A method and system for monitoring and evaluating the drought condition of grassland in a pastoral area

Abstract

The application discloses a kind of pasture grassland drought dynamic monitoring and evaluation method and system, through the time period cooperation of remote sensing satellite and unmanned aerial vehicle, construct the vegetation and excrement distribution monitoring network of " heaven and earth integration ", realize the dynamic monitoring of pasture scale;Pasture is divided based on multidimensional index, image processing and big data analysis are fused, dynamic planning unmanned aerial vehicle flight path, key patrol target plot, effectively improve timeliness;Fully consider the improvement effect of excrement on soil moisture and soil microenvironment, take excrement amount and excrement distribution as evaluation factor, combined with the vegetation cover of divided plot, drought situation of pasture is corrected in view of, effectively improve the accuracy of drought determination, promote the paradigm shift of grassland management from " passive drought resistance " to active adaptive regulation.

Description

Technical Field

[0001] This invention relates to the application of image processing and big data analysis technologies in the assessment of drought conditions in pastoral grasslands, and particularly to a method and system for dynamic monitoring and assessment of drought conditions in pastoral grasslands. Background Technology

[0002] Grassland drought monitoring technology systems based on vegetation cover have developed multi-scale and multi-dimensional assessment methods. These methods provide a scientific basis for drought assessment by capturing the physiological and ecological responses of vegetation to water stress. Currently, the mainstream technologies mainly include the following categories:

[0003] First, the NDVI / EVI index system: As the most widely used vegetation cover evaluation indicators, the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are calculated based on the reflectance characteristics of red and near-infrared bands, effectively reflecting changes in vegetation greenness and biomass. Second, LAI fusion technology: Addressing the shortcomings in simulating eco-hydrological processes in arid and cold regions, recent research has fused GLASS LAI (Leaf Area Index) data using the ESTARFM model to generate a high spatiotemporal resolution (1 day / 30 meters) LAI dataset. Third, the Temperature-Vegetation Drought Index (TVDI): Constructing an NDVI-LST (Land Surface Temperature) feature space, the coupling relationship between vegetation canopy temperature and soil moisture is quantified using the wet-dry edge equation. Fourth, multi-source data collaboration and hydrological model simulation: This includes the fusion of Standardized Precipitation Index (SPI) with remote sensing, and improvements to the SWAT model.

[0004] Although current drought monitoring technologies have formed a multi-dimensional framework, significant limitations remain in terms of dynamism, scale matching, and integration of ecological processes, restricting the accuracy and application value of assessment results. First, traditional assessments often rely on fixed thresholds or historical averages to classify drought levels, failing to fully consider the time-varying characteristics of vegetation responses to drought, resulting in a significant lag. Second, regional-scale remote sensing monitoring (such as 1km resolution) struggles to capture vegetation heterogeneity at the herder scale; when satellite imagery shows "overall normal," localized overgrazing areas may already be facing severe water stress. Third, the impact of grazing activities is ignored. Grazing activities significantly affect vegetation cover in the corresponding areas, and livestock excrement, as an important pathway for nutrient redistribution, can also significantly alter local soil moisture and nutrient balance, all of which influence the true drought situation in pastoral areas. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by providing a method and system for dynamic monitoring and assessment of drought conditions in pastoral grasslands.

[0006] This invention first provides a method for dynamic monitoring and assessment of drought conditions in pastoral grasslands, comprising the following steps:

[0007] S1: In the first time period, the vegetation cover of the first plot of land is obtained through remote sensing satellites, and the vegetation cover rate is calculated. The first plot of land is rectangular.

[0008] S2: Divide the first plot into several second plots of the same area, the side length of the second plot is L, and the side length L is positively correlated with the vegetation coverage of the first plot;

[0009] S3: In the second time period, a first low-altitude image of the first plot of land is acquired by a drone at a first flight altitude along a first path, the first path being determined by the vegetation cover of each of the second plots of land.

[0010] S4: Obtain the fecal coverage of the first plot based on the first low-altitude image, and calculate the fecal coverage rate;

[0011] S5: In the third time period, the UAV acquires a second low-altitude image of the first plot of land at a second flight altitude and along a second path, the second path being determined by the fecal coverage of each of the second plots of land;

[0012] S6: Based on the first low-altitude image and the second low-altitude image, dynamically correct the drought situation in pastoral grasslands.

[0013] Preferably, in step S3 above, in the second time period, the first low-altitude image of the first plot is acquired by the UAV at a first flight altitude along a first path, the first path being determined by the vegetation cover of each of the second plots, specifically as follows:

[0014] S31: Determine the first flight altitude based on the side length L;

[0015] S32: Calculate the vegetation coverage of each of the second plots, and plan the first path of the UAV in order of increasing vegetation coverage.

[0016] Preferably, in step S5 above, during the third time period, the drone acquires a second low-altitude image of the first plot of land at a second flight altitude and along a second path. The second path is determined by the fecal coverage of each of the second plots of land. Specifically:

[0017] S51: Determine the second flight altitude based on the side length L and the fecal coverage rate;

[0018] S52: Determine the scanning distance R based on the fecal coverage rate, wherein the scanning distance R is inversely correlated with the fecal coverage rate;

[0019] S53: Using the grid intersection points inside the first gridded plot as the center and the scanning distance R as the radius, draw a circle to form several scanning areas. Each scanning area includes four sub-scanning areas distributed in four adjacent second plots.

[0020] S54: Calculate the feces coverage area of ​​each second plot. If the overlap area between the vegetation coverage area and the feces coverage area in any sub-scanning area is greater than a set threshold, then the second plot where the sub-scanning area is located is determined as the target plot.

[0021] S55: Plan the second path of the UAV based on the target plot.

[0022] Preferably, in step S52 above, the relationship between the scanning distance R and the side length L is 0 < R ≤ L / 2.

[0023] Preferably, in step S54 above, the set threshold is positively correlated with the fecal coverage rate.

[0024] Preferably, in step S55 above, for all the target plots, the second path of the UAV is planned in descending order of the overlapping area of ​​the vegetation coverage and the manure coverage.

[0025] Preferably, in step S6 above, the changes in vegetation coverage of all the target plots in the first low-altitude image and the second low-altitude image are calculated, and a first correction parameter is determined based on the changes in vegetation coverage to dynamically correct the drought situation in the pastoral grassland.

[0026] Preferably, after step S6 above, the following step is further included:

[0027] S7: Divide off an adjacent plot of the same size as the first plot on one side of the first plot, and perform the above steps S1-S6 on the adjacent plot to determine the second correction parameter;

[0028] S8: Based on the first correction parameter and the second correction parameter, dynamically correct the drought situation in pastoral grasslands.

[0029] Preferably, in step S7 above, the orientation of the adjacent land parcel relative to the first land parcel is determined by the density of the target land parcel.

[0030] Meanwhile, the present invention also provides a system for realizing a method for dynamic monitoring and assessment of drought conditions in pastoral grasslands, comprising:

[0031] An image acquisition module, which includes remote sensing satellites and drones;

[0032] A block division module, which is used to divide land parcels into regions;

[0033] An image processing module is used to extract and analyze the vegetation cover and manure cover of the corresponding plot.

[0034] A central computing module is used to calculate the vegetation coverage rate, manure coverage rate, and correction parameters of the corresponding plot of land.

[0035] The decision analysis module is used to dynamically correct the drought situation in pastoral grasslands based on correction parameters.

[0036] Compared with existing technologies, this invention improves the monitoring and assessment scheme for drought in pastoral areas. By coordinating remote sensing satellites and drones in different time periods, it constructs an integrated "space-ground" monitoring network for vegetation and manure distribution, enabling dynamic monitoring at the pasture scale. Based on multi-dimensional indicators, pastoral areas are divided into plots, and image processing and big data analysis are integrated to dynamically plan drone flight paths, focusing on key target plots and effectively improving timeliness. Taking into full account the role of manure in maintaining soil moisture and improving the soil microenvironment, the amount and distribution of manure are used as assessment factors. Combined with the vegetation cover of the divided plots, targeted corrections are made to the drought situation in pastoral areas, effectively improving the accuracy of drought assessment and promoting a paradigm shift in grassland management from "passive drought resistance" to proactive adaptive regulation. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the method for dynamic monitoring and assessment of drought conditions in pastoral grasslands according to the present invention.

[0038] Figure 2 This is a schematic diagram of the land parcel division and scanning area according to the present invention. Detailed Implementation

[0039] The techniques described below can be modified in various ways and have multiple embodiments, which are described in detail below with reference to the accompanying drawings. However, this does not mean that the techniques described below are limited to the specific embodiments. It should be understood that the present invention includes all similar modifications, equivalents, and substitutions without departing from the spirit and scope of the techniques described below.

[0040] like Figure 1 As shown, this invention provides a method for dynamic monitoring and assessment of drought conditions in pastoral grasslands, comprising the following steps:

[0041] S1: In the first time period, the vegetation cover of the first plot of land is obtained through remote sensing satellites, and the vegetation cover rate is calculated. The first plot of land is rectangular.

[0042] Alternatively, the Normalized Difference Vegetation Index (NDVI) can be used to perform large-scale mapping calculations of vegetation cover.

[0043] Meanwhile, to facilitate subsequent gridding of the plots, conduct high spatial resolution vegetation and manure analysis, and accurately plan the drone path, rectangular plots were selected as the initial analysis objects.

[0044] S2: The first plot of land is gridded into several second plots of land with the same area. The side length of the second plot is L, and the side length L is positively correlated with the vegetation coverage of the first plot.

[0045] like Figure 2 As shown. The purpose of gridding the plots is twofold: first, to enable low-altitude monitoring and analysis of smaller plots, achieving drought assessment at the ranch scale; and second, to facilitate the division of scanning range and analysis area, providing a mathematical basis and physical evidence for subsequent UAV low-altitude monitoring path planning and the identification of plots of high interest (i.e., target plots).

[0046] In addition, a higher vegetation coverage rate often indicates a less severe drought. Therefore, by increasing the side length L, the cutting density of the first plot can be appropriately reduced, and the number of second plots can be decreased, thereby reducing the subsequent computational burden and the intensity of drone patrols.

[0047] S3: In the second time period, a first low-altitude image of the first plot of land is acquired by a drone at a first flight altitude along a first path, the first path being determined by the vegetation cover of each of the second plots of land.

[0048] A first interval period is set between the second and first time periods. The duration of the first interval period is inversely correlated with the vegetation cover of the first plot.

[0049] Unlike existing technologies where remote sensing satellites and low-altitude drones try to align image acquisition time as much as possible, this invention does not require such temporal synchronization. Instead, it aims to stagger the acquisition of remote sensing images from satellites and the initial low-altitude images from drones to better assess the potential temporal impacts of grazing behaviors, represented by manure, and herd characteristics on the soil and vegetation of the corresponding areas. The lower the vegetation cover, the longer the interval can be.

[0050] Therefore, when planning the first path of a drone, it is not required and unnecessary to traverse all second plots in the shortest possible time.

[0051] In a preferred embodiment, step S3 specifically includes:

[0052] S31: Determine the first flight altitude based on the side length L.

[0053] The resolution of drone aerial photography is directly related to its flight altitude. As mentioned above, if the vegetation coverage is high, the side length L can be larger, and correspondingly, the drone can fly at a higher altitude. That is, the first flight altitude is positively correlated with the side length L, thereby improving the efficiency of drone patrols when resolution is not so sensitive.

[0054] S32: Calculate the vegetation coverage of each of the second plots, and plan the first path of the UAV in order of increasing vegetation coverage.

[0055] Generally speaking, for areas with small vegetation cover, the causes of their formation and changes over a period of time are important reference indicators for assessing local drought conditions. The causes may be drought itself or the impact of local grazing practices. Therefore, when assessing drought conditions based on vegetation cover, it is necessary to conduct special investigations into the causes and changes in vegetation cover and prioritize such investigations.

[0056] Of course, when planning the drone's path, sorting by vegetation cover from smallest to largest, there might be situations where the corresponding second plots are not adjacent. In such cases, the principle of local optima can be applied to guide the drone's path. That is, among the plots surrounding the drone's current location, the plot with the smallest vegetation cover becomes the drone's next target. When there is no next target and all second plots have not yet been traversed, the drone is guided directly to the remaining second plots with the smallest vegetation cover for inspection.

[0057] S4: Obtain the fecal coverage of the first plot based on the first low-altitude image, and calculate the fecal coverage rate.

[0058] Current technologies often overlook the impact of grazing on pasture vegetation, yet this factor can be significant in some cases. However, real-time monitoring of grazing behavior is impractical; therefore, analyzing manure from various plots in pastoral areas using high-resolution drone aerial photography is particularly necessary.

[0059] Studies have shown that herds tend to defecate near watering sites or at their main feeding areas. Therefore, the distribution of feces is directly and strongly correlated with the degree of drought in that area.

[0060] This connection is mainly reflected in:

[0061] Firstly, biological consumption: If there is a lot of manure in an area with little vegetation, it may be due to consumption by herds. Therefore, this situation of little vegetation is temporary and should not be used to directly conclude that the area is arid.

[0062] Secondly, physical barriers: the accumulation of feces can reduce surface wind speed to a certain extent and reflect sunlight, thereby reducing soil moisture evaporation and alleviating drought.

[0063] Third, microenvironment improvement: the degradation of manure promotes the formation of soil aggregates, and the ammonium nitrogen provided by excrement promotes proline synthesis, which keeps the water potential of pasture leaves at a high level during the dry season.

[0064] S5: In the third time period, the UAV acquires a second low-altitude image of the first plot of land at a second flight altitude and along a second path, the second path being determined by the fecal coverage of each of the second plots of land.

[0065] A second interval period is set between the third and second time periods. The duration of the second interval period is positively correlated with the manure coverage rate of the first plot.

[0066] The higher the manure coverage, the longer the interval can be. In this way, there is enough time between the two monitoring sessions of the second plot to show the potential impact of grazing behavior and herd characteristics, represented by manure, on the soil and vegetation of the corresponding area in the temporal dimension.

[0067] In a preferred embodiment, step S5 specifically includes:

[0068] S51: Determine the second flight altitude based on the side length L and the fecal coverage rate.

[0069] Given that fecal distribution is crucial for mitigating drought conditions, secondary inspections should, on the one hand, adjust the drone's flight altitude based on plot size, and on the other hand, further improve aerial image resolution to enhance fecal identification accuracy. Especially in areas with low fecal coverage, the flight altitude needs to be further reduced to improve monitoring precision.

[0070] In other words, the second flight altitude is positively correlated with the side length L, and after determining the baseline value of the second flight altitude based on the side length L, it is further positively correlated with the fecal coverage rate. Specifically, the baseline value of the second flight altitude corresponds to the maximum reference value of the fecal coverage rate, which can be a historical experience value. That is to say, the second flight altitude is generally lower than the first flight altitude.

[0071] S52: Determine the scanning distance R based on the fecal coverage rate, wherein the scanning distance R is inversely correlated with the fecal coverage rate.

[0072] Determining the scanning distance is for the purpose of further dividing the second plot of land, accurately analyzing changes in vegetation coverage, and planning secondary drone patrol routes.

[0073] The relationship between the scanning distance R and the side length L is 0 < R ≤ L / 2.

[0074] S53: Using the grid intersection points inside the first gridded plot as the center and the scanning distance R as the radius, draw a circle to form several scanning areas. Each scanning area includes four sub-scanning areas distributed in four adjacent second plots.

[0075] Please refer to it again. Figure 2 By constraining the relationship between the scanning distance R and the side length L, it is ensured that no two circular scanning areas intersect. Circular scanning areas are planned using grid intersections and scanning distances, ensuring that any scanning area involves four adjacent second plots. Excluding the four second plots at the four corners of the first plot, each second plot contains at least two sub-scanning areas. This reduces computational load while maximizing the monitoring and analysis capabilities of each second plot, and also provides a basis for accurately planning the UAV's secondary patrol path, i.e., the second path.

[0076] S54: Calculate the manure coverage area of ​​each second plot. If the overlap between the vegetation coverage area and the manure coverage area in any of the sub-scanning areas is greater than a set threshold, then the second plot where the sub-scanning area is located is determined as the target plot.

[0077] Areas where vegetation cover and manure cover overlap are often areas where herds are frequently active and where vegetation growth has undergone two changes (from presence to absence, and then from absence to presence). These areas serve as important references for dynamically assessing and correcting drought conditions.

[0078] The set threshold is positively correlated with the fecal coverage rate.

[0079] The greater the manure coverage, the larger the manure coverage area, and the more likely it is to overlap with the vegetation coverage area. Therefore, it is necessary to raise the threshold accordingly to screen out the plots that need more precise monitoring.

[0080] S55: Plan the second path of the UAV based on the target plot.

[0081] Specifically, for all the target plots, the second path of the UAV is planned in descending order of the overlapping area between the vegetation coverage and the manure coverage.

[0082] Since the second flight altitude is generally lower than the first flight altitude, the efficiency of secondary drone patrols is inevitably affected. Patrolling only specific plots, i.e., target plots, can mitigate the lag in drought dynamic correction and reduce costs. The smaller the overlap between vegetation cover and manure cover, the greater the impact of grazing on that area; these areas can be patrolled last, allowing them as much time as possible for evolution.

[0083] Given that the target plots may not be continuous and become more discrete after sorting, the local optimum principle mentioned above can also be applied to the planning of the second path of the UAV.

[0084] S6: Based on the first low-altitude image and the second low-altitude image, dynamically correct the drought situation in pastoral grasslands.

[0085] Specifically, the changes in vegetation coverage of all target plots in the first low-altitude image and the second low-altitude image are calculated, and a first correction parameter is determined based on the changes in vegetation coverage to dynamically correct the drought situation in pastoral grasslands.

[0086] This invention focuses on dynamically monitoring changes in vegetation growth in areas where feces are distributed, and achieves accurate correction of assessment results through a three-step process of "monitoring-quantification-coupling".

[0087] In the quantification process, two parameters can be considered: the nutrient slow-release index and spatial uniformity. The nutrient slow-release index characterizes the potential water retention capacity of manure per unit area; a higher nutrient slow-release index indicates that drought conditions in the area are more easily alleviated. Spatial uniformity assesses the efficiency of manure distribution in alleviating drought conditions; a lower spatial uniformity indicates a misalignment between manure accumulation areas and high-risk drought areas, necessitating adjustments to grazing management.

[0088] In other words, the first correction parameter is not only related to changes in vegetation cover, but can also be set to be related to the distribution of excrement.

[0089] In the coupling step, an initial drought level map can be generated first based on the TVDI or SWAT model for basic drought calculation. Next, a dynamic correction module is introduced, taking into input real-time manure distribution data, livestock migration trajectories, soil type maps, and other information; at the processing end, a drought-manure response function is trained using a BP neural network, outputting a correction coefficient matrix; at the output end, the drought level is appropriately lowered based on the TVDI value and the nutrient slow-release index.

[0090] In a preferred embodiment, after step S6, the following is further included:

[0091] S7: Divide off an adjacent land parcel with the same size as the first land parcel on one side of the first land parcel, and perform the above steps S1-S6 on the adjacent land parcel to determine the second correction parameter.

[0092] To better assess and correct drought conditions, the scope of the monitoring area can be appropriately expanded. The first monitoring area can be used as a benchmark, and the assessment area can be continuously expanded outward using the method provided by this invention.

[0093] The orientation of the adjacent land parcel relative to the first land parcel is determined by the density of the target land parcel.

[0094] Within the first plot, areas with a higher density of target plots are more likely to have more eligible target plots. Therefore, when expanding the adjacent plots of the first plot, the density of target plots can be used as a reference for guiding the location of adjacent plots.

[0095] S8: Based on the first correction parameter and the second correction parameter, dynamically correct the drought situation in pastoral grasslands.

[0096] When multiple correction parameters exist, the final correction parameters can be determined by weighted summation. The weight of the correction parameter for each plot is positively correlated with the number of target plots contained within that plot.

[0097] Meanwhile, the present invention also provides a system for realizing a method for dynamic monitoring and assessment of drought conditions in pastoral grasslands, comprising:

[0098] An image acquisition module, which includes remote sensing satellites and drones.

[0099] A block division module is used to divide land parcels into regions.

[0100] The image processing module is used to extract and analyze the vegetation coverage and manure coverage of the corresponding plot.

[0101] The central computing module is used to calculate the vegetation coverage rate, manure coverage rate, and correction parameters of the corresponding plot.

[0102] The decision analysis module is used to dynamically correct the drought situation in pastoral grasslands based on correction parameters.

[0103] The modules mentioned above achieve information exchange through blockchain technology.

[0104] This invention incorporates the dynamic effects of livestock excrement into the assessment framework. Through nutrient slow-release mechanisms, microenvironment improvement, and dynamic planning of UAV paths, it can significantly improve the accuracy of drought assessment and reduce the misjudgment rate.

[0105] Although the present invention has been described in detail above with general descriptions and specific embodiments, some modifications or improvements can be made to it. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Other changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention are still included within the scope of protection of the present invention.

Claims

1. A method for monitoring and evaluating the drought condition of a pasture grassland, characterized in that, The method comprises the following steps: S1: obtaining the vegetation coverage of a first land block by a remote sensing satellite in a first period, and calculating the vegetation coverage rate, wherein the first land block is a rectangle; S2: griding the first land block into a plurality of second land blocks with the same area, wherein the side length of the second land block is L, and the side length L is positively correlated with the vegetation coverage rate of the first land block; S3: obtaining a first low-altitude image of the first land block by a UAV at a first flight height along a first path in a second period, wherein the first path is determined by the vegetation coverage of each second land block; S4: obtaining the excrement coverage of the first land block according to the first low-altitude image, and calculating the excrement coverage rate; S5: obtaining a second low-altitude image of the first land block by the UAV at a second flight height along a second path in a third period, wherein the second path is determined by the excrement coverage of each second land block; S6: generating an initial drought level map based on a TVDI or SWAT model, performing basic drought calculation, and dynamically correcting the grassland drought in the pastoral area based on the first low-altitude image and the second low-altitude image.

2. The method according to claim 1, wherein, In the step S3, the first low-altitude image of the first land block is obtained by the UAV at the first flight height along the first path in the second period, and the first path is determined by the vegetation coverage of each second land block, specifically: S31: determining the first flight height according to the side length L; S32: calculating the vegetation coverage range of each second land block, and planning the first path of the UAV in the order of the vegetation coverage range from small to large.

3. The method according to claim 2, wherein, In the step S5, the second low-altitude image of the first land block is obtained by the UAV at the second flight height along the second path in the third period, and the second path is determined by the excrement coverage of each second land block, specifically: S51: determining the second flight height according to the side length L and the excrement coverage rate; S52: determining the scanning distance R based on the excrement coverage rate, wherein the scanning distance R is inversely related to the excrement coverage rate; S53: taking the grid intersection in the interior of the grid first land block as the center and the scanning distance R as the radius to draw a circle, thereby forming a plurality of scanning areas, and any scanning area includes four sub-scanning areas distributed in four adjacent second land blocks; S54: calculating the excrement coverage range of each second land block, and determining the second land block in which any sub-scanning area is located as a target land block if the overlap area of the vegetation coverage range and the excrement coverage range in the sub-scanning area is greater than a set threshold; S55: planning the second path of the UAV based on the target land block.

4. The method according to claim 3, wherein, In the step S52, the scanning distance R and the side length L satisfy the relationship of 0 5. The method according to claim 4, wherein, In the step S54, the set threshold is positively correlated with the excrement coverage rate.

6. The method according to claim 5, wherein, In the step S55, for all target land blocks, the second path of the UAV is planned in the order of the overlap area of the vegetation coverage range and the excrement coverage range from large to small.

7. The method according to claim 6, wherein, In step S6, the change of vegetation coverage of all target plots in the first low-altitude image and the second low-altitude image is calculated, the first correction parameter is determined based on the change of vegetation coverage, and the pasture drought is dynamically corrected.

8. The method according to claim 7, wherein, After step S6, the following steps are further included: S7: A neighboring plot with the same size as the first plot is divided on one side of the first plot, and steps S1-S6 are performed on the neighboring plot to determine a second correction parameter; S8: The pasture drought is dynamically corrected based on the first correction parameter and the second correction parameter.

9. The method according to claim 8, wherein, In step S7, the orientation of the neighboring plot compared to the first plot is determined by the density of the target plot.

10. A system for implementing the method for monitoring and evaluating the drought condition of a pasture according to any one of claims 1-9, characterized in that, Comprise: An image acquisition module, the image acquisition module comprising a remote sensing satellite and a drone; A block division module, the block division module being used for regional division of plots; An image processing module, the image processing module being used for extraction and analysis of vegetation coverage and excrement coverage of corresponding plots; A central calculation module, the central calculation module being used for calculation of vegetation coverage, excrement coverage and correction parameters of corresponding plots; A decision analysis module, the decision analysis module being used for dynamic correction of the pasture drought according to the correction parameters.

Images