Method for realizing grassland vegetation coverage quantitative analysis based on GIS data

Abstract

The invention provides a method for realizing grassland vegetation coverage quantitative analysis based on GIS (Geographic Information System) data, which comprises the following steps: S1, acquiring geographic information GIS data, collecting grassland vegetation change data in the GIS data, and determining an attribute change component; and S2, determining grassland vegetation coverage weight distribution for the attribute change component, carrying out expectation measurement through a grassland vegetation coverage expectation model, and determining a threshold value to carry out grassland vegetation quantitative analysis. The grassland vegetation coverage quantitative analysis based on the GIS data has the advantages of high data comprehensiveness and accuracy, strong space-time analysis capability, high analysis precision, support of decision making and visual display of analysis results.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a method for quantitatively analyzing grassland vegetation coverage based on GIS data. Background Art

[0002] Natural resources are natural elements and conditions that can generate economic value at a certain time, place and conditions to improve the current and future welfare of human beings. They can also be understood as natural entities that can be harvested, mined or extracted to be directly used for production, consumption or accumulation in the economic system, or to provide space for economic activities. Grassland resource assets are a concept proposed based on the usefulness of grassland resources. The key to the transition from grassland resources to grassland resource assets is the extension and expansion of "assets". Assets are storage devices of value. Grassland resource assets refer to scarce material assets and services in the form of natural resources with clear property rights, clear boundaries, and benefits for human beings. Natural resource assets often exist in a natural state and are mostly public goods. They are exclusive in use, poor in substitutability and have rigid demand. Therefore, they have the characteristics of asset preservation and appreciation, and are spatially locational and non-homogeneous. The concept of natural resource assets originates from natural resources. Compared with the physical characteristics of natural resources, natural resource assets emphasize the value of natural resources. Grassland asset value refers to the value of material assets and services provided by grassland in the form of grassland resources, highlighting the value provided by grassland as an asset, mainly including direct economic value, ecological value and social value.

[0003] The key technology research of grassland quantitative analysis takes the grassland patches of change survey as the base map data, extracts the natural grassland, artificial grassland and other grassland patches in the change survey as the quantitative analysis objects, and studies the technical methods of demarcating grassland levels by multi-factor comprehensive evaluation. This urgently requires technical personnel in this field to solve the corresponding technical problems. Summary of the invention

[0004] The present invention aims to at least solve the technical problems existing in the prior art, and in particular innovatively proposes a method for quantitative analysis of grassland vegetation coverage asset value based on GIS data.

[0005] In order to achieve the above-mentioned object of the present invention, the present invention provides a method for quantitative analysis of grassland vegetation coverage based on GIS data, comprising:

[0006] S1, obtain geographic information GIS data, collect grassland vegetation change data in GIS data, and determine attribute change components;

[0007] S2, determine the grassland vegetation coverage weight distribution for the attribute change component, perform expected measurement through the grassland vegetation coverage expected model, and determine the threshold for quantitative analysis of grassland vegetation.

[0008] Preferably, the above technical solution is that S1 comprises:

[0009] S1-1, collect the precipitation and wind speed of a certain place according to GIS data, perform spatial interpolation processing, obtain raster data of precipitation and wind speed, convert the raster to surface or use the zoning statistical tool to assign attributes according to the grade classification standard, and perform overlay analysis with the grass map patch;

[0010]

[0011] Among them, according to the change component A after precipitation interpolation processing, the prediction parameter a of the n-time precipitation in a certain area is extracted n , is the Gaussian component of precipitation change, t is the real-time precipitation time, t0 is the initial precipitation time, and this formula describes the change of precipitation around the initial precipitation time t0. Due to the characteristics of the Gaussian function, it is very suitable for describing the precipitation change that reaches a peak at a certain moment and then gradually decreases. By adjusting a n The value of can flexibly adapt to different precipitation change patterns. υ is a random variable, W n is the random variable regression coefficient.

[0012] Preferably, the above technical solution, S1 further comprises:

[0013]

[0014] The wind speed change component B, b1 is the nighttime wind speed change value, b2 is the dynamic mean of wind speed, and f(b3) is the adjustment function of the wind speed interference component b3. It is the dynamic change periodic component of the daytime wind speed value b4.

[0015] Preferably, the above technical solution, S1 further comprises:

[0016] S1-2, quantify the soil and arable land fertility database. The collected county soil census or arable land fertility database is assigned a global value using the Kriging interpolation method. The grid is reclassified according to the annual soil texture, soil layer thickness, soil organic matter and other index classification scores. Finally, the grid is converted to a surface and overlaid with the grassland map to obtain the quantitative layer of soil texture, soil layer thickness, and soil organic matter index.

[0017]

[0018] Among them, in the soil change component C, τ0(c i ,t i ) is the initial regression coefficient of soil thickness ci changing with time ti, k is the number of soil thickness observations, (c i ,t i) is the i-th observation point at sampling time t, and x is the adjustment factor.

[0019] Preferably, the above technical solution, S1 further comprises:

[0020] S1-3 According to the slope and aspect of the slope, based on the DEM data, raster reclassification is performed according to the slope position and aspect index classification standards (azimuth can be used as the raster reclassification standard, -1 is flat land), and the raster is vectorized and overlaid with the grassland patch to obtain the slope position and aspect index layer. The second is slope and altitude. Based on the DEM data, the slope and altitude raster data of the entire district (county) are obtained, and the raster is converted to a surface. It is overlaid with the grassland patch through identification and analysis, and the slope and altitude index classification score standards are assigned;

[0021] D=ω1·f1(α)+ω2·f2(d)+Y

[0022] Where D is the slope influence component, is the slope, Δh is the elevation difference, I is the horizontal distance, d=azimuth(z) is the slope angle, z is the projection vector of the positive direction of the normal on the plane, f1(α) is the adjustment function of the attribute influencing component of the slope, f2(d) is the adjustment function of the attribute influencing component of the slope, Y is the residual term, ω1 is the slope adjustment weight, and ω2 is the slope adjustment weight.

[0023] Preferably, the above technical solution, S1 further comprises:

[0024] S1-4. Distance from settlements

[0025] Data source: data on urban villages and industrial and mining land in the annual land change survey (201), towns (202), villages (203), and roads.

[0026] Quantitative method: Use the proximity analysis tool to analyze the distance from the edge of the grassland patch to the nearest settlements of the city (201), town (202), and village (203). Use the attribute connection tool to assign the obtained distance to the grassland patch, and then assign it according to the indicator classification standard.

[0027] As cities, towns and villages have a wide range and their impacts on grassland vary greatly, the impact radius (maximum distance) is calculated according to the calculation method of the township impact of agricultural land, and the impact radius (maximum distance) is calculated according to the district and county government seats, central towns and general towns. With reference to relevant technical regulations, the impact radius of each settlement is calculated according to the following formula:

[0028] Where r is the radius of the settlement, S is the total area of ​​the settlement, and m is the total number of settlements.

[0029] Based on the distance of each settlement and the radius of action of roads at all levels, the quantitative score of the index is calculated using the exponential decay model, and the automatic calculation is performed through GIS software.

[0030] f(r)=(F i R ) 1-r

[0031] Among them: f(r) is the accessibility effect of the quantitative factor on point i (a point inside the patch);

[0032] F i R Functional score of this quantitative factor;

[0033] r is the relative distance from the quantitative factor to point i (a point inside the patch).

[0034] Preferably, the above technical solution, S1 further comprises:

[0035] S1-5, Road Accessibility

[0036] Data source: Mainly based on grassland patches and road data from the annual regional change survey.

[0037] Quantitative method: Use the "Proximity Analysis" tool in ArcGIS Neighborhood Analysis to analyze the grassland patches to the highway land (1003), urban and rural road land (1004), and rural road (1006) patches in the annual land change survey data, calculate the distance from the edge of the grassland patch to the nearest road, and use the attribute connection tool to assign the obtained distance to the grassland patch, and then assign it according to the indicator classification standard.

[0038] The functional points and effective radius of different road accessibility conditions are determined according to the classification standards of grade indicators. According to the function and role of the road, the functional points of the road are proportional to its role, with a value between 0-100. The role points of the roads with the best conditions (national highways, expressways) are 100, and the role points of the remaining types are set in descending order. The calculation of the effective radius of the road is publicized as follows:

[0039] Among them, r is the effective radius of the road, S is the built-up area, and L is the total length of the road;

[0040] Based on the distance from each level of roads and the radius of action of each level of roads, the quantitative score of the index is calculated using the exponential decay model, and the automatic calculation is performed through GIS software.

[0041] f(r′)=(F i R ) 1-r

[0042] Among them: f(r) is the accessibility effect of the quantitative factor on point i (a point inside the patch);

[0043] F i R The functional points of the quantitative factor; R is the relative distance from the quantitative factor to point i (a point inside the patch).

[0044] In the above technical solution, preferably, S2 comprises:

[0045] The expected model of grassland vegetation coverage is obtained as follows:

[0046]

[0047] Among them, AVG is the predicted value of grassland vegetation coverage; w A is the weight of precipitation, A T is the precipitation threshold; w B is the weight of wind speed, B T is the wind speed threshold; w C is the weight of soil change, C T is the threshold of soil change, σ1 is the weight of the impact of settlement on grassland vegetation coverage, σ2 is the weight of the impact of road accessibility on grassland vegetation coverage,

[0048] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0049] According to the expected results and actual situation of grassland vegetation coverage, reasonable thresholds can be determined to conduct quantitative analysis of grassland vegetation coverage. Visual display of analysis results: Through quantitative analysis, grassland vegetation coverage can be intuitively displayed in the form of numerical values ​​or charts, which is easy to understand and apply. Support for subsequent research and management: Quantitative analysis results can provide important references for subsequent grassland vegetation research and management, such as formulating targeted protection measures and evaluating restoration effects.

[0050] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0052] Figure 1 It is the overall flow chart of the present invention;

[0053] Figure 2 This is the grassland distribution map in the early stage of the present invention;

[0054] Figure 3is a precipitation distribution map of the present invention;

[0055] Figure 4 It is the soil content distribution map of the present invention;

[0056] Figure 5 It is the soil texture status distribution map of the present invention;

[0057] Figure 6 It is the slope status distribution map of the present invention;

[0058] Figure 7 It is the slope aspect status distribution map of the present invention;

[0059] Figure 8 It is the distribution map of the present invention from the settlements;

[0060] Fig. 9 is a road accessibility distribution map of the present invention;

[0061] Fig.10 This is a quantitative analysis data chart of grassland vegetation coverage. DETAILED DESCRIPTION

[0062] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0063] like Figures 1 to 10 As shown, the present invention discloses a method for quantitatively analyzing the value of grassland vegetation coverage assets based on GIS data, comprising the following steps:

[0064] S1, obtain geographic information GIS data, collect grassland vegetation change data in GIS data, and determine attribute change components;

[0065] S2, determining the attribute change degree of grassland vegetation for the attribute change component, thereby determining the grassland vegetation weight distribution;

[0066] S3, according to the grassland vegetation weight distribution, determine the expected model of grassland vegetation coverage, and conduct quantitative analysis of grassland vegetation according to the measurement threshold.

[0067] Grassland, as a natural resource asset, is used to study key technologies for grassland vegetation coverage assessment and provide technical support for quantitative analysis and assessment of grassland. At the same time, it explores methods for assessing the value of fresh grass on the ground and the acquisition of basic data.

[0068] There are five main steps to carry out grassland vegetation cover assessment:

[0069] The first is to prepare the questionnaire, base map and technical route. The second is to define the connotation of grassland vegetation coverage. The third is to investigate and organize the data. The fourth is to select the vegetation coverage assessment method and analyze the parameters. The fifth is to assess the vegetation coverage and establish a system for determining and correcting the vegetation coverage.

[0070] Preferably, the above technical solution is that S1 comprises:

[0071] The attribute change components of grassland vegetation include precipitation change component, wind speed change component, soil change component, slope influence component, aspect influence component, settlement influence component, and road influence component.

[0072] S1-1, collect the precipitation and wind speed of a certain place according to GIS data, perform spatial interpolation processing, obtain raster data of precipitation and wind speed, convert the raster to surface or use the zoning statistical tool to assign attributes according to the grade classification standard, and perform overlay analysis with the grass map patch;

[0073]

[0074] Among them, according to the change component A after precipitation interpolation processing, the prediction parameter a of the n-time precipitation in a certain area is extracted n , is the Gaussian component of precipitation change, t is the real-time precipitation moment, and t0 is the initial precipitation moment. This formula describes the change of precipitation around the initial precipitation moment t0. Due to the characteristics of the Gaussian function, it is very suitable for describing the change of precipitation that reaches a peak at a certain moment and then gradually decreases. By adjusting a n The value of can flexibly adapt to different precipitation change patterns. υ is a random variable, W n is the regression coefficient of the random variable;

[0075] Then calculate the wind speed change component:

[0076]

[0077] The wind speed change component B, b1 is the nighttime wind speed change value, b2 is the dynamic mean of wind speed, and f(b3) is the adjustment function of the wind speed interference component b3. is the dynamic change periodic component of the daytime wind speed value b4;

[0078] S1-2, such as Figure 4As shown in the figure, the current status of soil organic matter content and the acquisition of soil content change components are quantified through soil and cultivated land fertility databases. The Kriging interpolation method is used to assign values ​​to the entire region through the collected district and county soil surveys or cultivated land fertility databases, and the grids are reclassified according to the annual soil texture, soil layer thickness, soil organic matter and other index classification scores. Finally, the grids are converted to surfaces and overlaid with grassland map patches for analysis to obtain quantitative layers of soil texture, soil layer thickness, and soil organic matter indicators.

[0079]

[0080] Among them, in the soil change component C, τ0(c i ,t i ) is the soil thickness c i Variation over time i The initial regression coefficient is k, which is the number of soil thickness observations. i ,t i ) is the i-th observation point at sampling time t, x is the adjustment factor,

[0081] S1-3 According to the position and aspect of the slope, based on the DEM data, raster reclassification is performed according to the index classification standards of the position and aspect (azimuth is used as the raster reclassification standard, -1 is flat land), and the raster is vectorized and overlaid with the grassland patch to obtain the index layer of the position and aspect. The second is slope and altitude. Based on the DEM data, the slope and altitude raster data of the whole district (county) are obtained, and the raster is converted to a surface. It is overlaid with the grassland patch through the identification method, and the slope and altitude index classification score standard is assigned;

[0082] D=ω1·f1(α)+ω2·f2(d)+Y

[0083] Where D is the slope influence component, is the slope, Δh is the elevation difference, I is the horizontal distance, d=azimuth(z) is the slope angle, z is the projection vector of the positive direction of the normal on the plane, f1(α) is the slope attribute influence component adjustment function, f2(d) is the slope attribute influence component adjustment function, Y is the residual term, ω1 is the slope adjustment weight, ω2 is the slope adjustment weight,

[0084] S1-4. Distance from settlements

[0085] Data source: data on urban villages and industrial and mining land in the annual land change survey (201), towns (202), villages (203), and roads.

[0086] Quantitative method: Use the proximity analysis tool to analyze the distance from the edge of the grassland patch to the nearest settlements of the city (201), town (202), and village (203). Use the attribute connection tool to assign the obtained distance to the grassland patch, and then assign it according to the indicator classification standard.

[0087] As cities, towns and villages have a wide range and their impacts on grassland vary greatly, the impact radius (maximum distance) is calculated according to the calculation method of the township impact of agricultural land, and the impact radius (maximum distance) is calculated according to the district and county government seats, central towns and general towns. With reference to relevant technical regulations, the impact radius of each settlement is calculated according to the following formula:

[0088] Where r is the radius of the settlement, S is the total area of ​​the settlement, and m is the total number of settlements.

[0089] Based on the distance from each settlement and the radius of action of each level of roads, the quantitative score of the index is calculated using the exponential decay model, and the automatic calculation is performed through GIS software.

[0090] f(r)=(F i R ) 1-r

[0091] Among them: f(r) is the accessibility effect of the quantitative factor on point i (a point inside the patch);

[0092] F i R Functional score of this quantitative factor;

[0093] r is the relative distance from the quantitative factor to point i (a point inside the patch).

[0094] S1-5, Road Accessibility

[0095] Data source: Mainly based on grassland patches and road data from the annual regional change survey.

[0096] Quantitative method: Use the "Proximity Analysis" tool in ArcGIS Neighborhood Analysis to analyze the grassland patches to the highway land (1003), urban and rural road land (1004), and rural road (1006) patches in the annual land change survey data, calculate the distance from the edge of the grassland patch to the nearest road, and use the attribute connection tool to assign the obtained distance to the grassland patch, and then assign it according to the indicator classification standard.

[0097] The functional points and effective radius of different road accessibility conditions are determined according to the classification standards of grade indicators. According to the function and role of the road, the functional points of the road are proportional to its role, with a value between 0-100. The role points of the roads with the best conditions (national highways, expressways) are 100, and the role points of the remaining types are set in descending order. The calculation of the effective radius of the road is publicized as follows:

[0098] Among them, r is the effective radius of the road, S is the built-up area, and L is the total length of the road;

[0099] Based on the distance from each level of roads and the radius of action of each level of roads, the quantitative score of the index is calculated using the exponential decay model, and the automatic calculation is performed through GIS software.

[0100] f(r′)=(F i R ) 1-r

[0101] Among them: f(r) is the accessibility effect of the quantitative factor on point i (a point inside the patch);

[0102] F i R The functional points of the quantitative factor; R is the relative distance from the quantitative factor to point i (a point inside the patch).

[0103] The distribution and proportion of effect scores at different distances from settlements were analyzed by converting raster to surface. As most grasslands are far away from settlements, the effect scores are mainly 20-60 points, of which the score of 20 accounts for 88% of the total grassland area, and the scores of 80 and 100 are relatively small, less than 1% of the total area.

[0104] Preferably, the above technical solution, said S2 comprises: according to the above precipitation change component, wind speed change component, soil change component, slope influence component, thickness influence component, settlement influence component, and road influence component;

[0105] The expected model of grassland vegetation coverage is obtained as follows:

[0106]

[0107] Among them, AVG is the predicted value of grassland vegetation coverage; w A is the weight of precipitation, A T is the precipitation threshold; w B is the weight of wind speed, B T is the wind speed threshold; w C is the weight of soil change, C T is the threshold of soil change, σ1 is the weight of the impact of settlement on grassland vegetation coverage, σ2 is the weight of the impact of road accessibility on grassland vegetation coverage,

[0108] Through GIS, the differences in grassland vegetation coverage of influencing factors were analyzed to determine the impact of the characteristic differences of influencing factors on the changes in grassland vegetation coverage and the value range of influencing factors suitable for growth;

[0109] According to the expected results and actual situation of grassland vegetation coverage, reasonable thresholds can be determined to conduct quantitative analysis of grassland vegetation coverage. Visual display of analysis results: Through quantitative analysis, grassland vegetation coverage can be intuitively displayed in the form of numerical values ​​or charts, which is easy to understand and apply. Support for subsequent research and management: Quantitative analysis results can provide important references for subsequent grassland vegetation research and management, such as formulating targeted protection measures and evaluating restoration effects.

[0110] Determine the distance to the water source and conduct quantitative analysis. Use the "Proximity Analysis" tool in ArcGIS Neighborhood Analysis to analyze the distance from the grassland patch to the river surface (1101), lake surface (1102), reservoir surface (1103), pond surface (1104), ditch (1107), etc. in the annual land change survey data. Calculate the distance from the edge of the grassland patch to the nearest water source. Use the attribute connection tool to assign the obtained distance to the grassland patch, and then assign it according to the indicator classification standard.

[0111] Combined with the grassland vegetation coverage assessment method, it is necessary to collect information on four aspects: regional natural conditions and socio-economic development, grassland utilization, input-output data, and grassland transaction case data. Through data collection, collation and field investigation, the specific content, source and collation method of each data are as follows:

[0112] The grassland utilization status includes the location, boundaries, grassland scale, grassland shape, grassland grade, cadastral registration information, etc. of the grassland to be assessed. Grassland classification results, a forestry resource map, grassland title certificate information, and grassland map patches in the change survey. The grassland map patches in the change survey are superimposed with grassland classification results and title certificate results to obtain the boundaries, grassland shape, registration, and registration information of the grassland to be assessed.

[0113] A certain area has a subtropical humid monsoon climate, with rain and heat occurring at the same time and a distinct three-dimensional climate. It has the characteristics of mild climate, abundant rainfall, neither severe cold nor extreme heat, distinct four seasons, little frost and snow, long frost-free period, weak solar radiation, short sunshine time, more clouds and fog, less frost and snow, and rich climate resources.

[0114] There are 56 rivers of various sizes in a certain area, belonging to the Wujiang River and Yangtze River water systems. There are 26 main rivers, which are distributed in a tree-like pattern throughout the area. The river valleys are narrow, and there are many dangerous rapids along the way.

[0115] In 2023, the region's total GDP reached 42.142 billion yuan, up 3.4%. By industry, the primary industry achieved an added value of 6.599 billion yuan, up 2.6%; the secondary industry achieved an added value of 16.200 billion yuan, up 3.6%; and the tertiary industry achieved an added value of 19.343 billion yuan, up 3.7%. The three-industry structure is 15.7:38.4:45.9. The total output value of animal husbandry in the region in 2023 is 2.465 billion yuan, and 32,500 sheep will be slaughtered.

[0116] The total land area of ​​a certain region is 258,957.84 hectares, of which there are 233,924.15 hectares of agricultural land, 14,673.95 hectares of construction land, and 10,359.74 hectares of unused land. The agricultural land contains 314.97 hectares of grassland resources, including 3.43 hectares of artificial pasture and 311.54 hectares of other grassland.

[0117] Basic information on grassland: A certain area has 35,000 mu of natural grassland, grass hills and grass slopes, accounting for 1% of the total area. 14,000 mu of artificial grass is reserved, and 12,300 mu of cultivated land is planted with grass. The main types of forage grass include white clover, perennial ryegrass and pennisetum. According to the production statistics in 2020, the planting area of ​​perennial ryegrass is 1,100 mu, the planting area of ​​pennisetum is 1,200 mu, the planting area of ​​white clover is 4,400 mu, and the area of ​​grass planted in idle farmland is 12,100 mu.

[0118] Based on the distance between the grassland patch and the roads at all levels and the effective radius of the roads at all levels, the index score is calculated using the exponential decay model, and the score map is automatically calculated and obtained through GIS software.

[0119] 1. Calculation of road accessibility

[0120] The functional score and maximum impact radius are determined according to the road types at each level; the accessibility effect score is calculated by the exponential decay method and an accessibility effect score map is formed.

[0121] 2. Distance from water source

[0122] According to the distribution of water systems in a certain area, the Euclidean distance from each grassland patch to the nearest water source was calculated with the help of GIS analysis tools, and the distance from each patch to the water source was obtained, so the maximum distance from the patch to the water source was 2568m. The distribution of the distance to the water source in the county was standardized to determine the index classification standard and its score. The exponential decay model was used to calculate the effect score from the water source, and a water source effect score map was compiled.

[0123] 3. Distance from settlements

[0124] The grassland patches were converted into points using the Feature to Point (Interior) tool, and the proximity analysis tool was used to analyze the shortest distances from the points to cities (201), towns (202), and villages (203). The obtained distances were assigned to the grassland patches using the Attribute Connection tool, and then assigned according to the index classification standard. Since cities, towns, and villages have a wide range and their impacts on grassland vary greatly, the impact radius (maximum distance) was calculated based on the district and county government seats, central towns, and general towns, with reference to the calculation method for the impact of agricultural land townships. The impact radius of each settlement was calculated according to the following formula with reference to relevant technical regulations:

[0125] 4. Calculation of soil texture, soil layer thickness, and soil organic matter function

[0126] Based on the soil texture, soil thickness, and soil organic matter extracted from the soil survey database of a certain area, the Kriging interpolation method is used to perform global assignment to form a status map of soil texture, soil thickness, and soil organic matter in a certain area. The grid is reclassified according to the index classification score standard, and finally the grid is converted to a surface, superimposed with the grassland map, and the soil texture, soil thickness, and soil organic matter function score map is obtained using pure polygon calculation.

[0127] 5. Calculation of slope and slope effect

[0128] Based on DEM data, the current status map of slope and aspect of a certain area is obtained, and values ​​are assigned according to the slope and aspect index grade classification standards. After copying, the slope and aspect index layers are obtained by overlaying and analyzing the grassland map.

[0129] This invention actually studies the key technologies for quantifying grassland asset value, analyzes the urgent needs and existing problems of quantifying grassland asset value in combination with the current status of grassland asset management; analyzes the characteristics and applicable conditions of different grassland vegetation cover assessment methods, and combines actual analysis to develop methods suitable for grassland vegetation cover assessment in our city, as well as the technical points for compiling a vegetation cover correction system.

[0130] The Delphi method is a new expert forecasting method developed by the Rand Corporation of the United States. It solicits the opinions of experts through questionnaires. After the experts give their opinions, they give feedback. The organizers will organize the preliminary results and then give them feedback to the experts, asking them to reconsider and give their opinions again. After several rounds of feedback, the experts' opinions basically tend to be consistent, and the forecast results are obtained accordingly. That is, according to the experience and subjective judgment of the experts, a large number of factors that cannot be quantitatively analyzed are statistically analyzed, and the results are returned to the experts, so that the scattered opinions converge, give full play to the role of information feedback and information control, and finally focus on coordinated and consistent results.

[0131] The method requires experts to:

[0132] (1) The experts should be technical and management experts and high-level decision-makers in relevant industries such as grassland and socio-economic development. The overall authority of the experts should be high, with a total number of 10-30 people;

[0133] (2) Experts should score based on the background materials and scoring instructions of the corresponding work and score independently without consultation;

[0134] (3) Starting from the second round of scoring, scoring must refer to the results of the previous round of scoring;

[0135] (4) The scoring rounds are 2-3.

[0136] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for quantitative analysis of grassland vegetation coverage based on GIS data, characterized in that: include: S1, obtain geographic information GIS data, collect grassland vegetation change data in GIS data, and determine attribute change components; S2, determine the grassland vegetation coverage weight distribution for the attribute change component, perform expected measurement through the grassland vegetation coverage expected model, and determine the threshold for quantitative analysis of grassland vegetation.

2. The method for quantitatively analyzing grassland vegetation coverage based on GIS data according to claim 1 is characterized in that: The S1 includes: S1-1, collect the precipitation and wind speed of a certain place according to GIS data, perform spatial interpolation processing, obtain raster data of precipitation and wind speed, convert the raster to surface or use the zoning statistical tool to assign attributes according to the grade classification standard, and perform overlay analysis with the grass map patch; Among them, according to the change component A after precipitation interpolation processing, the prediction parameter a of the n-time precipitation in a certain area is extracted n , is the Gaussian component of precipitation change, t is the real-time precipitation moment, t0 is the initial precipitation moment, and the formula describes the change of precipitation around the initial precipitation moment t0. Due to the characteristics of the Gaussian function, it describes the change of precipitation that reaches a peak at a certain moment and then gradually decreases. By adjusting a n The value of is adapted to different precipitation change patterns, υ is a random variable, W n is the random variable regression coefficient.

3. The method for quantitatively analyzing grassland vegetation coverage based on GIS data according to claim 1 is characterized in that: The S1 further comprises: The wind speed change component B, b1 is the nighttime wind speed change value, b2 is the dynamic mean of wind speed, and f(b3) is the adjustment function of the wind speed interference component b3. It is the dynamic change periodic component of the daytime wind speed value b4.

4. The method for quantitatively analyzing grassland vegetation coverage based on GIS data according to claim 1 is characterized in that: The S1 further comprises: S1-2, quantify the soil and arable land fertility database. The collected county soil census or arable land fertility database is assigned a global value using the Kriging interpolation method. The grid is reclassified according to the annual soil texture, soil layer thickness, soil organic matter and other index classification scores. Finally, the grid is converted to a surface and overlaid with the grassland map to obtain the quantitative layer of soil texture, soil layer thickness, and soil organic matter index. Among them, in the soil change component C, τ0(c i ,t i ) is the initial regression coefficient of soil thickness ci changing with time ti, k is the number of soil thickness observations, (c i ,t i ) is the i-th observation point at sampling time t, and x is the adjustment factor.

5. The method for quantitatively analyzing grassland vegetation coverage based on GIS data according to claim 1 is characterized in that: The S1 further comprises: S1-3 According to the slope and aspect of the slope, the raster is reclassified based on the DEM data according to the slope position and aspect index classification standards, and the raster is vectorized and overlaid with the grassland patches to obtain the slope position and aspect index layer. The second is slope and altitude. The slope and altitude raster data are obtained based on the DEM data, and the raster is converted to a surface. It is overlaid with the grassland patches through the identification method and assigned according to the slope and altitude index classification score standard; D=ω1·f1(α)+ω2·f2(d)+Y Where D is the slope influence component, is the slope, Δh is the elevation difference, I is the horizontal distance, d=azimuth(z) is the slope angle, z is the projection vector of the positive direction of the normal on the plane, f1(α) is the adjustment function of the attribute influencing component of the slope, f2(d) is the adjustment function of the attribute influencing component of the slope, Y is the residual term, ω1 is the slope adjustment weight, and ω2 is the slope adjustment weight.

6. The method for quantitatively analyzing grassland vegetation coverage based on GIS data according to claim 1 is characterized in that: The S1 further comprises: S1-4. Distance from settlements Data source: data on urban and rural areas, industrial and mining land, cities, towns, villages, roads, etc. from the annual land change survey results. Quantitative method: Use the proximity analysis tool to analyze the distance from the edge of the grassland patch to the nearest settlement of the city, town, or village. Use the attribute connection tool to assign the obtained distance to the grassland patch, and then assign values ​​according to the indicator classification standard. Since cities, towns and villages have a wide range and their impact on grassland varies greatly, the impact radius is calculated according to the calculation method of the township impact of agricultural land, and the impact radius is calculated according to the district and county government seats, central towns and general towns respectively. With reference to relevant technical regulations, the impact radius of each settlement is calculated according to the following formula: Among them, r is the radius of action of the settlement, S is the total area of ​​the settlement, and m is the total number of settlements; Based on the distance of each settlement and the radius of action of roads at all levels, the quantitative score of the index is calculated using the exponential decay model, and the automatic calculation is performed through GIS software. f(r)=(F i R ) 1-r Where: f(r) is the effect of the quantitative factor on the accessibility of point i; i is a point inside the patch; F i R The functional score of the quantitative factor; r is the relative distance from the quantitative factor to point i.

7. The method for quantitatively analyzing grassland vegetation coverage based on GIS data according to claim 1 is characterized in that: The S1 further comprises: S1-5, Road Accessibility Data source: Mainly based on grassland patches and road data from the annual regional change survey. Quantitative method: Use the "Proximity Analysis" tool in ArcGIS Neighborhood Analysis to analyze the grassland patches to the highway land, urban and rural road land, and rural road patches in the annual land change survey data, calculate the distance from the edge of the grassland patch to the nearest road, and use the attribute connection tool to assign the obtained distance to the grassland patch, and then assign values ​​according to the indicator classification standard; The functional points and effective radius of different road accessibility conditions are determined according to the classification standards of grade indicators. According to the function and role of the road, the functional points of the road are proportional to its role, with a value between 0-100. The role points of the roads with the best conditions (national highways, expressways) are 100, and the role points of the remaining types are set in descending order. The calculation of the effective radius of the road is publicized as follows: Among them, r is the effective radius of the road, S is the built-up area, and L is the total length of the road; Based on the distance from each level of roads and the radius of action of each level of roads, the quantitative score of the index is calculated using the exponential decay model, and the automatic calculation is performed through GIS software. f(r′)=(F i R ) 1-r Where: f(r) is the effect of the quantitative factor on the accessibility of point i; i is a point inside the patch; F i R The functional score of the quantitative factor; r is the relative distance from the quantitative factor to point i; F i R The functional score of the quantitative factor; R is the relative distance from the quantitative factor to point i.

8. The method for quantitative analysis of grassland vegetation coverage based on GIS data according to claim 1 is characterized in that: The S2 includes: The expected model of grassland vegetation coverage is obtained as follows: Among them, AVG is the predicted value of grassland vegetation coverage; w A is the weight of precipitation, A T is the precipitation threshold; w B is the weight of wind speed, B T is the wind speed threshold; w C is the weight of soil change, C T is the threshold of soil change, σ1 is the impact weight of settlement on grassland vegetation coverage, and σ2 is the impact weight of road accessibility on grassland vegetation coverage.

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