A dynamic monitoring method for grassland-livestock balance

Abstract

The present invention provides a method for dynamically monitoring the balance between grass and livestock, which includes obtaining the theoretical livestock carrying capacity through remote sensing monitoring and obtaining the actual livestock carrying capacity through ground monitoring, and verifying the degree of overloading. The technical solution of the present invention can be accurate to the bearing capacity of the grassland contracted by each herdsman household, accurate to the household, and is relatively fair; when monitoring the balance between grass and livestock, it can eliminate the influence of artificial law enforcement, reflecting fairness and justice; in addition, it helps to monitor the degradation of the grassland and protect the grassland ecosystem.

Description

Technical Field

[0001] The present invention relates to a monitoring method, in particular to a dynamic grass-livestock balance monitoring method. Background Art

[0002] In order to protect, construct and rationally utilize grasslands, maintain and improve the ecological environment, and promote the sustainable development of animal husbandry, in accordance with the "Grassland Law of the People's Republic of China", the state has formulated the "Measures for the Administration of Grass-Livestock Balance". It refers to maintaining a dynamic balance between the total amount of available forage obtained by grassland users or contractors through grasslands and other channels and the amount of forage required for the livestock they raise within a certain period of time to maintain a virtuous cycle of the grassland ecosystem.

[0003] The grass-livestock balance system is a basic system for grassland management and ecological protection in China, but it is difficult to achieve the goal of "grass-livestock balance" in implementation. The grass-livestock balance management directly affects the decentralization relationship between managers and producers nowadays, and there are certain limitations in the current implementation of the grass-livestock balance management system. As a grassland management system with the regulation of livestock-carrying capacity as the core, the overloading rate of livestock in key national natural grasslands reaches 31.2%. As a measure for grassland ecological protection, the phenomena of grassland degradation, desertification, salinization, and rocky desertification in the country are still very serious. This situation requires reflection on the problems existing in the formulation and implementation of this system. There are mainly the following defects.

[0004] 1. Currently, the grass-livestock balance management system in China is defined according to the average productivity of grasslands in banners, counties and districts, and one sheep unit can be raised per unit area. The grassland types in a banner, county or district may be diverse, and each grassland type is different, and the above-ground productivity is also different, resulting in different carrying capacities of the grasslands contracted by each pastoral household, and different sheep units raised per unit area by each pastoral household.

[0005] 2. The grassland carrying capacity stipulated in the "Measures for the Administration of Grass-Livestock Balance" has always been a fixed value, without considering the impact of abundant, normal and poor years on grassland productivity. In a year with better rainfall, the grassland carrying capacity may be high, and in a year with poor rainfall, the grassland carrying capacity may be low. There is no variable amount of dynamic carrying capacity every year.

[0006] 3. In the supervision of grass-livestock balance, grazing bans, rotational grazing and grass-livestock balance subsidies are all directly related to grass-livestock balance. At present, the method of counting sheep manually is still used. The implementation of grass-livestock balance must be implemented at the scale of pastoral households. However, due to the heterogeneity and volatility of grassland resources, it is almost impossible to accurately measure the grass yield at the scale of pastoral households through existing means. In addition, herdsmen also question the rationality of the livestock-carrying capacity standards formulated by the government based on their own experience and livelihood needs. Therefore, it is unfair to punish herdsmen according to such a controversial standard. Summary of the Invention

[0007] In view of the deficiencies in the prior art, the present invention provides a dynamic grassland livestock balance monitoring method that is accurate, fair, can improve law enforcement efficiency, and helps protect the grassland ecosystem, specifically as follows:

[0008] The present invention discloses a dynamic grassland livestock balance monitoring method, which is characterized by comprising:

[0009] (1) Collect high-resolution remote sensing data, and after processing, form image data, and use software to interpret and automatically identify land use types, grassland types, and sub-types;

[0010] (2) Calculate the NDVI of different herdsmen's pastures according to the image data in step (1), perform a regression analysis based on the NDVI data and the above-ground biomass obtained through field surveys, establish a yield estimation model, and invert the grass yield data layer;

[0011] (3) Overlay the boundaries of the herdsmen's pastures, use the grass yield data in step (2), and refer to the calculation of the reasonable carrying capacity of natural grasslands in the agricultural industry standard NY / T635-2015 of the People's Republic of China to calculate the theoretical carrying capacity of the herdsmen;

[0012] (4) Apply the "cage method" to measure the seasonal dynamics and forage intake dynamics of the herdsmen's grazing pastures through internal and external comparisons. At the same time, obtain the seasonal dynamics of the grassland without grazing pressure, calculate the grazing intensity of the grazing pasture, investigate and obtain the actual carrying capacity of the herdsmen with a large grazing intensity (forage utilization rate greater than 50%), and compare and verify the overloading degree with the theoretical carrying capacity data in step (3);

[0013] The measurement of grazing intensity and above-ground biomass refers to the calculation of the reasonable carrying capacity of natural grasslands in the agricultural industry standard NY / T635-2015 of the People's Republic of China.

[0014] Further, the NDVI data is calculated using NDVI = (B4 - B3) / (B4 + B3); B4 represents the near-infrared band of the remote sensing image, and B3 represents the red light band of the remote sensing image.

[0015] Further, the spatial resolution of the high-resolution remote sensing data in step (1) is 2 meters.

[0016] Further, the collection of high-resolution remote sensing data in step (1) and the formation of image data after processing are specifically as follows: Use the high-resolution No. 1 remote sensing data in June, July, August, and September each year. The remote sensing base map is mosaicked by 4-phase remote sensing image data of the same month, and after radiation correction, atmospheric correction, and cropping, monthly image data is formed.

[0017] Further, the software in step (1) includes ENVI.

[0018] Further, the cage method in step (4) means placing a small fence of 5m×5m in the grazing pasture, and no grazing is carried out inside the fence.

[0019] Furthermore, the inner diameter of the iron ring on the fence of the cage method in step (4) is 30 mm and the length is 100 mm.

[0020] Furthermore, the upper and lower parts of the fence of the cage method in step (4) are reinforced by welding horizontal iron bars.

[0021] The present invention provides a method for monitoring dynamic grass-livestock balance, which has the following beneficial effects:

[0022] 1. It is accurate to the carrying capacity of the grassland contracted by each pastoral household, accurate to each household, and relatively fair. There is a scientific basis for how many sheep units each household should raise.

[0023] 2. When monitoring the grass-livestock balance, the influence of human law enforcement is eliminated, reflecting fairness and justice.

[0024] 3. Determining the livestock based on the grass, monitoring the changes in the grasslands of each pastoral household can effectively enforce the law and improve the law enforcement efficiency. The law enforcement efficiency can be increased by more than 90%. (Previously, 2-3 law enforcement officers were required to count the sheep one by one at the pastoral households. Now, it is only necessary to check which households are overloaded severely according to the remote sensing satellite images, and there is no need to count the sheep of each household.) It helps to monitor the degradation of the grassland and protect the grassland ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a grassland yield estimation model for inversely calculating the grass yield data layer; wherein, the grassland yield estimation model is Y = 498x 2 + 30.5x 0 ≤ x < 0.35 ( Figure 1 left side); Y = 1295x 2 - 917x + 271 0.35 ≤ x ≤ 1 ( Figure 1 right side); in the formula, Y is the aboveground biomass of the grassland, with the unit of g / m 2 ; x is the NDVI value of the grassland.

[0026] Figure 2 is the forage utilization rate distribution map of the present invention.

[0027] Figure 3 is the aboveground biomass distribution map of the present invention.

[0028] Figure 4 is a schematic diagram of the ground monitoring fence.

[0029] Figure 5 is the fence and component dimensions. DETAILED DESCRIPTION OF THE INVENTION

[0030] The present invention will be further described below by way of examples, but the present invention is not limited to the scope of the described examples. For the experimental methods without specific conditions noted in the following examples, they are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0031] The data analysis method of the present invention:

[0032] Vegetation growth data: Vegetation growth is calculated according to the normalized difference vegetation index (NDVI) using formula (1) NDVI = (B4 - B3) / (B4 + B3), where B4 represents the near-infrared band of the remote sensing image and B3 represents the red band of the remote sensing image. The NDVI value is between [-1, 1], and the larger the NDVI, the higher the vegetation coverage.

[0033] Pasture utilization mode data: According to the local haying time node, obtain the remote sensing images before and after this time node and calculate the NDVI value, and determine the haying pasture according to the characteristic that the NDVI value decreases rapidly after haying.

[0034] Coverage data: Formula (2) fc = (NDVI - NDVIsoil) / (NDVIveg - NDVIsoil), where NDVIsoil is the NDVI value of bare soil or areas without vegetation coverage; NDVIveg represents the NDVI value of pixels completely covered by vegetation.

[0035] Biomass data: There is a strong positive correlation between the normalized difference vegetation index (NDVI) and biomass. Therefore, regression analysis is performed based on the NDVI data and the field survey of biomass, and the vegetation yield estimation relationship model is determined according to the size of the R-square value, and the above-ground biomass data layer is inversely derived.

[0036] Carrying capacity data: According to technical parameters such as biomass data, actual grazing days, and daily food intake of livestock, in accordance with the national agricultural industry standard "Calculation of Reasonable Carrying Capacity of Natural Grasslands" NY_T635 - 2015.

[0037] Grassland utilization intensity data: Monitored by 2 methods: 1. The "cage method" is used to measure and compare the utilization of grazing pastures inside and outside the fence on the ground; 2. High-resolution remote sensing is applied in combination with ground yield measurement data. The forage intake of each grazing pasture is obtained by comparing the biomass of various haying pastures (where there is no livestock grazing and they are only used for haying, and various refer to grasslands and grassland types) with that of the grazing pasture of the same type. The ratio of the forage intake (the remaining grass yield after the livestock outside the fence have eaten) to the grass yield of the haying pasture is the grassland utilization rate of this area.

[0038] Example 1

[0039] The dynamic grass-livestock balance monitoring method includes:

[0040] I. Remote sensing monitoring:

[0041] The GF-1 remote sensing data of June, July, August, and September each year are adopted. The remote sensing base map is mosaicked by 4-phase remote sensing image data of the same month. After radiometric correction, atmospheric correction, and clipping, monthly image data are formed. Software such as ENVI is used for interpretation and automatic recognition of land use types, grassland categories, and sub-categories; the NDVI of pastures of different herders is calculated based on the image data, and regression analysis is performed on the NDVI data and the above-ground biomass investigated on-site to establish a yield estimation model ( Figure 1 ), and the forage yield data layer is inversely derived; the pasture boundaries of herders are overlaid, and the theoretical livestock carrying capacity of herders is calculated based on the above forage yield data, actual grazing days, daily food intake of livestock, and the area of various grasslands of herders' pastures. The theoretical livestock carrying capacity refers to the agricultural industry standard of the People's Republic of China (NY / T635—2015 Calculation of reasonable livestock carrying capacity of natural grasslands).

[0042] II. Ground monitoring:

[0043] The "cage method" is applied (small enclosures of 5m×5m are placed in the grazing land without grazing inside the enclosures) to measure the seasonal dynamics and forage intake dynamics of the grazing pastures of herders through comparison inside and outside the enclosures. At the same time, the seasonal dynamics of the grassland without grazing pressure are obtained, the grazing intensity of the grazing land is calculated, and the actual livestock carrying capacity of herders with a large grazing intensity is investigated and obtained, and compared with the remotely sensed estimated theoretical livestock carrying capacity data to verify the overloading degree. The grazing intensity refers to the agricultural industry standard of the People's Republic of China (NY / T635—2015 Calculation of reasonable livestock carrying capacity of natural grasslands). When the forage utilization rate is 50%-60%, it is considered mild overloading; when it is 60%-70%, it is considered moderate overloading; when it is greater than 70%, it is considered severe overloading.

[0044] The above ground monitoring is mainly based on sample plot and quadrat surveys. On the field survey routes, the sample plots are mainly arranged in representative types, including representative grassland categories (temperate meadow steppe, temperate steppe, temperate desert steppe, temperate steppe desert, temperate desert, etc.) and grassland types (plain and hilly meadow steppe sub-category, mountain meadow steppe sub-category, sandy land steppe sub-category, etc.). Several small enclosures are set up using the "cage method" to measure and compare the seasonal dynamics and forage intake dynamics of the vegetation in the grazing pastures inside and outside the enclosures.

[0045] One main plant species description quadrat and one species-specific yield measurement quadrat, and two non-species-specific yield measurement quadrats are randomly set in the sample plot. The sample plot table mainly records information such as longitude and latitude, altitude, grassland category, grassland sub-category, grassland type, topographic and geomorphic features, soil texture, grassland utilization method, and grassland utilization intensity.

[0046] The quadrat table mainly records the names of the main plant species, and indicators such as the coverage, height, and forage yield of the community. The forage yield is measured by cutting the above-ground part of the grass community at ground level and weighing the dry weight after drying.

[0047] Example 2

[0048] Establishment of regression analysis model

[0049] Select sample points (such as pastures of different herdsmen). In this embodiment, the grazing pastures of herdsmen in Wulagai area of Inner Mongolia are selected. Regression analysis is performed based on NDVI data and field-measured aboveground biomass to establish a yield estimation model and invert the aboveground grass yield data layer ( Figure 1 ).

[0050] The method for measuring aboveground biomass refers to the calculation of reasonable stocking rate of natural grasslands in the agricultural industry standard NY / T635—2015 of the People's Republic of China.

[0051] When 0.35 ≤ x ≤ 1, regression analysis is performed according to the data in Table 1, Y = 1295x 2 -917x + 271( Figure 1 right side), where Y is the aboveground biomass of the grassland, with the unit of g / m 2 ; x is the NDVI value of the grassland.

[0052] Table 1

[0053]

[0054]

Claims

1. A dynamic grassland-livestock balance monitoring method, characterized in that, Including: (1) Collect high-resolution remote sensing data, and after processing, form image data. Use software to interpret and automatically identify land use types, grassland types, and subtypes; (2) Calculate the NDVI of the grasslands of different herdsmen households based on the image data in step (1). Conduct a regression analysis based on the NDVI data and the above-ground biomass from on-site investigations, establish a yield estimation model, and invert the forage yield data layer; (3) Overlay the boundaries of the herdsmen households' grasslands, use the forage yield data in step (2), and refer to the reasonable livestock carrying capacity of natural grasslands in the agricultural industry standard NY / T635—2015 of the People's Republic of China to calculate the theoretical livestock carrying capacity of the herdsmen households; (4) Apply the "cage method", measure the seasonal dynamics and the dynamic of forage intake of the grazing grasslands of the herdsmen households through internal and external comparisons. At the same time, obtain the seasonal dynamics of the grassland without grazing pressure, calculate the grazing intensity of the grazing land, investigate and obtain the actual livestock carrying capacity of the herdsmen households with a forage utilization rate greater than 50%, and compare and verify the overloading degree with the theoretical livestock carrying capacity data in step (3); The measurement of grazing intensity and above-ground biomass refers to the calculation of the reasonable livestock carrying capacity of natural grasslands in the agricultural industry standard NY / T635—2015 of the People's Republic of China; Among them, the yield estimation model is Y = 498x 2 + 30.5x where 0 ≤ x < 0.35; Y = 1295x 2 - 917x + 271 where 0.35 ≤ x ≤ 1; In the formula, Y is the aboveground biomass of the grassland, with the unit of g / m 2 ; x is the NDVI value of the grassland.

2. The dynamic grassland-livestock balance monitoring method according to claim 1, wherein, The spatial resolution of the high-resolution remote sensing data in step (1) is 2 meters.

3. The dynamic grassland-livestock balance monitoring method according to claim 1, wherein, The specific process of collecting high-resolution remote sensing data in step (1) and forming image data after processing is as follows: Use the high-resolution satellite No. 1 remote sensing data in June, July, August, and September every year. The remote sensing base map is mosaicked by 4-phase remote sensing image data of the same month. After radiation correction, atmospheric correction, and cropping, monthly image data is formed.

4. The dynamic grassland-livestock balance monitoring method according to claim 1, wherein, The software in step (1) includes ENVI.

5. The dynamic grassland-livestock balance monitoring method according to claim 1, wherein, The NDVI data is calculated using NDVI = (B4 - B3) / (B4 + B3); B4 represents the near-infrared band of the remote sensing image, and B3 represents the red band of the remote sensing image.

6. The dynamic grassland-livestock balance monitoring method according to claim 1, wherein, The cage method in step (4) means placing a small fence of 5m × 5m in the grazing land without grazing inside the fence.

7. The dynamic grassland-livestock balance monitoring method according to claim 6, wherein, The inner diameter of the iron ring on the fence of the cage method in step (4) is 30mm, and the length is 100mm.

8. The dynamic grassland-livestock balance monitoring method according to claim 6, wherein, The upper and lower parts of the fence of the cage method in step (4) are reinforced by welding horizontal iron bars.

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