Grassland survey monitoring plot sampling method and system, and readable storage medium

Abstract

The present application provides a grassland survey and monitoring plot sampling method and system, and a readable storage medium, and relates to the technical field of grassland plot layout. The grassland survey and monitoring plot sampling method includes: determining the minimum sampling unit according to the plot area; laying out the geospatial balanced sampling grid according to the minimum sampling unit; allocating the number of plots in the geospatial balanced sampling grid according to the grassland area, grass yield and coverage in the geospatial balanced sampling grid; making a sampling data table according to the minimum sampling unit, the geospatial balanced sampling grid, and the number of plots; performing spatial attribute double balanced sampling according to the sampling data table to obtain the initial sampling results; and generating the final sampling results according to the initial sampling results. Through the technical solution of the present application, it is possible to solve the problems of small coverage and strong subjectivity of typical plot layout, and ensure that the plot sampling results cover the entire grassland area and meet the needs of objectively reflecting the overall condition of the grassland.

Description

Technical Field

[0001] The present application relates to the technical field of grassland sample plot layout, and in particular to a grassland survey and monitoring sample plot sampling method and system, and a readable storage medium. Background Art

[0002] There are two main methods for sampling and setting up grassland monitoring plots. The first is typical sampling, which relies on the understanding of local grasslands by technical personnel in each grassland area, refers to the distribution of different types of grasslands in the region, combines historical experience with on-site survey results, and selects several typical grasslands that can represent local soil and vegetation conditions for sample plot layout. The second is spatially balanced sampling, that is, sample plots are evenly arranged in a matrix form within the grassland, with fixed horizontal and vertical distances between sample plots, and the distribution range can cover the entire grassland area.

[0003] Typical sampling layout: Although it is typical, it mainly relies on human experience selection and lacks an objective stance. It is often greatly interfered by human factors. The sample plot layout often avoids grasslands with poor vegetation growth and degradation. Its survey results cannot objectively reflect the overall condition of the local grassland. In addition, human experience has limitations. Some grasslands are often sparsely visited. The lack of relevant experience will lead to the sample plot layout being unable to objectively reflect the actual overall condition of the local grassland.

[0004] Spatially balanced sampling layout: Grassland is an ecosystem with both ecological and production attributes. Although spatially uniform layout can survey grassland conditions more holistically, the method is too mechanical. The location of the sample plots is relatively fixed. Although it can effectively monitor the overall ecological base, it is impossible to form targeted and effective sample plot coverage for important grasslands with high grass yield and high coverage, and it is also impossible to form targeted monitoring of important pastures, resulting in the inability of the sample plot survey results to effectively guide production activities such as grazing. Summary of the invention

[0005] The present application aims to improve at least one of the technical problems existing in the prior art or related art.

[0006] To this end, the first purpose of this application is to provide a grassland survey and monitoring plot sampling method.

[0007] The second object of the present application is to provide a grassland survey and monitoring plot sampling system.

[0008] The third object of the present application is to provide a grassland survey and monitoring plot sampling system.

[0009] The fourth object of the present application is to provide a readable storage medium.

[0010] To achieve the first purpose of the present application, the technical solution of the first aspect of the present invention provides a sampling method for grassland survey and monitoring plots, including: determining the minimum sampling unit according to the area of ​​the plot; laying out a geographically spatially balanced sampling grid according to the minimum sampling unit; allocating the number of sample plots in the geographically spatially balanced sampling grid according to the grassland area, grass yield and coverage in the geographically spatially balanced sampling grid; making a sampling data table according to the minimum sampling unit, the geographically spatially balanced sampling grid and the number of sample plots; performing spatial attribute double balanced sampling according to the sampling data table to obtain an initial sampling result; judging the initial sampling result and the difference between the forest resource continuous inventory plot and the grassland historical inventory plot, and the sampling result of the grassland historical inventory plot. Whether the overlapping rate of historical sample plots is above the first preset percentage; if the overlapping rate of the initial sampling results with the continuous inventory sample plots of forest resources and the historical sample plots of grassland is not above the first preset percentage, adjust the sampling cost value and re-produce the sampling data table; if the overlapping rate of the initial sampling results with the continuous inventory sample plots of forest resources and the historical sample plots of grassland is above the first preset percentage, determine whether the deviation between the comprehensive vegetation coverage of grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage; if the deviation between the comprehensive vegetation coverage of grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage, generate the final sampling result based on the initial sampling result.

[0011] According to the grassland survey and monitoring plot sampling method provided in this application, first determine the minimum sampling unit based on the area of ​​the grassland survey and monitoring plot. Then, taking all grassland patches in the preset area as the whole, mechanically lay out a geographical spatial balanced sampling grid of equal area and covering all minimum sampling units. According to the differences in grassland area, grass yield and coverage in each sampling grid, allocate the number of grassland plots to them. Then, a sampling data table is prepared based on the minimum sampling unit, the geographical spatial balanced sampling grid, and the number of plots, and a spatial attribute double balanced sampling is performed based on the sampling data table to obtain the initial sampling results. Determine whether the overlap rate between the initial sampling results and the forest resource continuous inventory plots and the grassland historical plots is above the first preset percentage. If not, adjust the sampling cost value and re-prepare the sampling data table. If so, determine whether the grassland comprehensive vegetation coverage, grass yield back-calculation and plot statistical deviation are within the second preset percentage. If so, generate the final sampling result based on the initial sampling result. Based on the spatially balanced sampling layout, this application fully combines historical typical sample plots and adds productivity indicators such as grass yield and coverage. It can not only solve the subjective limitations of typical sample plot layout, such as small coverage and single grassland type, but also improve the problem that spatially balanced layout cannot carry out effective monitoring of important grasslands. On the basis of ensuring that the sample plot sampling results can cover all grassland areas, meet the requirements of reflecting the overall condition of grasslands and take into account the continuous monitoring requirements of historical sample plots, the technical methods of grassland sample plot layout are further optimized. The laid out sample plots can better cover important grassland areas such as grasslands with high grass yield and high coverage, pastures, etc. The sample plots laid out under this sampling method can better guide grassland production activities.

[0012] In addition, the technical solution provided by this application may also have the following additional technical features:

[0013] In the above technical scheme, the minimum sampling unit is determined according to the area of ​​the sample plot, including: screening the area of ​​the sample plot, screening out small classes with an area larger than the preset area from the grassland small class database of the preset area, and saving them as independent layers to obtain vector data; adding a unique value field to the vector data, and converting the vector data into raster data, and the raster data retains the unique value field; performing a raster-to-point operation on the raster data to obtain vector point data as the minimum sampling unit, and adding multiple fields to the vector point data; setting the forest resource continuous inventory sample plot and the grassland historical sample plot within the preset distance as a low-cost sample plot.

[0014] In this technical solution, the minimum sampling unit is determined according to the area of ​​the sample plot, specifically, the vector data of the grassland base database with a preset area or more is converted into raster data, and then the raster data is converted into point-type vector data as the minimum sampling unit. Specifically, the preset area can be 1 hectare.

[0015] In the above technical solution, a geographically spatially balanced sampling grid is laid out according to the minimum sampling unit, including: obtaining the number of sample plots in a preset area; calculating the number of sampling grids in the preset area according to the number of sample plots in the preset area and the distribution of grassland resources; and laying out a regular grid covering all minimum sampling units through geographic information system software.

[0016] In this technical solution, a geographically spatially balanced sampling grid is laid out according to the minimum sampling unit. Specifically, the number of sample plots in the preset area is first obtained. Then, according to the number of sample plots in the preset area and the distribution of grassland resources, the number of sampling grids in the preset area is calculated. Finally, a regular grid covering all minimum sampling units is laid out through the geographic information system software. Specifically, the total land area of ​​the preset area is divided by the number of sample plots in the preset area and then multiplied by 5 (or the total number of samples is divided by 5), and the overall grassland resource distribution is comprehensively considered. The number of sampling grids of the whole is calculated, and the regular grid covering all minimum sampling units is laid out using tools such as "fishnet" or "grid index" in the geographic information system software.

[0017] In the above technical scheme, the number of sample plots in the geographically balanced sampling grid is allocated according to the grassland area, grass yield and coverage in the geographically balanced sampling grid, including: calculating the grassland area proportion, grass yield variation coefficient, vegetation coverage variation coefficient and grassland area weight, grass yield variation coefficient weight and vegetation coverage variation coefficient weight in each geographically balanced sampling grid; weighting the grassland area weight, grass yield variation coefficient weight and vegetation coverage variation coefficient weight to obtain the comprehensive sampling grid weight of each geographically balanced sampling grid; and allocating the number of sample plots in the preset area to the geographically balanced sampling grid according to the comprehensive sampling grid weight.

[0018] In this technical solution, according to the balanced sampling in geographic space, the proportion of grassland area, grass yield, vegetation coverage coefficient of change and weight in each sampling grid are calculated respectively, and the three weights are weighted to obtain the comprehensive weight of each grid, and the number of sample plots in the province is allocated to each grid according to the comprehensive weight. Among them, the comprehensive weight of the sampling grid = grassland area weight × 0.6 + grass yield coefficient of change weight × 0.2 + vegetation coverage coefficient of change weight × 0.2, grassland area weight = grassland area in the sampling grid / total grassland area, grass yield coefficient of change weight = grass yield coefficient of change in the sampling grid / sum of grass yield coefficients of all grids, vegetation coverage coefficient of change weight = coverage coefficient of change in the sampling grid / sum of vegetation coverage coefficients of change in all grids.

[0019] In the above technical solution, the sampling data table includes multiple fields, and the fields include one of the following: keywords, sampling grid numbers, minimum sampling unit numbers, coordinates, sampling costs, vegetation coverage, and grass production.

[0020] In this technical solution, the sampling data table includes fields such as keywords, sampling grid numbers, minimum sampling unit numbers, coordinates, sampling costs, vegetation coverage, and grass production.

[0021] In the above technical scheme, spatial attribute double balanced sampling is performed according to the sampling data table to obtain the initial sampling result, including: extracting the characteristic value of each minimum sampling unit and identifying the minimum sampling unit; based on the principle of similar spatial location and similar attributes, using the spatial hierarchical clustering algorithm, clustering the sampling units in the geographical space balanced sampling grid to obtain the clustering result; sampling is performed through the Latin hypercube sampling model according to the clustering result to obtain the initial sampling result.

[0022] In this technical solution, spatial attribute double balanced sampling is performed according to the sampling data table to obtain the initial sampling results. Specifically, the characteristic value of each minimum sampling unit is first extracted and the minimum sampling unit is identified. Then, based on the principle of similar spatial location and similar attributes, the spatial hierarchical clustering algorithm is used to cluster the sampling units in the geographical spatial balanced sampling grid to obtain the clustering results. Finally, sampling is performed through the Latin hypercube sampling model according to the clustering results to obtain the initial sampling results.

[0023] In the above technical scheme, sampling is performed according to the clustering results through the Latin hypercube sampling model to obtain the initial sampling results, including: for the minimum sampling unit in each geographical space balanced sampling grid, the minimum sampling unit has a characteristic value, the characteristic value includes the clustering layer number characteristic value, the coverage characteristic value and the grass yield characteristic value, according to the cumulative probability function, each characteristic value is divided into a plurality of intervals with equal cumulative probability density, and a cumulative probability value is randomly selected in each interval with equal cumulative probability density, wherein the number of intervals with equal cumulative probability density is the number of sample plots in the geographical space balanced sampling grid; the specific attribute value corresponding to the cumulative probability value is calculated according to the inverse function of the cumulative probability function; the specific attribute value obtained for each variable is randomly or according to a certain rule. Pairing to form a sampling that evenly covers the feature space.

[0024] In this technical solution, the Latin hypercube sampling method is adopted, and the combination of continuous forest resource inventory sampling sites, the continuation of grassland survey and monitoring historical sampling sites, and the arrival conditions are comprehensively considered. The minimum sampling unit is extracted one by one in the sampling grid, and its central coordinate point is the central coordinate point of the grassland survey and monitoring sampling site. Latin hypercube sampling is completed using the Spcosa function package of the software developed in R language, and it can comprehensively consider the combination of continuous forest resource inventory sampling sites, the continuation of grassland survey and monitoring historical sampling sites, and the arrival conditions. Among them, R language is a programmable language. The Spcosa function package is a computer term. The method consists of the following three steps: for the smallest sampling unit in each sampling grid, which has three eigenvalues ​​(clustering layer number, coverage, and grass yield), each eigenvalue is divided into n (n=number of sample plots in the grid) intervals of equal cumulative probability density according to the cumulative probability function, and a cumulative probability value is randomly selected in each interval of equal cumulative probability density; the specific attribute value (clustering layer number, coverage, and grass yield) corresponding to the cumulative probability value is calculated according to the inverse function of the cumulative probability function; the n values ​​obtained for each variable are randomly or paired according to certain rules to form a sampling that evenly covers the feature space.

[0025] To achieve the second purpose of the present application, the technical solution of the second aspect of the present application provides a grassland survey and monitoring plot sampling system, including: an acquisition module, used to determine the minimum sampling unit according to the plot area; a grid layout module, used to layout the geospatial balanced sampling grid according to the minimum sampling unit; a plot quantity allocation module, used to allocate the number of plots in the geospatial balanced sampling grid according to the grassland area, grass yield and coverage in the geospatial balanced sampling grid; a data table creation module, used to create a sampling data table based on the minimum sampling unit, the geospatial balanced sampling grid, and the number of plots; a sampling module, used to perform spatial attribute double balanced sampling according to the sampling data table to obtain an initial sampling result; a judgment module , used to determine whether the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is above the first preset percentage; if the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is not above the first preset percentage, the sampling cost value is adjusted and the sampling data table is re-made; if the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is above the first preset percentage, it is determined whether the deviation between the comprehensive grassland vegetation coverage and the back-calculated grass yield and the statistical plot is within the second preset percentage; if the deviation between the comprehensive grassland vegetation coverage and the back-calculated grass yield and the statistical plot is within the second preset percentage, the final sampling result is generated according to the initial sampling result.

[0026] The grassland survey and monitoring plot sampling system provided in the present application includes an acquisition module, a grid layout module, a plot quantity allocation module, a data table production module, a sampling module and a judgment module. Among them, the acquisition module is used to determine the minimum sampling unit according to the plot area. The grid layout module is used to lay out the geospatial balanced sampling grid according to the minimum sampling unit. The plot quantity allocation module is used to allocate the number of plots in the geospatial balanced sampling grid according to the grassland area, grass production and coverage in the geospatial balanced sampling grid. The data table production module is used to produce a sampling data table based on the minimum sampling unit, the geospatial balanced sampling grid and the number of plots. The sampling module is used to perform spatial attribute double balanced sampling according to the sampling data table to obtain the initial sampling results. The judgment module is used to judge whether the overlap rate between the initial sampling results and the continuous inventory sample plots of forest resources and the historical grassland sample plots is above the first preset percentage; if the overlap rate between the initial sampling results and the continuous inventory sample plots of forest resources and the historical grassland sample plots is not above the first preset percentage, the sampling cost value is adjusted and the sampling data table is re-made; if the overlap rate between the initial sampling results and the continuous inventory sample plots of forest resources and the historical grassland sample plots is above the first preset percentage, it is judged whether the deviation between the comprehensive vegetation coverage of grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage; if the deviation between the comprehensive vegetation coverage of grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage, the final sampling result is generated according to the initial sampling result. Based on the spatially balanced sampling layout, this application fully combines historical typical sample plots and adds productivity indicators such as grass yield and coverage. It can not only solve the subjective limitations of typical sample plot layout, such as small coverage and single grassland type, but also improve the problem that spatially balanced layout cannot carry out effective monitoring of important grasslands. On the basis of ensuring that the sample plot sampling results can cover all grassland areas, meet the requirements of reflecting the overall condition of grasslands and take into account the continuous monitoring requirements of historical sample plots, the technical methods of grassland sample plot layout are further optimized. The laid out sample plots can better cover important grassland areas such as grasslands with high grass yield and high coverage, pastures, etc. The sample plots laid out under this sampling system can better guide grassland production activities.

[0027] In order to achieve the third purpose of the present application, the technical solution of the third aspect of the present application provides a grassland survey and monitoring plot sampling system, including: a memory and a processor, wherein the memory stores programs or instructions that can be run on the processor, and when the processor executes the program or instructions, it implements the steps of the grassland survey and monitoring plot sampling method of any one of the technical solutions of the first aspect, and thus has the technical effect of any of the technical solutions of the first aspect mentioned above, which will not be repeated here.

[0028] In order to achieve the fourth purpose of the present application, the technical solution of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the grassland survey and monitoring sample plot sampling method of any one of the technical solutions of the first aspect are implemented, and thus it has the technical effect of any one of the technical solutions of the first aspect mentioned above, which will not be repeated here.

[0029] Additional aspects and advantages of the present application will become apparent in the following description or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0031] Figure 1 This is a flowchart of the steps of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0032] Figure 2 This is a flowchart of the steps of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0033] Figure 3 This is a flowchart of the steps of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0034] Figure 4 This is a flowchart of the steps of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0035] Figure 5 This is a flowchart of the steps of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0036] Figure 6 This is a flowchart of the steps of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0037] Figure 7 This is a schematic structural diagram of a grassland survey and monitoring plot sampling system according to an embodiment of the present application;

[0038] Figure 8 A schematic block diagram of a grassland survey and monitoring plot sampling system according to another embodiment of the present application;

[0039] Fig. 9 This is a flowchart of the steps of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0040] Fig.10 A schematic diagram of grass yield distribution of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0041] Fig.11A schematic diagram of sampling within equal cumulative probability segments of a grassland survey and monitoring plot sampling method according to an embodiment of the present application;

[0042] Fig.12 A schematic diagram of a grassland survey and monitoring plot sampling method according to an embodiment of the present application.

[0043] in, Figure 7 and Figure 8 The corresponding relationship between the reference numerals and component names in the figure is:

[0044] 10: grassland survey and monitoring plot sampling system; 110: acquisition module; 120: grid layout module; 130: plot quantity allocation module; 140: data table making module; 150: sampling module; 160: judgment module; 20: grassland survey and monitoring plot sampling system; 300: memory; 400: processor. DETAILED DESCRIPTION

[0045] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0047] Refer to the following Figures 1 to 12 Describe the grassland survey and monitoring plot sampling method and system, and readable storage medium of some embodiments of the present application.

[0048] like Figure 1 As shown, a grassland survey and monitoring plot sampling method according to an embodiment of the present application includes the following steps:

[0049] S102: Determine the minimum sampling unit according to the sample plot area;

[0050] S104: Arrange a geographically spatially balanced sampling grid according to the minimum sampling unit;

[0051] S106: allocating the number of sample plots in the geographically spatially balanced sampling grid according to the grassland area, grass yield and coverage in the geographically spatially balanced sampling grid;

[0052] S108: Prepare sampling data table based on minimum sampling unit, geographical spatial balanced sampling grid, and number of sample plots;

[0053] S110: Performing spatial attribute double balanced sampling according to the sampling data table to obtain an initial sampling result;

[0054] S112: Determine whether the overlap rate between the initial sampling results and the forest resources continuous inventory sample plots and the grassland historical sample plots is above a first preset percentage;

[0055] S114: If the overlap rate between the initial sampling result and the forest resources continuous inventory sample plot and the grassland historical sample plot is not above the first preset percentage, the sampling cost value is adjusted and the sampling data table is re-created;

[0056] S116: If the overlap rate between the initial sampling result and the forest resources continuous inventory sample plot and the grassland historical sample plot is above the first preset percentage, then determine whether the grassland comprehensive vegetation coverage, grass yield back calculation and the statistical deviation of the plot are within the second preset percentage;

[0057] S118: If the grassland comprehensive vegetation coverage, grass yield back-calculation and the statistical deviation of the spot are within the second preset percentage, the final sampling result is generated according to the initial sampling result.

[0058] According to the grassland survey and monitoring plot sampling method provided in this application, first determine the minimum sampling unit based on the area of ​​the grassland survey and monitoring plot. Then, taking all grassland patches in the preset area as the whole, mechanically lay out a geographical spatial balanced sampling grid of equal area and covering all minimum sampling units. According to the differences in grassland area, grass yield and coverage in each sampling grid, allocate the number of grassland plots to them. Then, a sampling data table is prepared based on the minimum sampling unit, the geographical spatial balanced sampling grid, and the number of plots, and a spatial attribute double balanced sampling is performed based on the sampling data table to obtain the initial sampling results. Determine whether the overlap rate between the initial sampling results and the forest resource continuous inventory plots and the grassland historical plots is above the first preset percentage. If not, adjust the sampling cost value and re-prepare the sampling data table. If so, determine whether the grassland comprehensive vegetation coverage, grass yield back-calculation and plot statistical deviation are within the second preset percentage. If so, generate the final sampling result based on the initial sampling result. Based on the spatially balanced sampling layout, this embodiment fully combines historical typical sample plots and adds productivity indicators such as grass yield and coverage. It can not only solve the subjective limitations of typical sample plot layout, such as small coverage and single grassland type, but also improve the problem that spatially balanced layout cannot carry out effective monitoring of important grasslands. On the basis of ensuring that the sample plot sampling results can cover all grassland areas, meet the requirements of reflecting the overall condition of grasslands and take into account the continuous monitoring requirements of historical sample plots, the technical methods of grassland sample plot layout are further optimized. The laid out sample plots can better cover important grassland areas such as grasslands with high grass yields and high coverage, pastures, etc. The sample plots laid out under this sampling method can better guide grassland production activities.

[0059] The first preset percentage is 50%, and the second preset percentage is 5%.

[0060] like Figure 2 As shown, according to a grassland survey and monitoring plot sampling method according to an embodiment of the present application, the minimum sampling unit is determined according to the plot area, specifically comprising the following steps:

[0061] S202: Screening the sample plot area, screening out the small classes with an area larger than the preset area from the grassland small class database of the preset area, and saving them as an independent layer to obtain vector data;

[0062] S204: adding a unique value field to the vector data, and converting the vector data into raster data, and retaining the unique value field in the raster data;

[0063] S206: performing a raster-to-point operation on the raster data to obtain vector point data as a minimum sampling unit, and adding multiple fields to the vector point data;

[0064] S208: Set forest resource continuous inventory sample plots and grassland historical sample plots within the preset distance as low-cost sample plots.

[0065] In this embodiment, the minimum sampling unit is determined according to the area of ​​the sample plot, specifically, vector data with a preset area in the grassland base database is converted into raster data, and then the raster data is converted into point-type vector data as the minimum sampling unit. Specifically, the preset area can be 1 hectare.

[0066] like Figure 3 As shown, according to a grassland survey and monitoring plot sampling method according to an embodiment of the present application, a geographically spatially balanced sampling grid is arranged according to the minimum sampling unit, specifically comprising the following steps:

[0067] S302: Obtain the number of sample plots in a preset area;

[0068] S304: Calculating the number of sampling grids in the preset area according to the number of sample plots in the preset area and the distribution of grassland resources in the preset area;

[0069] S306: Laying out a regular grid covering all minimum sampling units through geographic information system software.

[0070] In this embodiment, a geographically spatially balanced sampling grid is laid out according to the minimum sampling unit, specifically, the number of sample plots in a preset area is first obtained. Then, according to the number of sample plots in the preset area and the distribution of grassland resources in the preset area, the number of sampling grids in the preset area is calculated. Finally, a regular grid covering all minimum sampling units is laid out through the geographic information system software. Specifically, the total land area of ​​the preset area is divided by the number of sample plots in the preset area and then multiplied by 5 (or the total number of samples is divided by 5), and the overall grassland resource distribution is comprehensively considered, and the number of sampling grids of the population is calculated. The regular grid covering all minimum sampling units is laid out using tools such as "fishnet" or "grid index" in the geographic information system software.

[0071] like Figure 4 As shown, according to a grassland survey and monitoring plot sampling method of an embodiment proposed in the present application, the number of plots in the geographically spatially balanced sampling grid is allocated according to the grassland area, grass yield and coverage in the geographically spatially balanced sampling grid, specifically including the following steps:

[0072] S402: Calculate the grassland area ratio, grass yield change coefficient, vegetation coverage change coefficient, grassland area weight, grass yield change coefficient weight, and vegetation coverage change coefficient weight in each geographical space balanced sampling grid respectively;

[0073] S404: weighting the grassland area weight, grass yield variation coefficient weight, and vegetation coverage variation coefficient weight to obtain a sampling grid comprehensive weight of each geographical space balanced sampling grid;

[0074] S406: Allocate the number of sample plots in the preset area to the geographically spatially balanced sampling grid according to the comprehensive weight of the sampling grid.

[0075] In this embodiment, according to the balanced sampling of geographic space, specifically, the proportion of grassland area, grass yield, vegetation coverage variation coefficient and weight in each sampling grid are calculated respectively, and the three weights are weighted to obtain the comprehensive weight of each grid, and the number of sample plots in the province is allocated to each grid according to the comprehensive weight. Among them, the comprehensive weight of the sampling grid = grassland area weight × 0.6 + grass yield variation coefficient weight × 0.2 + vegetation coverage variation coefficient weight × 0.2, grassland area weight = grassland area in the sampling grid / total grassland area, grass yield variation coefficient weight = grass yield variation coefficient in the sampling grid / sum of grass yield variation coefficients of all grids, vegetation coverage variation coefficient weight = coverage variation coefficient in the sampling grid / sum of vegetation coverage variation coefficients of all grids.

[0076] In some embodiments, the sampling data table includes fields such as keywords, sampling grid numbers, minimum sampling unit numbers, coordinates, sampling costs, vegetation coverage, and grass production.

[0077] like Figure 5As shown, according to a grassland survey and monitoring plot sampling method according to an embodiment of the present application, spatial attribute double balanced sampling is performed according to a sampling data table to obtain an initial sampling result, which specifically includes the following steps:

[0078] S502: extracting the characteristic value of each minimum sampling unit and marking the minimum sampling unit;

[0079] S504: Based on the principle of close spatial location and similar attributes, a spatial hierarchical clustering algorithm is used to cluster the sampling units in the geographical spatial balanced sampling grid to obtain a clustering result;

[0080] S506: Sampling is performed using a Latin hypercube sampling model according to the clustering result to obtain an initial sampling result.

[0081] In this embodiment, spatial attribute double balanced sampling is performed according to the sampling data table to obtain the initial sampling result, specifically, the characteristic value of each minimum sampling unit is first extracted, and the minimum sampling unit is identified. Then, based on the principle of close spatial location and similar attributes, the spatial hierarchical clustering algorithm is used to cluster the sampling units in the geographical spatial balanced sampling grid to obtain the clustering result. Finally, sampling is performed through the Latin hypercube sampling model according to the clustering result to obtain the initial sampling result.

[0082] like Figure 6 As shown, sampling is performed according to the clustering results through the Latin hypercube sampling model to obtain the initial sampling results, which specifically includes the following steps:

[0083] S602: for each minimum sampling unit in the geographical spatial balanced sampling grid, the minimum sampling unit has characteristic values, the characteristic values ​​include the characteristic value of the clustering layer number, the characteristic value of the coverage, and the characteristic value of the grass yield; according to the cumulative probability function, each characteristic value is divided into a plurality of intervals with equal cumulative probability density, and a cumulative probability value is randomly selected in each interval with equal cumulative probability density, wherein the number of intervals with equal cumulative probability density is the number of sample plots in the geographical spatial balanced sampling grid;

[0084] S604: Calculate the specific attribute value corresponding to the cumulative probability value according to the inverse function of the cumulative probability function;

[0085] S606: Pair the specific attribute values ​​obtained for each variable randomly or according to certain rules to form a sample that evenly covers the feature space.

[0086] In this embodiment, the Latin hypercube sampling method is adopted, and the combination of continuous inventory of forest resources, the continuation of historical sample plots for grassland investigation and monitoring, and the arrival conditions are comprehensively considered. The minimum sampling unit is extracted from each sampling grid, and its central coordinate point is the central coordinate point of the grassland investigation and monitoring sample plot. The Latin hypercube sampling is completed using the Spcosa function package of the software developed in R language, and the combination of continuous inventory of forest resources, the continuation of historical sample plots for grassland investigation and monitoring, and the arrival conditions can be comprehensively considered. The method is divided into the following three steps: for the minimum sampling unit in each sampling grid, it has 3 eigenvalues ​​(clustering layer number, coverage, grass production), according to the cumulative probability function, each eigenvalue is divided into n (n=number of sample plots in the grid) intervals of equal cumulative probability density, and a cumulative probability value is randomly selected in each interval of equal cumulative probability density; the specific attribute value (clustering layer number, coverage, grass production) corresponding to the cumulative probability value is calculated according to the inverse function of the cumulative probability function; the n values ​​obtained for each variable are randomly or paired according to certain rules to form a sampling that evenly covers the feature space.

[0087] like Figure 7 As shown, the embodiment of the second aspect of the present application provides a grassland survey and monitoring plot sampling system 10, including: an acquisition module 110, used to determine the minimum sampling unit according to the plot area; a grid layout module 120, used to layout the geospatial balanced sampling grid according to the minimum sampling unit; a plot quantity allocation module 130, used to allocate the number of plots in the geospatial balanced sampling grid according to the grassland area, grass yield and coverage in the geospatial balanced sampling grid; a data table preparation module 140, used to prepare a sampling data table according to the minimum sampling unit, the geospatial balanced sampling grid, and the number of plots; a sampling module 150, used to perform spatial attribute double balanced sampling according to the sampling data table to obtain an initial sampling result; and a judgment module 140. Module 160 is used to determine whether the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is above the first preset percentage; if the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is not above the first preset percentage, the sampling cost value is adjusted and the sampling data table is re-produced; if the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is above the first preset percentage, it is determined whether the deviation between the comprehensive vegetation coverage of the grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage; if the deviation between the comprehensive vegetation coverage of the grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage, the final sampling result is generated according to the initial sampling result.

[0088] In this embodiment, the grassland survey and monitoring plot sampling system 10 includes an acquisition module 110, a grid layout module 120, a plot quantity allocation module 130, a data table making module 140, a sampling module 150 and a judgment module 160. Among them, the acquisition module 110 is used to determine the minimum sampling unit according to the plot area. The grid layout module 120 is used to lay out the geospatial balanced sampling grid according to the minimum sampling unit. The plot quantity allocation module 130 is used to allocate the number of plots in the geospatial balanced sampling grid according to the grassland area, grass yield and coverage in the geospatial balanced sampling grid. The data table making module 140 is used to make a sampling data table based on the minimum sampling unit, the geospatial balanced sampling grid, and the number of plots. The sampling module 150 is used to perform spatial attribute double balanced sampling according to the sampling data table to obtain an initial sampling result. The judgment module 160 is used to judge whether the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is above the first preset percentage; if the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is not above the first preset percentage, the sampling cost value is adjusted and the sampling data table is re-produced; if the overlap rate between the initial sampling results and the continuous forest resource inventory sample plots and the grassland historical sample plots is above the first preset percentage, it is judged whether the deviation between the comprehensive vegetation coverage of the grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage; if the deviation between the comprehensive vegetation coverage of the grassland and the back calculation of grass yield and the statistical data of the map is within the second preset percentage, the final sampling result is generated according to the initial sampling result. On the basis of spatially balanced sampling and layout, this embodiment fully combines historical typical sample plots and adds productivity indicators such as grass yield and coverage. It can not only solve the subjective limitations of classic plot layout, such as small coverage and single grassland type, but also improve the problem that spatially balanced layout cannot carry out effective monitoring of important grasslands. On the basis of ensuring that the sample plot sampling results can cover all grassland areas, meet the requirements of reflecting the overall condition of grasslands and take into account the requirements of continuous monitoring of historical sample plots, the technical methods of grassland sample plot layout are further optimized. The laid out sample plots can better cover important grassland areas such as grasslands with high grass yield and high coverage, pastures, etc. The sample plots laid out under this sampling system can better guide grassland production activities.

[0089] like Figure 8 As shown, an embodiment of the third aspect of the present application provides a grassland survey and monitoring plot sampling system 20, comprising: a memory 300 and a processor 400, wherein the memory 300 stores a program or instruction that can be run on the processor 400, and when the processor 400 executes the program or instruction, it implements the steps of the grassland survey and monitoring plot sampling method of any one of the embodiments of the first aspect, and thus has the technical effect of any one of the embodiments of the first aspect mentioned above, which will not be repeated here.

[0090] The embodiment of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the grassland survey and monitoring plot sampling method of any one of the embodiments of the first aspect are implemented, and thus it has the technical effect of any one of the embodiments of the first aspect mentioned above, which will not be repeated here.

[0091] like Fig. 9 , Fig.10 , Fig.11 and Fig.12 As shown, according to a grassland survey and monitoring plot sampling method of a specific embodiment proposed in this application, on the basis of spatially balanced sampling layout, it fully combines historical typical plots and innovatively adds productivity indicators such as "grass yield" and "coverage", which can not only solve the subjective limitations of typical plot layout, such as small coverage and single grassland type, but also improve the problem that spatially balanced layout cannot carry out effective monitoring of important grasslands. On the basis of ensuring that the plot sampling results can cover all grassland areas, meet the requirements of reflecting the overall condition of the grassland, and take into account the requirements of continuous monitoring of historical plots, the technical method of grassland plot layout is further optimized, and the laid out plots can better cover important grassland areas such as grasslands with high grass yields and high coverage, pastures, etc. The plots laid out under this sampling method can better guide the development of grassland production activities.

[0092] Specifically, the method for calculating the number of sample plots is:

[0093] The number of sample plots in each region is calculated based on a confidence level of 95% to meet the sampling accuracy of vegetation coverage and grass production. The calculation formula is as follows:

[0094] ;

[0095] n: number of grassland survey and monitoring plots;

[0096] N: total number (total area of ​​the survey area divided by the area of ​​the sample plot);

[0097] t: Reliability index (confidence level), reliability is 95%, the population obeys normal distribution, then t=1.96;

[0098] C: coefficient of variation, 0.7 is taken if it is below 0.7, and 1.3 is taken if it is above 1.3;

[0099] E: relative error, i.e. 1-P, where P is the sampling accuracy;

[0100] Insurance factor: 15%;

[0101] Sample plot layout: The sampling method for grassland survey and monitoring plots adopts balanced sampling of geographic space and attribute space combined with continuous inventory of fixed plots. The steps are as follows:

[0102] 1. Determine the minimum sampling unit:

[0103] According to the area of ​​grassland survey and monitoring plots, the minimum sampling unit area is 1 hectare.

[0104] Taking 1 hectare as the pixel unit, the vector data larger than 1 hectare in the grassland basic condition database is converted into raster data, and then the raster data is converted into point-type vector data as the minimum sampling unit.

[0105] 2. Laying out a geographically balanced sampling grid:

[0106] Taking all the grassland patches in the preset area as the population, a geographically balanced sampling grid with equal area and covering all the minimum sampling units is mechanically laid out.

[0107] The total land area of ​​the preset area is divided by the number of sample plots in the preset area and then multiplied by 5 (or the total number of samples is divided by 5). Taking the overall grassland resource distribution into consideration, the number of sampling grids for the whole is calculated. Use tools such as "fishing net" or "grid index" in the geographic information system software to lay out regular grids covering all the minimum sampling units.

[0108] 3. Allocate the number of sample plots within the geographically balanced sampling grid:

[0109] According to the differences in grassland area, grass yield and coverage in each sampling grid, the number of grassland sample plots is allocated to them.

[0110] The proportion of grassland area, grass yield, vegetation coverage variation coefficient and weight in each sampling grid are calculated respectively, and the three weights are weighted to obtain the comprehensive weight of each grid. The number of sample plots in the province is allocated to each grid according to the comprehensive weight. The comprehensive weight calculation formula is as follows:

[0111] The comprehensive weight of sampling grid = grassland area weight × 0.6 + grass yield variation coefficient weight × 0.2 + vegetation coverage variation coefficient weight × 0.2;

[0112] Grassland area weight = grassland area in sampling grid / total grassland area;

[0113] Grass yield variation coefficient weight = grass yield variation coefficient in sampling grid / sum of grass yield variation coefficients in all grids;

[0114] Vegetation coverage variation coefficient weight = coverage variation coefficient in sampling grid / sum of vegetation coverage variation coefficients in all grids;

[0115] (IV) Assignment of minimum sampling unit attribute characteristics:

[0116] Based on the grassland baseline monitoring database, the characteristic values ​​of each minimum sampling unit are extracted; combined with the location of the continuous forest inventory sample plots, the minimum sampling units involved are identified.

[0117] Use geographic information software to extract the geographic spatial coordinates of each minimum sampling unit and two field values, including the vegetation coverage and fresh grass yield per unit area of ​​the grassland class where it is located, and add identification of required fields.

[0118] 5. Clustering of sampling units within each geographical space balanced sampling grid:

[0119] Based on the principle of close spatial location and similar attributes, a spatial hierarchical clustering algorithm is used to cluster all sampling units in each spatial balanced sampling grid.

[0120] Using software developed based on R language, the stratify function in the Spcosa function package was selected to cluster all the minimum sampling units in each sampling grid. The number of layers generated by clustering is equal to the number of sample plots in the sampling grid. After clustering, the layer numbers are assigned to all the minimum sampling units in the layer. Among them, the Spcosa function package and the stratify function are both computer terms.

[0121] (VI) Using methods such as “Latin Hypercube Sampling” to lay out sample plots:

[0122] The Latin hypercube sampling method is adopted, and factors such as the combination of continuous forest resource inventory plots, the continuation of historical grassland survey and monitoring plots, and arrival conditions are comprehensively considered. The minimum sampling unit is extracted from each sampling grid, and its central coordinate point is the central coordinate point of the grassland survey and monitoring plot.

[0123] Latin hypercube sampling was completed using the Spcosa function package of the software developed in R language, and it can comprehensively consider factors such as the combination of continuous forest resource inventory plots, the continuation of grassland survey and monitoring historical plots, and arrival conditions. The method is divided into the following three steps:

[0124] (1) For the smallest sampling unit in each sampling grid, which has three eigenvalues ​​(clustering layer number, coverage, and grass yield), each eigenvalue is divided into n (n = number of sample plots in the grid) equal cumulative probability density intervals according to the cumulative probability function, and a cumulative probability value is randomly selected in each equal cumulative probability density interval;

[0125] (2) Calculate the specific attribute value (clustering layer number, coverage, grass yield) corresponding to the cumulative probability value based on the inverse function of the cumulative probability function;

[0126] (3) Pair the n values ​​obtained for each variable randomly or according to certain rules to form a sample that evenly covers the feature space. Cumulative probability function , mean , x represents the sample.

[0127] In this application, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance; the term "plurality" refers to two or more, unless otherwise expressly defined. Terms such as "installed", "connected", "connected", and "fixed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0128] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "back", etc., indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore, should not be understood as a limitation on the present application.

[0129] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0130] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A grassland survey and monitoring plot sampling method, characterized in that: include: Determine the minimum sampling unit based on the sample plot area; Arrange a geographically spatially balanced sampling grid according to the minimum sampling unit; Allocate the number of sample plots in the geographically spatially balanced sampling grid according to the grassland area, grass yield and coverage in the geographically spatially balanced sampling grid; Prepare a sampling data table according to the minimum sampling unit, the geographically spatially balanced sampling grid, and the number of sample plots; Performing double balanced sampling of spatial attributes according to the sampling data table to obtain an initial sampling result; Determine whether the overlap rate between the initial sampling results and the forest resources continuous inventory sample plots and the grassland historical sample plots is above a first preset percentage; If the overlap rate between the initial sampling result and the forest resources continuous inventory sample plot and the grassland historical sample plot is not above a first preset percentage, the sampling cost value is adjusted and the sampling data table is re-created; If the overlap rate between the initial sampling result and the forest resources continuous inventory sample plot and the grassland historical sample plot is above the first preset percentage, then determine whether the grassland comprehensive vegetation coverage, grass yield back-calculation and plot statistical deviation are within the second preset percentage; If the grassland comprehensive vegetation coverage, the grass yield back-calculation and the spot statistical deviation are within a second preset percentage, a final sampling result is generated according to the initial sampling result; The performing of spatial attribute double balanced sampling according to the sampling data table to obtain an initial sampling result includes: Extracting the characteristic value of each minimum sampling unit and marking the minimum sampling unit; Based on the principle of close spatial location and similar attributes, a spatial hierarchical clustering algorithm is used to cluster the sampling units in the geographical spatial balanced sampling grid to obtain a clustering result; Sampling is performed according to the clustering result through a Latin hypercube sampling model to obtain an initial sampling result.

2. The grassland survey and monitoring plot sampling method according to claim 1 is characterized in that: Determining the minimum sampling unit according to the sample plot area includes: The sample plot area is screened, and the small classes with an area larger than the preset area are screened from the grassland small class database of the preset area, and saved as an independent layer to obtain vector data; Adding a unique value field to the vector data, and converting the vector data into raster data, wherein the raster data retains the unique value field; Performing a raster-to-point operation on the raster data to obtain vector point data as a minimum sampling unit, and adding a plurality of fields to the vector point data; Forest resource continuous inventory plots and grassland historical plots will be set as low-cost plots within the preset distance.

3. The grassland survey and monitoring plot sampling method according to claim 1, characterized in that: The step of laying out a geographically spatially balanced sampling grid according to the minimum sampling unit comprises: Get the number of sample plots in a preset area; Calculating the number of sampling grids in the preset area according to the number of sample plots in the preset area and the distribution of grassland resources in the preset area; A regular grid covering all minimum sampling units was laid out using geographic information system software.

4. The grassland survey and monitoring plot sampling method according to claim 1, characterized in that: The allocating the number of sample plots in the geographically spatially balanced sampling grid according to the grassland area, grass yield and coverage in the geographically spatially balanced sampling grid comprises: Calculate the grassland area ratio, grass yield change coefficient, vegetation coverage change coefficient and grassland area weight, grass yield change coefficient weight, and vegetation coverage change coefficient weight in each of the geographical space balanced sampling grids respectively; The grassland area weight, the grass yield variation coefficient weight, and the vegetation coverage variation coefficient weight are weighted to obtain the sampling grid comprehensive weight of each of the geographical space balanced sampling grids; The number of sample plots in a preset area is allocated to the geographically spatially balanced sampling grid according to the comprehensive weight of the sampling grid.

5. The grassland survey and monitoring plot sampling method according to claim 1, characterized in that: The sampling data table includes multiple fields, and the fields include one of the following: keywords, sampling grid numbers, minimum sampling unit numbers, coordinates, sampling costs, vegetation coverage, and grass production.

6. The grassland survey and monitoring plot sampling method according to claim 1, characterized in that: The step of sampling according to the clustering result by using a Latin hypercube sampling model to obtain an initial sampling result includes: For each minimum sampling unit in the geographical spatial balanced sampling grid, the minimum sampling unit has characteristic values, and the characteristic values ​​include the characteristic value of the clustering layer number, the characteristic value of the coverage, and the characteristic value of the grass yield. According to the cumulative probability function, each characteristic value is divided into a plurality of intervals of equal cumulative probability density, and a cumulative probability value is randomly selected in each interval of equal cumulative probability density, wherein the number of intervals of equal cumulative probability density is the number of sample plots in the geographical spatial balanced sampling grid; Calculating the specific attribute value corresponding to the cumulative probability value according to the inverse function of the cumulative probability function; The specific attribute values ​​obtained for each variable are paired randomly or according to certain rules to form a sample that evenly covers the feature space.

7. A grassland survey and monitoring plot sampling system, characterized in that: include: An acquisition module (110) is used to determine the minimum sampling unit according to the area of ​​the sample plot; A grid layout module (120), configured to layout a geographically spatially balanced sampling grid according to the minimum sampling unit; A sample plot quantity allocation module (130) is used to allocate the number of sample plots in the geographical spatial balanced sampling grid according to the grassland area, grass yield and coverage in the geographical spatial balanced sampling grid; A data table making module (140) is used to make a sampling data table according to the minimum sampling unit, the geographical spatial balanced sampling grid, and the number of sampling plots; A sampling module (150), configured to perform spatial attribute double balanced sampling according to the sampling data table to obtain an initial sampling result; A judgment module (160) is used to judge whether the overlap rate between the initial sampling result and the forest resources continuous inventory sample plot and the grassland historical sample plot is above a first preset percentage; If the overlap rate between the initial sampling result and the forest resources continuous inventory sample plot and the grassland historical sample plot is not above the first preset percentage, the sampling cost value is adjusted and the sampling data table is re-made; if the overlap rate between the initial sampling result and the forest resources continuous inventory sample plot and the grassland historical sample plot is above the first preset percentage, it is determined whether the grassland comprehensive vegetation coverage, grass yield back calculation and the statistical deviation of the map are within the second preset percentage; If the grassland comprehensive vegetation coverage, the grass yield back-calculation and the spot statistical deviation are within a second preset percentage, a final sampling result is generated according to the initial sampling result; The sampling module (150) is specifically used to: Extracting the characteristic value of each minimum sampling unit and marking the minimum sampling unit; Based on the principle of close spatial location and similar attributes, a spatial hierarchical clustering algorithm is used to cluster the sampling units in the geographical spatial balanced sampling grid to obtain a clustering result; Sampling is performed according to the clustering result through a Latin hypercube sampling model to obtain an initial sampling result.

8. A grassland survey and monitoring plot sampling system, characterized in that: include: A memory (300) and a processor (400), wherein the memory (300) stores a program or instruction that can be run on the processor (400), and when the processor (400) executes the program or the instruction, the steps of the grassland survey and monitoring plot sampling method according to any one of claims 1 to 6 are implemented.

9. A readable storage medium having a program or instruction stored thereon, characterized in that: When the program or the instruction is executed by the processor, the steps of the grassland survey and monitoring plot sampling method as described in any one of claims 1 to 6 are implemented.

Images