Grassland desertification condition analyzing and monitoring method and system

Abstract

The invention discloses a grassland desertification condition analyzing and monitoring method and system, and relates to the technical field of grassland desertification monitoring, growth data of three representative plants in a grassland monitoring area are sorted according to time, and time sequences of the three representative plants are formed; extracting a historical desertification area by using a historical remote sensing image of the grassland, and analyzing the historical desertification area to obtain grassland desertification variation in each time period; respectively obtaining growth data variation of the three representative plants corresponding to each time period, and further analyzing by combining the grassland desertification variation of each time period to obtain response degree factors of the three representative plants to grassland desertification; analyzing the response degree factors of the three representative plants to the grassland desertification, and monitoring the grassland desertification condition. The problems that fine changes of early grassland desertification are difficult to recognize, feedback and analysis periods of part of monitoring methods are long, and instant or near-real-time desertification monitoring information cannot be provided are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of grassland desertification monitoring, and in particular to a grassland desertification status analysis and monitoring method and system. Background Art

[0002] Grassland is one of the important ecosystems, with multiple ecological functions such as regulating climate, protecting water and soil, and maintaining biodiversity. However, due to the influence of climate change, overgrazing, unreasonable development and utilization, desertification and land degradation have occurred in some grassland areas, seriously threatening the stability and sustainable development of grassland ecosystems. The problem of grassland desertification is becoming increasingly serious, leading to the deterioration of the ecological environment. Therefore, it is urgent to effectively monitor and analyze the desertification of grasslands in order to take corresponding control measures to protect grassland ecosystems.

[0003] With the rapid development of remote sensing technology, it has become possible to monitor large areas and long time series using satellite images, drone aerial photography and other means. Remote sensing data can provide high-resolution images to help researchers obtain dynamic changes in grassland desertification in real time. At the same time, the development of big data and artificial intelligence technology has provided new means for monitoring and analyzing grassland desertification. Through data mining, machine learning and other technologies, useful information can be extracted from a large amount of remote sensing data to predict the degree of desertification and analyze trends. However, although remote sensing data can provide a wide coverage and regular monitoring capabilities, its spatial resolution may not be sufficient to capture small-scale grassland desertification processes, especially localized desertification phenomena. Currently, commonly used monitoring indicators such as vegetation coverage and LAI can indirectly reflect the degree of grassland desertification, but these indicators are not always able to directly and accurately reflect subtle changes or early trends in desertification. At the same time, the feedback and analysis cycle of some monitoring methods is long, and they cannot provide immediate or near-real-time desertification monitoring information, which is insufficient when emergency intervention or disaster warning is needed.

[0004] Therefore, in response to the above problems, a method and system for analyzing and monitoring grassland desertification conditions is urgently needed. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method and system for analyzing and monitoring grassland desertification conditions, which solves the problems of difficulty in identifying subtle changes in early grassland desertification, and the long feedback and analysis cycles of some monitoring methods, which cannot provide immediate or near real-time desertification monitoring information.

[0006] To achieve the above purpose, the present invention is implemented through the following technical solutions: a method for analyzing and monitoring grassland desertification, comprising the following steps: selecting three representative plants of a grassland ecosystem, obtaining growth data of the three representative plants in a grassland monitoring area, and sorting the growth data of the three representative plants according to time to form a time series of the three representative plants; identifying the increase and decrease trends of the three representative plant time series, and then identifying whether the grassland has a desertification trend based on the influence of the three representative plants on grassland desertification; based on the identification of the existence of a desertification trend in the grassland, collecting historical remote sensing images of the grassland, and then using the historical remote sensing images to extract the historical desertification area, analyzing the historical desertification area, and obtaining the grassland desertification change in each time period; respectively obtaining the growth data change of the three representative plants corresponding to each time period, and then analyzing in combination with the grassland desertification change in each time period to obtain the response degree factors of the three representative plants to grassland desertification; real-time monitoring of the growth data of the three representative plants, and then analyzing in combination with the response degree factors of the three representative plants to grassland desertification, and monitoring the grassland desertification condition.

[0007] Furthermore, the specific requirements for the three representative plants are: Plant One is a dominant species in the desertified grassland, and when the grassland becomes desertified, the number of Plant One will increase; Plant Two and Plant Three are inferior species in the desertified grassland, and when the grassland becomes desertified, the numbers of Plant Two and Plant Three will decrease.

[0008] Furthermore, the specific selection method of the three representative plants is analyzed as follows: three representative plants of the grassland ecosystem are selected based on the analysis of the multidimensional characteristics of plants, and the multidimensional characteristics of plants specifically include climate adaptation characteristics and community ecological mutual influence characteristics; the specific plant multidimensional characteristic analysis is as follows: identifying grassland plant species, and then collecting multidimensional characteristic data of various types of grassland plants that can reflect the multidimensional characteristics of plants, and the multidimensional characteristic data specifically include climate adaptation characteristic data and community ecological mutual influence characteristic data; the climate adaptation characteristic data specifically include the climate adaptability index, and the community ecological mutual influence characteristic data specifically include the interspecific association index; the climate adaptation characteristic data and the community ecological mutual influence characteristic data are de-dimensionalized and valued, and then the climate adaptability index and the plant multidimensional performance evaluation value are combined into The logic that the interspecific association index is inversely proportional to the plant multidimensional performance evaluation value is used to calculate the plant multidimensional performance evaluation value. The plant multidimensional performance evaluation value represents the inverse proportion of the degree of mutual influence between climate and community ecology on plants, and is used to numerically represent the representative ability of plants for grassland desertification monitoring. According to the relationship between plants as dominant species or inferior species in desertified grasslands, grassland plant species are divided into dominant species categories and inferior species categories, and the plant multidimensional performance evaluation values ​​of grassland plants in the dominant species category and the inferior species category are ranked respectively. The plant with the largest plant multidimensional performance evaluation value of grassland plants in the dominant species category is selected as plant one among the representative plants, and the plants with the largest and second largest plant multidimensional performance evaluation values ​​of grassland plants in the inferior species category are selected as plant two and plant three among the representative plants, respectively.

[0009] Furthermore, the growth data of three representative plants in the grassland monitoring area are obtained, and the growth data of the three representative plants are sorted according to time to form a specific analysis of the time series of the three representative plants: the growth data of the three representative plants are specifically the plant density of the three representative plants in the grassland monitoring area; the plant density of the three representative plants in the grassland monitoring area is sorted according to time sequence, and then the time series of the three representative plants are constructed respectively, and the horizontal axis of the time series is time, and the vertical axis is the plant density of the three representative plants in the grassland monitoring area.

[0010] Furthermore, the increase and decrease trends of the three representative plant time series are identified, and then based on the influence of the three representative plants on grassland desertification, it is identified whether the grassland has a desertification trend. The specific analysis is as follows: the influence of the three representative plants on grassland desertification is specifically based on the specific requirements of the three representative plants, that is, when: the time series of plant one shows an increasing trend and the time series of plant two and plant three show a decreasing trend, the grassland has a desertification trend.

[0011] Furthermore, the specific acquisition and analysis of the response degree factors of the three representative plants to grassland desertification are as follows: the changes in the growth data of the three representative plants corresponding to each time period are compared with the changes in grassland desertification in each time period, and then the average values ​​are taken to obtain the response degree factors of the three representative plants to grassland desertification. The response degree factors are used to quantify the response degree of plants to grassland desertification, and indicate the correlation between changes in plant growth data and changes in grassland desertification.

[0012] Furthermore, the growth data of three representative plants are monitored in real time, and then analyzed in combination with the response degree factors of the three representative plants to grassland desertification. The specific analysis for monitoring the grassland desertification situation is: obtain the growth data of the three representative plants monitored in real time, that is, the plant density of the three representative plants in the grassland monitoring area; multiply the plant density of the three representative plants in the grassland monitoring area by the corresponding response degree factors and take the average value to obtain the current grassland desertification degree coefficient. The current grassland desertification degree coefficient is used to quantify the degree of change of the current grassland desertification.

[0013] A grassland desertification status analysis and monitoring system, which applies the above-mentioned grassland desertification status analysis and monitoring method, includes: a representative plant determination module, which is used to select three representative plants of the grassland ecosystem, obtain the growth data of the three representative plants in the grassland monitoring area, and sort the growth data of the three representative plants according to time to form a time series of the three representative plants; a desertification trend determination module, which is used to identify the increase and decrease trend of the three representative plant time series, and then identify whether the grassland has a desertification trend based on the influence relationship of the three representative plants on grassland desertification; a grassland desertification history analysis module, which is used to identify the existence of a grassland desertification trend based on the relationship between the three representative plants and the influence relationship between the three representative plants and the grassland desertification history. The system collects historical remote sensing images of grasslands, and then uses the historical remote sensing images to extract the historical desertified areas, analyzes the historical desertified areas, and obtains the changes in grassland desertification in each time period; the representative plant impact association calculation module is used to obtain the changes in growth data of three representative plants corresponding to each time period, and then analyzes the changes in grassland desertification in each time period to obtain the response degree factors of the three representative plants to grassland desertification; the grassland desertification real-time monitoring module is used to monitor the growth data of the three representative plants in real time, and then analyzes the response degree factors of the three representative plants to grassland desertification to monitor the grassland desertification situation.

[0014] The present invention has the following beneficial effects:

[0015] The present invention relates to a method and system for analyzing and monitoring the desertification of grasslands. By selecting representative plants and monitoring their growth data, the method and system can identify at an early stage whether the grassland has a tendency to desertify. This early warning mechanism provides valuable time for taking necessary protection and restoration measures, and helps prevent the further deterioration of the desertification problem. The method and system combine the time series analysis of plant growth data and the data extraction of historical remote sensing images, making the analysis of the desertification of grasslands more scientific and accurate. The time series analysis can reveal the long-term trend of plant growth changes, while the remote sensing images provide large-scale, high-resolution surface coverage information. The combination of the two greatly improves the reliability of the monitoring results. By calculating the change in grassland desertification in each time period and the change in the growth data of representative plants, and then obtaining the plant response degree factor to grassland desertification, this quantitative evaluation method can be used to analyze the desertification of grasslands. The assessment process provides a scientific basis for formulating targeted governance measures. Different plants may have different response mechanisms to desertification. Understanding these differences will help to adopt more accurate and effective protection strategies. Real-time monitoring of the growth data of representative plants and analysis of their response degree factors to desertification can achieve dynamic monitoring of grassland desertification. The real-time monitoring mechanism enables management departments to grasp the changes in grassland ecology at any time and adjust protection and management measures in time according to the monitoring results to ensure the stability and sustainability of the grassland ecosystem. Through effective desertification monitoring and timely intervention measures, this method helps to promote the recovery and reconstruction of grassland ecosystems, which not only protects precious grassland resources, but also maintains regional ecological balance, provides a sustainable source of livelihood for local communities, and promotes the coordinated development of economy and environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a method for analyzing and monitoring grassland desertification.

[0017] Figure 2 This is a structural diagram of a grassland desertification analysis and monitoring system according to the present invention. DETAILED DESCRIPTION

[0018] The embodiment of the present application uses a grassland desertification analysis and monitoring method and system to identify whether the grassland has a desertification trend by monitoring the growth data of three representative plants, thereby helping to discover possible desertification problems in the grassland ecosystem at an early stage, so as to take timely control and protection measures to prevent further deterioration of desertification.

[0019] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0020] Three representative plants in the grassland ecosystem are selected as monitoring objects. These plants are usually widely distributed in the area, have sensitive growth conditions, and can reflect the overall health of the grassland ecosystem; the growth data of the three representative plants in the grassland monitoring area are collected, and the growth data of each plant are sorted in chronological order to form a time series. By analyzing the trends of these time series, the changes in the growth status of plants in the long and short term can be observed, and then whether there is a desertification trend can be determined; historical remote sensing images are collected and analyzed, and the desertification area data of the grassland in different time periods are extracted to evaluate the degree of grassland desertification and the rate of change; the response degree factor of each plant to grassland desertification is calculated by combining the historical desertification area data and the change in the growth data of the three plants. The response degree factor quantifies the differences in the sensitivity of different plants to desertification, so as to more accurately evaluate the desertification situation; the real-time growth data of the three plants are used to monitor the desertification status of the grassland in real time, and desertification problems can be discovered in time and corresponding control measures can be taken.

[0021] See also Figure 1 The embodiment of the present invention provides a technical solution: a method for analyzing and monitoring grassland desertification, comprising the following steps: selecting three representative plants of a grassland ecosystem, obtaining growth data of the three representative plants in a grassland monitoring area, and sorting the growth data of the three representative plants according to time to form a time series of the three representative plants; identifying the increase and decrease trends of the time series of the three representative plants, and then identifying whether the grassland has a desertification trend based on the influence of the three representative plants on grassland desertification; based on the identification of the existence of a desertification trend in the grassland, collecting historical remote sensing images of the grassland, and then using the historical remote sensing images to extract the historical desertification area, analyzing the historical desertification area, and obtaining the grassland desertification change in each time period; respectively obtaining the growth data change of the three representative plants corresponding to each time period, and then analyzing in combination with the grassland desertification change in each time period to obtain the response degree factors of the three representative plants to grassland desertification; real-time monitoring of the growth data of the three representative plants, and then analyzing in combination with the response degree factors of the three representative plants to grassland desertification, and monitoring the grassland desertification condition.

[0022] Specifically, the specific requirements for the three representative plants are: Plant One is the dominant species in the desertified grassland, and when the grassland becomes desertified, the number of Plant One will increase; Plant Two and Plant Three are the inferior species in the desertified grassland, and when the grassland becomes desertified, the number of Plant Two and Plant Three will decrease.

[0023] In this implementation plan, multiple sample plots are divided in the study area, for example, each sample plot is 10x10 meters, and plant sample data in the sample plots are collected regularly to record the number and distribution of plant 1, plant 2, and plant 3. By monitoring the changes in representative plant species, early warning of grassland desertification trends can be provided, providing timely information for ecological protection and restoration, helping to formulate effective grassland management and restoration measures, slowing down the desertification process, and protecting grassland ecosystems; through monitoring and management, plant diversity in desertified grasslands can be protected, the recovery of disadvantaged plants can be promoted, and the stability and health of grassland ecosystems can be maintained.

[0024] Specific representative plant selection examples are designed as follows: Plant one can choose Artemisia ordosica, which is dominant in desertified grasslands, is drought-resistant, and has strong adaptability, and its number will increase when the grassland becomes desertified; Plant two can choose Stipa krylovii, which is common in non-desertified grasslands but is not drought-resistant, and its number will decrease when the grassland becomes desertified; Plant three can choose Artemisia frigida, which is common in non-desertified grasslands but has high requirements for soil conditions, and its number will decrease when the grassland becomes desertified.

[0025] Specifically, the specific selection method of the three representative plants is analyzed as follows: three representative plants of the grassland ecosystem are selected based on the analysis of the multidimensional characteristics of plants, and the multidimensional characteristics of plants specifically include climate adaptation characteristics and community ecological mutual influence characteristics; the specific plant multidimensional characteristic analysis is: identify grassland plant species, and then collect multidimensional characteristic data of various types of grassland plants that can reflect the multidimensional characteristics of plants, and the multidimensional characteristic data specifically include climate adaptation characteristic data and community ecological mutual influence characteristic data; climate adaptation characteristic data specifically include climate adaptability index, and community ecological mutual influence characteristic data specifically include interspecific association index; de-dimensionalize the climate adaptation characteristic data and community ecological mutual influence characteristic data, and then use the climate adaptability index to be proportional to the plant multidimensional performance evaluation value. , the logic that the interspecific association index is inversely proportional to the plant multidimensional performance evaluation value is used to calculate the plant multidimensional performance evaluation value, which represents the inverse proportion of the degree of mutual influence between climate and community ecology on plants, and is used to numerically represent the representative ability of plants for grassland desertification monitoring; according to the relationship between plants as dominant species or inferior species in desertified grasslands, grassland plant species are divided into dominant species categories and inferior species categories, and the plant multidimensional performance evaluation values ​​of grassland plants in the dominant species category and the inferior species category are ranked respectively, and the plant with the largest plant multidimensional performance evaluation value of grassland plants in the dominant species category is selected as plant one among the representative plants, and the plants with the largest and second largest plant multidimensional performance evaluation values ​​of grassland plants in the inferior species category are selected as plant two and plant three among the representative plants, respectively.

[0026] In this implementation scheme, in addition to obtaining the plant multidimensional performance evaluation value by inputting and analyzing the climate adaptation characteristic data and the community ecological mutual influence characteristic data using random forests, the plant multidimensional performance evaluation value can also be obtained by analyzing the degree of influence of the climate adaptation characteristic data and the community ecological mutual influence characteristic data on the plant multidimensional performance evaluation value, respectively, and obtaining the influencing factors of the climate adaptation characteristic data and the community ecological mutual influence characteristic data, and then respectively calculating and summing the climate adaptation characteristic data and the community ecological mutual influence characteristic data according to the logical relationship with the plant multidimensional performance evaluation value. The specific plant multidimensional performance evaluation value calculation formula can be expressed as: Where XDW represents the multi-dimensional performance evaluation value of plants, CAI represents the climate adaptability index, ICI represents the interspecific association index, α1 represents the impact factor of the climate adaptability index on the multi-dimensional performance evaluation value of plants, and α2 represents the impact factor of the interspecific association index on the multi-dimensional performance evaluation value of plants.

[0027] The influencing factors of the climate adaptability index and the interspecific association index on the multidimensional performance evaluation value of plants are specifically obtained and analyzed as follows: the climate adaptability index and interspecific association index of grassland plant species are collected through field surveys, literature and ecological monitoring records, and the correlation between the climate adaptability index and the interspecific association index and the multidimensional performance evaluation value of plants is analyzed using statistical methods or data mining techniques. Specifically, correlation coefficients, regression analysis or machine learning algorithms can be used to explore the relationship between data; based on the results of the analysis, an influencing factor model is established to quantify the degree of influence of climate adaptation characteristic data and community ecological mutual influence characteristic data on the multidimensional performance evaluation value of plants; the accuracy and stability of the established influencing factor model are verified through historical data, or real-time monitoring and feedback are carried out in a field environment to adjust and optimize the model parameters; the obtained influencing factors are applied to the actual multidimensional performance evaluation of plants to explain the degree of influence of different factors on the ecological characteristics of plants.

[0028] The climate adaptability index and interspecific association index are used to evaluate the adaptability of biological populations or communities under specific environmental conditions and the relationships between populations. The specific climate adaptability index measures the adaptability of a biological population or species to changes in specific climatic conditions. Therefore, the larger the climate adaptability index of a plant, the more it can ignore the impact of climate on plants, and the impact of grassland desertification on plants can be mainly considered. The larger the multi-dimensional performance evaluation value of the plant, the smaller the climate adaptability index of the plant, the greater the impact of climate on the plant, and the smaller the multi-dimensional performance evaluation value of the plant when the plant growth data changes cannot be determined solely as a factor caused by grassland desertification. The specific climate adaptability index is obtained by collecting ecological data of target species or communities through long-term ecological monitoring and experiments. The ecological data include growth rate, population density, and reproductive success rate, analyzing the correlation between these data and climate factors (such as temperature, precipitation, and seasonal changes), and based on these analyses, using statistical methods or building models to calculate The climate adaptability index reflects the adaptability of the species or community under different climatic conditions; the specific interspecific association index is used to evaluate the correlation and interaction between different species in space or time. Therefore, the larger the interspecific association index of a plant, the greater the impact of the extinction, reduction or increase of other plants on the plant. It cannot be determined solely as the impact caused by grassland desertification. The smaller the plant's interspecific association index, the less affected the plant is by changes in other plant growth data. The impact of grassland desertification on plants can be mainly considered. The larger the plant's multidimensional performance evaluation value. The specific interspecific association index is obtained by determining the distribution and community structure of multiple species in the study area, analyzing the degree of spatial or temporal association between these species, such as by calculating their symbiosis, dispersion or co-occurrence patterns. Based on these analyses, use appropriate statistical methods or index calculation formulas to obtain the interspecific association index and evaluate the strength and mode of the relationship between species.

[0029] Through quantitative data analysis and evaluation, we ensure that the selection of plants is scientifically based and objective, avoiding subjective preference and arbitrariness; provide numerical indicators for evaluating the representativeness of plants in grassland desertification monitoring, which will help guide the formulation of relevant monitoring and protection strategies; through classification and sorting, optimize resource allocation and improve research and monitoring efficiency.

[0030] An example of selecting representative plants is as follows: the dominant species of plants in the grassland with respect to desertification include Mongolian willow, leek sedge, and sheep fescue. The multi-dimensional performance evaluation values ​​of the three plants were calculated respectively, and the multi-dimensional performance evaluation value of Mongolian willow was the highest, so it was selected as representative plant one; the disadvantageous species of plants in the grassland with respect to desertification include alfalfa and gluttonous grass. The multi-dimensional performance evaluation value of alfalfa ranked first and gluttonous grass ranked second, and they were selected as representative plant two and plant three, respectively.

[0031] Specifically, the growth data of three representative plants in the grassland monitoring area are obtained, and the growth data of the three representative plants are sorted according to time to form a specific analysis of the time series of the three representative plants: the growth data of the three representative plants are specifically the plant density of the three representative plants in the grassland monitoring area; the plant density of the three representative plants in the grassland monitoring area is sorted according to time sequence, and then the time series of the three representative plants are constructed respectively, and the horizontal axis of the time series is time, and the vertical axis is the plant density of the three representative plants in the grassland monitoring area.

[0032] In this embodiment, in grassland monitoring, the plant density of the three representative plants refers to the number of plants of a certain species per unit area (for example, per square meter), which is used to reflect the population density and distribution of the plant in a specific habitat. The specific acquisition method is: in the grassland monitoring area, a number of sample points or plots are set to cover different habitat types and vegetation coverage, and a number of fixed-area plots (such as 1m x 1m or 2m x 2m) are randomly selected in each sample point as sample observation units. A detailed plant survey is conducted in each plot. The plant survey includes recording the number of each plant of each species, or using a sampling method to estimate the plant density. For each representative plant, the above process is repeated until all sample points and plots in the monitoring area are covered; the plant density of each representative plant in each plot is calculated by simple counting, and the data of all sample points and plots are integrated to obtain the average plant density of the representative plants in the monitoring area and its spatial distribution pattern.

[0033] Through time series analysis, the changing trend of the density of each representative plant can be observed, thereby predicting possible growth trends in the future; it provides quantitative indicators of the dynamics of key plant populations in grassland ecosystems, which is helpful to monitor changes in the ecological environment and formulate protection and management strategies; time series analysis provides the data basis required for scientific research and supports a deep understanding of the structure and dynamics of plant communities in grassland ecosystems.

[0034] Specifically, the increase and decrease trends of the three representative plant time series are identified, and then based on the influence of the three representative plants on grassland desertification, it is identified whether the grassland has a desertification trend. The specific analysis is as follows: the influence of the three representative plants on grassland desertification is specifically based on the specific requirements of the three representative plants, that is, when: the time series of plant one shows an increasing trend and the time series of plant two and plant three show a decreasing trend, the grassland has a desertification trend.

[0035] In this implementation plan, by monitoring the time series changes of plants, the trend of grassland desertification can be discovered early, which helps to take early protection and control measures to prevent the further deterioration of the grassland ecological environment; it provides a scientific basis based on data to help government departments and environmental protection organizations formulate scientific and effective desertification control strategies and policies; by identifying desertification trends, it can optimize the allocation of resources and invest funds and manpower in a targeted manner to protect and control the grassland ecological environment. A specific example of desertification trend identification is: assuming that plant one represents a sand-dwelling plant, its plant density increases over time; plant two and plant three represent drought-tolerant plants and wind-resistant plants, respectively, and their plant density shows a downward trend over time. Based on the trend analysis of these time series, it can be concluded that the grassland has a desertification trend, so that corresponding environmental protection and control measures can be taken.

[0036] Specifically, the specific acquisition and analysis of the response degree factors of the three representative plants to grassland desertification are as follows: the growth data changes of the three representative plants corresponding to each time period are compared with the grassland desertification changes in each time period, and then the average values ​​are taken to obtain the response degree factors of the three representative plants to grassland desertification. The specific calculation formula of the response degree factors is: Where ε represents the response degree factor, i represents the number of each time period, i = 1, 2, 3, ..., n, n represents the total number of time periods, ΔZ i Indicates the change in plant growth data in the i-th time period, ΔS i It represents the change of grassland desertification in the i-th time period. The response degree factor is used to quantify the response degree of plants when the grassland is desertified, indicating the correlation between the change of plant growth data and the change of grassland desertification degree.

[0037] In this implementation plan, the calculated response degree factors are used to quantify the response degree of plants to grassland desertification. These factors can be used to compare the performance of different plants under different environmental conditions. By quantifying the response degree factors of plants, the degree of grassland desertification can be evaluated more scientifically, providing data support for environmental protection decision-making. Identifying plant species with higher response degrees can guide grassland ecological restoration projects and select suitable plants for vegetation restoration and desertification control. Based on the response degree factors of plants, grassland desertification monitoring and management strategies can be optimized to achieve precise control and sustainable development.

[0038] Specifically, the growth data of three representative plants are monitored in real time, and then the response degree factors of the three representative plants to grassland desertification are analyzed. The specific analysis of monitoring the grassland desertification status is as follows: the growth data of the three representative plants monitored in real time, that is, the plant density of the three representative plants in the grassland monitoring area, are obtained; the plant density of the three representative plants in the grassland monitoring area is multiplied by the corresponding response degree factor and the average value is taken to obtain the current grassland desertification degree coefficient. The specific calculation formula of the current grassland desertification degree coefficient is: Where PSH represents the current grassland desertification degree coefficient, M1, M2, and M3 represent the plant density of plant one, plant two, and plant three in the grassland monitoring area, respectively, ε1, ε2, and ε3 represent the response degree factors of plant one, plant two, and plant three, respectively. The current grassland desertification degree coefficient is used to quantify the degree of change of the current grassland desertification.

[0039] In this embodiment, a growth data monitoring device is installed in the grassland monitoring area, which can be an automatic monitoring device or a regular field observation record; the growth data of three representative plants are obtained in real time, and based on historical data analysis, the response factor of each plant is calculated, that is, the correlation index or ratio between the growth data change and the desertification change; the growth data of the three plants in the current period are multiplied by the corresponding response factor, and then the average value is taken. The average value is used as the grassland desertification degree coefficient, which is used to quantify the current degree of change of grassland desertification and reflect the comprehensive response of plants to desertification; the calculated grassland desertification degree coefficient is visualized, such as in the form of a chart or report, to facilitate decision makers and researchers to understand the current grassland desertification situation.

[0040] Timely monitoring of changing trends in grassland desertification and providing timely early warning information will help to take rapid and effective protection and control measures; based on the quantified desertification degree coefficient, it will help environmental protection departments and decision makers to formulate precise grassland ecological protection strategies and measures; it will provide scientific data support, promote in-depth research on the changing mechanisms of grassland ecosystems, and contribute data and insights to the progress in the fields of environmental science and ecology. An example of understanding the current status of grassland desertification is as follows: assuming that real-time monitoring data shows that the growth density of plant one has decreased, while the growth density of plants two and three shows different changing trends. By calculating the response degree factor of each plant and combining it with real-time data, the current grassland desertification degree coefficient is obtained. If the coefficient shows that the degree of grassland desertification is increasing, the environmental management department can respond quickly and take corresponding prevention and control measures to protect the stability and health of the grassland ecosystem.

[0041] A grassland desertification status analysis and monitoring system, which applies the above-mentioned grassland desertification status analysis and monitoring method, includes: a representative plant determination module, which is used to select three representative plants of the grassland ecosystem, obtain the growth data of the three representative plants in the grassland monitoring area, and sort the growth data of the three representative plants according to time to form a time series of the three representative plants; a desertification trend determination module, which is used to identify the increase and decrease trend of the three representative plant time series, and then identify whether the grassland has a desertification trend based on the influence relationship of the three representative plants on grassland desertification; a grassland desertification history analysis module, which is used to identify the existence of a grassland desertification trend based on the relationship between the three representative plants and the influence relationship between the three representative plants and the grassland desertification history. The system collects historical remote sensing images of grasslands, and then uses the historical remote sensing images to extract the historical desertified areas, analyzes the historical desertified areas, and obtains the changes in grassland desertification in each time period; the representative plant impact association calculation module is used to obtain the changes in growth data of three representative plants corresponding to each time period, and then analyzes the changes in grassland desertification in each time period to obtain the response degree factors of the three representative plants to grassland desertification; the grassland desertification real-time monitoring module is used to monitor the growth data of the three representative plants in real time, and then analyzes the response degree factors of the three representative plants to grassland desertification to monitor the grassland desertification situation.

[0042] In summary, this application has at least the following effects:

[0043] By selecting three representative plants in the grassland ecosystem and comprehensively considering their growth conditions to analyze the desertification trend, the limitation of single indicator monitoring is overcome. Different plants have different sensitivities to desertification. The desertification status of grassland can be more comprehensively evaluated through the data of multiple plants. By analyzing the time series of plant growth data, long-term and short-term change trends can be observed, so as to more accurately judge whether there is a desertification trend in grassland. Taking into account the dynamic changes of the ecosystem, abnormalities in plant growth status can be discovered in time. Using the desertification area data extracted from historical remote sensing images and combining the growth data changes of three representative plants, the change amount of grassland desertification in different time periods can be analyzed, which is helpful to understand the development trend and rate of desertification and provide background information of historical changes. By calculating the response degree factors of three representative plants to grassland desertification, the correlation between plant growth data and desertification status can be quantified, which helps to determine the effectiveness and sensitivity of different plants in monitoring desertification and provide a scientific basis for the formulation of desertification prevention and control measures. Combined with real-time monitoring of the growth data and response degree factors of three plants, the desertification status of grassland can be monitored in real time. This method has an early warning function, which can detect desertification problems early and take corresponding control measures, helping to protect the stability and sustainable development of the grassland ecosystem.

[0044] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods and systems. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0045] The present invention is described with reference to the flowcharts and structure diagrams of the methods and systems according to the embodiments of the present invention. It should be understood that each process and combination of modules in the flowcharts and structure diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 process or processes and structures Figure 1 A device that specifies functionality in a module or modules.

[0046] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 process or processes and structures Figure 1 A function specified in a module or multiple modules.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 process or processes and structures Figure 1 Steps to specify functionality in a module or multiple modules.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0049] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for analyzing and monitoring grassland desertification, characterized in that: The following steps are involved: Select three representative plants of the grassland ecosystem, obtain the growth data of the three representative plants in the grassland monitoring area, sort the growth data of the three representative plants by time, and form a time series of the three representative plants; Identify the increase and decrease trends of three representative plant time series, and then identify whether the grassland has a desertification trend based on the influence of the three representative plants on grassland desertification; Based on the identification of the desertification trend of grassland, historical remote sensing images of grassland are collected, and then the historical desertification area is extracted and analyzed using the historical remote sensing images to obtain the change of grassland desertification in different time periods; The growth data changes of three representative plants in each time period were obtained respectively, and then combined with the grassland desertification changes in each time period for analysis, the response degree factors of the three representative plants to grassland desertification were obtained; The growth data of three representative plants are monitored in real time, and then the response degree factors of the three representative plants to grassland desertification are analyzed to monitor the grassland desertification status.

2. A grassland desertification analysis and monitoring method according to claim 1, characterized in that: The specific requirements for the three representative plants are: Plant One is the dominant species in the desertified grassland, and when the grassland becomes desertified, the number of Plant One will increase; Plant Two and Plant Three are the inferior species in the desertified grassland, and when the grassland becomes desertified, the number of Plant Two and Plant Three will decrease.

3. A grassland desertification analysis and monitoring method according to claim 2, characterized in that: The specific selection method of the three representative plants is as follows: Three representative plants of grassland ecosystems were selected based on the analysis of multi-dimensional characteristics of plants, which specifically include climate adaptation characteristics and community ecological interaction characteristics; The specific plant multi-dimensional characteristic analysis is as follows: identifying grassland plant species, and then collecting multi-dimensional characteristic data of various grassland plants that can reflect the multi-dimensional characteristics of plants. The multi-dimensional characteristic data specifically includes climate adaptation characteristic data and community ecological interaction characteristic data; The climate adaptation characteristic data specifically include the climate adaptability index, and the community ecological interaction characteristic data specifically include the interspecific association index; The climate adaptation characteristic data and community ecological mutual influence characteristic data were de-dimensionalized and quantified, and then the plant multi-dimensional performance evaluation value was calculated based on the logic that the climate adaptability index is proportional to the plant multi-dimensional performance evaluation value, and the interspecific association index is inversely proportional to the plant multi-dimensional performance evaluation value. The plant multi-dimensional performance evaluation value represents the inverse proportion of the degree of mutual influence between the climate and community ecology on the plant, and is used to numerically represent the representative ability of the plant for grassland desertification monitoring; According to whether plants are dominant or inferior species in desertified grasslands, grassland plant species are divided into dominant species categories and inferior species categories, and the multidimensional performance evaluation values ​​of grassland plants in the dominant species category and inferior species category are ranked respectively. The plant with the largest multidimensional performance evaluation value of grassland plants in the dominant species category is selected as the first representative plant, and the plants with the largest and second largest multidimensional performance evaluation values ​​of grassland plants in the inferior species category are selected as the second and third representative plants respectively.

4. The method for analyzing and monitoring grassland desertification according to claim 1, characterized in that: The growth data of three representative plants in the grassland monitoring area were obtained, and the growth data of the three representative plants were sorted according to time. The specific analysis of the time series of the three representative plants was as follows: The growth data of the three representative plants are specifically the plant density of the three representative plants in the grassland monitoring area; The plant densities of the three representative plants in the grassland monitoring area were sorted in chronological order, and then the time series of the three representative plants were constructed respectively. The horizontal axis of the time series is time, and the vertical axis is the plant density of the three representative plants in the grassland monitoring area.

5. The method for analyzing and monitoring grassland desertification according to claim 1, characterized in that: Identify the increase and decrease trends of the three representative plant time series, and then identify whether the grassland has a desertification trend based on the influence of the three representative plants on grassland desertification. The specific analysis is as follows: the influence of the three representative plants on grassland desertification is specifically based on the specific requirements of the three representative plants, that is, when: the time series of plant one shows an increasing trend and the time series of plant two and plant three show a decreasing trend, the grassland has a desertification trend.

6. The method for analyzing and monitoring grassland desertification according to claim 1, characterized in that: The specific acquisition and analysis of the response degree factors of the three representative plants to grassland desertification are as follows: the changes in the growth data of the three representative plants in each time period are compared with the changes in grassland desertification in each time period, and then the average values ​​are taken to obtain the response degree factors of the three representative plants to grassland desertification. The response degree factors are used to quantify the response degree of plants to grassland desertification, and indicate the correlation between changes in plant growth data and changes in grassland desertification.

7. The method for analyzing and monitoring grassland desertification according to claim 1, characterized in that: The growth data of three representative plants are monitored in real time, and then the response degree factors of the three representative plants to grassland desertification are analyzed. The specific analysis of grassland desertification monitoring is as follows: Obtaining the growth data of three representative plants monitored in real time, that is, the plant density of the three representative plants in the grassland monitoring area; The plant density of three representative plants in the grassland monitoring area was multiplied by the corresponding response degree factor and the average value was taken to obtain the current grassland desertification degree coefficient, which was used to quantify the degree of change of current grassland desertification.

8. A grassland desertification status analysis and monitoring system, using a grassland desertification status analysis and monitoring method according to any one of claims 1 to 7, characterized in that: include: The representative plant determination module is used to select three representative plants of the grassland ecosystem, obtain the growth data of the three representative plants in the grassland monitoring area, and sort the growth data of the three representative plants according to time to form a time series of the three representative plants; The desertification trend determination module is used to identify the increase and decrease trends of the three representative plant time series, and then identify whether the grassland has a desertification trend based on the influence of the three representative plants on grassland desertification; The grassland desertification history analysis module is used to identify the grassland desertification trend, collect the grassland's historical remote sensing images, and then use the historical remote sensing images to extract the historical desertification area, analyze the historical desertification area, and obtain the grassland desertification change in each time period; The representative plant impact correlation calculation module is used to obtain the growth data changes of the three representative plants in each time period, and then analyze the grassland desertification changes in each time period to obtain the response degree factors of the three representative plants to grassland desertification; The grassland desertification real-time monitoring module is used to monitor the growth data of three representative plants in real time, and then analyze the response degree factors of the three representative plants to grassland desertification to monitor the grassland desertification status.

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