Desertification dynamic monitoring system and method based on multi-tree-species matching prevention and control

Abstract

The invention relates to the technical field of desertification dynamic monitoring, in particular to a desertification dynamic monitoring system and method based on multi-tree-species matching prevention and control, and the system comprises a sample plot arrangement module, a region type module, a region regression module, a state classification module and a prevention and control evaluation module. Dividing a plurality of sample plots according to the area of the current area to be prevented and treated, and acquiring prevention and treatment parameters of the sample plots; mapping the prevention and control parameters and the region types of the sample plot into a plurality of state intervals, and identifying the conversion trend of each region type relative to desertification improvement; according to the conversion trend of the region type, on the basis of the change rate of the current conversion trend, judging region target features of the region type during desertification improvement; state classification is carried out according to the regional target features, and desertification prevention and control behaviors are set according to the data proportion under each state classification; according to the divided desertification prevention and control behaviors, various prevention and control parameters are evaluated, and a prevention and control configuration strategy is obtained; the regional adaptability and accuracy of desertification monitoring are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic monitoring of desertification, and in particular to a dynamic monitoring system and method for desertification based on multi-species combination prevention and control. Background Art

[0002] Desertification control has long relied on a single tree species or mechanical sand fixation measures. The prevention and control of desertification are susceptible to climate impacts, have low coverage efficiency, and have monitoring lags, which reduces the accuracy and practicality of desertification monitoring and affects the accuracy of remote sensing monitoring of desertification dynamics.

[0003] For example, Chinese patent publication number CN118534086A discloses a dynamic monitoring system for desertification in the Horqin Sandy Land. The system comprises the following steps: remote sensing monitoring → ground monitoring → soil sand content ratio data monitoring → desertification comprehensive index → ​​data analysis → early warning system. The system utilizes Landsat remote sensing data to calculate the vegetation index of the Horqin Sandy Land based on spectral characteristics; uses a wind erosion model (RWEQ) to calculate the wind erosion modulus of the Horqin Sandy Land; uses an ASD spectrometer and GF-5 remote sensing data to obtain soil sand content ratio data; then uses soil sand content and land use data to establish a quantitative relationship and calculate soil sand content ratio data; and uses the analytic hierarchy process to determine the weights of different factors, constructing a comprehensive desertification index that integrates vegetation, dynamics, and soil factors, accurately and objectively monitoring the long-term dynamics of desertification in the Horqin Sandy Land.

[0004] For example, Chinese patent publication number CN118258766A discloses a method, system, and device for remote sensing monitoring of land desertification in arid areas. The method includes the following steps: obtaining remote sensing data and soil nutrient data at each monitoring time node during the entire monitoring period, constructing a remote sensing monitoring indicator system for land desertification in arid areas based on the remote sensing data, and obtaining the normalized vegetation index, surface albedo, surface temperature, and temperature-vegetation drought index at each monitoring time node; determining the weight of each monitoring indicator pixel by pixel based on the spatial heterogeneity of the second law of geography and soil nutrient data; calculating the land desertification index at each monitoring time node based on the monitoring indicators and weights; analyzing the spatiotemporal variation trend of land desertification based on the land desertification index using a linear regression analysis method; and classifying the land desertification grade according to the spatiotemporal variation trend of land desertification.

[0005] The existing technology describes the use of multiple parameters for hierarchical analysis, and the use of multi-factor weights to explain the comprehensive desertification index to provide early warning for desertification; and the use of surface reflection for spatiotemporal change analysis to determine the level of land desertification; the existing technology tends to analyze desertification prevention and control scenarios based on single parameters such as soil sand content and normalized vegetation index, but these data analyses cannot conduct a comprehensive analysis of the spatial continuity of each data source and the changing trends in the time dimension, resulting in insufficient accuracy in desertification monitoring, difficulty in quantifying the improvement in each region, and limited adaptability of desertification monitoring. Summary of the Invention

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a dynamic monitoring system for desertification based on multi-tree species combination prevention and control, including: a sample plot layout module, which is used to divide multiple sample plots with a fixed grid size according to the area of ​​the current area to be controlled, and obtain the prevention and control parameters of the sample plots.

[0007] The regional type module is used to map the control parameters with the regional types of the sample sites, divide the mapped data into the state intervals of each control parameter in the form of a long time series, and identify the transformation trend of each regional type relative to desertification improvement based on the transition probability of each control parameter in the corresponding state interval.

[0008] The regional regression module is used to determine the regional target characteristics of the regional type when desertification is improved based on the transformation trend of the regional type and the change rate of the current transformation trend.

[0009] The state classification module is used to classify states according to regional target characteristics, divide each regional target characteristic into multiple state categories based on the data type it contains, and set desertification prevention and control behaviors according to the data proportion under each state category.

[0010] The prevention and control evaluation module is used to evaluate the prevention and control parameters of various areas according to the divided desertification prevention and control behaviors, and obtain the prevention and control configuration strategy.

[0011] The dynamic monitoring method for desertification based on multi-tree species combination control includes: S1, dividing a plurality of sample plots with a fixed grid size according to the area of ​​the current control area, and obtaining control parameters of the sample plots.

[0012] S2: Map the control parameters to the regional types of the sample sites, divide the mapped data into state intervals for each control parameter in the form of a long time series, and identify the transformation trend of each regional type relative to desertification improvement based on the transition probability of each control parameter in the corresponding state interval.

[0013] S3, according to the transformation trend of the regional type and based on the change rate of the current transformation trend, judge the regional target characteristics of the regional type when desertification is improved.

[0014] S4, classify the states according to the regional target characteristics, divide each regional target characteristic into multiple state categories according to the data type it contains, and set desertification prevention and control behaviors according to the data proportion under each state category.

[0015] S5, based on the divided desertification control behaviors, evaluate the control parameters of various areas and obtain the control configuration strategy.

[0016] The beneficial effects of the present invention are: 1. The present invention divides the sample plots into fixed grids, integrates four types of prevention and control parameters: normalized vegetation index, surface albedo, soil moisture, and effective soil thickness, and realizes data continuity splicing by binding spatial positions and multi-tree species configurations, thereby improving the comprehensiveness of current data coverage and providing a basis for subsequent analysis of various sample plots.

[0017] 2. The present invention establishes a mapping relationship between prevention and control parameters and regional types through technical effects, and divides state intervals based on desertification thresholds and improvement thresholds; the data in multiple state intervals are respectively described by setting transition probabilities and threshold balance degrees, and the relative situation of the current desertification improvement is described, and then the quantitative effect of the current desertification prevention and control is described, so as to monitor the dynamic evolution of desertification and identify the improvement in desertification prevention and control in advance.

[0018] 3. The present invention distinguishes between continuous and discrete boundary areas through boundary connectivity judgment, and further describes the transformation trend of desertification improvement by displaying the direction of the boundary area. It uses the maximum directional boundary length and parameter combination to describe the values ​​of the prevention and control parameters after the current desertification improvement, explaining the problem of unclear prevention and control effects caused by ignoring boundary dynamics during desertification treatment. It links the configuration strategy with the location of desertification improvement to describe the data basis for spatial optimization under multi-tree species matching.

[0019] 4. The present invention divides regional target features into dominant mode, normal mode and edge mode based on similarity measurement, and uses the output content of each mode to further illustrate whether the multiple tree species currently configured to the sample site are compatible with the desertification control in the corresponding area, and uses the output multiple modes as the basis for scheme configuration allocation during subsequent desertification control optimization to improve the efficiency of desertification control strategy utilization; finally, by updating the difference before and after, the pre-stored instructions are called to generate a configuration sub-strategy, and the minimum common subset of the covering set is calculated. Under the premise of ensuring the compatibility of the strategy, the current configuration strategy can adapt to problems such as regional terrain differences, thereby improving the adaptability of the scheme during implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below with reference to the accompanying drawings and examples.

[0021] Figure 1This is a schematic diagram of a dynamic desertification monitoring system based on multi-tree species combination prevention and control.

[0022] Figure 2 This is a flow chart of the regional type module of the dynamic desertification monitoring system based on multi-tree species combination prevention and control.

[0023] Figure 3 This is a flow chart of the regional regression module of the dynamic desertification monitoring system based on multi-tree species combination prevention and control.

[0024] Figure 4 This is a flow chart of the state classification module of the dynamic desertification monitoring system based on multi-tree species combination prevention and control.

[0025] Figure 5 This is a flow chart of the dynamic monitoring method for desertification based on multi-tree species combination prevention and control. DETAILED DESCRIPTION

[0026] The following embodiments of the present invention are described in detail. The embodiments described below are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, the techniques or conditions described in the literature in the art or in the product specifications shall be followed.

[0027] See Figure 1 The dynamic monitoring system for desertification based on multi-tree species combination prevention and control includes: a sample plot layout module, a regional type module, a regional regression module, a state classification module and a prevention and control evaluation module; among them, the output end of the sample plot layout module is connected to the regional type module, the output end of the regional type module is connected to the regional regression module, the output end of the regional regression module is connected to the state classification module, and the output end of the state classification module is connected to the prevention and control evaluation module.

[0028] The sample plot layout module is used to divide the sample plots into multiple sample plots with a fixed grid size according to the area of ​​the current area to be controlled, and obtain the control parameters of the sample plots.

[0029] The regional type module is used to map the control parameters with the regional types of the sample sites, divide the mapped data into the state intervals of each control parameter in the form of a long time series, and identify the transformation trend of each regional type relative to desertification improvement based on the transition probability of each control parameter in the corresponding state interval.

[0030] The regional regression module is used to determine the regional target characteristics of the regional type when desertification is improved based on the transformation trend of the regional type and the change rate of the current transformation trend.

[0031] The state classification module is used to classify states according to regional target characteristics, divide each regional target characteristic into multiple state categories based on the data type it contains, and set desertification prevention and control behaviors according to the data proportion under each state category.

[0032] The prevention and control evaluation module is used to evaluate the prevention and control parameters of various areas according to the divided desertification prevention and control behaviors, and obtain the prevention and control configuration strategy.

[0033] The control parameters include the Normalized Difference Vegetation Index (NDVI), surface albedo (Albedo), soil moisture (Wet) and effective soil thickness, which are used to illustrate the vegetation index of the current vegetation coverage area after control, the surface albedo index that reflects the surface structure, and related parameters that illustrate soil moisture and thickness. They are used to explain the soil preservation effect of the current area under the control of multiple tree species, and whether there is a serious problem of soil erosion, so as to prevent and control soil desertification.

[0034] Preferably, when dividing the land into various sizes, the current area to be controlled is displayed as a grid area with every 50m as a grid, and satellite remote sensing images of the area to be controlled are obtained, and the normalized vegetation index and surface albedo are extracted from the satellite remote sensing images; soil sampling is used to obtain soil moisture and effective soil thickness, and soil moisture is measured by a humidity sensor; the effective soil thickness is determined by representing the thickness of the soil suitable for the development of plant roots, such as digging a soil profile, observing and measuring the distance from the surface to the layer below that is not suitable for plant growth in the soil, such as bedrock, hard clay layer or groundwater level, and this distance is regarded as the effective soil thickness, or the proportion of sand, silt and clay in the soil that is not suitable for plant growth is analyzed. These data will be obtained by directly querying the plant-related data set from the database according to the type of plant currently being controlled.

[0035] Preferably, the implementation method of the sample plot arrangement module also includes: taking the spatial location of each sample plot as the basic condition, the tree species bound to each sample plot as the configuration condition, and splicing the sample plots according to the time points when the data in the prevention and control parameters continuously change.

[0036] The regional type corresponding to each prevention and control parameter is set based on the number of sample plots in the same data dimension after splicing.

[0037] The number of sample plots under the same data dimension described above is used to illustrate the data at the same time point and spatial position when the normalized vegetation index, surface albedo, soil moisture and effective soil thickness and other data are continuously changing. The time point of continuous change represents the magnitude of the change of any value of the normalized vegetation index, surface albedo, soil moisture and effective soil thickness over time, which serves as the subject of subsequent analysis. The configuration condition of tree species combination is to facilitate the segmentation of sample plots with the same tree species configuration. At this time, the number of tree species set at different locations may be different when desertification prevention and control is carried out. The sample plots with the same configuration need to be analyzed to illustrate the effects produced for different regional types.

[0038] In one embodiment of the present invention, the area type indicates whether the current location is a fixed dune, semi-mobile dune, mobile dune, or non-desertified dune, indicating whether the area type at the corresponding location has changed over a long period of time. This area type represents the label assigned to the sample site at the beginning of the control period. This label is used to describe the improvement and progress of the current sample site in the multiple monitoring cycles after the control period.

[0039] like Figure 2 As shown, the implementation method of the regional type module includes: based on the value of the parameters in the prevention and control parameters, mapping the value range of each parameter in the prevention and control parameters with the regional type, and setting multiple regional status indexes.

[0040] The state interval corresponding to each area type is set using the desertification threshold and improvement threshold pointed to by the area state index.

[0041] It should be noted that the prevention and control parameters will set multiple state intervals according to the values ​​of normalized vegetation index, surface albedo, soil moisture and effective soil thickness.

[0042] For example, the Normalized Difference Vegetation Index (NDVI) sets desertification thresholds and improvement thresholds based on its current value, indicating the improvement of the NDVI after clear desertification and after control measures. These thresholds are then assigned to plots at different locations. For example, the sandification threshold for moderate to severe desertification is NDVI ≤ 0.1, corresponding to a vegetation cover of less than 30%, indicating the relative vegetation cover in the area under control. A vegetation cover of less than 30% indicates a significant decline in vegetation cover in the area, making severe desertification likely. For the moderate desertification threshold, an NDVI between 0.1 and 0.2 corresponds to a vegetation cover of 30% to 50%, meaning that mildly degraded grassland corresponds to moderate desertification. Improvement thresholds are then divided into initial improvement and significant improvement. For example, initial improvement is NDVI ≥ 0.2, while significant improvement is NDVI ≥ 0.4. Significant improvement is achieved when vegetation cover reaches 50%, indicating a clear control effect.

[0043] The desertification threshold and improvement threshold of surface albedo are similar to those used in the Normalized Difference Vegetation Index (NDVI), and are also divided into values ​​such as moderate desertification and severe desertification. For example, severe desertification is defined as Albedo ≥ 0.25, indicating that the surface is bare or sparsely covered, mainly sand or rock, and has a high albedo. In grasslands and forests, Albedo is usually 0.1-0.2, which is used to describe bare areas with little vegetation cover. Moderate desertification is defined as Albedo between 0.2 and 0.25, indicating that some vegetation cover exists but the area is still desertified. The improvement threshold is divided into initial improvement (Albedo ≤ 0.2) and significant improvement (Albedo ≤ 0.15). Initial improvement indicates that vegetation cover has increased after desertification prevention and control, resulting in a decrease in surface albedo. Significant improvement indicates that vegetation has recovered well and is in a relatively stable state.

[0044] The NDVI and Albedo values ​​described above can be directly derived from existing methods for processing satellite remote sensing images, so we will not elaborate on them here. The multiple thresholds for NDVI and Albedo values ​​described above can be set based on historical data on the classification of severe and moderate desertification in the current area under control. The data described above is for illustrative purposes only.

[0045] The desertification threshold of soil moisture can be set according to the soil moisture of desertified land. For example, soil moisture ≤ 10% is considered the desertification threshold for severe desertification, and soil moisture between 10% and 15% is considered the desertification threshold for moderate desertification. Severe desertification indicates that drought has led to insufficient soil moisture, and moderate desertification indicates that the soil has poor water retention and vegetation is difficult to maintain. The desertification threshold set at this time can be determined by sampling the Gobi and mobile sand dunes in the current area. The thresholds for moderate and severe desertification can be adjusted according to the location of the area to be controlled. As for the improvement threshold of soil moisture, it can be divided into preliminary improvement of soil moisture ≥ 15% and significant improvement of soil moisture ≥ 20%. Preliminary improvement represents the initial stage of vegetation recovery and an increase in soil moisture. This indicates that the corresponding data in the control parameters have rebounded and improved. Other data can be synchronized to determine whether there is improvement under its regional type. As for significant improvement, it can indicate that the soil water retention capacity has increased and the plant roots can consolidate water, indicating that there is a certain effect under multi-species control.

[0046] The desertification threshold and improvement threshold set by the effective soil thickness need to be set according to the thickness of the soil layer in which plants can grow. For example, soil with severe desertification and moderate desertification is sampled, and its value range is converted into a confidence interval, which is distinguished by its upper limit value. At this time, the confidence interval can adopt a 95% confidence level; then the desertification threshold for severe desertification can be ≤5cm, and the desertification threshold for moderate desertification can be 5-10cm. These two desertification thresholds will represent land with mostly sandy texture and weak wind resistance. At this time, the desertification threshold will be based on the data collected at the beginning and the multi-time point data collected subsequently to check whether the current prevention and control is conducive to plant growth; as for the improvement threshold, the initial improvement of effective soil thickness ≥10cm and the significant improvement of effective soil thickness ≥15cm can be selected respectively to illustrate the effective thickness and stability of the soil under the current prevention and control.

[0047] At this time, according to the different prevention and control parameters collected under each sample site, the sample sites will be divided into multiple parameter combination status intervals according to the set improvement threshold and desertification threshold to illustrate the status of each sample site relative to the normalized vegetation index, surface albedo, soil moisture and effective soil thickness, and indicate whether each area is developing normally.

[0048] According to the value range of the prevention and control parameters in the current state interval under the preset detection cycle, the quantitative values ​​of the prevention and control parameters in each state interval at multiple time points are detected, and the threshold balance between improvement and desertification in the corresponding state interval is set.

[0049] At this time, the threshold balance can be set based on the balance point method of the ROC curve and the dynamic threshold method based on elasticity theory; for example, the balance point method based on the ROC curve is to identify the maximum difference between the proportion of sample plots correctly identified as improved and the proportion of sample plots whose desertification status is misjudged as improved in the current area to be identified, to illustrate the form of dynamic improvement of desertification.

[0050] The dynamic threshold rule based on elasticity theory uses the number of plots in an improved state divided by the total number of plots to illustrate the value of the improved state in the current area to be identified, thereby illustrating the threshold balance of the state interval. This threshold balance will dynamically illustrate the classification standards for desertification and improvement, and be used to further monitor the improvement of desertification in the current area; that is, it indicates that the current threshold balance represents the proportion of the corresponding parameter in an improved state. When analyzing a single parameter, the threshold balance will represent the proportion of a single parameter in an improved state. If multiple parameters are described, it indicates the proportion of multiple parameters that are below the improvement threshold.

[0051] A weighted calculation is performed based on the transition probability of each control parameter and the threshold balance of the state interval, which is used as the indicator value of the transformation trend. The slope corresponding to the indicator value at multiple time points is regarded as the output transformation trend.

[0052] At this point, the transition probability of each plot's corresponding value changes within a fixed detection period, such as when NDVI changes from 0.1 to 0.2, is calculated using a Markov chain. If all four parameters corresponding to the NDVI, surface albedo, soil moisture, and effective soil thickness change at this time, the transition probabilities of these four values ​​are weighted and summed. The sum of the weighted sum and the threshold balance is used as the indicator of the transition trend at the current time point. The slope of the indicator values ​​at multiple time points is used to represent the overall improvement trend. The weights for the NDVI, surface albedo, soil moisture, and effective soil thickness can be set based on the frequency of each data collection relative to the frequency of all data collection when identifying regional desertification improvement, or fixed values ​​such as 0.3, 0.2, 0.3, and 0.2 can be set in that order to reflect the relatively complete trend change pattern of the current regional type. The weight of the threshold balance is the ratio of the frequency of the current value to the total frequency in the historical data.

[0053] At this time, through the indicator value of the conversion trend, we can know whether the current regional state is mainly desertification or improvement. A positive slope value represents continuous improvement, while a negative slope value indicates partial degradation and instability, and it is necessary to strengthen environmental prevention and control interventions.

[0054] It should be noted that the above processing content will be processed according to the sample plots under the same regional type, or without considering the changes in regional types, the area included in the current control area will be directly judged to determine the improvement form of the current area under control. At the same time, calculations such as state transition probability can be displayed for a single sample plot. The slope value corresponding to the transformation trend is combined into a matrix in the form of a matrix, and the change form of the slope value of each sample plot is combined into a matrix. The value of the matrix in each time period is observed to evaluate its transformation method, or the weighted summation is used to obtain the index value of the transformation trend. After the normalized vegetation index, surface albedo, soil moisture and effective soil thickness in all sample plots are individually weighted and averaged, the four average values ​​are weighted with the threshold balance to obtain the corresponding index value to illustrate the relative improvement of desertification.

[0055] Preferably, in order to make the current conversion trend output more comprehensive, the trends of changes in the four parameters corresponding to the normalized vegetation index, surface albedo, soil moisture and effective soil thickness will be divided. For any one of the four parameters that changes in value, the corresponding parameter and the threshold balance of the state interval will be weighted and summed in the form of a weighted sum. At this time, the weight can be set based on the scenario where all four parameters change to obtain the relative parameters when the current desertification is improved; that is, the output conversion trend may be expressed as the improvement of a single parameter, or the improvement of multiple parameters in combination, to more comprehensively indicate which parameters have obvious improvement trends after desertification prevention and control treatment in the current area, and whether the overall improvement is normal. At this time, for the time points of data analysis and collection, the preset detection cycle will be in months and years to describe the conversion trends at multiple time points.

[0056] Preferably, the implementation method of identifying the conversion trend of the regional type further includes: based on the regional type corresponding to the conversion trend, when the slope value corresponding to the current conversion trend is positive, outputting data in the continuous time series corresponding to the current conversion trend.

[0057] When the slope value corresponding to the current conversion trend is negative, the data of the extreme point corresponding to the current conversion trend is output.

[0058] At this time, the slope value of the curve at each recorded time point will be judged according to the curve combined in each time period, and multiple values ​​related to the current conversion trend will be output. In the subsequent judgment of the improvement of desertification under the conversion trend, the most obvious boundary features of the current area type can be found based on the obtained slope value and the actual improvement. That is, the actual effect of multi-species prevention and control in areas with desertification such as fixed sand dunes, semi-mobile sand dunes and mobile sand dunes can be identified, and the current prevention and control work can be monitored and recorded.

[0059] Preferably, since the current transformation trend will form a composite state interval by crossing the values ​​of multiple prevention and control parameters, and then each sample plot is marked according to the value range corresponding to the desertification threshold and the improvement threshold, the quantity included in the state interval is used to record the proportion of values ​​that are considered to be improved under the multiple state intervals divided by the desertification threshold and the improvement threshold, to illustrate the proportion of values ​​that are reasonably allocated to the improved conditions in the multiple state intervals currently divided; the state interval is only used to illustrate the prevention and control parameters under different value ranges, and should be regarded as any description of desertification and improvement.

[0060] In one embodiment of the present invention, when receiving the transformation trend formed by any parameter in the prevention and control parameters, the transformation trends of other parameters are analyzed at the time point of the current transformation trend input. The content of the analysis includes but is not limited to identifying the critical points of the change rates of different parameters, and the various parameters near the critical points. These points are regarded as the target tasks of the current analysis to identify the values ​​and positions of the corresponding trends when they change. The regional target characteristics that affect desertification improvement under the current regional type are obtained using multi-attribute regression and parameter analysis.

[0061] Under the current desertification control, a continuous area of ​​several kilometers long may be divided into multiple sample plots, and the regional type of each sample plot may be marked. At this time, in addition to indicating whether it belongs to the mobile sand dune type, the marked regional type can also be divided into multiple areas with different tree species configurations, and each area is set with a regional type in the form of a label to illustrate the difference in desertification improvement between multiple tree species combination control. At this time, the length, shape and spatial continuity of the area after clustering the critical points and mutation points are used to illustrate the situation under different tree species control.

[0062] like Figure 3 As shown, the implementation method of the regional regression module includes: for the prevention and control parameters included in the current conversion trend, taking the change rate of any parameter in the prevention and control parameters in the conversion trend as the starting point, identifying the critical points of each prevention and control parameter in the conversion trend, at this time the critical point represents the part of the conversion trend where the change rate exceeds the set threshold, and this set threshold can be set as the part that is higher than the moving average and standard deviation of the current overall conversion trend. At this time, the set threshold regards data greater than the average value of the change rate and twice the standard deviation as its boundary point, and conducts a centralized analysis of the conversion trend formed by its prevention and control parameters in each state interval to determine the change of the conversion trend.

[0063] The area mapped by each critical point is considered a boundary area, and the boundary length, boundary shape, and boundary connectivity of each boundary area are obtained. The area mapped by each critical point represents the sample plot from which the corresponding control parameters were collected, and the sample plots with critical points constitute the boundary area. The boundary shape obtained at this time is used to assist in identifying whether each boundary area is connected. The boundary length and boundary shape are combined to mark the relative trend of the current desertification control and illustrate the effectiveness of using multiple tree species for control.

[0064] When boundary connectivity exists in the boundary area, the current boundary length and boundary shape are traversed, and the direction vector is set according to the difference in the transformation trend index values ​​of two adjacent boundary points in the boundary area. Multiple adjacent boundary points are directional fitted, and the boundary length in the maximum direction after fitting is used as the output regional target feature.

[0065] When there is no boundary connectivity in the boundary area, a parameter response plane is established. At this time, a continuous parameter field is set with the values ​​of the control parameters corresponding to each critical point. Then, the optimal part of the multi-parameter combination is used as the output regional target feature, that is, the optimal combination of each control parameter under the parameter response plane is used as the output regional target feature.

[0066] Preferably, the implementation method for obtaining boundary connectivity also includes: setting a judgment threshold based on the ratio of the connected area of ​​the mapped boundary area to the total area of ​​the boundary area; when the corresponding ratio is greater than the judgment threshold, the current boundary area is regarded as having boundary connectivity, otherwise it is regarded as not having boundary connectivity.

[0067] Preferably, the above-mentioned judgment of boundary connectivity is set based on the ratio of the area of ​​the connected boundary area to the total area of ​​the boundary area. When most of the boundary areas are connected, the current boundary area is regarded as having boundary connectivity. For example, 0.7 is used as the judgment threshold at this time. When the area ratio of the connected boundary area is 0.7, it means that the areas of desertification improvement and obvious changes occupy a relatively large part in the currently divided area, and most areas have obvious improvements within a specific period. At this time, you only need to check the length of the boundary area projection in the main improvement direction to know the size of the area corresponding to the current obvious improvement, to illustrate the trend under desertification improvement and prevention.

[0068] When it is less than 0.7, it means that the areas with obvious improvement are relatively scattered and located in multiple locations. At this time, it is necessary to obtain parameter combinations distributed in multiple locations, and use these parameter combinations to perform regression analysis or other forms of analysis to find the optimal parameter combination with the smallest error under multiple iterations. This will be used as the output content to illustrate the values ​​of the prevention and control parameters in the areas with obvious improvement, to capture local heterogeneity, and to adapt to the treatment methods of desertification prevention and control of multiple tree species under complex conditions.

[0069] Similarly, the judgment threshold can be set based on the confidence interval of the ratio of the connected area of ​​the boundary area to the total area of ​​the boundary area under the current boundary connectivity analysis. That is, the value range of the data in the historical data under the corresponding area ratio is set to a confidence interval with a 95% confidence level, and the middle value of the confidence interval is used as the current judgment threshold.

[0070] Preferably, the above-mentioned direction vector takes the difference in the index values ​​of the conversion trend of the two boundary points as the length of its direction vector, takes the angle of the line segment connecting the two boundary points as its angle, and fits multiple direction vectors in the form of principal component analysis. After calculating the covariance matrix and the corresponding eigenvalues, the direction of the maximum variance is regarded as the maximum direction of the current fitting, and then the length value of the boundary area projected to this direction is regarded as the output regional target feature.

[0071] Preferably, the optimal combination of each control parameter is achieved by taking soil moisture among the current control parameters as the dependent variable, other control parameters as independent variables, and taking a set of control parameters with the smallest error under linear regression calculation as the current optimal combination.

[0072] The main reason for choosing soil moisture at this time is that soil moisture is a key indicator of vegetation survival and directly affects NDVI and the corresponding surface albedo. As for soil thickness, although it represents the relative conditions for soil root growth, its relative effect can only be reflected in scenarios with high soil moisture. That is, the predicted value is calculated by linear regression based on the data in the current multiple boundary areas. The error is calculated between the predicted value and the actual value of soil moisture in the corresponding boundary area, and the group of areas with the smallest error is found. This area will represent the area where desertification control is most obvious under the current combination of multiple tree species.

[0073] In one embodiment of the present invention, when dividing the status classification, it is used to describe the proportion of various data in the total data after using multiple tree species to control different forms of desertification, explain the impact of the main tree species on desertification control after growth, and distinguish the desertification boundaries after the impact, and analyze the desertification effect under multiple tree species control.

[0074] like Figure 4 As shown in FIG, the implementation method of the state classification module includes: taking the regional target feature as a candidate classification point, using the similarity measure between the candidate classification point and the control parameter to perform state classification, setting the state classification corresponding to the candidate classification point, and dividing the collected data into multiple modes through the corresponding classification point in the regional target feature to describe multiple groups of description forms under the current regional target feature; since the data output by the regional target feature exists in two forms, namely the projected boundary length and the optimal parameter combination, the optimal parameter combination can divide the parameters under the optimal soil moisture change under multiple time conditions as candidate classification points, and the boundary length will use the boundary point fitted when obtaining the boundary length as the candidate classification point to divide the value of various desertification improvement conditions, classify these data and the collected control parameters according to the similarity measure, and perform cluster analysis on the corresponding data and the current collected data using the control parameter corresponding to the current candidate classification point in each classification to adapt to the dynamic changes of multiple parameters under desertification control.

[0075] At this time, the cosine similarity method is used to calculate the similarity between the values ​​under the current candidate classification point and other collected prevention and control parameters to obtain a similarity measure. At this time, when clustering, the parts with cosine similarity greater than 0.6 are used to combine multiple groups of data that are obviously similar to the current candidate classification point to discover the dynamic behavior patterns of governance under the combination of multiple tree species in the data.

[0076] Derive the data proportion under each state classification, compare the data proportion with the preset mode range, and classify the current state into dominant mode, normal mode and marginal mode in turn.

[0077] The data with the largest proportion of state classification data is considered the dominant mode, and the candidate classification points of the dominant mode are used as the output desertification control behavior. The dominant mode mainly outputs the core parameter combination, which describes the main forms of improvement in desertification control. This data serves as the basis for subsequent adjustments to intervention methods.

[0078] Data falling within the preset mode range is considered the normal mode, and the direction of each control parameter under the normal mode at multiple time periods is regarded as the output desertification control behavior. The direction at multiple time periods here indicates that the corresponding parameter is in an increasing or decreasing state over consecutive time periods. Through time series analysis, linear regression can be used to determine the slope value and trend item of the current control parameter. These contents are used as the output desertification control behavior to illustrate the cyclical changes under the current state classification.

[0079] When the value is less than the lower limit of the preset mode range, the data in the corresponding state classification is considered an edge mode, and the state transitions of each control parameter in the edge mode are output as desertification control behaviors using a chain structure. At this time, the state transitions of the control parameters are recorded to illustrate the changes of each control parameter under desertification control. The chain structure is preferred to illustrate the NDVI less than 10% → NDVI recovery to the baseline value, or NDVI less than 10% → NDVI recovery monitoring. The chain structure records the intervention content experienced when the control parameter changes. This part is used as the content for rapid response to degradation risks, focusing on identifying whether the corresponding control parameters have returned to normal under the chain structure. The chain structure output also includes the actual behaviors currently implemented under multi-species control, such as the recovery of control parameters under measures such as drone watering and seeding robot replanting.

[0080] It should be noted that when candidate classification points are used for classification, a corresponding text label will be set for each candidate classification point, and candidate classification points under the same description will be regarded as belonging to the same state classification.

[0081] Preferably, the currently set preset mode range will be based on the current input state classification, and multiple value ranges will be set in sequence, such as selecting 15%-40% as the current preset mode range, or selecting the conventional form of multi-tree species matching under desertification control in historical data, and expressing the amount of data contained in the conventional form each time using a confidence interval, and using the upper and lower limits of the confidence interval at a 95% confidence level as the preset mode range at this time to represent the relative behavior mainly due to desertification control within the corresponding range.

[0082] The dominant mode then shows a high degree of similarity in most cases, which represents the main reason for selecting strategies under desertification prevention and control. As for the normal mode, it tends to obtain the specific environment and is explained using multi-time change trajectories. Here, the multi-time direction is used to represent the time change trajectory to illustrate the changes under the normal mode; as for the edge mode, it will represent the highlighted mutation content. At this time, it is in the form of a state chain to facilitate subsequent processing of the mutation form to prevent the deterioration of desertification.

[0083] In one embodiment of the present invention, when setting the prevention and control configuration strategy, it is necessary to describe the output desertification prevention and control behaviors represented by these data in different model representation forms, such as using quick-effect improvement type, gradual stability type, fluctuation maintenance type and degradation risk type expressions to describe the relative behaviors that can be produced under the current multi-species combination, thereby converting them into corresponding strategies.

[0084] The quick-acting improvement type represents short-term parameter jumps, the gradual stabilization type represents long-term linear changes, the fluctuation maintenance type represents threshold range oscillations, and the degradation risk type represents key parameter attenuation. At this time, the dominant mode and edge mode contained in the state classification will serve as the main monitoring contents of the fluctuation maintenance type and the degradation risk type. The configuration strategies that need to be set are viewed based on these two types. As for the normal mode, the effectiveness of prevention and control is evaluated through the quick-acting improvement type and the gradual stabilization type, and the corresponding configuration strategies are set. These configuration strategies will be set in the database and directly compared and extracted based on the parameters of the currently input desertification prevention and control behavior.

[0085] The implementation method of the prevention and control evaluation module includes: when the current desertification prevention and control behavior is in the updating state, according to the time interval between each update of the current desertification prevention and control behavior, checking the difference between the desertification prevention and control behavior before and after the update. At this time, the difference in the desertification prevention and control behavior represents the difference in the corresponding prevention and control parameters; using the difference in the desertification prevention and control behavior, calling the pre-stored information instructions to form the configuration sub-strategy of the current desertification prevention and control behavior.

[0086] When the current desertification prevention and control behavior is not in an updating state, the configuration sub-strategy is obtained based on the prevention and control parameter range of the current desertification prevention and control behavior; the covering set of each configuration sub-strategy is calculated, and the minimum common subset in the covering set is used as the output prevention and control configuration strategy.

[0087] Preferably, when obtaining the configuration strategy for the current desertification prevention behavior, an extraction is performed based on the state classification corresponding to the desertification prevention behavior, and configuration strategies for processing the normalized vegetation index, surface albedo, soil moisture, and effective soil thickness are obtained from the database according to the state classification. The configuration strategy consistent with the current state classification is retrieved, and subsequent configuration sub-strategies are formed according to the pre-stored information instructions contained in the configuration strategy. The pre-stored information instructions are used to explain how to intervene in the current multi-species prevention and control, and the relevant instructions are set according to the specific values ​​of the prevention and control parameters.

[0088] Preferably, when calculating the covering set of each configuration sub-strategy, the parameters covered in each configuration sub-strategy are used, and the set of covered corresponding parameters is regarded as the covering set. Then, the strategy corresponding to the minimum common subset in the covering set is obtained as the subsequent output prevention and control configuration strategy to achieve desertification prevention and control while satisfying the common characteristics of the overall desertification prevention and control behavior.

[0089] Preferably, the above-mentioned update status indicates that there is an update in the data of the currently obtained desertification prevention and control behavior, representing the dynamic changes in the trend of desertification improvement and governance, and generating a configuration sub-strategy based on each dynamic change to deal with the problem of lack of real-time response, thereby realizing real-time tracking of desertification improvement.

[0090] like Figure 5 As shown, the present invention also provides a dynamic monitoring method for desertification based on multi-tree species combination control, including: S1, dividing multiple sample plots with a fixed grid size according to the area of ​​the current area to be controlled, and obtaining control parameters of the sample plots.

[0091] S2: Map the control parameters to the regional types of the sample sites, divide the mapped data into state intervals for each control parameter in the form of a long time series, and identify the transformation trend of each regional type relative to desertification improvement based on the transition probability of each control parameter in the corresponding state interval.

[0092] S3, according to the transformation trend of the regional type and based on the change rate of the current transformation trend, judge the regional target characteristics of the regional type when desertification is improved.

[0093] S4, classify the states according to the regional target characteristics, divide each regional target characteristic into multiple state categories according to the data type it contains, and set desertification prevention and control behaviors according to the data proportion under each state category.

[0094] S5, based on the divided desertification control behaviors, evaluate the control parameters of various areas and obtain the control configuration strategy.

[0095] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered by the scope of protection of the present invention.

Claims

1. A dynamic monitoring system for desertification based on multi-species combination prevention and control, characterized by: include: The sample plot layout module is used to divide the sample plots into multiple plots with a fixed grid size according to the area of ​​the current control area, and obtain the control parameters of the sample plots; The regional type module is used to map control parameters to the regional types of the sample plots. The mapped data is divided into state intervals for each control parameter in the form of a long time series. Based on the transition probability of each control parameter in the corresponding state interval, the transformation trend of each regional type relative to desertification improvement is identified; The regional regression module is used to determine the regional target characteristics of the regional type when desertification is improved based on the transformation trend of the regional type and the change rate of the current transformation trend; The state classification module is used to classify states according to regional target characteristics. Each regional target characteristic is divided into multiple state categories based on the data type it contains. Desertification prevention and control actions are set according to the data proportion in each state category. The prevention and control evaluation module is used to evaluate the prevention and control parameters of various areas according to the divided desertification prevention and control behaviors, and obtain the prevention and control configuration strategy.

2. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 1 is characterized in that: The prevention and control parameters include normalized vegetation index, surface albedo, soil moisture and effective soil thickness, among which the effective soil thickness is the thickness of the soil suitable for the development of plant roots.

3. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 1 is characterized in that: The implementation of the sample plot layout module also includes: The spatial location of each plot is used as the basic condition, and the tree species bound to each plot are used as the configuration condition. The plots are spliced ​​according to the time points when the data in the control parameters continuously change. The regional type corresponding to each prevention and control parameter is set based on the number of sample plots in the same data dimension after splicing.

4. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 1 is characterized in that: The implementation of the region type module includes: Based on the value of the parameters in the prevention and control parameters, the mapping relationship between the value range of each parameter in the prevention and control parameters and the regional type is set to set multiple regional status indexes; The desertification threshold and improvement threshold pointed to by the regional status index are used to set the status interval corresponding to each regional type; According to the value range of the prevention and control parameters in the current state interval under the preset detection cycle, the quantitative value of the prevention and control parameters in each state interval at multiple time points is detected, and the threshold balance between improvement and desertification in the corresponding state interval is set; A weighted calculation is performed based on the transition probability of each control parameter and the threshold balance of the state interval, which is used as the indicator value of the transformation trend. The slope corresponding to the indicator value at multiple time points is regarded as the output transformation trend.

5. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 4 is characterized in that: Other ways to identify conversion trends by region include: Based on the area type corresponding to the conversion trend, when the slope value corresponding to the current conversion trend is positive, the data in the continuous time series corresponding to the current conversion trend is output; When the slope value corresponding to the current conversion trend is negative, the data of the extreme point corresponding to the current conversion trend is output.

6. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 1 is characterized in that: The implementation of the regional regression module includes: For the control parameters included in the current conversion trend, taking the change rate of any one of the control parameters in the conversion trend as the starting point, identify the critical points of each control parameter in the conversion trend; The area mapped by each critical point is regarded as the boundary area, and the boundary length, boundary shape and boundary connectivity of each boundary area are obtained; When there is boundary connectivity in the boundary area, the current boundary length and boundary shape are traversed, and the direction vector is set based on the difference in the conversion trend index values ​​of two adjacent boundary points in the boundary area. Multiple adjacent boundary points are directional fitted, and the boundary length in the maximum direction after fitting is used as the output regional target feature; When there is no boundary connectivity in the boundary area, a parameter response plane is established, and the optimal combination of each control parameter under the parameter response plane is used as the output regional target feature.

7. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 6 is characterized in that: The implementation of obtaining boundary connectivity also includes: The judgment threshold is set by the ratio of the connected area of ​​the mapped boundary region to the total area of ​​the boundary region. When the corresponding ratio is greater than the judgment threshold, the current boundary region is considered to have boundary connectivity, otherwise it is considered to have no boundary connectivity.

8. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 1 is characterized in that: The implementation of the status classification module includes: The regional target features are used as candidate classification points, and the similarity measurement between the candidate classification points and the control parameters is used to perform state classification, and the state classification corresponding to the candidate classification points is set; Deducing the data proportions under each state classification, comparing the data proportions with the preset mode range, and classifying the current state into dominant mode, normal mode, and marginal mode in turn; The data with the largest proportion in the state classification is regarded as the dominant mode, and the candidate classification points of the dominant mode are used as the output desertification prevention and control behavior; The data that falls within the preset mode range is regarded as the normal mode, and the directions of each control parameter under the normal mode at multiple times are regarded as the output desertification control behavior; When the value is less than the lower limit of the preset mode range, the data in the corresponding state classification is regarded as an edge mode, and the state transition of each prevention and control parameter in the edge mode is output as a desertification prevention and control behavior in a chain structure.

9. The dynamic monitoring system for desertification based on multi-species combination prevention and control according to claim 1 is characterized in that: The implementation methods of the prevention and control assessment module include: When the current desertification prevention and control behavior is in an updating state, the difference between the desertification prevention and control behavior before and after the update is checked according to the time interval between each update of the current desertification prevention and control behavior. The difference in the desertification prevention and control behavior is used to call pre-stored information instructions to form a configuration sub-strategy for the current desertification prevention and control behavior. When the current desertification prevention and control behavior is not in an updating state, the configuration sub-strategy is obtained based on the prevention and control parameter range of the current desertification prevention and control behavior; the covering set of each configuration sub-strategy is calculated, and the minimum common subset in the covering set is used as the output prevention and control configuration strategy.

10. A dynamic monitoring method for desertification based on multi-species combination prevention and control, characterized in that: include: S1, dividing the area to be controlled into multiple plots with a fixed grid size according to the area of ​​the current control area, and obtaining the control parameters of the plots; S2: Mapping the control parameters to the regional types of the sample plots. The mapped data is divided into state intervals for each control parameter in the form of a long time series. Based on the transition probability of each control parameter in the corresponding state interval, the transformation trend of each regional type relative to desertification improvement is identified; S3, according to the transformation trend of the regional type and based on the change rate of the current transformation trend, determine the regional target characteristics of the regional type when desertification is improved; S4: Classify the states according to the regional target characteristics. Divide each regional target characteristic into multiple state categories based on the data type it contains. Set desertification prevention and control actions based on the data proportion in each state category. S5, based on the divided desertification control behaviors, evaluate the control parameters of various areas and obtain the control configuration strategy.

Images