Desert ecological environment monitoring and early warning method and system

Abstract

The invention relates to the technical field of desert environment monitoring, and discloses a desert ecological environment monitoring and early warning method and system, and the method comprises the steps: determining the vegetation index and soil spectrum characteristics of a monitored region according to an aerial image of the monitored region, and evaluating the monitoring score of the monitored region; and according to a relationship between the monitoring score and a historical monitoring score, determining to carry out environment monitoring on the monitored area, and when the monitoring score is smaller than the historical monitoring score, carrying out environment monitoring on the monitored area. And according to the soil data and the environment data of the monitored area, determining a soil score and an environment score of the monitored area, and evaluating a quarantine score of the monitored area. And obtaining a historical quarantine score of the monitoring area in adjacent time periods, and determining an early warning level of the monitoring area according to a quarantine score difference value between the quarantine score and the historical quarantine score. According to the method, the ecological state is evaluated in time by comprehensively analyzing the data of the monitored area, so that early warning and effective management of desertification are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of desert environment monitoring, and in particular to a method and system for monitoring and early warning of a desert ecological environment. Background Art

[0002] In the field of desert ecological environment monitoring, with the impact of global climate change and human activities, desertification is becoming increasingly serious. This phenomenon not only threatens the stability of the ecosystem, but also has a profound impact on agricultural production, drinking water safety and local economic development. For example, soil erosion and plant withering in arid areas will lead to a decline in agricultural output, thus affecting farmers' livelihoods and even causing social problems. Therefore, scientifically and effectively responding to desertification has become a challenge that needs to be urgently addressed worldwide.

[0003] At present, in order to effectively respond to desertification, timely obtaining ecological change information in the monitoring area and conducting early warnings have become important research directions. However, current monitoring methods mainly rely on ground station observations or regular manual surveys. These traditional methods usually have a small coverage and cannot fully reflect the dynamic changes of large-scale desertification. In addition, the frequency of data updates is often not high enough, resulting in the failure to timely feedback on ecological changes, which makes the response measures lag behind and makes it difficult to meet the needs of rapid assessment and real-time warning of wide-area desertification. For example, monitoring stations in some areas may not be established due to funding or technical limitations, resulting in the loss of important ecological change data. At the same time, manual surveys require a lot of manpower and material resources, and the results are often affected by subjective factors, with large data deviations. Therefore, this traditional monitoring method seems to be increasingly unsuitable for the needs of modern ecological monitoring.

[0004] Therefore, there is an urgent need to invent a monitoring and early warning technology for the desert ecological environment to solve the problem that the existing desertification monitoring methods rely on ground stations and manual surveys, resulting in small data coverage, low update frequency, and delayed feedback, which cannot meet the needs of rapid assessment and real-time warning. Summary of the invention

[0005] In view of this, the present invention proposes a desert ecological environment monitoring and early warning method and system, aiming to solve the problem that the desertification monitoring method in the current technology relies on ground stations and manual surveys, resulting in small data coverage, low update frequency, delayed feedback, and cannot meet the needs of rapid assessment and real-time early warning.

[0006] The present invention proposes a desert ecological environment monitoring and early warning method, comprising:

[0007] Acquire an aerial image of the monitoring area, determine a vegetation index and soil spectral characteristics of the monitoring area based on the aerial image, and evaluate a monitoring score of the monitoring area based on the vegetation index and soil spectral characteristics;

[0008] Obtain the historical monitoring scores of the monitoring area in adjacent time periods, and determine whether to perform environmental monitoring on the monitoring area according to the relationship between the monitoring score and the historical monitoring score, wherein:

[0009] When the monitoring score is less than the historical monitoring score, environmental monitoring is performed on the monitoring area;

[0010] Acquiring soil data and environmental data of the monitoring area, determining a soil score of the monitoring area according to the soil data, determining an environmental score of the monitoring area according to the environmental data, and determining a wasteland score of the monitoring area according to the soil score and the environmental score;

[0011] The historical wasteland scores of the monitoring area in adjacent time periods are obtained, and the warning level of the monitoring area is determined according to the wasteland score difference between the wasteland score and the historical wasteland score.

[0012] Furthermore, when evaluating the monitoring score of the monitoring area according to the vegetation index and the soil spectral characteristics, it includes:

[0013] Dividing the aerial image into several regions based on a preset distance R, obtaining the reflection frequency of the red light band and the infrared reflectivity of each region in the aerial image, and determining the vegetation index and soil reflectivity characteristics of each region according to the reflection frequency of the red light band and the infrared reflectivity;

[0014] According to the vegetation index and soil reflectivity characteristics of each of the regions, the vegetation health value of each of the regions is determined, and the vegetation health value of each of the regions is substituted into Formula I to determine the monitoring score, wherein Formula I is as follows:

[0015]

[0016] Among them, S is the monitoring score, n is the total number of each area, Vi is the vegetation health value of the i-th area, and Wi is the weight of the i-th area.

[0017] Furthermore, when determining the soil score of the monitoring area according to the soil data, it includes:

[0018] Acquire the soil collection depth and the corresponding soil moisture in the soil data, and perform soil stratification according to the soil collection depth and the corresponding soil moisture;

[0019] Obtaining the soil layer thickness of the surface soil in each of the soil layer stratifications, obtaining the historical soil layer thickness of the surface soil in adjacent time periods, and determining the soil score based on the relationship between the soil layer thickness and the historical soil layer thickness;

[0020] When the soil layer thickness is less than or equal to the historical soil layer thickness, the soil score is determined to be Xmin;

[0021] When the soil layer thickness is greater than the historical soil thickness, the thickness difference between the soil layer thickness and the historical soil thickness is obtained, and the soil score is determined based on the thickness difference.

[0022] Further, when determining the soil score according to the thickness difference, it includes:

[0023] Determining the soil score according to a relationship between the thickness difference and a preconfigured first preset thickness difference and a second preset thickness difference;

[0024] When the thickness difference is less than the first preset thickness difference, the soil score is determined to be X1;

[0025] When the thickness difference is greater than or equal to the first preset thickness difference, and the thickness difference is less than the second preset thickness difference, determining that the soil score is X2;

[0026] When the thickness difference is greater than or equal to the second preset thickness difference, the soil score is determined to be X3;

[0027] The first preset thickness difference is smaller than the second preset thickness difference, and 0<Xmin<X1<X2<X3.

[0028] Further, when the soil score is determined to be Xi, i=1, 2, 3, including:

[0029] Obtaining the mean soil particle size of the surface soil and the collected volume of the surface soil, and determining the soil porosity value of the surface soil according to the mean soil particle size and the collected volume;

[0030] Obtaining historical soil porosity values ​​of surface soil in adjacent time periods, and determining whether to adjust the soil score Xi according to the relationship between the soil porosity value and the historical soil porosity value;

[0031] When the soil porosity value is less than or equal to the historical soil porosity value, it is determined that the soil score Xi is not adjusted;

[0032] When the soil porosity value is greater than the historical soil porosity value, an adjustment coefficient is determined according to the porosity value difference between the soil porosity value and the historical soil porosity value, and the soil score Xi is adjusted according to the adjustment coefficient.

[0033] Furthermore, when determining the adjustment coefficient according to the pore value difference between the soil pore value and the historical soil pore value, it includes:

[0034] Determining the adjustment coefficient according to the relationship between the pore value difference and the first preset pore value difference and the second preset pore value difference that are preconfigured;

[0035] When the pore value difference is less than the first preset pore value difference, the adjustment coefficient is determined to be K1;

[0036] When the pore value difference is greater than or equal to the first preset pore value difference, and the pore value difference is less than the second preset pore value difference, the adjustment coefficient is determined to be K2;

[0037] When the pore value difference is greater than or equal to the second preset pore value difference, the adjustment coefficient is determined to be K3;

[0038] The first preset pore value difference is smaller than the second preset pore value difference, and 1<K1<K2<K3.

[0039] Furthermore, when determining the environmental score of the monitoring area according to the environmental data, it includes:

[0040] Obtaining the number of sandstorms, the total amount of precipitation, and the average temperature value in the monitoring area within a preset period, and determining the environmental score based on the relationship between the average temperature value and the preset temperature value;

[0041] When the average temperature value is less than the preset temperature value, the environmental score is determined to be Jmin;

[0042] When the average temperature value is greater than or equal to the preset temperature value, the number of sandstorms, the total precipitation and the average temperature value are substituted into Formula II to determine the environmental score, wherein Formula II is as follows:

[0043] Ji = z1×a+z2×d+z3×f;

[0044] Wherein, Ji is the environmental score, i=1, 2, 3, a is the number of sandstorms, d is the total precipitation, f is the average temperature value, z1-z3 are weight coefficients, and the sum of z1-z3 is 1.

[0045] Furthermore, when determining the wasteland score of the monitored area according to the soil score and the environmental score, it includes:

[0046] According to the relationship between the environmental score and the preset first preset environmental score and the second preset environmental score, a correction coefficient is determined, the soil score is corrected according to the correction coefficient, and the corrected soil score is determined as the wasteland score of the monitoring area, wherein:

[0047] When the environmental score is less than or equal to the first preset environmental score, the correction coefficient is determined to be P1;

[0048] When the environment score is greater than the first preset environment score, and the first preset environment score is less than or equal to the second preset environment score, the correction coefficient is determined to be P2;

[0049] When the environmental score is greater than the second preset environmental score, the correction coefficient is determined to be P3;

[0050] The first preset environment score is smaller than the second preset environment score, and 1<P1<P2<P3.

[0051] Furthermore, when determining the early warning level of the monitoring area according to the difference between the wasteland score and the historical wasteland score, it includes:

[0052] Pre-configuring a first preset wasteland score difference and a second preset wasteland score difference, and determining the early warning level of the monitoring area according to the relationship between the wasteland score difference and the first preset wasteland score difference and the second preset wasteland score difference;

[0053] When the wasteland score difference is less than the first preset wasteland score difference, it is determined that the warning level of the monitoring area is a low level;

[0054] When the wasteland score difference is greater than or equal to the first preset wasteland score difference, and the wasteland score difference is less than the second preset wasteland score difference, it is determined that the warning level of the monitoring area is medium;

[0055] When the wasteland score difference is greater than or equal to the second preset wasteland score difference, it is determined that the warning level of the monitored area is a high level;

[0056] The first preset wasteland score difference is smaller than the second preset wasteland score difference, and low level < medium level < high level.

[0057] Compared with the prior art, the beneficial effect of the present invention is that by acquiring aerial images, the vegetation growth status and soil characteristics can be quickly captured in a large range. These data can reflect key ecological indicators such as vegetation coverage, health, and soil moisture and nutrient status, helping scientists to fully understand the ecological health status of the monitoring area. Compared with traditional ground monitoring methods, aerial photography technology can not only save a lot of manpower and material resources, but also reduce data deviations caused by human interference, making the ecological monitoring results more objective and reliable. Secondly, combined with historical monitoring data, the current monitoring score can be compared with the historical monitoring score to judge the changing trend of the current ecological state. This dynamic comparison method makes the monitoring work no longer isolated, but is integrated into the time series analysis, which can effectively identify the potential risks of ecological deterioration. For example, when the monitoring score is less than the historical monitoring score, an environmental monitoring warning will be automatically triggered, so that environmental changes can be responded to in a timely manner, ensuring that necessary management measures are taken to prevent further spread of desertification. In addition, soil data and environmental data are integrated to calculate soil scores and environmental scores, thereby evaluating desertification scores. Soil and environmental factors are important factors affecting ecological health. By scientifically calculating these scores, we can fully understand the multiple factors affecting the ecology of the monitoring area. This multi-dimensional assessment method can not only identify potential ecological problems, but also provide more targeted intervention measures for environmental management. Finally, by comparing with the historical desertification scores, the warning level of the current monitored area can be determined. This mechanism ensures the continuous monitoring and assessment of desertification risks, and enables monitoring personnel to promptly detect changes in the ecosystem and adjust management strategies according to the changes.

[0058] On the other hand, the present application also provides a desert ecological environment monitoring and early warning system, including:

[0059] A first evaluation module is used to obtain an aerial image of a monitoring area, determine a vegetation index and a soil spectral feature of the monitoring area according to the aerial image, and evaluate a monitoring score of the monitoring area according to the vegetation index and the soil spectral feature;

[0060] a central control module, electrically connected to the first evaluation module, and configured to obtain historical monitoring scores of adjacent time periods of the monitoring area, and determine whether to perform environmental monitoring on the monitoring area according to a relationship between the monitoring score and the historical monitoring score, wherein: when the monitoring score is less than the historical monitoring score, the central control module performs environmental monitoring on the monitoring area;

[0061] a second evaluation module, electrically connected to the central control module, the second evaluation module being used to obtain soil data and environmental data of the monitored area, determine a soil score of the monitored area according to the soil data, determine an environmental score of the monitored area according to the environmental data, and determine a wasteland score of the monitored area according to the soil score and the environmental score;

[0062] An early warning module and the second evaluation module, wherein the early warning module is used to obtain the historical wasteland scores of the monitoring area in adjacent time periods, and determine the early warning level of the monitoring area according to the wasteland score difference between the wasteland score and the historical wasteland score.

[0063] It can be understood that the desert ecological environment monitoring and early warning method and system in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0065] Figure 1 A flowchart of a desert ecological environment monitoring and early warning method provided by an embodiment of the present invention;

[0066] Figure 2 A functional block diagram of a desert ecological environment monitoring and early warning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0068] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a desert ecological environment monitoring and early warning method, including:

[0069] Step S100: Acquire an aerial image of the monitoring area, determine the vegetation index and soil spectral characteristics of the monitoring area based on the aerial image, and evaluate the monitoring score of the monitoring area based on the vegetation index and soil spectral characteristics.

[0070] Specifically, when evaluating the monitoring score of the monitoring area based on the vegetation index and soil spectral characteristics, it includes: dividing the aerial image into several areas based on the preset distance R, obtaining the reflection frequency and infrared reflectivity of the red light band in each area of ​​the aerial image, and determining the vegetation index and soil reflectivity characteristics of each area based on the reflection frequency and infrared reflectivity of the red light band. According to the vegetation index and soil reflectivity characteristics of each area, the vegetation health value of each area is determined, and the vegetation health value of each area is substituted into Formula I to determine the monitoring score, where Formula I is as follows:

[0071]

[0072] Among them, S is the monitoring score, n is the total number of regions, Vi is the vegetation health value of the i-th region, and Wi is the weight of the i-th region.

[0073] It can be seen that the monitoring area is divided into multiple small areas by using aerial images at preset distances, and then the red light band reflection frequency and infrared reflectivity of each area are obtained to calculate the vegetation index and soil reflectivity characteristics. These characteristics reflect the vegetation health status of each area. By combining the vegetation health value of each area with the corresponding weight, the overall monitoring score is calculated using the weighted average method. This process realizes the quantitative assessment of the ecological status of the monitoring area and can effectively reflect the changes in vegetation health and soil conditions.

[0074] It is understandable that by using aerial images, the monitoring area is divided into multiple small areas by preset distances, and then the red light band reflection frequency and infrared reflectivity of each area are obtained, so as to calculate the vegetation index and soil reflectivity characteristics. These characteristics reflect the health of vegetation in each area and provide important data support for the evaluation of the overall monitoring score. Specifically, through high-resolution aerial photography technology, detailed monitoring of a wide area can be achieved, avoiding the limitations of traditional ground surveys. Secondly, in the specific implementation process, the acquired aerial images are processed and the images are divided into several areas according to the preset distance R. This process ensures that the ecological characteristics of different areas can be analyzed independently, making the data more accurate. In each area, the vegetation index (such as the normalized vegetation index NDVI) and soil reflectivity characteristics are obtained by analyzing the reflection frequency and infrared reflectivity of the red light band. The vegetation index is an important indicator for measuring the growth status of plants, which is usually calculated by reflectance spectrum data. Soil reflectivity reflects the composition and state of the soil, which is of great significance in assessing land degradation and ecological health. In addition, the vegetation health values ​​of each region are obtained and substituted into formula I to calculate the monitoring score S. In the formula, n represents the total number of regions, Vi represents the vegetation health value of the i-th region, and Wi represents the weight of the i-th region. This weighted average method can effectively reflect the relative importance of each region in the overall ecological health, thereby ensuring that the final monitoring score can accurately reflect the ecological status of the entire monitoring area. The weight setting can be adjusted according to the importance of the region or the difference in ecological characteristics to meet different monitoring needs. Finally, by regularly acquiring aerial images and calculating monitoring scores, dynamic tracking of ecological environmental changes can be achieved.

[0075] Step S200, obtaining the historical monitoring scores of the monitoring area in adjacent time periods, and determining whether to conduct environmental monitoring of the monitoring area based on the relationship between the monitoring score and the historical monitoring score, wherein: when the monitoring score is less than the historical monitoring score, environmental monitoring of the monitoring area is conducted.

[0076] Step S300: Acquire soil data and environmental data of the monitored area, determine the soil score of the monitored area according to the soil data, determine the environmental score of the monitored area according to the environmental data, and determine the desertification score of the monitored area according to the soil score and the environmental score.

[0077] Specifically, when determining the soil score of the monitored area based on soil data, it includes: obtaining the soil collection depth and the corresponding soil moisture in the soil data, and stratifying the soil according to the soil collection depth and the corresponding soil moisture. Obtain the soil layer thickness of the surface soil in each soil layer, obtain the historical soil layer thickness of the surface soil in adjacent time periods, and determine the soil score based on the relationship between the soil layer thickness and the historical soil layer thickness: when the soil layer thickness is less than or equal to the historical soil layer thickness, the soil score is determined to be Xmin. When the soil layer thickness is greater than the historical soil thickness, the thickness difference between the soil layer thickness and the historical soil thickness is obtained, and the soil score is determined based on the thickness difference.

[0078] Specifically, when determining the soil score according to the thickness difference, it includes: determining the soil score according to the relationship between the thickness difference and the pre-configured first preset thickness difference and the second preset thickness difference. When the thickness difference is less than the first preset thickness difference, the soil score is determined to be X1. When the thickness difference is greater than or equal to the first preset thickness difference, and the thickness difference is less than the second preset thickness difference, the soil score is determined to be X2. When the thickness difference is greater than or equal to the second preset thickness difference, the soil score is determined to be X3. Among them, the first preset thickness difference is less than the second preset thickness difference, and 0<Xmin<X1<X2<X3.

[0079] Specifically, when the soil score Xi is determined, i = 1, 2, 3, including: obtaining the mean soil particle size of the surface soil and the collection volume of the surface soil, and determining the soil porosity value of the surface soil according to the mean soil particle size and the collection volume. Obtain the historical soil porosity value of the surface soil in adjacent periods, and determine whether to adjust the soil score Xi according to the relationship between the soil porosity value and the historical soil porosity value. When the soil porosity value is less than or equal to the historical soil porosity value, it is determined that the soil score Xi is not adjusted. When the soil porosity value is greater than the historical soil porosity value, the adjustment coefficient is determined according to the porosity value difference between the soil porosity value and the historical soil porosity value, and the soil score Xi is adjusted according to the adjustment coefficient.

[0080] Specifically, when determining the adjustment coefficient based on the pore value difference between the soil pore value and the historical soil pore value, it includes: determining the adjustment coefficient based on the relationship between the pore value difference and the pre-configured first preset pore value difference and the second preset pore value difference. When the pore value difference is less than the first preset pore value difference, the adjustment coefficient is determined to be K1. When the pore value difference is greater than or equal to the first preset pore value difference, and the pore value difference is less than the second preset pore value difference, the adjustment coefficient is determined to be K2. When the pore value difference is greater than or equal to the second preset pore value difference, the adjustment coefficient is determined to be K3. Among them, the first preset pore value difference is less than the second preset pore value difference, and 1<K1<K2<K3.

[0081] It can be seen that different scoring criteria are set according to the relationship between the soil layer thickness and the historical soil layer thickness to determine the soil scores Xmin, X1, X2 and X3. This process takes into account the changes in soil layer thickness and can effectively reflect the actual situation of the soil. In addition, by analyzing the mean particle size and collection volume of the surface soil, the soil porosity value is calculated and compared with the historical data to determine whether to adjust the soil score. The setting of the adjustment coefficient is based on the relationship between the porosity value difference and the preset threshold, ensuring the dynamic adaptability of the soil score. This method comprehensively considers the influence of soil layer thickness, porosity and historical data, realizes an accurate assessment of soil conditions, reflects changes in soil health and stability, and provides a scientific basis for desertification monitoring and ecological management.

[0082] It is understandable that the soil data obtained by the soil collection depth and corresponding humidity can reflect the actual moisture status of the soil. By stratifying the soil, the physical properties of soil at different levels can be understood in more detail, which is crucial for evaluating soil quality. Secondly, by analyzing the relationship between the thickness of the surface soil and the thickness of the historical soil layer, multiple threshold scoring standards are set, including Xmin, X1, X2 and X3. This method not only takes into account the impact of historical data, but also reflects the changes in the soil in a timely manner, ensuring that the scoring has strong timeliness and adaptability. Further, when determining the soil score, the porosity value of the soil is calculated by obtaining the mean particle size and collection volume of the surface soil. This indicator is a key factor in evaluating the soil's water retention capacity and air permeability. By comparing the soil porosity value with the historical porosity value, the health status of the soil can be determined, and it can be judged whether the score needs to be adjusted. The advantage of this process is that it not only evaluates based on absolute values, but also introduces historical data reference, making the scoring more scientific and accurate. Finally, the determination of the adjustment coefficient is based on the relationship between the porosity value difference and the preset threshold. By classifying the porosity difference into different levels, the soil score can be adjusted dynamically according to the actual situation. For example, when the porosity difference is less than the preset first threshold, a lower adjustment coefficient K1 will be used; when the difference is larger, it will be increased to K2 or K3. This hierarchical adjustment mechanism ensures the flexibility of soil scoring, enabling it to reflect the changing state of the soil in a timely manner and meet the needs of dynamic monitoring.

[0083] Specifically, when determining the environmental score of the monitoring area based on environmental data, it includes: obtaining the number of sandstorms, total precipitation and average temperature values ​​in the monitoring area within a preset period, and determining the environmental score based on the relationship between the average temperature value and the preset temperature value: when the average temperature value is less than the preset temperature value, the environmental score is determined to be Jmin. When the average temperature value is greater than or equal to the preset temperature value, the number of sandstorms, total precipitation and average temperature value are substituted into Formula II to determine the environmental score, where Formula II is as follows: Ji = z1×a+z2×d+z3×f. Where Ji is the environmental score, i = 1, 2, 3, a is the number of sandstorms, d is the total precipitation, f is the average temperature value, z1-z3 are weight coefficients, and the sum of z1-z3 is 1.

[0084] It is understandable that an environmental scoring model is established by comprehensively analyzing the environmental data of the monitored area (including the number of sandstorms, total precipitation and average temperature values) to evaluate the ecological environment of the area. Specifically, two scoring criteria are set based on the comparison between the average temperature value and the preset temperature value: when the average temperature is lower than the preset value, the environmental score is set to the minimum value Jmin to reflect the potential ecological risk; when the average temperature is higher than or equal to the preset value, a more detailed scoring calculation is performed using Formula II. This formula weights the number of sandstorms, total precipitation and average temperature values ​​to form a comprehensive environmental score Ji. Among them, the setting of weight coefficients z1, z2 and z3 enables the influence of different factors on the environmental score to be quantified, ensuring the scientificity and rationality of the score. In this way, the environmental changes and potential threats in the monitored area can be effectively reflected, providing data support for ecological management and decision-making.

[0085] Specifically, when determining the wasteland score of the monitored area based on the soil score and the environmental score, it includes: determining a correction coefficient based on the relationship between the environmental score and the preset first preset environmental score and the second preset environment, correcting the soil score based on the correction coefficient, and determining the corrected soil score as the wasteland score of the monitored area, wherein: when the environmental score is less than or equal to the first preset environmental score, the correction coefficient is determined to be P1. When the environmental score is greater than the first preset environmental score, and the first preset environmental score is less than or equal to the second preset environmental score, the correction coefficient is determined to be P2. When the environmental score is greater than the second preset environmental score, the correction coefficient is determined to be P3. Among them, the first preset environmental score is less than the second preset environment, and 1<P1<P2<P3.

[0086] It can be understood that by combining environmental changes with soil quality, the overall ecological status of the monitored area can be reflected through the dynamic adjustment of the correction coefficient. Specifically, by determining the core role of the environmental score in the assessment of the desertification score. When the environmental score is high, it means that the area may face severe ecological challenges, such as persistent drought, frequent sandstorms or soil erosion, so the soil score needs to be significantly revised to ensure the timely and effective ecological management measures. At this stage, the setting of the correction coefficient P3 is crucial, which can guide decision makers to take timely actions to prevent further ecological deterioration. When the environmental score is at an intermediate level (between the first preset environmental score and the second preset environmental score), the introduction of the correction coefficient P2 provides a relatively flexible response mechanism. In this case, although the soil score needs to be corrected, the amplitude is small, which means that the ecological environment is improving and the ecological restoration potential of the monitored area still exists. At this time, the relevant departments can formulate appropriate management and restoration plans based on this score to encourage sustainable land use on the basis of protecting the existing ecosystem. Finally, when the environmental score is higher than the second preset environmental score, the use of the correction coefficient P1 means that the ecosystem in the monitored area is relatively healthy and the soil score remains unchanged, which provides a good basis for continued ecological protection and management. This assessment process not only focuses on soil quality itself, but also emphasizes the impact of environmental factors on soil health, promoting the construction of an overall ecological monitoring system. Through this comprehensive assessment mechanism, the wasteland scores of the monitored areas can timely reflect changes in soil and environment, enabling relevant decision makers to formulate more scientific and effective ecological protection measures and promote the realization of sustainable development goals.

[0087] Step S400: Obtain historical wasteland scores of the monitored area in adjacent time periods, and determine the warning level of the monitored area based on the wasteland score difference between the wasteland score and the historical wasteland score.

[0088] Specifically, when determining the early warning level of the monitoring area according to the difference between the wasteland score and the historical wasteland score, it includes: pre-configuring a first preset wasteland score difference and a second preset wasteland score difference, and determining the early warning level of the monitoring area according to the relationship between the wasteland score difference and the first preset wasteland score difference and the second preset wasteland score difference. When the wasteland score difference is less than the first preset wasteland score difference, the early warning level of the monitoring area is determined to be a low level. When the wasteland score difference is greater than or equal to the first preset wasteland score difference, and the wasteland score difference is less than the second preset wasteland score difference, the early warning level of the monitoring area is determined to be a medium level. When the wasteland score difference is greater than or equal to the second preset wasteland score difference, the early warning level of the monitoring area is determined to be a high level. Among them, the first preset wasteland score difference is less than the second preset wasteland score difference, and the low level < medium level < high level.

[0089] It is understandable that by comparing the difference in desertification scores with the preset difference, the trend of ecological change can be clearly identified. This method is not only simple and easy to implement, but also can guide the formulation of management measures by setting different warning levels. For example, if the difference in desertification scores in the monitored area is at a low level, the relevant departments can choose to conduct routine monitoring and maintenance to prevent the problem from further deteriorating. When the difference reaches the medium level, more active intervention measures need to be taken, such as implementing vegetation restoration, soil improvement and other projects to reduce ecological risks. A high level means that immediate action is needed to mobilize more resources for governance to prevent ecological collapse. Secondly, the dynamic nature of ecological monitoring makes the early warning mechanism more adaptable. By combining real-time monitoring and historical data, the health status of the ecosystem can be more accurately assessed. This dynamic assessment method can help decision makers adjust strategies in a timely manner and avoid decision-making errors caused by information lags. For example, if the difference in desertification scores monitored rises suddenly during a certain period of time, the relevant departments can quickly take targeted measures, such as strengthening soil and water conservation, implementing targeted planting strategies, etc., to slow down the process of desertification. Finally, the successful implementation of the early warning mechanism depends not only on scientific assessment methods, but also on effective data support and technical means. With the rapid development of remote sensing technology and big data analysis, environmental changes in the monitored areas can be collected in real time through high-resolution satellite images and automated monitoring equipment. The combination of these technologies makes ecological monitoring not limited to traditional manual surveys, but also able to reflect the ecological status more comprehensively and accurately. Through the analysis and processing of big data, decision makers can gain deeper insights and provide data support for the formulation of long-term sustainable ecological management plans.

[0090] In the above embodiment, by acquiring aerial images, the vegetation growth status and soil characteristics can be quickly captured in a large range. These data can reflect key ecological indicators such as vegetation coverage, health, and soil moisture and nutrient status, helping scientists to fully understand the ecological health status of the monitoring area. Compared with traditional ground monitoring methods, aerial photography technology can not only save a lot of manpower and material resources, but also reduce data deviations caused by human interference, making the ecological monitoring results more objective and reliable. Secondly, combined with historical monitoring data, the current monitoring score can be compared with the historical monitoring score to determine the changing trend of the current ecological state. This dynamic comparison method makes the monitoring work no longer isolated, but is integrated into the time series analysis, which can effectively identify the potential risks of ecological deterioration. For example, when the monitoring score is less than the historical monitoring score, an environmental monitoring warning will be automatically triggered, so that environmental changes can be responded to in a timely manner, ensuring that necessary management measures are taken to prevent further spread of desertification. In addition, soil data and environmental data are integrated to calculate soil scores and environmental scores, thereby evaluating desertification scores. Soil and environmental factors are important factors affecting ecological health. By scientifically calculating these scores, we can fully understand the multiple factors affecting the ecology of the monitoring area. This multi-dimensional assessment method can not only identify potential ecological problems, but also provide more targeted intervention measures for environmental management. Finally, by comparing with the historical desertification scores, the warning level of the current monitored area can be determined. This mechanism ensures the continuous monitoring and assessment of desertification risks, and enables monitoring personnel to promptly detect changes in the ecosystem and adjust management strategies according to the changes.

[0091] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides a desert ecological environment monitoring and early warning system, including: a first evaluation module, a central control module, a second evaluation module and an early warning module.

[0092] Specifically, the first evaluation module is used to obtain aerial images of the monitoring area, determine the vegetation index and soil spectral characteristics of the monitoring area based on the aerial images, and evaluate the monitoring score of the monitoring area based on the vegetation index and soil spectral characteristics. The central control module is electrically connected to the first evaluation module, and the central control module is used to obtain the historical monitoring scores of the monitoring area in adjacent periods, and determine whether to conduct environmental monitoring of the monitoring area based on the relationship between the monitoring score and the historical monitoring score, wherein: when the monitoring score is less than the historical monitoring score, the central control module conducts environmental monitoring of the monitoring area. The second evaluation module is electrically connected to the central control module, and the second evaluation module is used to obtain soil data and environmental data of the monitoring area, determine the soil score of the monitoring area based on the soil data, determine the environmental score of the monitoring area based on the environmental data, and determine the wasteland score of the monitoring area based on the soil score and the environmental score. The early warning module is connected to the second evaluation module, and the early warning module is used to obtain the historical wasteland score of the monitoring area in adjacent periods, and determine the early warning level of the monitoring area based on the wasteland score difference between the wasteland score and the historical wasteland score.

[0093] It can be understood that the desert ecological environment monitoring and early warning method and system in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.

[0095] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, 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 generate 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] 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 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A desert ecological environment monitoring and early warning method, characterized in that: include: Acquire an aerial image of the monitoring area, determine a vegetation index and soil spectral characteristics of the monitoring area based on the aerial image, and evaluate a monitoring score of the monitoring area based on the vegetation index and soil spectral characteristics; Obtain the historical monitoring scores of the monitoring area in adjacent time periods, and determine whether to perform environmental monitoring on the monitoring area according to the relationship between the monitoring score and the historical monitoring score, wherein: When the monitoring score is less than the historical monitoring score, environmental monitoring is performed on the monitoring area; Acquiring soil data and environmental data of the monitoring area, determining a soil score of the monitoring area according to the soil data, determining an environmental score of the monitoring area according to the environmental data, and determining a wasteland score of the monitoring area according to the soil score and the environmental score; The historical wasteland scores of the monitoring area in adjacent time periods are obtained, and the warning level of the monitoring area is determined according to the wasteland score difference between the wasteland score and the historical wasteland score.

2. The desert ecological environment monitoring and early warning method according to claim 1, characterized in that: When evaluating the monitoring score of the monitoring area based on the vegetation index and soil spectral characteristics, it includes: Dividing the aerial image into several regions based on a preset distance R, obtaining the reflection frequency of the red light band and the infrared reflectivity of each region in the aerial image, and determining the vegetation index and soil reflectivity characteristics of each region according to the reflection frequency of the red light band and the infrared reflectivity; According to the vegetation index and soil reflectivity characteristics of each of the regions, the vegetation health value of each of the regions is determined, and the vegetation health value of each of the regions is substituted into Formula I to determine the monitoring score, wherein Formula I is as follows: Among them, S is the monitoring score, n is the total number of each area, Vi is the vegetation health value of the i-th area, and Wi is the weight of the i-th area.

3. The desert ecological environment monitoring and early warning method according to claim 1, characterized in that: Determining the soil score of the monitoring area based on the soil data includes: Acquire the soil collection depth and the corresponding soil moisture in the soil data, and perform soil stratification according to the soil collection depth and the corresponding soil moisture; Obtaining the soil layer thickness of the surface soil in each of the soil layer stratifications, obtaining the historical soil layer thickness of the surface soil in adjacent time periods, and determining the soil score based on the relationship between the soil layer thickness and the historical soil layer thickness; When the soil layer thickness is less than or equal to the historical soil layer thickness, the soil score is determined to be Xmin; When the soil layer thickness is greater than the historical soil thickness, the thickness difference between the soil layer thickness and the historical soil thickness is obtained, and the soil score is determined based on the thickness difference.

4. The desert ecological environment monitoring and early warning method according to claim 3, characterized in that: Determining the soil score according to the thickness difference includes: Determining the soil score according to a relationship between the thickness difference and a preconfigured first preset thickness difference and a second preset thickness difference; When the thickness difference is less than the first preset thickness difference, the soil score is determined to be X1; When the thickness difference is greater than or equal to the first preset thickness difference, and the thickness difference is less than the second preset thickness difference, determining that the soil score is X2; When the thickness difference is greater than or equal to the second preset thickness difference, the soil score is determined to be X3; The first preset thickness difference is smaller than the second preset thickness difference, and 0<Xmin<X1<X2<X3.

5. The desert ecological environment monitoring and early warning method according to claim 4, characterized in that: When the soil score is determined to be Xi, i=1, 2, 3, including: Obtaining the mean soil particle size of the surface soil and the collected volume of the surface soil, and determining the soil porosity value of the surface soil according to the mean soil particle size and the collected volume; Obtaining historical soil porosity values ​​of surface soil in adjacent time periods, and determining whether to adjust the soil score Xi according to the relationship between the soil porosity value and the historical soil porosity value; When the soil porosity value is less than or equal to the historical soil porosity value, it is determined that the soil score Xi is not adjusted; When the soil porosity value is greater than the historical soil porosity value, an adjustment coefficient is determined according to the porosity value difference between the soil porosity value and the historical soil porosity value, and the soil score Xi is adjusted according to the adjustment coefficient.

6. The desert ecological environment monitoring and early warning method according to claim 5, characterized in that: When determining the adjustment coefficient according to the pore value difference between the soil pore value and the historical soil pore value, it includes: Determining the adjustment coefficient according to the relationship between the pore value difference and the first preset pore value difference and the second preset pore value difference that are preconfigured; When the pore value difference is less than the first preset pore value difference, the adjustment coefficient is determined to be K1; When the pore value difference is greater than or equal to the first preset pore value difference, and the pore value difference is less than the second preset pore value difference, the adjustment coefficient is determined to be K2; When the pore value difference is greater than or equal to the second preset pore value difference, the adjustment coefficient is determined to be K3; The first preset pore value difference is smaller than the second preset pore value difference, and 1<K1<K2<K3.

7. The desert ecological environment monitoring and early warning method according to claim 6, characterized in that: When determining the environmental score of the monitoring area based on the environmental data, it includes: Obtaining the number of sandstorms, the total amount of precipitation, and the average temperature value in the monitoring area within a preset period, and determining the environmental score based on the relationship between the average temperature value and the preset temperature value; When the average temperature value is less than the preset temperature value, the environmental score is determined to be Jmin; When the average temperature value is greater than or equal to the preset temperature value, the number of sandstorms, the total precipitation and the average temperature value are substituted into Formula II to determine the environmental score, wherein Formula II is as follows: Ji=z1×a+z2×d+z3×f; Among them, Ji is the environmental score, i=1, 2, 3, a is the number of sandstorms, d is the total precipitation, f is the average temperature value, z1-z3 are weight coefficients, and the sum of z1-z3 is 1.

8. The desert ecological environment monitoring and early warning method according to claim 1, characterized in that: When determining the wasteland score of the monitoring area based on the soil score and the environmental score, it includes: According to the relationship between the environmental score and the preset first preset environmental score and the second preset environmental score, a correction coefficient is determined, the soil score is corrected according to the correction coefficient, and the corrected soil score is determined as the wasteland score of the monitoring area, wherein: When the environmental score is less than or equal to the first preset environmental score, the correction coefficient is determined to be P1; When the environment score is greater than the first preset environment score, and the first preset environment score is less than or equal to the second preset environment score, the correction coefficient is determined to be P2; When the environmental score is greater than the second preset environmental score, the correction coefficient is determined to be P3; The first preset environment score is smaller than the second preset environment score, and 1<P1<P2<P3.

9. The desert ecological environment monitoring and early warning method according to claim 1, characterized in that: When determining the early warning level of the monitoring area according to the difference between the wasteland score and the historical wasteland score, it includes: Pre-configuring a first preset wasteland score difference and a second preset wasteland score difference, and determining the early warning level of the monitoring area according to the relationship between the wasteland score difference and the first preset wasteland score difference and the second preset wasteland score difference; When the wasteland score difference is less than the first preset wasteland score difference, it is determined that the warning level of the monitored area is a low level; When the wasteland score difference is greater than or equal to the first preset wasteland score difference, and the wasteland score difference is less than the second preset wasteland score difference, it is determined that the warning level of the monitoring area is medium; When the wasteland score difference is greater than or equal to the second preset wasteland score difference, it is determined that the warning level of the monitored area is a high level; The first preset wasteland score difference is smaller than the second preset wasteland score difference, and low level < medium level < high level.

10. A desert ecological environment monitoring and early warning system, suitable for executing a desert ecological environment monitoring and early warning method as claimed in any one of claims 1 to 9, characterized in that: include: A first evaluation module is used to obtain an aerial image of a monitoring area, determine a vegetation index and a soil spectral feature of the monitoring area according to the aerial image, and evaluate a monitoring score of the monitoring area according to the vegetation index and the soil spectral feature; a central control module, electrically connected to the first evaluation module, and configured to obtain historical monitoring scores of adjacent time periods of the monitoring area, and determine whether to perform environmental monitoring on the monitoring area according to a relationship between the monitoring score and the historical monitoring score, wherein: when the monitoring score is less than the historical monitoring score, the central control module performs environmental monitoring on the monitoring area; a second evaluation module, electrically connected to the central control module, the second evaluation module being used to obtain soil data and environmental data of the monitored area, determine a soil score of the monitored area according to the soil data, determine an environmental score of the monitored area according to the environmental data, and determine a wasteland score of the monitored area according to the soil score and the environmental score; An early warning module and the second evaluation module, wherein the early warning module is used to obtain the historical wasteland scores of the monitoring area in adjacent time periods, and determine the early warning level of the monitoring area according to the wasteland score difference between the wasteland score and the historical wasteland score.

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