30m sample plots in forest sample plots in different mountainous areas, measure each tree with a breast diameter greater than 5cm in the sample plots, record the tree species name, number, and breast diameter, and summarize the average breast diameter and number of each tree species in forests in different mountainous areas;

[0091] Biomass estimation. Refer to the biomass equation constructed by Zhou Guoyi et al. A univariate power function model between biomass and DBH was used to estimate the biomass of tree trunks (including bark), branches, leaves, and roots.

[0092] The expression of the biomass equation is:

[0093] W=aD b ;

[0094] Among them, W is the biomass of a certain organ of the tree, in kg; D is the tree's diameter at breast height; a is a coefficient related to tree density; and b is a coefficient related to the growth environment.

[0095] Taking the survey plot of Jinyun Mountain in Chongqing as an example, the biomass equation coefficients of each tree species are shown in Table 1, which is as follows:

[0096] Table 1 Biomass equation coefficients of various tree species

[0097]

[0098] Based on the aboveground and underground biomass estimated from the survey of the tree layer in different mountainous areas, the aboveground carbon density and underground carbon density values ​​of forests in different mountainous areas were optimized and corrected. The aboveground carbon density and underground carbon density values ​​of the forests on the top of the slope were 142.75t / hm 2 and 29.12t / hm 2 ; The forest land in the middle of the slope is 125.35t / hm 2 and 26.19t / hm 2 ; Valley forest land is 74.64t / hm 2 and 15.56t / hm 2 ; Forest land at the bottom of the slope is 72.97t / hm 2 and 21.35t / hm 2 .

[0099] Further, such as Figure 3-4 As shown in the figure, the modified habitat suitability parameters are used, based on the HabitatQuality module of the InVEST model, by establishing a connection between habitats and threat factors, based on the sensitivity of each land use type to threat factors and the intensity of external threats, to analyze the response of different habitats to threat factors and their spatial distribution changes, so as to obtain a habitat quality index that reflects the regional biodiversity maintenance service. Habitat quality depends on the relative impact and weight of each threat factor on the habitat, the habitat suitability of different land use types and their sensitivity to threat factors, etc.

[0100] The calculation expression of the habitat quality is:

[0101]

[0102]

[0103] Among them, Q xj represents the habitat quality of grid x in land use type j; H j D is habitat suitability; xjis the degree of habitat degradation; k is a half-saturation constant, generally set to 1 / 2 of the maximum degree of habitat degradation; z is the default parameter of the model, usually set to 2.5. R represents the number of threat factors; Y r is the total number of grids with threat factor r; w r is the weight; r y is the number of threat factors on grid y; i rxy is the influence degree of threat factor r in grid y on grid x; β x is the degree of habitat protection; S jr is the sensitivity of land use type j to threat factor r.

[0104] Furthermore, the revised carbon density values ​​of forest land in different mountainous areas were used to estimate the carbon sequestration of different land use types based on the Carbon module of the InVEST model;

[0105] The calculation expression of carbon fixation is:

[0106] C=C above +C below +C soil +C dead ;

[0107] Where C is the total carbon fixation; C above is the carbon stock of all living vegetation above ground; C below is the carbon storage of underground vegetation roots; C soil is the soil organic carbon storage; C dead It is the carbon storage of dead organic matter such as dead standing trees, fallen trees and litter, in t / hm 2 .

[0108] The parameter settings of the InVEST model were modified by combining field survey data of forests in different mountainous areas to achieve optimal evaluation of two key ecosystem services: biodiversity maintenance services and carbon sequestration services in mountainous areas. Figure 5 As shown, by comparing the biodiversity maintenance service results using the original method and the method of the present invention, it can be seen that the method of the present invention improves the accuracy of the ecosystem service assessment in Chongqing's main city.

[0109] The beneficial effects of the present invention are as follows:

[0110] The present invention provides an ecosystem service optimization assessment method based on the analysis of mountain micro-geomorphological characteristics, comprising: performing landform division on mountainous areas to obtain landform types of mountainous areas; performing micro-geomorphological division on mountainous areas based on the Geomorphons method according to the landform types of mountainous areas to obtain mountain location information; obtaining mountain forest types according to the mountain location information; based on the mountain forest types, obtaining the coefficient of variation of the regional ratio vegetation index and the tree layer biomass of each mountain forest type according to multi-spectral unmanned aerial vehicle image acquisition and processing and tree layer investigation; using the coefficient of variation of the regional ratio vegetation index and the tree layer biomass of each mountain forest type to respectively correct the habitat suitability parameters and carbon density values ​​of the InVEST model to obtain the optimal InVEST model; using the optimal InVEST model to obtain the optimization assessment results of ecosystem services, wherein the optimization assessment results include: carbon fixation and habitat quality. The present invention can subdivide forest land according to different mountainous areas by classifying landform types and analyzing mountain micro-landform characteristics; by conducting field surveys of forest land in different mountainous areas and combining the survey data, the InVEST model parameter settings can be corrected, thereby improving the accuracy of ecosystem service assessment in mountainous areas.

[0111] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0112] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An ecosystem service optimization assessment method based on mountain micro-geomorphological characteristics analysis, characterized in that: include: Divide the mountainous area into topographic and geomorphic categories to obtain the topographic and geomorphic types of the mountainous area; According to the topographic and geomorphic types of the mountainous area, the micro-topographic types of the mountainous area are divided based on the Geomorphons method to obtain the mountainous location information; According to the mountain location information, the mountain forest type is obtained; Based on the mountain forest type, the coefficient of variation of the regional ratio vegetation index and the biomass of the tree layer of each mountain forest type were obtained according to the multispectral UAV image acquisition and processing and the tree layer investigation; The coefficient of variation of the regional ratio vegetation index and the biomass of the tree layer of each mountain forest type were used to modify the habitat suitability parameters and carbon density values ​​of the InVEST model, and the optimal InVEST model was obtained. The optimal InVEST model is used to obtain the optimal assessment results of ecosystem services, wherein the optimal assessment results include: carbon sequestration and habitat quality; The mountainous area is divided into topographic and geomorphic types to obtain the topographic and geomorphic types of the mountainous area, including: According to SimDTA software, the terrain data of the mountainous area was obtained; The rectangular moving window method and mean change point method are used to determine the optimal analysis window; Based on the optimal analysis window and the terrain data, determining the principal component factors by using the dimensionality reduction technique according to the principal component analysis; Combining the principal component factors by bands based on a band synthesis tool to obtain multi-band raster data; The multi-band raster data are unsupervisedly classified using the ISODATA iterative self-organizing data analysis method to obtain the topographic and geomorphic types of the mountainous area.

2. The method for optimizing and evaluating ecosystem services based on mountain micro-geomorphological characteristics analysis according to claim 1 is characterized in that: According to the topographic and geomorphic types of the mountainous area, based on the Geomorphons method, the micro-topographic types of the mountainous area are divided to obtain the mountainous location information, including: Based on the Geomorphons method, the elevation relationship between the central grid and the neighboring grids in the other eight directions is determined by the terrain openness index to obtain the spatial structure information of the landform in the mountainous area. The mountain location information is obtained by overlaying and analyzing the topographic and geomorphic types of mountainous areas and the spatial structure information of the geomorphic morphology of mountainous areas.

3. The method for optimizing and evaluating ecosystem services based on mountain micro-geomorphological characteristics analysis according to claim 1 is characterized in that: The mountain forest types include: Forest land on the top of the slope, forest land in the middle of the slope, forest land at the bottom of the slope and forest land in the valley.

4. The method for optimizing and evaluating ecosystem services based on mountain micro-geomorphological characteristics analysis according to claim 1 is characterized in that: Based on the mountain forest type and the tree layer survey, the tree layer biomass of each mountain forest type area was obtained, including: Based on the mountain forest type, all trees in the forest sample areas of different mountainous parts in the mountainous area were measured to obtain the average breast diameter and number of each tree species in the forests of different mountainous parts; Based on the biomass equation, the biomass of the tree layer in each mountain forest type area was obtained according to the average breast diameter and number of each tree species in the forests in different mountainous areas.

5. The method for optimizing and evaluating ecosystem services based on mountain micro-geomorphological characteristics analysis according to claim 1 is characterized in that: Based on the mountain forest type and the multispectral UAV image acquisition and processing, the coefficient of variation of the regional ratio vegetation index of each mountain forest type is obtained, including: Based on the types of mountain forests, multispectral UAV image collection and processing of forests in different mountainous areas were carried out to obtain UAV image data; Based on the spectral variation hypothesis, the coefficient of variation of the regional ratio vegetation index of each mountain forest type was calculated according to the UAV image data.

6. The method for optimizing and evaluating ecosystem services based on mountain micro-geomorphological characteristics analysis according to claim 1 is characterized in that: The expression of the biomass equation is: ； in, W is the biomass of a certain organ of the tree, in kg ; D is the tree's diameter at breast height; a is the coefficient related to tree density; b is a coefficient related to the growth environment.

7. The method for optimizing and evaluating ecosystem services based on mountain micro-geomorphological characteristics analysis according to claim 1 is characterized in that: The calculation expression of the habitat quality is: ； ； in, Q xj Indicates land use type j Medium Grid x habitat quality; H j is habitat suitability; D xj is the degree of habitat degradation; k is the half-saturation constant; z are the default parameters of the model; R Indicates the number of threat factors; Y r Threat factor r The total number of grids; w r is the weight; r y For Grid y The number of threat factors; i rxy For Grid y Medium Threat Factor r To grid x the extent of the impact; β x The degree of habitat protection; S jr Land use type j Threat Factors r sensitivity.

8. The method for optimizing and evaluating ecosystem services based on mountain micro-geomorphological characteristics analysis according to claim 1 is characterized in that: The calculation expression of carbon fixation is: ； in, C is the total carbon fixation; C above is the carbon stock of all living vegetation above ground; C below is the carbon storage in the root system of underground vegetation; C soil is the soil organic carbon storage; C dead It is the carbon storage of dead organic matter in dead standing trees, fallen trees and litter.

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