Alpine grassland bearing capacity evaluation and optimization method based on hyperspectral remote sensing

Abstract

An alpine grassland bearing capacity evaluation and optimization method based on hyperspectral remote sensing comprises the steps of evaluating a nutritional value corresponding to an alpine grassland quality index (a general nutrient index), calculating a ratio of the nutritional value to a standard grassland nutritional value, and optimizing the alpine grassland bearing capacity by adopting the ratio. And the alpine grassland bearing capacity based on quality index optimization has higher confidence. Firstly, an optimal pasture yield and quality parameter inversion model is selected; determining formula parameters according to industrial standards in combination with actual conditions, and calculating the theoretical bearing capacity of the alpine grassland in the research area based on the optimal pasture yield modeling result and the formula parameters; and finally, correcting the bearing capacity of the alpine grassland based on the optimal pasture quality modeling result and the general nutrient parameters, and determining the bearing capacity of the alpine grassland as the optimized bearing capacity of the alpine grassland by taking the spatial resolution of the hyperspectral image as a calculation unit. According to the method, an effective solution is provided for evaluation and optimization of the bearing capacity of the alpine grassland, and references are provided for the animal husbandry amount and pasturing area configuration.

Description

Technical Field

[0001] The present invention belongs to the field of hyperspectral remote sensing, and in particular relates to a method for evaluating and optimizing the carrying capacity of an alpine grassland based on hyperspectral remote sensing. Background Art

[0002] The alpine grassland ecology is fragile and sensitive. Overgrazing causes grassland ecological degradation and threatens ecological security. Carrying out alpine grassland carrying capacity evaluation is the basis for stocking control and pasture selection, and is of great significance to ensuring the sustainable development of animal husbandry and ecological security. The current grassland carrying capacity estimation methods are mostly based on production indicators such as grass yield (Wu Dan, 2015), net primary productivity (NPP) (Wang Qi et al., 2019) and normalized difference vegetation index (NDVI) (Ren et al., 2021), but ignore the importance of quality indicators for grassland carrying capacity, and cannot effectively and objectively evaluate the health status of the actual grassland ecosystem. Compared with ordinary forage, high-quality forage has a higher crude protein (CP) content, but lower acid detergent fiber (ADF) and neutral detergent fiber (NDF), soft grass, good grazing resistance, and higher feeding value (Chen Gu et al., 2010).

[0003] With the development of hyperspectral remote sensing technology, thanks to its advantages of high spectral resolution, wide wavelength coverage, unified map and spectrum, and continuous imaging, the ability to distinguish and identify ground objects has been greatly improved, and the number of imaging channels has been greatly increased, which makes it possible to accurately invert grassland yield indicators and quality indicators (approximate nutrient parameters) (Gao Jinlong, 2020), and also brings the possibility of optimizing yield based on forage quality. Therefore, compared with the grassland carrying capacity directly estimated by yield indicators, the grassland carrying capacity optimized by quality indicators is more reasonable. Since different approximate nutrients of forage affect digestible energy to different degrees, and the number of approximate nutrient parameters is very large, how to determine the optimal number and value of approximate nutrient parameters through method design to accurately and reliably estimate the carrying capacity of alpine grassland is a problem worth studying.

[0004] The references for the background technology are as follows:

[0005] [1] Chen Gu, Tai Jianhui. Forage quality and quality standards in commercial applications in the United States [J]. China Animal Husbandry Newsletter, 2010, 48-49.

[0006] [2] Gao Jinlong, Liu Yin, Ge Hou, Feng Liang. Research progress on hyperspectral remote sensing of nutritional quality of natural grassland forage[J]. Acta Prataculturae Sinica, 2020, 29:172-185.

[0007] [3] Wang Qi, Wu Chengyong, Chen Kelong, Zhang Xiao, Zhang Lele, Ding Junxia. Estimation of grass yield and livestock carrying capacity in Qinghai Lake Basin based on MODIS NPP data[J]. Ecological Science, 2019, 38: 178-185.

[0008] [4] Wu Dan. Study on the origin and carrying capacity of grassland in Guanshan grassland, Long County[M]. Northwest Agriculture and Forestry University. 2015. Summary of the invention

[0009] In view of the shortcomings of existing alpine grassland carrying capacity estimation methods, the present invention provides an alpine grassland carrying capacity assessment and optimization method based on hyperspectral remote sensing, which solves the difficult problem of how to optimize yield using forage quality indicators. The optimal number and values ​​of approximate nutrient parameters are determined through method design to accurately and reliably estimate the alpine grassland carrying capacity.

[0010] The technical solution adopted by the present invention is: a method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing, comprising the following steps:

[0011] Step 1: Select the optimal forage yield inversion model Y(X), where X is the inversion parameter and Y is the forage yield;

[0012] Step 2: According to the agricultural industry standard "Calculation of Reasonable Carrying Capacity of Natural Grassland" (NY / T635-2015), combined with the actual situation of the study area, determine the pasture parameters, which include reasonable grassland utilization rate G, proportion of edible grass in grassland E, standard sheep unit I and grassland grazing time D;

[0013] Step 3: Based on the optimal modeling results of the inversion parameter X and the pasture parameters determined in step 2, the theoretical carrying capacity C of the alpine grassland of each grid unit in the study area is calculated according to the agricultural industry standard "Calculation of Reasonable Carrying Capacity of Natural Grassland" (NY / T635-2015), where C = {C1, C2, C3, ..., C i}, i represents the i-th grid unit, and its calculation formula is as follows:

[0014]

[0015] Where: C is the theoretical carrying capacity of grassland, (SU·hm -2 ); Y is the optimal grass yield per unit area (g·m -2 ); G is the annual grassland utilization rate (%); E is the proportion of edible grass in the grassland (%); I is the standard sheep unit; D is the grassland grazing time (d).

[0016] Step 4: Select the optimal forage quality inversion model *(X), where * is the approximate nutrient index of the forage, the approximate nutrient index of the forage includes crude protein content CP, neutral detergent fiber content NDF and acid detergent fiber content ADF, and X is the inversion parameter;

[0017] Step 5: Consult references and determine the approximate nutrient parameters that have not been inverted based on the actual situation in the study area, including 48-hour in vitro neutral detergent fiber digestibility NDFD and crude fat content EE;

[0018] Step 6: Based on the optimal forage quality modeling results of the inversion parameter X and the uninverted approximate nutrient parameters determined in step 5, calculate the relative forage quality RFQ of the alpine grassland in each grid cell in the study area, RFQ = {RFQ1, RFQ2, RFQ3, ..., RFQ i}, i represents the i-th grid unit, and its calculation formula is as follows:

[0019] RFQ=DMI×TDN / 1.23 (2)

[0020]

[0021] Wherein: CP is crude protein content (%); NDF is neutral detergent fiber content (%); ADF is acid detergent fiber content (%); RFQ is relative feeding quality (%); TDN is total digestible nutrients (%); DMI is ad libitum intake of roughage dry matter (%); NDFn is nitrogen-free neutral detergent fiber content (%), NDFn = NDF × 0.93; NDFD is 48-hour in vitro neutral detergent fiber digestibility (%); NFC is non-fiber carbohydrate content (%), NFC = 100-(NDFn + CP + EE + ash), ash is crude ash content (%); EE is crude fat content (%)

[0022] Step 7: Based on the dominant species of grassland in the study area, take it as the standard forage type, assume its approximate nutrient parameters as standard values, and calculate the relative feeding quality RFQ of the standard forage according to step 6 s ;

[0023] Step 8: Calculate the relative forage quality (RFQ) for each grid cell i RFQ forage quality relative to standard forage s The ratio R i ;

[0024] R i =RFQ i / RFQ s (5)

[0025] Step 9: Using the Ratio R iCorrect the theoretical carrying capacity of alpine grassland C in each grid unit i , and the optimized theoretical carrying capacity of alpine grassland C is obtained oi , and its calculation formula is as follows:

[0026] C oi =C i ×R i (6)

[0027] Step 10: Repeat steps 8 to 9 until all grid cells in the hyperspectral remote sensing image are calculated;

[0028] Step 11: Output the theoretical carrying capacity C of the alpine grassland of each grid cell after optimization oi The theoretical carrying capacity of the alpine grassland after optimization is C o ={C o1 , C o2 , C o3 , …, C oi}.

[0029] Furthermore, the selection range of the optimal forage yield inversion model Y(X) includes: stepwise regression, partial least squares method, principal component analysis, physical model, neural network, and random forest.

[0030] Furthermore, the selection range of the optimal forage quality inversion model *(X) includes: stepwise regression, partial least squares method, principal component analysis, physical model, neural network, and random forest.

[0031] Furthermore, when the model Y(X) or *(X) is selected in step 1 or step 4, the correlation coefficient R is used to determine the model based on the laboratory analysis data after sampling of the actual alpine grassland and reference to relevant scientific research papers and other literature. 2 , root mean square error RMSE determines the optimal inversion model.

[0032] Furthermore, the selection range of the inversion parameter X of the model in step 1 or step 4 includes: spectral characteristic variables and vegetation indices, wherein the spectral characteristic variables include band reflectance, high-order differentials of reflectance, absorption or reflection position, absorption or reflection depth, absorption or reflection width, and absorption or reflection symmetry; the vegetation indices include normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), conversion vegetation index (TVI), modified soil adjusted vegetation index (MSAVI), and optimized soil adjusted vegetation index (OSAVI).

[0033] Furthermore, the relative feeding quality RFQ in step 6 is based on the concept of total digestible nutrients TDN relative to standard forage. Forage with higher relative feeding quality RFQ can provide more energy relative to standard forage. The default standard forage is alfalfa in full bloom, and its relative feeding quality RFQ is 100.

[0034] Furthermore, in step 7, according to the different dominant species in the study area, the type of standard forage should be based on the dominant species, and its relative feeding quality RFQ is no longer 100 and needs to be recalculated.

[0035] The beneficial effects of the present invention are:

[0036] Compared with the existing technology, the proposed method combines the industry standard based on the concept that the relative feeding quality RFQ of forage is the total digestible nutrients (TDN) relative to the standard forage, determines the optimal number and values ​​of approximate nutrient parameters, optimizes the grassland carrying capacity calculated only based on forage yield, solves the difficulty of using forage quality indicators to formulate and correct grassland carrying capacity, and achieves the goal of being closer to the actual situation of grassland. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing according to an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of the carrying capacity of alpine grassland before optimization in an implementation case of the present invention;

[0039] Figure 3 It is a schematic diagram of the optimized alpine grassland carrying capacity in the implementation case of the present invention. DETAILED DESCRIPTION

[0040] In order to facilitate ordinary technicians in this field to understand and implement the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation cases. It should be understood that the implementation cases described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0041] like Figure 1 As shown, the embodiment of the present invention proposes a method for evaluating and optimizing the carrying capacity of an alpine grassland based on hyperspectral remote sensing. The technical solution provided by the present invention is described in detail below with respect to an implementation case. The implementation case is an evaluation and optimization of the carrying capacity of an alpine grassland, but the protection scope of the present invention is not limited to the embodiment.

[0042] Implementation Cases:

[0043] Step 1: Select the multivariate linear regression model Y(X) as the optimal forage yield inversion model, where X is the inversion parameter and its function expression is as follows:

[0044] Y=379.747EVI-561.583ρ1396+104.802PC3+17.824

[0045] Where: EVI represents the enhanced vegetation index, ρ1396 represents the spectral reflectance of the band with a central wavelength of 1396 nm, and PC3 represents the third principal component in the principal component analysis of all bands of the hyperspectral image.

[0046] Step 2: Based on the agricultural industry standard "Calculation of Reasonable Carrying Capacity of Natural Grassland" (NY / T635-2015), combined with the actual situation of the study area, the reasonable grassland utilization rate G is 50%, the proportion of edible grass in grassland E is 90%, the standard sheep unit I is the daily food intake of livestock per sheep unit, 1.8 kg of standard hay with a moisture content of 14% is converted into 1.548 kg of completely water-free hay, and the grassland grazing time D is 365 days;

[0047] Step 3: Based on the optimal modeling result (grass yield Y) of the inversion parameter X and the pasture parameters determined in step 2, the theoretical carrying capacity C of the alpine grassland of each grid unit in the study area is calculated according to the agricultural industry standard "Calculation of Reasonable Carrying Capacity of Natural Grassland" (NY / T635-2015), where C = {C1, C2, C3, ..., C i}, i represents the i-th grid unit, and its calculation formula is as follows:

[0048]

[0049] Where: C is the theoretical carrying capacity of grassland, (SU·hm -2 ); Y is the optimal grass yield per unit area (g·m -2 ); G is the annual grassland utilization rate (%); E is the proportion of edible grass in the grassland (%); I is the standard sheep unit; D is the grassland grazing time (d).

[0050] Step 4: Select the multiple linear regression model CP(X), NDF(X) and ADF(X) as the optimal forage quality inversion model, where X is the inversion parameter and its function expression is as follows:

[0051] CP=46.735ρ1497-77.126ρ733-99.366ρ2516+326.639ρ1918+21.464

[0052] NDF=1414.260ρ1884-112.185ρ2347+114.522ρ404+43.558

[0053] ADF=-494.601ρ1918+68.076ρ2415+34.942

[0054] Where: ρ* represents the spectral reflectance of the band with a central wavelength of *nm.

[0055] Step 5: Consult references and, based on the actual situation in the study area, take the 48-hour in vitro neutral detergent fiber digestibility NDFD as 55%, the crude fat content EE as 2%, and the crude ash content ash as 9%;

[0056] Step 6: Based on the optimal forage quality modeling results (crude protein content CP, neutral detergent fiber content NDF and acid detergent fiber content ADF) and the uninverted approximate nutrient parameters determined in step 5, calculate the relative feeding quality RFQ of the alpine grassland in each grid cell in the study area, RFQ = {RFQ1, RFQ2, RFQ3, ..., RFQ i}, i represents the i-th grid unit, and its calculation formula is as follows:

[0057] RFQ=DMI×TDN / 1.23 (2)

[0058]

[0059] Wherein: CP is crude protein content (%); NDF is neutral detergent fiber content (%); ADF is acid detergent fiber content (%); RFQ is relative feeding quality (%); TDN is total digestible nutrients (%); DMI is ad libitum intake of roughage dry matter (%); NDFn is nitrogen-free neutral detergent fiber content (%), NDFn = NDF × 0.93; NDFD is 48-hour in vitro neutral detergent fiber digestibility (%); NFC is non-fiber carbohydrate content (%), NFC = 100-(NDFn + CP + EE + ash), ash is crude ash content (%); EE is crude fat content (%);

[0060] Step 7: Based on the dominant species of grassland in the study area, take it as the standard forage type, assume its approximate nutrient parameters as standard values, and calculate the relative feeding quality RFQ of the standard forage according to step 6 s =125.30;

[0061] Step 8: Calculate the relative forage quality (RFQ) of each grid cell (within the spatial range of the hyperspectral remote sensing image resolution) i RFQ forage quality relative to standard forage s The ratio R i ;

[0062] R i =RFQ i / RFQ s (5)

[0063] Step 9: Using the Ratio R i Optimize the theoretical carrying capacity of alpine grassland C in each grid unit (within the spatial range of hyperspectral remote sensing image resolution) i , and the optimized theoretical carrying capacity of alpine grassland C is obtained oi , and its calculation formula is as follows:

[0064] C oi =C i ×R i (6)

[0065] Step 10: Repeat steps 8 to 9 until all grid cells in the hyperspectral remote sensing image are calculated.

[0066] Step 11: Output the theoretical carrying capacity C of the alpine grassland of each grid cell after optimization oi The theoretical carrying capacity of the alpine grassland after optimization is C o ={C o1 , C o2 , C o3 , …, C oi}.

[0067] To verify the effectiveness of the proposed method, the hyperspectral remote sensing satellite images of the Dangqu Basin were used, combined with field sampling and laboratory analysis data to verify the effectiveness of the proposed method. The results showed that the average theoretical carrying capacity C of the alpine grassland in the study area calculated based only on forage yield was 0.51 SU·hm -2 (like Figure 2 The theoretical carrying capacity of alpine grassland after yield index optimization is C o The average value is 0.63SU·hm -2 (like Figure 3 Optimized alpine grassland carrying capacity C o The high values ​​are mainly concentrated in the southeast of the basin. The optimization method can improve the reasonable livestock carrying capacity of high-quality forage, which is of great significance for the rational utilization of alpine grasslands, sustainable development of animal husbandry and ecological environment protection. This shows that the alpine grassland carrying capacity obtained by the method proposed in the present invention has higher data value and is better than previous estimation methods.

[0068] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing, characterized in that: The following steps are involved: Step 1: Select the optimal forage yield inversion model Y(X), where X is the inversion parameter and Y is the forage yield; Step 2: According to the agricultural industry standard "Calculation of Reasonable Carrying Capacity of Natural Grassland" (NY / T635-2015), combined with the actual situation of the study area, determine the pasture parameters, which include reasonable grassland utilization rate G, proportion of edible grass in grassland E, standard sheep unit I and grassland grazing time D; Step 3: Based on the optimal modeling results of the inversion parameter X and the pasture parameters determined in step 2, the theoretical carrying capacity C of the alpine grassland of each grid unit in the study area is calculated according to the agricultural industry standard "Calculation of Reasonable Carrying Capacity of Natural Grassland" (NY / T635-2015), where C = {C1, C2, C3, ..., C i }, i represents the i-th grid unit, and its calculation formula is as follows: In the formula: C is the theoretical carrying capacity of grassland; Y is the optimized grass yield per unit area of ​​grassland; G is the annual grassland utilization rate; E is the proportion of edible grass in grassland; I is the standard sheep unit; D is the grassland grazing time; Step 4: Select the optimal forage quality inversion model *(X), where * is the approximate nutrient index of the forage, the approximate nutrient index of the forage includes crude protein content CP, neutral detergent fiber content NDF and acid detergent fiber content ADF, and X is the inversion parameter; Step 5: Consult references and determine the approximate nutrient parameters that have not been inverted based on the actual situation in the study area, including 48-hour in vitro neutral detergent fiber digestibility NDFD and crude fat content EE; Step 6: Based on the optimal forage quality modeling results of the inversion parameter X and the uninverted approximate nutrient parameters determined in step 5, calculate the relative forage quality RFQ of the alpine grassland in each grid cell in the study area, RFQ = {RFQ1, RFQ2, RFQ3, ..., RFQ i }, i represents the i-th grid unit, and its calculation formula is as follows: RFQ=DMI×TDN / 1.23 (2) Wherein: CP is crude protein content; NDF is neutral detergent fiber content; ADF is acid detergent fiber content; RFQ is relative feeding quality; TDN is total digestible nutrients; DMI is ad libitum intake of roughage dry matter; NDFn is nitrogen-free neutral detergent fiber content, NDFn = NDF × 0.93; NDFD is 48-hour in vitro neutral detergent fiber digestibility; NFC is non-fiber carbohydrate content, NFC = 100-(NDFn + CP + EE + ash), ash is crude ash content; EE is crude fat content; Step 7: Based on the dominant species of grassland in the study area, take it as the standard forage type, assume its approximate nutrient parameters as standard values, and calculate the relative feeding quality FRQ of the standard forage according to step 6 s ; Step 8: Calculate the relative forage quality (FRQ) of each grid cell i RFQ forage quality relative to standard forage s The ratio R i ; R i =RFQ i / RFQ s (5) Step 9: Using the Ratio R i Correct the theoretical carrying capacity of alpine grassland C in each grid unit i , and the optimized theoretical carrying capacity of alpine grassland C is obtained oi , and its calculation formula is as follows: C oi =C i ×R i (6) Step 10: Repeat steps 8 to 9 until all grid cells in the hyperspectral remote sensing image are calculated; Step 11: Output the theoretical carrying capacity C of the alpine grassland of each grid cell after optimization oi The theoretical carrying capacity of the alpine grassland after optimization is C o ={C o1 , C o2 , C o3 , …, C oi }.

2. The method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing according to claim 1, characterized in that: The selection range of the optimal forage yield inversion model Y(X) includes: stepwise regression, partial least squares method, principal component analysis, physical model, neural network, and random forest.

3. The method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing according to claim 1, characterized in that: The selection range of the optimal forage quality inversion model *(X) includes: stepwise regression, partial least squares method, principal component analysis, physical model, neural network, and random forest.

4. The method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing according to claim 1, characterized in that: When model Y(X) or *(X) is selected in step 1 or step 4, the correlation coefficient R is calculated based on the actual laboratory analysis data after sampling of alpine grassland and reference to relevant scientific research papers and other literature. 2 , root mean square error RMSE determines the optimal inversion model.

5. The method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing according to claim 1, characterized in that: The selection range of the inversion parameter X of the model in step 1 or step 4 includes: spectral characteristic variables and vegetation indexes, wherein the spectral characteristic variables include band reflectance, high-order differentials of reflectance, absorption or reflection position, absorption or reflection depth, absorption or reflection width, and absorption or reflection symmetry; the vegetation index includes the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), conversion vegetation index (TVI), modified soil adjusted vegetation index (MSAVI), and optimized soil adjusted vegetation index (OSAVI).

6. The method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing according to claim 1, characterized in that: The relative feeding quality RFQ in step 6 is based on the concept of total digestible nutrients TDN relative to standard forage. Forage with higher relative feeding quality RFQ can provide more energy relative to standard forage. The default standard forage is alfalfa in full bloom, and its relative feeding quality RFQ is 100.

7. The method for evaluating and optimizing the carrying capacity of alpine grassland based on hyperspectral remote sensing according to claim 6, characterized in that: In step 7, according to the dominant species in the study area, the type of standard forage should be based on the dominant species, and its relative feeding quality RFQ is no longer 100 and needs to be recalculated.

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