GEP refined visualization method and system based on objectified ecological pattern spots

Abstract

The invention belongs to the technical field of remote sensing image processing, and discloses a GEP refined visualization method and system based on objectified ecological pattern spots, and the method comprises the steps: obtaining a meter-scale / sub-meter-scale high-resolution remote sensing image based on a target region; the method comprises the following steps: dividing a remote sensing image into a plurality of ecological pattern spots with the same size, identifying the plurality of pattern spots with the same size based on a land utilization cover classification system to obtain a plurality of ecological pattern spots, and allocating a classification label to each ecological pattern spot; acquiring biological parameters of each ecological pattern spot; calculating the index parameter of each ecological pattern spot by using an InVEST model, and determining and displaying the GEP of each ecological pattern spot based on the index parameter of each ecological pattern spot; and taking the GEP sum of all the ecological pattern spots as the GEP of the target area and displaying the GEP. According to the method, the difference of ecological service supply capacities among different land covers is fully considered, and more detailed land utilization classification is formulated, so that more related information about GEP is displayed more visually.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and more specifically, to a GEP refined visualization method and system based on objectified ecological patches. Background Art

[0002] Ecosystem value accounting involves multiple aspects such as spatial elements, social culture, economic benefits, and asset value, and includes multiple fields such as ecology, economics, and geography. In order to illustrate its complexity and unify conceptual cognition so that GEP can be well calculated, existing technologies divide GEP into three parts, namely material services, regulatory services, and intangible cultural services. Subsequently, in GEP accounting, the existing ecosystem asset data and ecosystem service models are combined to evaluate the amount of ecosystem services, and finally the final GEP quantity and price are determined through supply and demand.

[0003] At present, the accounting boundaries for GEP are all based on administrative units or economies, but the spatial distribution of natural ecological patches is ignored, resulting in the GEP calculation results only reflecting the total value of administrative units, but losing the specific information of each ecological patch in space, such as the location, scope, spatial configuration and status of a grassland, the supply of material products, the value of services, etc., and thus losing the traceable GEP supply chain, making it difficult to determine the ecological value provider and specific information in ecological transactions, such as how to determine what has been destroyed and what should be compensated in ecological compensation, and then determine who should protect and who should be compensated. Therefore, how to improve the display accuracy of GEP so that users can intuitively obtain more useful information is a technical problem that urgently needs to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a GEP refined visualization method and system based on objectified ecological maps to improve the accuracy of GEP, fully consider the differences in ecological service supply capacity between different land covers, formulate more detailed land use classifications, and use more detailed biological parameters and spatial parameters, combined with model software (InVest) to calculate ecosystem service supply, so as to provide more scientific and reasonable data support for decision-making.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] According to a first aspect of the present invention, a GEP fine-tuning visualization method based on objectified ecological patches is provided, the method comprising:

[0007] Based on the target area, obtain meter-level / sub-meter-level high-resolution remote sensing images;

[0008] Divide the remote sensing image into a plurality of spots of the same size, identify the plurality of spots of the same size based on the land use and cover classification system, obtain a plurality of ecological spots, and assign a classification label to each ecological spot; wherein the size of the ecological spot is determined according to the set accuracy, and the classification label is used to characterize the land cover type of the ecological spot, and the classification label includes a high-coverage grassland label, a medium-coverage grassland label, a low-coverage grassland label, a shrub label, a river label, a reservoir pond label, a lake label, a beach label, a swamp label, an urban and rural industrial and mining residential land label, and an unused land label;

[0009] Obtain biological parameters of each ecological patch;

[0010] Based on the biological parameters of each ecological spot, the indicator parameters of each ecological spot are calculated using the InVEST model, and the GEP of each ecological spot is determined based on the indicator parameters of each ecological spot;

[0011] The sum of the GEPs of all ecological patches is taken as the GEP of the target area.

[0012] Preferably, after taking the sum of the GEPs of all ecological spots as the GEP of the target area, the method further comprises:

[0013] Based on the GEP of the target area, the GEP value is divided into a plurality of GEP value levels using a natural breakpoint method;

[0014] According to the set multiple GEP value levels, multiple ecological regions of the same size are graded, and ecological regions at different GEP value levels are assigned different colors to obtain the first GEP distribution map of the target area; wherein the ecological region is composed of at least one ecological spot or multiple continuous ecological spots.

[0015] Preferably, the plurality of GEP value levels include a first GEP value level, a second GEP value level, a third GEP value level, a fourth GEP value level, a fifth GEP value level, a sixth GEP value level, a seventh GEP value level and an eighth GEP value level;

[0016] The method of grading a plurality of ecological regions of the same size according to the set plurality of GEP value levels includes:

[0017] If 0 yuan / km 2 ≤G i <6.5×10 6 yuan / km 2 , then determine the i-th ecological region G i Belongs to the first GEP value level;

[0018] If 6.5×10 6 yuan / km 2 ≤G i <2.6×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the second GEP value level;

[0019] If 2.6×10 7 yuan / km 2 ≤G i <6.7×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the third GEP value level;

[0020] If 6.7×10 7 yuan / km 2 ≤G i <1.7×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fourth GEP value level;

[0021] If 1.7×10 8 yuan / km 2 ≤G i <3.8×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fifth GEP value level;

[0022] If 3.8×10 8 yuan / km 2 ≤G i <7.2×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the sixth GEP value level;

[0023] If 7.2×10 8 yuan / km 2 ≤G i <1.4×10 9 yuan / km 2 , then determine the i-th ecological region G i Belongs to the seventh GEP value level;

[0024] If G i ≥1.4×10 9 yuan / km 2, then determine the i-th ecological region G i Belongs to the eighth GEP value level.

[0025] Preferably, after taking the sum of the GEPs of all ecological spots as the GEP of the target area, the method further comprises:

[0026] According to the set multiple unit area levels, multiple ecological regions of the same size are graded, and ecological regions at different unit area levels are assigned different colors to obtain a second GEP distribution map of the target area; wherein the ecological region is composed of at least one ecological spot or multiple continuous ecological spots.

[0027] Preferably, the set multiple unit area levels are respectively a first unit area level, a second unit area level, a third unit area level, a fourth unit area level, a fifth unit area level, a sixth unit area level, a seventh unit area level and an eighth unit area level; and grading multiple ecological areas of the same size according to the set multiple unit area levels includes:

[0028] If 0 yuan / m 2 ≤G j <129yuan / m 2 , then determine the jth ecological region G j Belongs to the first unit area level;

[0029] If 129 yuan / m 2 ≤G j <153yuan / m 2 , then determine the jth ecological region G j Belongs to the second unit area level;

[0030] If 153 yuan / m 2 ≤G j <178yuan / m 2 , then determine the jth ecological region G j Belongs to the third unit area level;

[0031] If 178 yuan / m 2 ≤G j <205yuan / m 2 , then determine the jth ecological region G j Belongs to the fourth unit area level;

[0032] If 205 yuan / m 2 ≤G j <242yuan / m 2 , then determine the jth ecological region G j Belongs to the fifth unit area level;

[0033] If 242 yuan / m 2 ≤G j <287yuan / m 2 , then determine the jth ecological region G j It belongs to the sixth unit area level;

[0034] If 287 yuan / m 2 ≤G j <488yuan / m 2 , then determine the jth ecological region G j It belongs to the seventh unit area level;

[0035] If G i ≥488 yuan / m 2 , then determine the jth ecological region G j Belongs to the eighth unit area level.

[0036] Preferably, the indicator parameters include primary indicators, and the primary indicators include material production indicators, freshwater resource supply indicators, ecological energy indicators, water conservation indicators, soil conservation indicators, carbon fixation and oxygen release indicators, flood regulation indicators, water quality purification indicators, species conservation indicators, leisure and tourism indicators, and scientific research value indicators.

[0037] Preferably, the method further comprises:

[0038] The Moran index was used to conduct global and local spatial autocorrelation analysis on the GEP per unit area, water conservation value, soil conservation value, carbon fixation and oxygen release value, and water purification value of the ecological patches. When the Moran index was greater than zero, it indicated a positive correlation, and when the Moran index was less than zero, it indicated a negative correlation.

[0039] The calculation formula of Moran Index is:

[0040]

[0041] Where Ii is the Moran index, xi and xj are the GEP values ​​of the i-th and j-th ecological patches, is the average value, Wij is the spatial weight between the ith and jth elements, and n is the number of ecological patches.

[0042] Preferably, the method further comprises:

[0043] The bivariate spatial correlation between the GEP per unit area of ​​ecological patches and various ecosystem services was analyzed by the following formula to determine the spatial binary clustering relationship between the GEP of ecological patches and the four ecosystem services:

[0044]

[0045] Where: is the global bivariate autocorrelation index, i and j represent two variables, w and q represent an ecological patch respectively, P wq represents the spatial weight matrix of ecological patches w and q, and represents the value of attribute i of ecological patch w, represents the value of attribute j of ecological patch q, and σ represent the mean and variance, The value range is [-1, 1].

[0046] According to a second aspect of the present invention, a GEP refined visualization system based on objectified ecological spots is provided, the system comprising:

[0047] The remote sensing image acquisition module is configured to acquire meter-level / sub-meter-level high-resolution remote sensing images based on the target area;

[0048] The ecological spot acquisition module is configured to divide the remote sensing image into a plurality of spots of the same size, identify the plurality of spots of the same size based on the land use and cover classification system, obtain a plurality of ecological spots, and assign a classification label to each ecological spot; wherein the size of the ecological spot is determined according to the set accuracy, and the classification label is used to characterize the land cover type of the ecological spot, and the classification label includes a high-coverage grassland label, a medium-coverage grassland label, a low-coverage grassland label, a shrub label, a river label, a reservoir pond label, a lake label, a beach label, a swamp label, an urban and rural industrial and mining residential land label, and an unused land label;

[0049] A biological parameter acquisition module is configured to acquire biological parameters of each ecological patch;

[0050] A first GEP visual module is configured to calculate the index parameters of each ecological spot using the InVEST model based on the biological parameters of each ecological spot, and determine and display the GEP of each ecological spot based on the index parameters of each ecological spot;

[0051] The second GEP visual module is configured to use the sum of the GEPs of all ecological spots as the GEP of the target area and display it.

[0052] The present invention has at least the following beneficial effects:

[0053] The present invention adopts higher-quality and high-precision basic data to reduce calculation errors, conducts more detailed land cover classification, adopts more detailed biological parameters to calculate GEP, and in terms of mapping method, breaks through the existing technology of GEP mapping with large-scale pixel grids, and summarizes the final GEP with objectified ecological patches as the basic spatial units, so that the GEP management units correspond to the actual ecological patch units; the various GEP distribution maps determined based on this accounting method can provide detailed and accurate scientific basis for ecological protection, ecological management and ecological transactions in the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 An application scenario diagram of the prior art is shown.

[0055] Figure 2 A flow chart of a GEP refinement visualization method based on objectified ecological patches according to an embodiment of the present invention is shown.

[0056] Figure 3 A geographical location map of a target area according to an embodiment of the present invention is shown.

[0057] Figure 4 The target area GEP accounting spatial base map data according to an embodiment of the present invention is shown.

[0058] Figure 5 A graph showing contributions of different land cover types to different ecosystem service values ​​in a target area according to an embodiment of the present invention is shown.

[0059] Figure 6 A first GEP distribution diagram of a target area according to an embodiment of the present invention is shown.

[0060] Figure 7 A second GEP distribution diagram of a target area according to an embodiment of the present invention is shown.

[0061] Figure 8 It shows the division of target area into key areas according to an embodiment of the present invention.

[0062] Fig. 9 A schematic diagram showing the relationship between the spatial autocorrelation of GEP and the service values ​​of four ecosystems according to an embodiment of the present invention is shown.

[0063] Fig.10 A spatial binary clustering relationship diagram of GEP and four ecosystem services according to an embodiment of the present invention is shown.

[0064] Fig.11 A structural diagram of a GEP refined visualization system based on objectified ecological patches according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a causal relationship between each other, the order in which they are described as examples herein should not be regarded as a limitation, and those skilled in the art should know that they can be adjusted in order, as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0066] The embodiment of the present invention provides a GEP refined visualization method based on objectified ecological patches.

[0067] See also Figure 1 , is an application scenario diagram in the prior art. The application scenario can implement the GEP refined visualization method based on objectified ecological spots. The application scenario includes a data acquisition module 101, a data processing terminal 102 and a display terminal 103. The data acquisition module 101 is used to acquire remote sensing images of set accuracy based on the target area. The data processing terminal 102 is connected to the data acquisition module 101 and is used to divide the remote sensing images into multiple spots of the same size. Based on the land use and cover classification system, multiple spots of the same size are identified to obtain multiple ecological spots, and a classification label is assigned to each ecological spot. The size of the ecological spot is determined according to the set accuracy. The classification label is used to characterize the land cover type of the ecological spot. The classification labels include high-coverage grassland labels, medium-coverage grassland labels, low-coverage grassland labels, shrub labels, river and canal labels, reservoir and pond labels, lake labels, beach labels, swamp labels, urban and rural industrial and mining residential land labels, and unused land labels to obtain biological parameters of each ecological spot. Based on the biological parameters of each ecological spot, the indicator parameters of each ecological spot are calculated using the InVEST model, and the GEP of each ecological spot is determined based on the indicator parameters of each ecological spot, and the sum of the GEPs of all ecological spots is used as the GEP of the target area. Finally, the data processing terminal 102 obtains the GEP of each ecological map after fine processing and its total GEP in the target area, which can be transmitted to the display terminal 103 in the form of pictures for display. Compared with the traditional way of displaying GEP based on administrative divisions alone, the GEP displayed in this embodiment, combined with remote sensing images, can more intuitively highlight the GEP status of different land cover types in the target area, so that users can more directly extract more information that is conducive to policy formulation and business planning.

[0068] Specifically, see Figure 2, is a flow chart of a GEP refined visualization method based on objectified ecological patches. The method includes the following steps S1 to S5, which are described in detail as follows:

[0069] S1. Based on the target area, obtain meter-level / sub-meter-level high-resolution remote sensing images.

[0070] In this embodiment, the specific method of obtaining a remote sensing image of a set accuracy based on the target area can be: first obtain a first remote sensing image containing the target area, and then crop the first remote sensing image according to the geographic information of the target area, so that the cropped remote sensing image only contains the target area, that is, a remote sensing image of a set accuracy is obtained, so that it only reflects the geographic characteristics of the target area. The purpose of obtaining meter-level / sub-meter-level high-resolution remote sensing images, for example, 2m, is to facilitate the subsequent identification of ecological spots in remote sensing images.

[0071] It should be noted that the selection of the target area is generally determined according to the administrative planning of the country or region. Taking China as an example, a district, city or province can be selected as the target area according to the administrative planning of the district, city or province, and then high-resolution remote sensing images can be obtained. High-precision remote sensing images come from existing data. The data source will be described in detail in the subsequent specific examples, so it will not be expanded in detail here.

[0072] S2. Divide the remote sensing image into multiple patches of the same size, identify the multiple patches of the same size based on the land use and cover classification system, obtain multiple ecological patches, and assign a classification label to each ecological patch; wherein the size of the ecological patch is determined according to the set accuracy, and the classification label is used to characterize the land cover type of the ecological patch, and the classification labels include high-coverage grassland labels, medium-coverage grassland labels, low-coverage grassland labels, shrub labels, river and canal labels, reservoir and pond labels, lake labels, beach labels, swamp labels, urban and rural industrial and mining residential land labels, and unused land labels.

[0073] In this embodiment, the remote sensing image can be divided into a plurality of spots of the same size by a grid division method, and the remote sensing image is divided into unit grids of a set size, and each unit grid and its corresponding information (such as land type, location information, etc.) are used as spots. Of course, the remote sensing image may not be completely divided into a plurality of spots of the same size. In this case, the very few smaller spots can be deleted or approximately considered to be of the same size.

[0074] The land use and cover classification system can refer to the land use and cover classification system of the Chinese Academy of Sciences.

[0075] S3. Obtain the biological parameters of each ecological patch.

[0076] In this embodiment, biological parameters are used to calculate GEP, and the selection of biological parameters is determined according to the calculation method of GEP. The method of calculating GEP adopts the existing method. This application does not improve the calculation method of GEP. This application aims to combine the calculated GEP with the remote sensing image containing the land cover type to provide users with more detailed GEP information. The calculation method of GEP will give its source in detail in the subsequent specific examples, so it will not be expanded here.

[0077] S4. Based on the biological parameters of each ecological patch, the indicator parameters of each ecological patch are calculated using the InVEST model, and the GEP of each ecological patch is determined and displayed based on the indicator parameters of each ecological patch.

[0078] In this embodiment, the selection of indicator parameters can be based on the "Technical Guidelines for Calculating Gross Ecosystem Product (GEP)" and the "Technical Specifications for Calculating Gross Ecosystem Product".

[0079] S5. The sum of the GEPs of all ecological patches is used as the GEP of the target area and displayed.

[0080] In some embodiments, after taking the sum of the GEPs of all ecological spots as the GEP of the target area, the method further includes: based on the GEP of the target area, dividing the GEP value into multiple GEP value levels using the natural breakpoint method; grading multiple ecological regions of the same size according to the set multiple GEP value levels, assigning different colors to ecological regions at different GEP value levels, and obtaining a first GEP distribution map of the target area; wherein the ecological region is composed of at least one ecological spot or multiple continuous ecological spots.

[0081] In this embodiment, considering that a high number of ecological spots are divided in remote sensing images, if the GEP of each ecological spot is displayed in a processed remote sensing image, this display method is still not very intuitive. Therefore, it is planned to merge ecological spots with the same level by setting the GEP value level, and the ecological spots of the same level are merged into an ecological region, and the GEP of an ecological region can be displayed. The GEP of the ecological region is the sum of the GEPs of the ecological spots it contains.

[0082] In some embodiments, the plurality of GEP value levels include a first GEP value level, a second GEP value level, a third GEP value level, a fourth GEP value level, a fifth GEP value level, a sixth GEP value level, a seventh GEP value level, and an eighth GEP value level;

[0083] According to the set GEP value levels, multiple ecological regions of the same size are classified, including:

[0084] If 0 yuan / km 2 ≤G i <6.5×10 6 yuan / km 2 , then determine the i-th ecological region G i Belongs to the first GEP value level;

[0085] If 6.5×10 6 yuan / km 2 ≤G i <2.6×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the second GEP value level;

[0086] If 2.6×10 7 yuan / km 2 ≤G i <6.7×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the third GEP value level;

[0087] If 6.7×10 7 yuan / km 2 ≤G i <1.7×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fourth GEP value level;

[0088] If 1.7×10 8 yuan / km 2 ≤G i <3.8×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fifth GEP value level;

[0089] If 3.8×10 8 yuan / km 2 ≤G i <7.2×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the sixth GEP value level;

[0090] If 7.2×10 8 yuan / km 2 ≤G i <1.4×109 yuan / km 2 , then determine the i-th ecological region G i Belongs to the seventh GEP value level;

[0091] If G i ≥1.4×10 9 yuan / km 2 , then determine the i-th ecological region G i Belongs to the eighth GEP value level.

[0092] In some embodiments, after taking the sum of the GEPs of all ecological spots as the GEP of the target area, the method further includes: grading multiple ecological areas of the same size according to a set plurality of unit area levels, assigning different colors to ecological areas at different unit area levels, and obtaining a second GEP distribution map of the target area; wherein the ecological area is composed of at least one ecological spot or multiple continuous ecological spots.

[0093] In this embodiment, the unit area levels are divided based on the unit area, and the ecological patches with the same unit area level are made into an ecological region, so that the GEP distribution pattern of the target area can be more finely displayed from the unit area distribution of GEP.

[0094] In some embodiments, the set multiple unit area levels are respectively a first unit area level, a second unit area level, a third unit area level, a fourth unit area level, a fifth unit area level, a sixth unit area level, a seventh unit area level and an eighth unit area level; and according to the set multiple unit area levels, multiple ecological areas of the same size are graded, including:

[0095] If 0 yuan / m 2 ≤G j <129yuan / m 2 , then determine the jth ecological region G j Belongs to the first unit area level;

[0096] If 129 yuan / m 2 ≤G j <153yuan / m 2 , then determine the jth ecological region G j Belongs to the second unit area level;

[0097] If 153 yuan / m 2 ≤G j <178yuan / m 2 , then determine the jth ecological region G j Belongs to the third unit area level;

[0098] If 178 yuan / m 2 ≤G j <205yuan / m 2 , then determine the jth ecological region G j Belongs to the fourth unit area level;

[0099] If 205 yuan / m 2 ≤G j <242yuan / m 2 , then determine the jth ecological region G j Belongs to the fifth unit area level;

[0100] If 242 yuan / m 2 ≤G j <287yuan / m 2 , then determine the jth ecological region G j It belongs to the sixth unit area level;

[0101] If 287 yuan / m 2 ≤G j <488yuan / m 2 , then determine the jth ecological region G j It belongs to the seventh unit area level;

[0102] If G i ≥488 yuan / m 2 , then determine the jth ecological region G j Belongs to the eighth unit area level.

[0103] In some embodiments, the method further comprises:

[0104] The Moran index was used to conduct global and local spatial autocorrelation analysis on the GEP per unit area, water conservation value, soil conservation value, carbon fixation and oxygen release value, and water purification value of the ecological patches. When the Moran index was greater than zero, it indicated a positive correlation, and when the Moran index was less than zero, it indicated a negative correlation.

[0105] The calculation formula of Moran Index is:

[0106]

[0107] Where Ii is the Moran index, xi and xj are the GEP values ​​of the i-th and j-th ecological patches, is the average value, Wij is the spatial weight between the ith and jth elements, and n is the number of ecological patches.

[0108] In some embodiments, the method further comprises:

[0109] The bivariate spatial correlation between the GEP per unit area of ​​ecological patches and various ecosystem services was analyzed by the following formula to determine the spatial binary clustering relationship between the GEP of ecological patches and the four ecosystem services:

[0110]

[0111] Where: is the global bivariate autocorrelation index, i and j represent two variables, w and q represent an ecological patch respectively, P wq represents the spatial weight matrix of ecological patches w and q, and represents the value of attribute i of ecological patch w, represents the value of attribute j of ecological patch q, and σ represent the mean and variance, The value range is [-1,1].

[0112] In this embodiment, the spatial binary clustering relationships between the GEP of the ecological patches and the four ecosystem services include low-low, low-high, high-low, high-high and not significant.

[0113] The following embodiments will further illustrate the feasibility and advancement of the present invention in conjunction with specific examples.

[0114] This specific example is implemented by the following steps:

[0115] Step 1: Selection of target area.

[0116] This example selects a certain county as the target area. Figure 3 As shown, the target area is located at 33°50′~35°40′N, 96°50′~99°20E, with a total area of ​​about 2.53*103km 2 There are 4,077 lakes and wetlands in the target area, with an outstanding ecological status. The target area belongs to the alpine grassland climate, with an average altitude of more than 4,200m. It is divided into only two seasons, cold and warm, without four seasons. The average annual temperature is -5.6℃ and the average annual precipitation is 303.9mm. It has the characteristics of high evaporation, little precipitation, low temperature and strong solar radiation. The land use types are mainly grassland and wetland, of which grassland accounts for about 79% of the county's area. The main industry is traditional animal husbandry. The county is rich in biodiversity, with more than 50 species of wild animals and more than 120 medicinal plants. It is one of the hot spots for studying alpine ecosystems.

[0117] Step 2: Acquisition of data.

[0118] This embodiment uses 2m-level high-resolution remote sensing images as the basis, and the classification standard refers to the land use and cover classification system of the Chinese Academy of Sciences. Since the grassland ecosystem and wetland ecosystem in the target area occupy a certain area of ​​the target area, in order to reflect the ecological characteristics of the target area, the land cover is subdivided into high-coverage grassland, medium-coverage grassland, low-coverage grassland, shrub forest, river canal, reservoir pond, lake, beach, swamp, urban and rural industrial and mining residential land, unused land (sand land, bare rock texture), and finally 49,671 ecological patches are identified, thereby forming the GEP accounting spatial base map data (i.e., remote sensing image) of the target area, as shown in FIG. Figure 4 In addition, terrain data was downloaded from the geospatial data cloud platform, meteorological data came from the Resources and Environmental Science Data Center of the Chinese Academy of Sciences, evaporation data came from the National Qinghai-Tibet Plateau Science Data Center, and soil data came from the World Soil Database.

[0119] The statistical data of agriculture, animal husbandry, tourism income and other statistics in the target area are derived from relevant departments and the Statistical Bulletin of ** County National Economic and Social Development in 2015. Price parameters are obtained through two methods, namely, collection from the above relevant departments and market research, and are used to calculate the value of GEP.

[0120] In order to accurately calculate the GEP in the target area, the spatial characteristics of the ecosystem in the target area and the differences in ecological service capabilities of different land covers are reflected. This embodiment collects the biological parameters required for the biophysical model from the China National Ecological Science Data Center and existing studies. First, the literature research in the target area and geographically similar areas is considered. In order to reduce the error of the carbon density data, the biological carbon density data is corrected by using a correction formula, and finally the detailed biological parameters are obtained for model calculation, as shown in Table 1, root_d is the maximum root depth of the plant, Kc is the crop coefficient, C_above is the carbon density of the aboveground biomass, C_below is the carbon density of the underground biomass, C_soil is the carbon density of the soil, C_dead is the carbon density of the dead wood, load_p is the phosphorus nutrient load, load_n is the nitrogen nutrient load, eff_ is the maximum nutrient ratio, LULC_veg is whether the land cover type is a vegetation type code; 1 represents yes, otherwise it is not.

[0121] Table 1 Biological parameters

[0122]

[0123] Step 3, calculation of GEP.

[0124] Based on the Technical Guidelines for Calculating Gross Ecosystem Product (GEP) of Terrestrial Ecosystems and the Technical Specifications for Calculating Gross Ecosystem Product, the GEP of the target area is divided into three categories: material supply, regulation services, and cultural services, with a total of 11 first-level indicators and 16 second-level indicators. Considering the outstanding ecological value of the target area, the indicator system weakens its material supply services and highlights the ecological regulation services and scientific research value, as shown in Table 2.

[0125] Table 2 GEP indicator system

[0126]

[0127] This embodiment is based on a detailed spatial base map and detailed biological parameters (see Table 1). The InVEST model is used to calculate water conservation, soil and water conservation, water purification, carbon fixation and oxygen release. The improved regional species conservation and renewal service method is used to calculate the species conservation value. The remaining indicators are calculated with reference to the "Technical Guidelines for Calculating Gross Terrestrial Ecosystem Product (GEP)", as shown in Table 3.

[0128] Table 3 GEP calculation method

[0129]

[0130]

[0131] Step 4: GEP spatial heterogeneity analysis.

[0132] In order to analyze the spatial differentiation of GEP and various ecosystem services, Moran's I was used for spatial autocorrelation analysis. This model can measure the global and local spatial autocorrelation within a region. This paper uses global and local autocorrelation analysis to further analyze the spatial agglomeration or dispersion of GEP and various ecosystem services. The calculation formula is as follows:

[0133]

[0134] Where Ii is the Moran index, xi and xj are the GEP values ​​of the i-th and j-th ecological patches, is the average value, Wij is the spatial weight between the ith and jth elements, and n is the number of ecological patches.

[0135] Each ecosystem service affects the calculation results of GEP and also shows a certain correlation in space. In order to explore the spatial clustering distribution pattern of each ecosystem service and GEP, so as to better guide regional planning management and policy formulation, this embodiment uses bivariate spatial autocorrelation analysis for analysis, and the relevant formula is as follows:

[0136]

[0137] Where: is the global bivariate autocorrelation index, i and j represent two variables, w and q represent an ecological patch respectively, P wq represents the spatial weight matrix of ecological patches w and q, and represents the value of attribute i of ecological patch w, represents the value of attribute j of ecological patch q, and σ represent the mean and variance, The value range is [-1,1]. The spatial clustering methods are divided into: low-low, low-high, high-low, high-high, and not significant.

[0138] Based on the above specific examples, the visualization results are as follows:

[0139] The total GEP of the target area in 2015 was 128.99 billion yuan, and the GEP per unit area was 3.6226 million yuan / km -2 The composition of GEP is shown in Table 4. The value of material supply is 81 million yuan, the value of cultural services is 75 million yuan, and the value of ecosystem regulation services is 124.336 billion yuan, which is the core component of GEP, accounting for 96.3% of the total value. Among the ecosystem regulation services, the value of carbon fixation and oxygen release is 79.201 billion yuan, accounting for 61.4% of the total GEP; followed by water conservation value of 15.804 billion yuan, accounting for 12.2%; flood regulation value of 14.353 billion yuan, accounting for 11.2%, soil conservation value of 10.138 billion yuan, accounting for 7.9%, and species conservation value of 4.53 billion yuan, accounting for 3.49%. This means that the target area needs to protect the ecosystem but cannot obtain benefits from it, and GEP has not been incorporated into the local economic and social development system. In 2015, the GDP of the target area was 232 million yuan, and the green gold index was 555.99, which shows that the conversion efficiency between the two is low.

[0140] Among the eleven land cover types classified in the target area, the GEP value of swampland is the largest, such as Figure 5 , accounting for 66.14 billion, accounting for 51.3% of the total GEP, followed by high-coverage grassland, with a value of 20.683 billion yuan, accounting for 16%, medium-coverage grassland 18.867 billion yuan, accounting for 14.6%, low-coverage grassland 9.249 billion yuan, accounting for 7%; lakes 7.078 billion yuan, accounting for 5.5%; the GEP value of these five types of land cover accounts for 94.58% of the total, and other types account for a relatively small proportion. Therefore, from a more detailed perspective, the GEP composition of each type of land cover is analyzed and found that swamps and lakes provide the greatest value of carbon fixation and oxygen release, and high, medium and low-coverage grasslands provide the greatest value of water conservation and soil conservation. This provides data support for the formulation of more detailed ecological protection policies in target areas, and helps to formulate more targeted GEP economic benefit conversion mechanisms.

[0141] In terms of the GEP value per unit area of ​​different land covers, the value per unit area of ​​swampland is 18.1009 million yuan / km 2 , followed by beach, reservoir pond, lake, river channel, medium-covered grassland, high-covered grassland, and low-covered grassland, which were 14.2709 million yuan / km 2 , 2.9917 million yuan·km 2 , 2.8173 million yuan·km 2 , 1.801 million yuan·km 2 , 1.767 million yuan·km 2 , 1.6934 million yuan·km -2 . The differences in ecosystem regulation service capacity of each type of land cover are subdivided. The water conservation and soil and water conservation capacity of grassland ecosystems are much greater than those of wetland ecosystems. This is related to the high evaporation and low rainfall climate in the target area. The GEP per unit area of ​​wetland ecosystems in the target area is higher than that of grassland ecosystems, and the GEP per unit area of ​​different land covers within wetland ecosystems is significantly different. The results show the differences in the value of ecosystem services provided by different types of land covers, indicating the necessity of GEP accounting at the land cover scale, rather than staying at the land use scale.

[0142] The natural breakpoint method was used to divide the GEP values ​​into 8 levels, and finally the GEP distribution map of each ecological patch in the target area was obtained, as shown in Figure 6 . From the perspective of spatial distribution, high-value patches are mainly distributed around Eling Lake and Zhaling Lake in the northwest, around Donggecuo Lake in the northeast, and in the wetland area in the southwest. The total GEP value of patches in the middle area is relatively low. This is because the patches in the middle area are fragmented and mostly land cover types with weak GEP supply energy, such as grassland and unused land. The spatial distribution of high-value areas is consistent with the results calculated in the document "Value Accounting of Ecological Products in National Parks", while the results calculated in the document "Optimization of management by analyzing ecosystem service value variations in different watersheds in the Three-River Headwaters Basin" do not show high-value patches in the southwest. Compared with previous research results, the results of this embodiment show more and finer spatial distribution details, such as Figure 8The GEP value of wetland ecosystem in region I is relatively high, but it is relatively low in the research results of the literature "Optimization of management by analyzing ecosystem service value variations in different watersheds in the Three-River Headwaters Basin". In the research results of the literature "National Park Ecological Product Value Accounting", the GEP value of this region is high but the area is relatively large; the GEP of grassland ecosystem in region II is relatively high because the region is connected to Donggecuo Lake and the habitat is in good condition, the grassland ecosystem is complete and the grassland coverage rate is high. This detail cannot be reflected in the research results of the first three, and the same is true for region III. Compared with the large-scale grids of 250m, 1km, and 10km used in existing literature, this embodiment uses 2m high-precision patches as the basic unit to calculate and display GEP, which more accurately reflects the spatial pattern of GEP and more spatial details.

[0143] The spatial distribution of GEP per unit area shows the GEP supply capacity of the patch. The overall spatial pattern is high in the west and low in the east, and the spatial pattern is low-high-low-high from north to south, such as Figure 8 . The GEP per unit area in the vicinity of Eling Lake and Zhaling Lake, Donggecuo Lake and area I is still high, indicating that the ecological patches in these areas are highly complete and the ecological conditions are good. The reason why area III changes from a high GEP total value area to a low unit area area is that the target area as a whole shows that the GEP supply capacity of grassland ecosystems is lower than that of wetland ecosystems. The grassland ecosystem patches in this area are highly complete, and the patches of wetland ecosystems are fragmented, resulting in a high total value and a low mean. The same is true for area v. From the GEP per unit area distribution map, it can be observed that the GEP supply capacity of grassland ecosystem patches in the north is lower than that in the south, indicating that the grasslands in the north need better maintenance, which is also related to the spatial differentiation of precipitation, evaporation and topography. The results of the spatial distribution map combining the total GEP value of the patch and the unit area value show that in areas IV and VI, ecological governance should be strengthened, grassland coverage should be improved, and wetland connectivity should be enhanced to obtain better ecological benefits.

[0144] Since statistical data has no spatial attributes and there was no rainstorm (>50mm) in the target area in 2015, the vegetation flood storage was zero, and there was only lake flood storage. Therefore, according to formula (1), only the unit area GEP, water conservation value, soil conservation value, carbon fixation and oxygen release value, and water purification value of the patch were analyzed globally and locally for spatial autocorrelation, and Moran's I significant level LISA diagram was drawn. When Moran's I>0, it indicates positive correlation, and when Moran's I<0, it indicates negative correlation. The results of the global autocorrelation analysis show that the target area GEP and the main ecosystem services passed the 1% significance level test, as shown in Table 4, indicating that there is a strong spatial aggregation and there are significant hot spots or cold spots. Fig. 9 The local spatial autocorrelation analysis chart shows that the GEP per unit area mainly presents low-low clustering in the south and north, low-high clustering in the central and western parts, and few and scattered high-high clustering and high-low clustering, mainly located in the western wetland area. The distribution trend of the spatial correlation of the GEP per unit area and the carbon fixation and oxygen release value is similar. The distribution of the spatial correlation of the unit area value of water conservation, soil conservation, and water purification is similar. They all present high-low clustering in the central region, low-low clustering in the surrounding areas, and high-high clustering in the north and south. Among them, there is an obvious low-high clustering phenomenon in the south and north of water purification, which is mainly concentrated around Donggecuo Lake and Eling Lake; compared with other types of ecosystem service values, soil conservation has the least low-high clustering, and high-low clustering is more obvious in the high-cover grassland in the east.

[0145] Table 4 Global Moran I index variables for GEP and four ecosystem services

[0146] Ecosystem service categories Moran'sI Zscore P value GEP 0.14 230.10 0.01 level significant correlation Water conservation 0.35 589.44 0.01 level significant correlation Soil Conservation 0.36 625.37 0.01 level significant correlation Carbon fixation and oxygen release 0.15 257.74 0.01 level significant correlation Water purification 0.34 597.02 0.01 level significant correlation

[0147] Formula (2) was used to analyze the bivariate spatial correlation between GEP per unit area and various ecosystem services. The results are as follows: Fig.10. The high-high clustering and low-low clustering areas shown in the figure indicate that GEP is positively correlated with the ecosystem service, while the high-low clustering and low-low clustering areas indicate negative correlation. Overall, the high-high clustering phenomenon of the four major ecosystem services and GEP is not obvious, and the spatial distribution is scattered. The spatial distribution trends of the spatial correlation between GEP and water conservation, water purification, and soil conservation services are similar. In the central region, it is mainly low-low clustering, and high-low clustering is mainly embedded in low-low clustering. The north and south are mainly low-high clustering. The spatial correlation distribution trend of GEP and carbon fixation and oxygen release is opposite to the first three. In the southwestern wetland area with the highest total GEP value, GEP and soil conservation services show obvious high-high clustering, and carbon fixation and oxygen release are not significantly distributed, and water conservation and water purification show high-low spatial distribution. From the perspective of patch area, the patch area where carbon fixation and oxygen release services are positively correlated with GEP is 1.57 times, 1.64 times, and 1.32 times that of water conservation, water purification, and soil conservation. The above results show that the impact of different ecosystem services on GEP in space presents obvious regional characteristics.

[0148] In summary, the method provided in this application uses a more sophisticated process to calculate the GEP of the target area. Based on the 49,671 ecological patches divided, the total GEP of the target area is 128.99 billion yuan, and the GEP per unit area is 3.6226 million yuan km -2 The Green Gold Index is 555.99, indicating the important ecological value of the target area. Existing literature has disclosed that the GEP per unit area of ​​the target area is 3.694 million yuan / km -2 , which is consistent with the results of this embodiment, confirming the effectiveness of the present invention. However, in terms of the spatial distribution of GEP, the present invention shows more accurate spatial details, calculates GEP in the form of objectified ecological patches, and shows its potential and value as a spatial base map for GEP-related policies. It can provide users with clearer spatial locations, boundaries, and total GEP values ​​of geographic entities, and can provide a reliable basis for regional ecological protection, management system formulation, and ecological transactions.

[0149] This embodiment calculates GEP at the land cover scale. In order to show the differences in GEP supply capacity of different land covers, more detailed biological parameters are given at the same time. The results show that the GEP supply capacity of the main ecological patches in the target area is ranked as swamps > beaches > lakes > reservoirs and ponds > medium-coverage grasslands > high-coverage grasslands > low-coverage grasslands, and there is an obvious spatial differentiation phenomenon in the same land cover type in space. For example, the GEP supply capacity of grasslands in the south is stronger than that of grasslands in the north, and the GEP supply capacity of wetlands in the west is stronger than that of wetlands in the east. Therefore, calculating GEP at the land use scale will ignore the differences between different land covers under the same land use type, which will lead to errors in the calculation results. At the same time, the carbon fixation and oxygen release values ​​in the research results are larger than those in the same region. This is because it is generally believed that water bodies have no carbon fixation and oxygen release functions and are assigned a zero value in biological parameters. In the literature [Sun Xing, Jiang Xiaoming, Song Gaofei, et al. Spatiotemporal pattern of phytoplankton community in Qinghai Lake and its environmental driving factors. Lake Science, 2023, 35(2): 540.556.], it was found that the water ecosystem of the Qinghai-Tibet Plateau has a large number of phytoplankton, including diatoms, green algae and cyanobacteria, which can play a role in carbon fixation and oxygen release through photosynthesis. Therefore, its related ecological functions cannot be ignored, and obtaining more accurate biological parameters will make the accounting results more accurate.

[0150] This embodiment analyzes the spatial characteristics of the GEP of the target area from the total value of the patch GEP and the value per unit area. The results show obvious spatial regional characteristics, which are the basis for the planning and management of the target area. The GEP supply capacity in the north is significantly lower than that in the south, and in the east is lower than that in the west. There are patches with high total value and low mean, and low total value and high mean. One reason is that the integrity of some patches is high, but the ecosystem service capacity is poor. These patches are mostly distributed in the north and east. The second reason is that the ecological patches in some areas are fragmented, but the ecosystem service capacity is strong. These patches are mostly distributed in the central region. This is because the northern region of the target area has moderately and severely degraded alpine grasslands and alpine meadows, low vegetation coverage and low habitat quality, resulting in poor ecosystem service capacity. Although the central region has fragmented patches, it is dominated by alpine meadows, alpine grasslands, lakes, and swamp wetland ecosystems. The ecosystem is generally stable. Although there are no large-scale ecological patches, the ecological environment is excellent. The ecological restoration and conservation project vigorously promotes the restoration of park grasslands and improves the water conservation and biodiversity service functions. The research results help decision makers to accurately identify the ecological problems of patches, implement specific measures in a targeted manner, improve the overall ecological benefits, and increase the output value of GEP. For example, the central region can implement wetland closure protection and expand the area of ​​patches. The north can carry out necessary artificial intervention to strengthen grassland insect and rodent control and increase vegetation coverage.

[0151] The spatial correlation between GEP and various ecosystem services also points out the ecological functions that need to be restored in different regions. For example, in the central region, GEP is significantly positively correlated with water conservation, water purification, and soil conservation services, which also shows that the three types of ecosystem services in this region have a synergistic effect on GEP. The southern and northern regions mainly provide carbon fixation and oxygen release services. Therefore, it is necessary to implement ecological protection measures according to the differences in the value of ecosystem services and improve ecological service functions. For example, the central region should pay attention to improving the richness of the ecosystem and comprehensively enhancing various ecological service functions; in the wetland area of ​​the southwest, the focus should be on improving soil conservation services; in the north and south, attention should be paid to maintaining carbon fixation and oxygen release functions. Clear spatial differentiation is also an important basis for effective regional management, and it also provides guiding standards for the formulation of differentiated ecological compensation strategies. When formulating ecological standards, it is necessary to consider the opportunity costs of local governments and indigenous residents who lose due to ecological protection. Clear spatial differentiation results guide where and what type of ecological protection or restoration is carried out, and include specific patch attributes, which provide a basis for measuring the risks that local governments, residents, and enterprises need to bear, so that ecological compensation standards can be formulated for different objects according to local characteristics, and all stakeholders can be mobilized to actively participate in measures to realize the value of GEP.

[0152] The realization of GEP value involves various situations such as local grasslands, lakes, wetlands, rivers, ecological environment quality, pollution emissions, soil erosion control, economic and social development, etc. It also requires the coordination of multiple departments including the Ministry of Ecology and Environment, the Ministry of Water Resources, and the National Forestry and Grassland Administration, and involves multiple subjects including governments, individuals, and enterprises. In the face of multiple subjects and multiple factors to form an accurate spatial base map, the establishment of a comprehensive management platform for the GEP value realization mechanism will help the implementation of relevant policies. This paper calculates GEP using objectified ecological patches as spatial units. Compared with related studies in the same region that use large-scale grids as spatial units, this embodiment shows more detailed spatial details. The final result provides the starting position of the GEP, clarifies the boundaries, area and other attribute information, and corresponds the actual management units to the geographic entities to form a detailed GEP spatial base map. On this basis, it helps various departments to coordinate various information elements to form a comprehensive database, which can effectively link ecosystem service providers and beneficiaries. For example, Province A and the government of Province B signed a horizontal ecological compensation agreement at the source of a river basin to monitor the ecosystem service value flow of the main stream and first-level tributaries of the basin. The detailed GEP spatial base map displayed by the method of this embodiment can help Province A (beneficiary) display the obtained ecosystem service projects, spatial information, and value, so that the amount of ecological compensation paid directly to Province B (provider) can be paid.

[0153] The embodiment of the present invention also provides a GEP refined visualization system based on objectified ecological spots, such as Fig.11 As shown, the system includes:

[0154] The remote sensing image acquisition module 1001 is configured to acquire meter-level / sub-meter-level high-resolution remote sensing images based on the target area;

[0155] The ecological spot acquisition module 1002 is configured to divide the remote sensing image into a plurality of spots of the same size, identify the plurality of spots of the same size based on the land use and cover classification system, obtain a plurality of ecological spots, and assign a classification label to each ecological spot; wherein the size of the ecological spot is determined according to the set accuracy, and the classification label is used to characterize the land cover type of the ecological spot, and the classification label includes a high-coverage grassland label, a medium-coverage grassland label, a low-coverage grassland label, a shrub label, a river label, a reservoir pond label, a lake label, a beach label, a swamp label, an urban and rural industrial and mining residential land label, and an unused land label;

[0156] The biological parameter acquisition module 1003 is configured to acquire biological parameters of each ecological spot;

[0157] The first GEP visual module 1004 is configured to calculate the index parameters of each ecological spot using the InVEST model based on the biological parameters of each ecological spot, and determine and display the GEP of each ecological spot based on the index parameters of each ecological spot;

[0158] The second GEP visual module 1005 is configured to use the sum of the GEPs of all ecological spots as the GEP of the target area and display it.

[0159] In some embodiments, the device also includes a first mapping module, which is configured to: classify multiple ecological regions of the same size according to a set plurality of GEP value levels, assign different colors to ecological regions at different GEP value levels, and obtain a first GEP distribution map of the target area; wherein the ecological region is composed of at least one ecological spot or multiple continuous ecological spots.

[0160] In some embodiments, the plurality of GEP value levels include a first GEP value level, a second GEP value level, a third GEP value level, a fourth GEP value level, a fifth GEP value level, a sixth GEP value level, a seventh GEP value level, and an eighth GEP value level;

[0161] The first mapping module is further configured to:

[0162] If 0 yuan / km 2 ≤G i <6.5×10 6 yuan / km 2, then determine the i-th ecological region G i Belongs to the first GEP value level;

[0163] If 6.5×10 6 yuan / km 2 ≤G i <2.6×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the second GEP value level;

[0164] If 2.6×10 7 yuan / km 2 ≤G i <6.7×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the third GEP value level;

[0165] If 6.7×10 7 yuan / km 2 ≤G i <1.7×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fourth GEP value level;

[0166] If 1.7×10 8 yuan / km 2 ≤G i <3.8×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fifth GEP value level;

[0167] If 3.8×10 8 yuan / km 2 ≤G i <7.2×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the sixth GEP value level;

[0168] If 7.2×10 8 yuan / km 2 ≤G i <1.4×10 9 yuan / km 2 , then determine the i-th ecological region G i Belongs to the seventh GEP value level;

[0169] If G i ≥1.4×10 9 yuan / km 2 , then determine the i-th ecological region G i Belongs to the eighth GEP value level.

[0170] In some embodiments, the device also includes a second mapping module, which is configured to classify multiple ecological regions of the same size according to a set multiple unit area levels, assign different colors to ecological regions at different unit area levels, and obtain a second GEP distribution map of the target area; wherein the ecological region is composed of at least one ecological spot or multiple continuous ecological spots.

[0171] In some embodiments, the set multiple unit area levels are respectively a first unit area level, a second unit area level, a third unit area level, a fourth unit area level, a fifth unit area level, a sixth unit area level, a seventh unit area level and an eighth unit area level; the second mapping module is further configured to:

[0172] If 0 yuan / m 2 ≤G j <129yuan / m 2 , then determine the jth ecological region G j Belongs to the first unit area level;

[0173] If 129 yuan / m 2 ≤G j <153yuan / m 2 , then determine the jth ecological region G j Belongs to the second unit area level;

[0174] If 153 yuan / m 2 ≤G j <178yuan / m 2 , then determine the jth ecological region G j Belongs to the third unit area level;

[0175] If 178 yuan / m 2 ≤G j <205yuan / m 2 , then determine the jth ecological region G j Belongs to the fourth unit area level;

[0176] If 205 yuan / m 2 ≤G j <242yuan / m 2 , then determine the jth ecological region Gj Belongs to the fifth unit area level;

[0177] If 242 yuan / m 2 ≤G j <287yuan / m 2 , then determine the jth ecological region G j It belongs to the sixth unit area level;

[0178] If 287 yuan / m 2 ≤G j <488yuan / m 2 , then determine the jth ecological region G j It belongs to the seventh unit area level;

[0179] If G i ≥488 yuan / m 2 , then determine the jth ecological region G j Belongs to the eighth unit area level.

[0180] In some embodiments, the indicator parameters include primary indicators, which include material production indicators, freshwater resource supply indicators, ecological energy indicators, water conservation indicators, soil conservation indicators, carbon fixation and oxygen release indicators, flood regulation indicators, water quality purification indicators, species conservation indicators, leisure and tourism indicators, and scientific research value indicators.

[0181] In some embodiments, the device further includes a first analysis module, which is configured to: use the Moran index to perform global and local spatial autocorrelation analysis on the GEP per unit area, water conservation value, soil conservation value, carbon fixation and oxygen release value, and water purification value of the ecological map patch, when the Moran index is greater than zero, it indicates a positive correlation, and when the Moran index is less than zero, it indicates a negative correlation;

[0182] The calculation formula of Moran Index is:

[0183]

[0184] Where Ii is the Moran index, xi and xj are the GEP values ​​of the i-th and j-th ecological patches, is the average value, Wij is the spatial weight between the ith and jth elements, and n is the number of ecological patches.

[0185] In some embodiments, the device further includes a second analysis module, which is configured to: analyze the bivariate spatial correlation between the GEP per unit area of ​​the ecological patch and each ecosystem service by the following formula to determine the spatial binary clustering relationship between the GEP of the ecological patch and the four ecosystem services:

[0186]

[0187] Where: is the global bivariate autocorrelation index, i and j represent two variables, w and q represent an ecological patch respectively, P wq represents the spatial weight matrix of ecological patches w and q, and represents the value of attribute i of ecological patch w, represents the value of attribute j of ecological patch q, and σ represent the mean and variance, The value range is [-1, 1].

[0188] In some embodiments, the spatial binary clustering relationship between the GEP of the ecological map and the four ecosystem services includes low-low, low-high, high-low, high-high and not significant.

[0189] It should be noted that the various system structures described in this embodiment belong to the same technical concept as the previously described method, and achieve the same technical effect through the same principle, which will not be repeated here.

[0190] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims will be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the implementation of this application, and the examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0191] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present invention. This should not be interpreted as a feature of an invention that is not claimed for protection being necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of the embodiments of a specific invention. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently used as a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the full scope of the equivalent forms of the attached claims and these claims.

Claims

1. A GEP refined visualization method based on objectified ecological patches, characterized in that: The method comprises: Based on the target area, obtain meter-level / sub-meter-level high-resolution remote sensing images; Divide the remote sensing image into a plurality of spots of the same size, identify the plurality of spots of the same size based on the land use and cover classification system, obtain a plurality of ecological spots, and assign a classification label to each ecological spot; wherein the size of the ecological spot is determined according to the set accuracy, and the classification label is used to characterize the land cover type of the ecological spot, and the classification label includes a high-coverage grassland label, a medium-coverage grassland label, a low-coverage grassland label, a shrub label, a river label, a reservoir pond label, a lake label, a beach label, a swamp label, an urban and rural industrial and mining residential land label, and an unused land label; Obtain biological parameters of each ecological patch; Based on the biological parameters of each ecological spot, the indicator parameters of each ecological spot are calculated using the InVEST model, and the GEP of each ecological spot is determined and displayed based on the indicator parameters of each ecological spot; The sum of the GEPs of all ecological patches is taken as the GEP of the target area and displayed.

2. The method according to claim 1, characterized in that After taking the sum of the GEPs of all ecological spots as the GEP of the target area, the method further comprises: Based on the GEP of the target area, the GEP value is divided into a plurality of GEP value levels using a natural breakpoint method; According to the set multiple GEP value levels, multiple ecological regions of the same size are graded, and ecological regions at different GEP value levels are assigned different colors to obtain the first GEP distribution map of the target area; wherein the ecological region is composed of at least one ecological spot or multiple continuous ecological spots.

3. The method according to claim 2, characterized in that The plurality of GEP value levels include a first GEP value level, a second GEP value level, a third GEP value level, a fourth GEP value level, a fifth GEP value level, a sixth GEP value level, a seventh GEP value level, and an eighth GEP value level; The method of grading a plurality of ecological regions of the same size according to the set plurality of GEP value levels includes: If 0 yuan / km 2 ≤G i <6.5×10 6 yuan / km 2 , then determine the i-th ecological region G i Belongs to the first GEP value level; If 6.5×10 6 yuan / km 2 ≤G i <2.6×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the second GEP value level; If 2.6×10 7 yuan / km 2 ≤G i <6.7×10 7 yuan / km 2 , then determine the i-th ecological region G i Belongs to the third GEP value level; If 6.7×10 7 yuan / km 2 ≤G i <1.7×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fourth GEP value level; If 1.7×10 8 yuan / km 2 ≤G i <3.8×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the fifth GEP value level; If 3.8×10 8 yuan / km 2 ≤G i <7.2×10 8 yuan / km 2 , then determine the i-th ecological region G i Belongs to the sixth GEP value level; If 7.2×10 8 yuan / km 2 ≤G i <1.4×10 9 yuan / km 2 , then determine the i-th ecological region G i Belongs to the seventh GEP value level; If G i ≥1.4×10 9 yuan / km 2 , then determine the i-th ecological region G i Belongs to the eighth GEP value level.

4. The method according to claim 1, characterized in that After taking the sum of the GEPs of all ecological spots as the GEP of the target area, the method further comprises: According to the set multiple unit area levels, multiple ecological regions of the same size are graded, and ecological regions at different unit area levels are assigned different colors to obtain a second GEP distribution map of the target area; wherein the ecological region is composed of at least one ecological spot or multiple continuous ecological spots.

5. The method according to claim 4, characterized in that The set multiple unit area levels are respectively a first unit area level, a second unit area level, a third unit area level, a fourth unit area level, a fifth unit area level, a sixth unit area level, a seventh unit area level and an eighth unit area level; The method of grading a plurality of ecological regions of the same size according to the set plurality of unit area levels includes: If 0 yuan / m 2 ≤G j <129yuan / m 2 , then determine the jth ecological region G j Belongs to the first unit area level; If 129 yuan / m 2 ≤G j <153yuan / m 2 , then determine the jth ecological region G j Belongs to the second unit area level; If 153 yuan / m 2 ≤G j <178yuan / m 2 , then determine the jth ecological region G j Belongs to the third unit area level; If 178 yuan / m 2 ≤G j <205yuan / m 2 , then determine the jth ecological region G j Belongs to the fourth unit area level; If 205 yuan / m 2 ≤G j <242yuan / m 2 , then determine the jth ecological region G j Belongs to the fifth unit area level; If 242 yuan / m 2 ≤G j <287yuan / m 2 , then determine the jth ecological region G j It belongs to the sixth unit area level; If 287 yuan / m 2 ≤G j <488yuan / m 2 , then determine the jth ecological region G j Belongs to the seventh unit area level; If G i ≥488 yuan / m 2 , then determine the jth ecological region G j Belongs to the eighth unit area level.

6. The method according to claim 1, characterized in that The indicator parameters include primary indicators, which include material production indicators, freshwater resource supply indicators, ecological energy indicators, water conservation indicators, soil conservation indicators, carbon fixation and oxygen release indicators, flood regulation indicators, water quality purification indicators, species conservation indicators, leisure and tourism indicators, and scientific research value indicators.

7. The method according to claim 1, characterized in that The method further comprises: The Moran index was used to conduct global and local spatial autocorrelation analysis on the GEP per unit area, water conservation value, soil conservation value, carbon fixation and oxygen release value, and water purification value of the ecological patches. When the Moran index was greater than zero, it indicated a positive correlation, and when the Moran index was less than zero, it indicated a negative correlation. The calculation formula of Moran Index is: Where Ii is the Moran index, xi and xj are the GEP values ​​of the i-th and j-th ecological patches, is the average value, Wij is the spatial weight between the ith and jth elements, and n is the number of ecological patches.

8. The method according to claim 1, characterized in that: The method further comprises: The bivariate spatial correlation between the GEP per unit area of ​​ecological patches and various ecosystem services was analyzed by the following formula to determine the spatial binary clustering relationship between the GEP of ecological patches and the four ecosystem services: Where: is the global bivariate autocorrelation index, i and j represent two variables, w and q represent an ecological patch respectively, P wq represents the spatial weight matrix of ecological patches w and q, and represents the value of attribute i of ecological patch w, represents the value of attribute j of ecological patch q, and σ represent the mean and variance, The value range is [-1,1].

9. The method according to claim 8, characterized in that: The spatial binary clustering relationships between the GEP of the ecological patches and the four ecosystem services include low-low, low-high, high-low, high-high and not significant.

10. A GEP refined visualization system based on objectified ecological patches, characterized in that: The system comprises: The remote sensing image acquisition module is configured to acquire meter-level / sub-meter-level high-resolution remote sensing images based on the target area; The ecological spot acquisition module is configured to divide the remote sensing image into a plurality of spots of the same size, identify the plurality of spots of the same size based on the land use and cover classification system, obtain a plurality of ecological spots, and assign a classification label to each ecological spot; wherein the size of the ecological spot is determined according to the set accuracy, and the classification label is used to characterize the land cover type of the ecological spot, and the classification label includes a high-coverage grassland label, a medium-coverage grassland label, a low-coverage grassland label, a shrub label, a river label, a reservoir pond label, a lake label, a beach label, a swamp label, an urban and rural industrial and mining residential land label, and an unused land label; A biological parameter acquisition module is configured to acquire biological parameters of each ecological patch; A first GEP visual module is configured to calculate the index parameters of each ecological spot using the InVEST model based on the biological parameters of each ecological spot, and determine and display the GEP of each ecological spot based on the index parameters of each ecological spot; The second GEP visual module is configured to use the sum of the GEPs of all ecological spots as the GEP of the target area and display it.

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