Natural resource intelligent monitoring and early warning system based on satellite remote sensing image

Abstract

The invention discloses a natural resource intelligent monitoring and early warning system based on a satellite remote sensing image, and belongs to the technical field of image recognition and processing, and the system comprises an image data obtaining module which is used for obtaining the satellite remote sensing image of forest and grass resources in a preset region, marking the edge points of the satellite remote sensing image, and outputting the marked edge points; the marking method comprises the following steps: selecting at least 5-8 edge points from a satellite remote sensing image for marking, and distinguishing the marked points at the same time; the regional data acquisition module responds to the satellite remote sensing image acquired by the image data acquisition module and is used for carrying out regional division on the satellite remote sensing image; the division mode comprises region division according to the color concentration presented by the satellite remote sensing image. According to the invention, through an intelligent and multi-dimensional monitoring technology, the efficiency and accuracy of forest and grass resource monitoring are improved, and a powerful guarantee is provided for ecological protection and resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition and processing, and particularly to an intelligent monitoring and early warning system for natural resources based on satellite remote sensing images. Background Art

[0002] China has a wide variety of natural resources in large quantities, including natural resources such as mines, forests and grasslands, water bodies, and farmlands. Among them, forests and grasslands not only provide habitats and living environments for organisms, but also play an irreplaceable role in maintaining ecological balance, protecting biodiversity, and promoting the sustainable development of the economy and society.

[0003] To monitor the destruction of forest and grassland resources by illegal persons, the traditional method relies on manual inspections, which has problems such as low recognition efficiency and great difficulty in discovery. In recent years, with the development and use of satellite remote sensing images, they have played an important role in the protection of natural resources. Among them, the application document with the technical application number CN202411251742.1 provides an intelligent recognition system and method based on remote sensing image features. This technical solution includes an image acquisition module, a ship size feature recognition module, a ship-related information acquisition module, an alarm module, and a warning module; this technical solution can use the optical camera and depth camera set on the unmanned aerial vehicle to simultaneously take pictures of the ships below, obtain the optical image of the ship taken from above and the depth image of the ship taken from above, and realize the intelligent management of the operating status of the ships based on remote sensing images.

[0004] Another application document with the technical application number CN201910785683.9 provides an intelligent monitoring and early warning system based on a satellite remote sensing system, which includes an image receiving module, an image processing module, and an image generating module; this technical solution has obvious advantages in fire point monitoring, which are mainly reflected in several aspects such as high spatial resolution, high temporal resolution, and high observation timeliness.

[0005] However, there are still some deficiencies in the existing satellite remote sensing image technology in monitoring the destruction of forest and grassland resources. For example, there are deficiencies in the spatial recognition algorithm for forests and grasslands, and it is difficult to identify small-scale destruction of forests and grasslands, such as small-scale illegal logging or local grassland degradation. For some complex forest and grassland ecosystems, such as natural secondary forests in the south or some mountainous areas, small-scale illegal logging activities may only involve a few trees. In the case of deficiencies in the spatial recognition algorithm for forests and grasslands, satellite remote sensing images may not be able to accurately identify these changes, resulting in a lag in early warning and being unable to guide relevant personnel to make timely disposals, and further leading to losses of forest and grassland resources. Summary of the Invention

[0006] In view of the above problems existing in the existing image recognition and processing technology field, the present invention is proposed.

[0007] Therefore, one of the objectives of the present invention is to provide an intelligent monitoring and early warning system for natural resources based on satellite remote sensing images. Through edge point marking and area division techniques, it can quickly identify concentrated and sparse areas of forest and grass, distinguish key monitoring areas from general areas, improve monitoring efficiency, and generate a database by calculating the distances between adjacent forest and grass objects. The system can dynamically update data, timely detect abnormal changes, and provide strong guarantees for ecological protection and resource management.

[0008] To solve the above technical problems, the present invention provides the following technical solutions:

[0009] The present invention provides an intelligent monitoring and early warning system for natural resources based on satellite remote sensing images, including:

[0010] An image data acquisition module, which is used to acquire satellite remote sensing images of forest and grass resources in a preset area and mark the edge points of the satellite remote sensing images. The marking method includes selecting at least 5 to 8 edge points in the satellite remote sensing images for marking and differentiating the marked points at the same time;

[0011] An area data collection module, which responds to the satellite remote sensing images acquired by the image data acquisition module and is used to divide the satellite remote sensing images into areas; the division method includes dividing the areas according to the color concentration presented in the satellite remote sensing images, dividing the areas with high color concentration into concentrated forest and grass areas; marking the areas with low color concentration as sparse forest and grass areas; and marking the concentrated forest and grass areas as key monitoring areas at the same time;

[0012] A data fusion and analysis unit, which responds to the key monitoring areas and is used to obtain the forest and grass distribution data between the key monitoring areas; the data fusion and analysis unit includes an object positioning data acquisition module, a calculation module, and a data interception module;

[0013] The object positioning data acquisition module is used to obtain the coordinate positioning data of a single forest and grass object between the key monitoring areas, divide the coordinate positioning data into θ 1 , θ 2 ,..., θ n , where n represents the nth forest and grass object; and obtain the image pictures of each forest and grass object;

[0014] The calculation module responds to the acquired coordinate positioning data, calculates the distances between adjacent forest and grass objects according to the coordinate positioning data, generates a database, and updates the database;

[0015] The data interception module responds to the spacing calculated by the calculation module, and is used to intercept at least three coordinate positioning data between each of the key monitoring areas; and determine the status of forest and grass resources according to the intercepted coordinate positioning data.

[0016] As a preferred solution of the present invention, in the image data acquisition module, the marked points are classified into ▽ 1 , ▽ 2 ,..., ▽ n , where n represents the nth marked point. Based on the satellite remote sensing image, the spacing between adjacent marked points is obtained, the number of forest and grass is counted within the spacing, and the number of forest and grass is updated daily.

[0017] As a preferred solution of the present invention, in the data interception module, the status of forest and grass resources is determined according to the coordinate positioning data. The determination method includes: when satellite remote sensing images of the forest and grass resources in the preset area are obtained in the future, if the data difference of the coordinate positioning data of each single forest and grass object between adjacent key monitoring areas shows an increasing trend, the system determines that the forest and grass resources in the preset area are cut and damaged; and issues a warning; otherwise, no determination is made.

[0018] As a preferred solution of the present invention, when it is not determined that the forest and grass resources in the preset area are cut and damaged, in the satellite remote sensing image of the forest and grass resources obtained in the preset area, an image picture of a forest and grass object is selected at the edge opposite side of adjacent key monitoring areas, the edge features of the forest and grass object image picture are analyzed, and an image picture of a forest and grass object in the middle position is selected between the two forest and grass object image pictures, and the image picture of the forest and grass object in the middle position is marked as a reference image picture; at the same time, the edge features of the image picture are analyzed; among them, the edge features of any image picture of a forest and grass object include the shape, texture and smoothness of the object contour in the image picture; and two monitoring points are selected based on the edge of the reference image picture, and the distances between the reference image picture and the forest and grass objects in the other two image pictures are calculated based on the two monitoring points, and are calculated according to the following formula:

[0019] where m represents the coordinate positioning data, and the coordinate positioning data are the coordinate positioning data of the reference image picture and the other two image pictures;

[0020] In the formula, d y represents the distance calculation value of the yth edge point and the dth time of the other two image pictures based on the monitoring point, x represents the change feature of the calculation value, k represents the position change data of the two monitoring points in the reference image picture, and p represents the resolution when the three image pictures are obtained.

[0021] When calculating the distance at the same resolution for a future time period, if the calculated distance value increases, the system determines that the forest and grass resources in the preset area have been cut and damaged; and issues a warning; otherwise, it does not determine.

[0022] As a preferred solution of the present invention, when it is determined that the forest and grass resources in the preset area have been cut and damaged, calculate the number of forest and grass objects between the reference image picture and the forest and grass objects corresponding to the other two image pictures, and obtain the coordinate positioning data of each forest and grass object from the number; mark the directions on one side of the forest and grass objects corresponding to the reference image picture and the other two image pictures as the first direction and the second direction. When obtaining satellite remote sensing images of the forest and grass resources in the preset area in a future time period, if the coordinate positioning data of the forest and grass objects in a certain direction shows a decreasing trend, the system determines that the forest and grass resources in the corresponding direction have been cut and damaged; and issues a warning.

[0023] As a preferred solution of the present invention, when the forest and grass resources in the other direction have not been cut and damaged, obtain the image pictures of the forest and grass objects one by one based on the coordinate positioning data of each forest and grass object in the direction, and label the image pictures in the way of recording with Arabic numerals. When obtaining satellite remote sensing images of the forest and grass resources in the preset area in a future time period, if the image picture of the forest and grass object corresponding to a certain label is not obtained, the system determines that the forest and grass resources in the direction have been cut and damaged; and issues a warning; otherwise, it does not determine.

[0024] As a preferred solution of the present invention, connect each marked point along the edge of the satellite remote sensing image of the forest and grass resources obtained in the preset area to obtain the area size formed by each connected marked point; obtain the image pictures of at least 5 to 10 forest and grass plants at the central position in the area, and label the image pictures as δ 影像图片 When obtaining satellite remote sensing images of the forest and grass resources in the preset area in a future time period, if the δ 影像图片 shows a decreasing trend and there is no decrease in the edge image pictures in the formed area, the system determines that the forest and grass resources in the preset area have been cut and damaged from the center outwards, and issues a warning.

[0025] As a preferred solution of the present invention, when the system determines that the forest and grass resources in the preset area have been cut and damaged from the center outwards, obtain δ 影像图片The image picture of the forest and grass objects at the very center, analyze the deforestation and damage trajectory based on the image picture, and at the same time predict the deforestation route according to the trajectory, obtain the image pictures of the forest and grass objects in the route. When satellite remote sensing images of the forest and grass resources in the preset area are acquired in the future, if the image picture of the forest and grass object closest to the trajectory in the route is not obtained, the system determines that the route prediction is correct and issues a warning; otherwise, it determines that the route prediction is incorrect.

[0026] As a preferred solution of the present invention, wherein: when the system determines that the route prediction is incorrect, calculate the change in the distance between the coordinate positioning data of the forest and grass object image picture at the very center and the coordinate positioning data of the surrounding forest and grass object image pictures. Among the coordinate positioning data of the surrounding forest and grass object image pictures, if the distance between the coordinate positioning data of the forest and grass object image picture at the very center and the coordinate positioning data of a certain forest and grass object image picture shows an expanding trend change, the system predicts the route on the side corresponding to the position of the distance change.

[0027] As a preferred solution of the present invention, wherein: when the system predicts the route on the side corresponding to the position of the distance change, calculate the distance between adjacent forest and grass objects according to the coordinate positioning data of each forest and grass object image picture in this route, and generate a data set based on the distance; in the data set, verify the predicted route based on the first 3 to 5 distance values. If the distances between the 3 to 5 forest and grass objects to be cut are the same as the first 3 to 5 distance values in the data set, the system determines that the predicted route is the correct route; otherwise, it does not determine.

[0028] Beneficial effects:

[0029] 1. Through the methods of edge point marking and area division, the present invention can quickly identify the concentrated areas and sparse areas of forest and grass, distinguish the key monitoring areas from the general areas, and improve the monitoring efficiency;

[0030] 2. By combining various data such as the coordinate positioning data of forest and grass objects, the edge features of image pictures, and distance changes, the system can analyze the state of forest and grass resources from multiple angles, improve the accuracy and reliability of monitoring; at the same time, by calculating the distances between adjacent forest and grass objects and generating a database, the system can dynamically update the data and timely detect abnormal changes;

[0031] 3. The present invention can predict the deforestation and damage trajectories and routes based on historical data and real-time monitoring data, issue early warnings in advance, and provide a time window for protection measures; when the predicted route is incorrect, the system can automatically adjust the prediction direction, recalculate and verify the route based on new data, and improve the flexibility and accuracy of prediction;

[0032] 4. For the deforestation and damage behavior from the center outwards, the system can analyze the image pictures in the central area to timely detect and give early warnings about this kind of damage behavior with strong concealment. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0034] Figure 1 It is a schematic diagram of the system modular structure of the embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the process structure of the embodiment of the present invention;

[0036] Reference numerals in the drawings: 110 - image data acquisition module; 120 - area data collection module; 130 - data fusion and analysis unit; 1301 - object positioning data acquisition module; 1302 - calculation module; 1303 - data interception module. DETAILED IMPLEMENTATION MANNER

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention.

[0038] Due to the deficiencies in the spatial recognition algorithms for forest and grass in the prior art, it is difficult to identify the damage to small areas of forest and grass, such as small-scale illegal logging or local grassland degradation. For some complex forest and grass ecosystems, such as natural secondary forests in the south or some mountainous areas, small-scale illegal logging activities may only involve a few trees. In the case of deficiencies in the spatial recognition algorithms for forest and grass, satellite remote sensing images may not be able to accurately identify these changes, resulting in a lag in early warnings and being unable to guide relevant personnel for timely handling, thereby causing losses of forest and grass resources.

[0039] Based on this, the present invention proposes an intelligent monitoring and early warning system for natural resources based on satellite remote sensing images. Through edge point marking and regional division techniques, it can quickly identify concentrated and sparse areas of forest and grass, distinguish key monitoring areas from general areas, improve monitoring efficiency, and by calculating the distances between adjacent forest and grass objects and generating a database, the system can dynamically update data, timely detect abnormal changes, and provide strong guarantees for ecological protection and resource management.

[0040] The following further specifically describes this solution through embodiments in combination with the drawings.

[0041] Refer to Figures 1 to 2 , which is an embodiment of the present invention. This embodiment provides an intelligent monitoring and early warning system for natural resources based on satellite remote sensing images, including:

[0042] An image data acquisition module 110, which is used to acquire satellite remote sensing images of forest and grass resources in a preset area and mark the edge points of the satellite remote sensing images. The marking method includes selecting at least 5 to 8 edge points in the satellite remote sensing image for marking and distinguishing the marked points at the same time;

[0043] It should be noted in this embodiment that the marked points are distinguished as ▽ 1 , ▽ 2 ,..., ▽ n , where n represents the nth marked point. Based on the satellite remote sensing image, the distances between adjacent marked points are obtained, the number of forest and grass is counted within the distances, and the number of forest and grass is updated daily;

[0044] A regional data acquisition module 120, which responds to the satellite remote sensing image acquired by the image data acquisition module 110 and is used to divide the satellite remote sensing image into regions; the division method includes dividing the regions according to the color concentration presented by the satellite remote sensing image, dividing the regions with high color concentration into concentrated forest and grass areas; marking the regions with low color concentration as sparse forest and grass areas; and at the same time marking the concentrated forest and grass areas as key monitoring areas;

[0045] A data fusion and analysis unit 130, which responds to the key monitoring areas and is used to obtain the forest and grass distribution data between the key monitoring areas; the data fusion and analysis unit 130 includes an object positioning data acquisition module 1301, a calculation module 1302, and a data interception module 1303;

[0046] The object positioning data acquisition module 1301 is used to obtain the coordinate positioning data of a single forest and grass object between the key monitoring areas, and divide the coordinate positioning data into θ 1 , θ 2 ,..., θ n, where n represents the nth forest and grass object; and obtain the image pictures of each forest and grass object;

[0047] The calculation module 1302 responds to the obtained coordinate positioning data, and is used to calculate the spacing between adjacent forest and grass objects according to the coordinate positioning data, generate a database, and update the database;

[0048] The data interception module 1303 responds to the spacing calculated by the calculation module, and is used to intercept at least 3 coordinate positioning data between each key monitoring area; and determine the status of forest and grass resources according to the intercepted coordinate positioning data;

[0049] It should be noted in this embodiment that when determining the status of forest and grass resources according to the coordinate positioning data, the determination method includes: when satellite remote sensing images of the forest and grass resources in the preset area are obtained in the future time period, if the data difference of the coordinate positioning data of each single forest and grass object between adjacent key monitoring areas shows an increasing trend, the system determines that the forest and grass resources in the preset area have been cut and damaged; and issue a warning; otherwise, no determination is made;

[0050] In reality, in forest and grasslands, if the forest and grass are cut, the spacing between adjacent forest and grass will increase. In this case, the data difference of the coordinate positioning data of each forest and grass object will show an increasing trend, which means that the forest and grass become sparse. Therefore, in this embodiment, it is of practical significance to determine that the forest and grass have been cut and damaged based on the increasing trend of the data difference of the coordinate positioning data of each single forest and grass object between adjacent key monitoring areas;

[0051] In another case, if the forest and grass are not cut and damaged, the forest and grass will maintain their original density. In this case, the data difference of the coordinate positioning data of each single forest and grass object between adjacent key monitoring areas will not show an increasing trend;

[0052] On the above basis, in this embodiment, further, when it is not determined that the forest and grass resources in the preset area have been cut and damaged, in the satellite remote sensing image of the forest and grass resources obtained in the preset area, select an image picture of a forest and grass object at the opposite side of the edge of adjacent key monitoring areas, analyze the edge features of the image picture of the forest and grass object, and select an image picture of a forest and grass object in the middle position between two image pictures of the forest and grass object, and mark the image picture of the forest and grass object in the middle position as the reference image picture; at the same time, analyze the edge features of the image picture; wherein, the edge features of any image picture of a forest and grass object include the shape, texture and smoothness of the object contour in the image picture; and based on the edge of the reference image picture, select two monitoring points, calculate the distance between the reference image picture and the forest and grass objects in the other two image pictures based on the two monitoring points, and calculate according to the following formula:

[0053] Among them, m represents the coordinate positioning data, which is the coordinate positioning data of the reference image and the other two image pictures;

[0054] In the formula, d y represents the distance calculation value of the d-th time for the y-th edge point in the other two image pictures based on the monitoring point, x represents the change characteristics of the calculation value, k represents the position change data regarding two monitoring points in the reference image picture, and p represents the resolution when obtaining the three image pictures;

[0055] When the distance calculation is performed at the same resolution in the future period, if the distance calculation value increases, the system determines that the forest and grass resources in the preset area have been cut and damaged; and issues a warning; otherwise, it does not determine;

[0056] It should be noted in this embodiment that for the shape change of the object contour, if the edge of the forest and grass object changes from a regular natural form (such as circular, elliptical) to an irregular serrated or linear shape, it may imply that the area has been artificially interfered, such as cutting or fire;

[0057] Texture change, if the texture of the edge changes from continuous vegetation texture to a fragmented or blurred state, it may indicate that the vegetation has been damaged;

[0058] Smoothness change, including contrast change, if the contrast between the edge and the surrounding background suddenly increases, it may be due to the removal of vegetation, exposing the soil or other non-vegetation surfaces;

[0059] However, it should be noted again that according to the experience of forest and grass resource protection, relying solely on edge features for judgment has certain limitations, because the edge features of forest and grass resources may be affected by natural factors, such as wind disasters, pests and diseases, natural growth, etc., resulting in changes in the edge form. These natural changes may be similar to the characteristics of human damage and are prone to misjudgment. Therefore, relevant personnel need to intervene and handle it in a timely manner to verify the judgment result;

[0060] Among them, when it is determined that the forest and grass resources in the preset area have been cut and damaged, calculate the number of forest and grass between the forest and grass objects corresponding to the reference image and the other two image pictures, and obtain the coordinate positioning data of each forest and grass object from the quantity; mark the directions on one side of the forest and grass objects corresponding to the reference image and the other two image pictures as the first direction and the second direction. When satellite remote sensing images of the forest and grass resources in the preset area are obtained in the future period, if the coordinate positioning data of the forest and grass objects in a certain direction shows a decreasing trend, the system determines that the forest and grass resources in the corresponding direction have been cut and damaged; and issues a warning;

[0061] Specifically, in this embodiment, when the forest and grass resources in the other direction are not cut and damaged, the image pictures of the forest and grass objects are obtained one by one based on the coordinate positioning data of the forest and grass objects in each direction, and the image pictures are numbered in the way of recording with Arabic numerals. When satellite remote sensing images of the forest and grass resources in the preset area are obtained in the future, if the image picture of the forest and grass object corresponding to a certain label is not obtained, the system determines that the forest and grass resources in this direction are cut and damaged; and issues a warning; otherwise, it does not determine;

[0062] It should be emphasized in this embodiment that the marked points are connected along the edge of the satellite remote sensing image of the forest and grass resources obtained in the preset area to obtain the area size formed by the connected marked points; at least 5 to 10 images of forest and grass in the central position are obtained from the area, and the image pictures are marked as δ 影像图片 , when satellite remote sensing images of the forest and grass resources in the preset area are obtained in the future, if δ 影像图片 shows a decreasing trend of change, and there is no decrease in the edge image pictures in the formed area, the system determines that the forest and grass resources in the preset area are cut and damaged from the center to the outside, and issues a warning;

[0063] It should be noted that in real life, illegal loggers usually start cutting from hidden places in the forest land. Choosing to start cutting from the center may be to hide the cutting traces and avoid exposing the traces that are easily discovered when starting cutting from the edge;

[0064] Further in this embodiment, when the system determines that the forest and grass resources in the preset area are cut and damaged from the center to the outside, the image picture of the forest and grass object at the very center of δ 影像图片 is obtained, and the cutting and damage trajectory is analyzed based on the image picture. At the same time, the cutting route is predicted according to the trajectory, and the image pictures of the forest and grass objects in the route are obtained. When satellite remote sensing images of the forest and grass resources in the preset area are obtained in the future, if the image picture of the forest and grass object closest to the trajectory is not obtained in the route, the system determines that the route prediction is correct and issues a warning; otherwise, it determines that the route prediction is incorrect;

[0065] It should be noted that predicting the cutting route is beneficial for relevant personnel to block the loggers along this route in the later stage, so that relevant personnel can conveniently maintain the forest and grass on the relevant route according to the target route;

[0066] At the same time, when the system determines that the route prediction is incorrect, the distance change between the coordinate positioning data of the forest and grass object image picture at the very center and the coordinate positioning data of the surrounding forest and grass object image pictures is calculated. Among the coordinate positioning data of the surrounding forest and grass object image pictures, if the distance between the coordinate positioning data of the forest and grass object image picture at the very center and the coordinate positioning data of a certain forest and grass object image picture shows an increasing trend of change, the system predicts the route on the side corresponding to the position of the distance change;

[0067] On the basis above, when the system predicts the route based on the side corresponding to the position with spacing change, calculate the distance between adjacent forest and grass objects according to the coordinate positioning data of the images of forest and grass objects in this route, and generate a data set based on the distance; in the data set, verify the predicted route based on the first 3 to 5 distance values. If the distance between the 3 to 5 forest and grass objects that have been cut down is the same as the first 3 to 5 distance values in the data set, the system determines that the predicted route is the correct route, otherwise, it does not determine;

[0068] Finally, it should be noted that on the basis of satellite remote sensing images, by obtaining the coordinate positioning data of forest and grass, the position of forest and grass objects can be accurately obtained. These data can help monitor the distribution and dynamic changes of forest and grass resources. If the coordinate positioning data of forest and grass objects in a certain area changes during the monitoring period (such as the reduction of coordinate points or the expansion of the spacing), it indicates that the forest and grass resources in this area have been cut down or damaged.

[0069] In summary, this application improves the efficiency and accuracy of forest and grass resource monitoring through intelligent and multi-dimensional monitoring technologies, providing a strong guarantee for ecological protection and resource management.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A natural resource intelligent monitoring and early warning system based on satellite remote sensing images, characterized in that: include: An image data acquisition module is used to acquire satellite remote sensing images of forest and grass resources in a preset area and mark edge points of the satellite remote sensing images, wherein the marking method includes selecting at least 5 to 8 edge points in the satellite remote sensing images for marking and distinguishing the marked points; A regional data acquisition module, the regional data acquisition module responds to the satellite remote sensing image acquired by the image data acquisition module, and is used to divide the satellite remote sensing image into regions; the division method includes dividing the regions according to the color concentration presented by the satellite remote sensing image, dividing the region with high color concentration into a forest and grass concentrated region; marking the region with low color concentration as a forest and grass sparse region; and marking the forest and grass concentrated region as a key monitoring region; A data fusion analysis unit, the data fusion analysis unit responds to the key monitoring area and is used to obtain forest and grass distribution data between the key monitoring areas; the data fusion analysis unit includes an object positioning data acquisition module, a calculation module and a data interception module; The object positioning data acquisition module is used to acquire the coordinate positioning data of a single forest and grass object between each of the key monitoring areas, and divide each of the coordinate positioning data into θ1, θ2, ..., θ n , where n represents the nth forest and grass object; and obtain the image picture of each forest and grass object; The calculation module responds to the acquired coordinate positioning data, and is used to calculate the spacing between adjacent forest and grass objects according to the coordinate positioning data, and to generate a database, and to update the database; The data interception module responds to the spacing calculated by the calculation module, and is used to intercept at least 3 coordinate positioning data between each of the key monitoring areas; and determines the status of forest and grass resources based on the intercepted coordinate positioning data.

2. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 1, characterized in that: In the image data acquisition module, the marking points are divided into ▽1, ▽2, ..., ▽ n , where n represents the nth marking point. Based on the satellite remote sensing image, the distance between each adjacent marking point is obtained, the number of forests and grasses within the distance is counted, and the number of forests and grasses is updated daily.

3. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 1, characterized in that: In the data capture module, the status of forest and grass resources is determined based on the coordinate positioning data, and the determination method includes: when satellite remote sensing images of the forest and grass resources in the preset area are acquired in the future, if the data difference of the coordinate positioning data of each individual forest and grass object between adjacent key monitoring areas shows an expanding trend, the system determines that the forest and grass resources in the preset area have been cut down and destroyed; and issues an early warning; otherwise, no determination is made.

4. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 3, characterized in that: When it is not determined that the forest and grass resources in the preset area have been cut down and destroyed, an image of a forest and grass object is selected at the opposite side of the edge of the adjacent key monitoring area from the satellite remote sensing image of the forest and grass resources obtained in the preset area, the edge features of the image of the forest and grass object are analyzed, and an image of a forest and grass object in the middle position is selected between the two image images of the forest and grass object, and the image of the forest and grass object in the middle position is marked as a reference image; the edge features of the image are analyzed at the same time; wherein, the edge features of any image of the forest and grass object include the shape, texture and smoothness of the object contour in the image; and two monitoring points are selected based on the edge of the reference image, and the distance between the reference image and the forest and grass objects in the other two images is calculated based on the two monitoring points, and the calculation is obtained according to the following formula: Wherein, m represents coordinate positioning data, and the coordinate positioning data is the coordinate positioning data of the reference image and the remaining two image images; Where, d y represents the d-th distance calculation value of the y-th edge point in the remaining two image pictures based on the monitoring point, x represents the change characteristics of the calculation value, k represents the position change data of the two monitoring points in the reference image picture, and p represents the resolution when obtaining the three image pictures; When distance calculation is performed at the same resolution in future time periods, if the distance calculation value increases, the system determines that the forest and grass resources in the preset area have been cut down and destroyed, and issues a warning; otherwise, no determination is made.

5. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 4, characterized in that: When it is determined that the forest and grass resources in the preset area have been cut down and destroyed, the number of forest and grass objects between the reference image and the forest and grass objects corresponding to the other two images is calculated, and the coordinate positioning data of each forest and grass object is obtained from the number; the direction of one side of the forest and grass object corresponding to the reference image and the other two images is marked as the first direction and the second direction. When satellite remote sensing images of the forest and grass resources in the preset area are acquired in the future, if the coordinate positioning data of the forest and grass objects in a certain direction shows a decreasing trend, the system determines that the forest and grass resources in the corresponding direction have been cut down and destroys, and issues an early warning.

6. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 5, characterized in that: When the forest and grass resources in another direction have not been cut down and damaged, the image pictures of the forest and grass objects are obtained one by one based on the coordinate positioning data of each forest and grass object in the direction, and the image pictures are numbered in Arabic numerals. When satellite remote sensing images of the forest and grass resources in the preset area are obtained in the future, if the image picture of the forest and grass object corresponding to a certain number is not obtained, the system determines that the forest and grass resources in the direction have been cut down and damaged, and issues an early warning; Otherwise, no judgment is made.

7. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 2, characterized in that: Connect the marked points along the edge of the satellite remote sensing image of the forest and grass resources obtained in the preset area to obtain the size of the area formed by the marked points; obtain an image of at least 5 to 10 trees and grasses at the center of the area, and mark the image as δ 影像图片 , when satellite remote sensing images of forest and grass resources in the preset area are obtained in the future, if the δ 影像图片 If the trend is decreasing and there is no decrease in the edge image in the formed area, the system determines that the forest and grass resources in the preset area are being cut down and destroyed from the center outward, and issues an early warning.

8. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 7, characterized in that: When the system determines that the forest and grass resources in the preset area are being cut down from the center outward, obtain δ 影像图片 The image of the forest and grass object at the center is obtained, and the felling destruction trajectory is analyzed based on the image. At the same time, the felling route is predicted according to the trajectory, and the image of the forest and grass object in the route is obtained. When the satellite remote sensing image of the forest and grass resources in the preset area is obtained in the future, if the image of the forest and grass object closest to the trajectory is not obtained in the route, the system determines that the route prediction is correct and issues an early warning; Otherwise, it is determined that the route prediction is wrong.

9. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 8, characterized in that: When the system determines that the route prediction is wrong, it calculates the change in the distance between the coordinate positioning data of the forest and grass object image picture at the most center and the coordinate positioning data of the surrounding forest and grass object image pictures. Among the coordinate positioning data of the surrounding forest and grass object image pictures, if the distance between the coordinate positioning data of the forest and grass object image picture at the most center and the coordinate positioning data of a certain forest and grass object image picture shows an expanding trend, the system performs route prediction on the side corresponding to the distance change position.

10. The natural resource intelligent monitoring and early warning system based on satellite remote sensing images as claimed in claim 9, characterized in that: When the system predicts the route on the side corresponding to the position of the spacing change, the distance between adjacent forest and grass objects is calculated based on the coordinate positioning data of the image of each forest and grass object in this route, and a data set is generated based on the distance; in the data set, the predicted route is verified based on the first 3 to 5 distance values. If the distance between the 3 to 5 cut down forest and grass objects is the same as the first 3 to 5 distance values ​​in the data set, the system determines that the predicted route is the correct route, otherwise, it will not make a determination.

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