A remote monitoring system for forest and grass coverage based on satellite remote sensing images

Abstract

The present invention relates to the technical field of image analysis systems, and in particular to a remote monitoring system for forest and grass coverage based on satellite remote sensing images, including a radar remote sensing sensor and a multispectral remote sensing sensor, and also including: an image acquisition module, an image analysis module, a key marking module, and a monitoring and control module. The present invention selects a standard optical monitoring image, that is, a standard optical image, according to the cloud cover, atmospheric light transmittance, and solar altitude angle at different optical monitoring image acquisition times, so that the standard optical image can be clear enough and the features of various objects in the image are obvious enough, thereby reducing the influence of weather changes and atmospheric cloud changes during the optical monitoring image acquisition process, and facilitating the use of the optical monitoring image as an analysis object to obtain an accurate and realistic forest and grass coverage.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis systems, and in particular to a remote monitoring system for forest and grass coverage based on satellite remote sensing images. Background Art

[0002] Satellite remote sensing images are data visualization results of the earth's surface information captured and transmitted back to the ground by remote sensing sensors carried on satellite platforms. Satellite remote sensing images show different characteristics of land objects through different spectral bands. Each pixel in the image represents a specific area on the surface, and the grayscale value or color of the pixel reflects the reflective spectral characteristics of the land objects in the area. In satellite remote sensing images, we can see different types of land objects, such as water bodies, vegetation, urban buildings and roads, which are then applied to agricultural monitoring, forest resource management, disaster warning and urban planning. With the continuous advancement of remote sensing technology, the resolution of satellite remote sensing images is also constantly improving, from the initial tens of meters to today's sub-meter level. At the same time, different types of remote sensing sensors such as multispectral, hyperspectral and radar also provide a richer source of information for satellite remote sensing images.

[0003] Remote monitoring of regional forest and grass coverage through satellite remote sensing images can achieve large-scale monitoring of changes in forest and grass coverage, thereby evaluating the greening situation in the region and discovering phenomena that endanger forest safety, such as illegal logging and fire. However, the monitoring system in the existing technology is easily affected by the weather and atmospheric environment in the region, resulting in large errors in the calculation of forest and grass coverage. The area for monitoring forest and grass coverage is generally wide, and it is difficult to intelligently partition it with existing technology. The computing power required for direct monitoring is huge, resulting in average monitoring efficiency. Summary of the invention

[0004] In response to the above-mentioned shortcomings of the prior art, the present invention provides a remote monitoring system for forest and grass coverage based on satellite remote sensing images, which can effectively solve the problems in the prior art that the monitoring system has poor calculation accuracy of forest and grass coverage and general efficiency in monitoring sensitive changes in forest and grass coverage.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] The present invention provides a remote monitoring system for forest and grass coverage based on satellite remote sensing images, which at least includes: a radar remote sensing sensor and a multi-spectral remote sensing sensor, and also includes: an image acquisition module, an image analysis module, a key marking module, and a monitoring and control module;

[0007] The image acquisition module obtains the radar remote sensing image of the ground, obtains the optical remote sensing image of the ground, sets the monitoring area, and intercepts the monitoring area of ​​the radar remote sensing image and the optical remote sensing image to obtain the radar monitoring image and the optical monitoring image;

[0008] The image analysis module includes a standard acquisition unit and an image processing unit. The standard acquisition unit is used to extract the standard optical image in the optical monitoring image. The image processing unit extracts the three most recently acquired standard optical images and performs grayscale processing to obtain the first standard grayscale image, the second standard grayscale image, and the third standard grayscale image. The proportion of pixels in the three standard grayscale images that belong to the preset green grayscale value interval is analyzed to obtain the forest and grass coverage rate of the monitoring area.

[0009] The key marking module divides the third standard grayscale image into multiple square unit areas, and marks them as forest areas, affiliated areas of forest areas or scattered areas according to the proportion of pixels belonging to the green grayscale value interval and their center positions in the square unit areas. The regional value parameters of the forest areas are obtained according to the transportation distance parameters, personnel activity parameters and tree value parameters of the forest areas, and the forest areas and their affiliated areas are marked as strictly controlled areas, key areas or ordinary areas according to the regional value parameters.

[0010] The monitoring and control module divides the supervision levels into strict control areas, key areas and ordinary areas and records them uniformly as monitoring areas. Different monitoring cycles are set according to the monitoring levels to conduct fire monitoring and felling monitoring in the monitoring areas.

[0011] Furthermore, the standard acquisition unit extraction process is specifically as follows:

[0012] The standard acquisition unit evenly sets up multiple meteorological monitoring points in the monitoring area, regularly obtains the optical monitoring images of the monitoring area and the meteorological data of each meteorological monitoring point at that time, and when the meteorological data of each meteorological monitoring point in the same optical monitoring image meet the cloud cover below , atmospheric transmittance is higher than And the solar altitude angle is within the range [ , ], the optical monitoring image is recorded as the standard optical image, where , , All are preset values. It depends on the local air pollution index, latitude and altitude. The calculation formula is: ,in Preset values ​​for constants, and is the preset weight coefficient, Indicates altitude, Indicates the local average air pollution index in the last month, represents the latitude value, and and After normalization.

[0013] Furthermore, the processing process of the image processing unit is specifically as follows:

[0014] The number of pixels in each standard grayscale image whose grayscale values ​​are within the green grayscale value interval is divided by the total number of pixels to obtain the green proportion value in each standard grayscale image, which are recorded as the first green proportion value , the second green ratio , the third green ratio , substitute into the formula The forest and grass coverage rate of the monitoring area is calculated ,in The evaluation formulas are , , ,in They respectively represent the time difference between the first standard grayscale image acquisition time and the current time, the time difference between the second standard grayscale image acquisition time and the current time, and the time difference between the third standard grayscale image acquisition time and the current time.

[0015] Furthermore, a leaf-falling time interval is preset. If the current time is within the leaf-falling time interval, the forest and grass coverage rate collected last time before the leaf-falling time interval is extracted. As the comparison value to be determined, the standard optical image collected last time before the leaf-falling time interval is extracted as the comparison image, which is grayed, and the pixels whose gray values ​​are within the green gray value interval in the image are marked to obtain multiple marked areas composed of marked pixels. The first standard grayscale image, the second standard grayscale image, and the third standard grayscale image are respectively compared with the comparison image in the marked area. The comparison process is as follows:

[0016] Obtain the grayscale distribution bar graph in the marked area of ​​the third standard grayscale map. A dead leaf grayscale value interval is preset. The grayscale values ​​that are neither in the dead leaf grayscale value interval nor in the green grayscale value interval are recorded as special grayscale values. The total proportion of characteristic grayscale values ​​is counted. When it is less than or equal to the preset characteristic proportion threshold, the undetermined comparison value is used as the current forest and grass coverage rate. When it is greater than the preset characteristic proportion threshold, substitute it into the formula Calculate the current forest and grass coverage rate , where Z represents the total proportion of characteristic grayscale values.

[0017] Furthermore, the marking process of the key point marking module is as follows:

[0018] Extract the third standard grayscale image and perform greening processing on the pixels whose grayscale values ​​belong to the green grayscale value interval to obtain green pixels. Segment the third standard grayscale image with grid lines whose unit grid length is n to obtain multiple square unit areas, where n is a preset value and is greater than or equal to 50m. Calculate the proportion of green pixels in each square unit area, which is recorded as the regional green proportion value.

[0019] When the regional green percentage value is greater than or equal to the preset green percentage threshold, the square unit area is recorded as a forest area, and the center point of each forest area is used as its measurement point. When the regional green percentage value is less than the preset green percentage threshold, the distance from the center point of the square unit area to the nearest measurement point is calculated. If the distance is equal to 50m, the square unit area is recorded as a subsidiary area of ​​the corresponding forest area. If the distance is greater than 50m, the square unit area is recorded as a scattered area.

[0020] According to the satellite remote sensing map, the roads with a width greater than or equal to 5m are marked in the third standard grayscale map as transportation roads, and the shortest distance from each metering point to the nearest transportation road is calculated and recorded as the transportation distance parameter of the metering point;

[0021] A circular area is drawn with the measuring point as the center and the preset length value R as the radius, and is recorded as the activity range area. The population number in the activity range area is calculated and recorded as the personnel activity parameter of the measuring point.

[0022] The types and proportions of trees with a diameter greater than or equal to 10CM in the forest area corresponding to the measurement point are counted. A tree type value table is preset, which contains multiple tree types and each tree type corresponds to a price value. The proportion of each tree type in the forest area corresponding to the measurement point is multiplied by its price value to obtain the regional reference value of the tree type, and then the regional reference values ​​of all tree types in the forest area are summed to obtain the tree value parameters of the forest area;

[0023] The transport distance parameter of the same forest area , Personnel activity parameters , Tree value parameters After normalization, it is inserted into the formula Calculate and get the regional value parameters ,in All of them are preset weight factors greater than 0, and there are preset strict control intervals, key intervals, and ordinary intervals. The forest areas and their affiliated areas are marked as strictly controlled areas, key areas or ordinary areas according to the intervals to which they belong.

[0024] Furthermore, the monitoring and control module supervision process is as follows:

[0025] Regularly carry out key monitoring of strictly controlled areas, key areas and ordinary areas and record them as monitoring areas. The supervision levels of strictly controlled areas, key areas and ordinary areas are marked as level 3, level 2 and level 1 respectively. Different monitoring cycles are set according to different levels to monitor them. In one monitoring cycle, various monitoring areas of the same level are monitored cyclically. The monitoring cycles of level 3, level 2 and level 1 are 3T, 2T and T respectively, where T is the preset cycle length.

[0026] Sequentially obtain infrared images of all monitoring areas at the same supervision level, and when the infrared images contain fire image features, generate a fire alarm signal and send the location of the monitoring area to the monitoring center;

[0027] The radar monitoring images of all monitoring areas of the same supervision level are obtained in sequence, and the similarity between the radar monitoring image currently collected in the same monitoring area and the radar monitoring image collected in the previous monitoring cycle is calculated. When the similarity is less than or equal to the preset similarity threshold, a felling signal is generated and the location of the monitoring area is sent to the monitoring center.

[0028] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0029] 1. The present invention selects standard optical monitoring images, that is, standard optical images, according to the cloud cover, atmospheric transmittance, and solar altitude conditions at different optical monitoring image acquisition times, so that the standard optical images can be clear enough and the features of various objects in the images can be obvious enough, thereby reducing the impact of changes in weather conditions and atmospheric cloud changes on the optical monitoring image acquisition process, making it convenient to use it as an analysis object to obtain accurate and practical forest and grass coverage, and then better divide the monitoring area according to optical characteristics. In addition, the present invention can also intelligently distinguish the area occupied by deciduous trees in the standard optical image during the leaf fall period and add it to the calculation range of the forest and grass coverage, so that the forest and grass coverage can still be accurately calculated even when the leaves of the deciduous trees are withered and yellowed.

[0030] 2. The present invention can comprehensively calculate the regional value parameters of each forest area according to the different situation parameters of each forest area, and is used to establish an intuitive measurement standard for the value of each forest area to make quantitative judgments on it, thereby facilitating the regional division and measurement of the entire forest, so as to facilitate the formulation of relevant monitoring and protection strategies. The regional value parameters are used to reflect the value and danger level of a forest area and its affiliated areas. The larger the regional value parameter, the more susceptible the forest area is to the impact of human activities, such as deforestation, accidental fires, etc. Therefore, the level of monitoring and the intensity of control should be higher, so that the forest regulatory authorities can adjust the monitoring intensity and attention according to the degree of danger. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] The present invention will be further described below in conjunction with the embodiments.

[0035] See also Figure 1 A remote monitoring system for forest and grass coverage based on satellite remote sensing images at least includes: a radar remote sensing sensor and a multi-spectral remote sensing sensor, and also includes: an image acquisition module, an image analysis module, a key marking module, and a monitoring and control module.

[0036] The image acquisition module obtains the satellite radar data signal collected by the radar remote sensing sensor and processes it into a ground radar remote sensing image, obtains the ground optical remote sensing image collected by the multispectral remote sensing sensor, sets multiple positioning points with different coordinates, the number of positioning points should be more than three and all are located within the range of the radar remote sensing image and the optical remote sensing image, marks the positioning points in the radar remote sensing image and the optical remote sensing image, and aligns the two remote sensing images with the corresponding positioning points with the same coordinates as the alignment points, records the overlapping area of ​​the two remote sensing images as the monitoring area, and intercepts the monitoring area of ​​the radar remote sensing image and the optical remote sensing image to obtain the radar monitoring image and the optical monitoring image.

[0037] When the radar flies with a carrier (aircraft or satellite), it transmits a radar beam to the surface of the earth and then receives the reflected signal from the ground. The radar image, also known as the SAR image, is generated through radar wave signal processing technology. Then, through synthetic aperture radar interferometry technology, two SAR images of the same area are used as basic processing data to calculate the phase difference between the two SAR images and obtain the interference image. After phase unwrapping, the spatial earth observation data of the terrain elevation data is obtained from the interference fringes to obtain the surface radar image.

[0038] The image analysis module includes a standard acquisition unit and an image processing unit.

[0039] The standard acquisition unit evenly sets up multiple meteorological monitoring points in the monitoring area, regularly obtains the optical monitoring images of the monitoring area and the meteorological data of each meteorological monitoring point at that time, and when the meteorological data of each meteorological monitoring point in the same optical monitoring image meet the cloud cover below , atmospheric transmittance is higher than And the solar altitude angle is within the range [ , ], the optical monitoring image is recorded as the standard optical image (the optical monitoring image under this condition can clearly and completely display the ground landscape, so as to facilitate the feature recognition of objects in the image in the subsequent process to determine the coverage area of ​​woodland and grassland), where , , are preset values, and in a specific embodiment, they are 5%, 70°, and 90° respectively. It depends on the local air pollution index, latitude and altitude. The calculation formula is: ,in Preset values ​​for constants, and is the preset weight coefficient, Indicates altitude, Indicates the local average air pollution index (air quality index) in the past month. represents the latitude value, and and After normalization.

[0040] It should be noted that due to differences in geographical factors and air environment in different regions, for example, some areas are seriously polluted and are surrounded by haze all year round, so the atmospheric transmittance in this area will be significantly lower than that in other areas. The calculation formula can comprehensively consider the latitude, altitude and air pollution conditions of the area, so that the atmospheric transmittance of the standard optical image can meet the conditions and be more regionally adaptable, and will not cause the inability to collect standard optical images in certain areas.

[0041] The image processing unit extracts the three standard optical images whose acquisition dates are closest to the current one and performs grayscale processing to obtain the grayscale value corresponding to each pixel, which are recorded as the first standard grayscale image, the second standard grayscale image, and the third standard grayscale image in chronological order. A green grayscale value interval is preset. The number of pixels whose grayscale values ​​are within the green grayscale value interval in each standard grayscale image is divided by the total number of pixels to obtain the green proportion value in each standard grayscale image, which are recorded as the first green proportion value. , the second green ratio , the third green ratio , substitute into the formula The forest and grass coverage rate of the monitoring area is calculated ,in The evaluation formulas are , , ,in They respectively represent the time difference between the first standard grayscale image acquisition time and the current time, the time difference between the second standard grayscale image acquisition time and the current time, and the time difference between the third standard grayscale image acquisition time and the current time.

[0042] It should be noted that the comprehensive calculation of the forest and grass coverage rate based on the green proportion values ​​at three different collection times can reduce the impact of external temporary factors on its value compared to directly using the current green proportion value as the forest and grass coverage rate. By adjusting the weights of different green proportion values ​​for the forest and grass coverage rate and increasing the weight of the green proportion value closer to the current time, the calculation of the forest and grass coverage rate can be more accurate and fit the current situation.

[0043] Furthermore, there is a preset leaf-falling time interval (the time interval from when the leaves of deciduous trees start to change color to when they fall). If the current time is within the leaf-falling time interval, the forest and grass coverage rate collected last time before the leaf-falling time interval is extracted. As the comparison value to be determined, the standard optical image collected last time before the leaf-falling time interval is extracted as the comparison image, which is grayed, and the pixels whose gray values ​​are within the green gray value interval in the image are marked to obtain multiple marked areas composed of marked pixels. The first standard grayscale image, the second standard grayscale image, and the third standard grayscale image are respectively compared with the comparison image in the marked area. The comparison process is as follows:

[0044] Obtain the grayscale distribution bar graph (the percentage of each grayscale value) in the marked area of ​​the third standard grayscale map. Preset the grayscale value interval of dead leaves (the grayscale value range of various colors of dead and yellow trees and grass). Record the grayscale values ​​that are neither in the grayscale value interval of dead leaves nor in the grayscale value interval of green as special grayscale values. Count the total proportion of characteristic grayscale values. When it is less than or equal to the preset characteristic proportion threshold, use the to-be-determined comparison value as the current forest and grass coverage rate. When it is greater than the preset characteristic proportion threshold, substitute it into the formula Calculate the current forest and grass coverage rate , where Z represents the total proportion of characteristic grayscale values.

[0045] By independently analyzing the leaf-falling time interval, we can make up for the inaccurate analysis caused by the color-changing and leaf-falling characteristics of deciduous trees within this time interval. By setting up a dead leaf gray value interval to supplement the green gray value interval, the combined forest and grass area judgment range of the two intervals is more extensive and specific, reducing the impact of tree and grass color changes on the forest and grass coverage range and improving judgment accuracy.

[0046] The key marking module extracts the third standard grayscale image and performs greening processing on the pixels whose grayscale values ​​belong to the green grayscale value interval to obtain green pixels. The third standard grayscale image is divided into multiple square unit areas by grid lines with a unit grid length of n, where n is a preset value and is greater than or equal to 50m. The proportion of green pixels in each square unit area is calculated and recorded as the regional green proportion value.

[0047] When the regional green percentage value is greater than or equal to the preset green percentage threshold, the square unit area is recorded as a forest area, and the center point of each forest area is used as its measurement point. When the regional green percentage value is less than the preset green percentage threshold, the distance from the center point of the square unit area to the nearest measurement point is calculated. If the distance is equal to 50m, the square unit area is recorded as a subsidiary area of ​​the corresponding forest area. If the distance is greater than 50m, the square unit area is recorded as a scattered area.

[0048] It should be noted that a length value n greater than or equal to 50 m can ensure that the ground area corresponding to each square unit area is greater than or equal to 2,500 square meters, so that the green plant area corresponding to each forest area meets a certain threshold, thereby screening out single or small areas of green plants and reducing their impact on judgment in subsequent processes.

[0049] According to the satellite remote sensing map, roads with a width greater than or equal to 5m are marked in the third standard grayscale image as transportation roads (transportation roads can meet the normal one-way traffic of vehicles). The shortest distance from each metering point to the nearest transportation road is calculated and recorded as the transportation distance parameter of the metering point. The transportation distance parameter is used to reflect the convenience of connecting the forest area with the external roads. The smaller the transportation distance parameter, the more convenient the transportation is and the easier it is to be illegally logged.

[0050] A circular area is drawn with the measuring point as the center and the preset length R as the radius, and is recorded as the activity range area. The population number in the activity range area is calculated and recorded as the personnel activity parameter of the measuring point. The personnel activity parameter is used to reflect the frequency of personnel activities in the forest area. The larger the personnel activity parameter is, the higher the frequency of personnel activities in the forest area is, and the higher the probability of events in which trees in the area are affected by humans is.

[0051] The types and proportions of trees with a diameter greater than or equal to 10CM in the forest area corresponding to the measurement point are statistically analyzed. The statistical methods include but are not limited to various methods in the prior art such as regional sampling method and exhaustive method. A tree type value table is preset, which contains multiple tree types and each tree type corresponds to a price value. The tree type value table is obtained according to the value of each type of tree wood in the market. The proportion of each tree type in the forest area corresponding to the measurement point is multiplied by its price value to obtain the regional reference value of the tree type. The regional reference values ​​of all tree types in the forest area are summed to obtain the tree value parameters of the forest area. The tree value parameters are used to reflect the felling value of trees in the forest area. The higher the tree value parameter, the easier it is to be illegally felled.

[0052] It should be noted that the diameter of trees that meet the species statistics requirements should be above 10CM, which is convenient for statistics. Trees that meet this requirement have a higher felling value and are easily targeted by felling workers.

[0053] The transport distance parameter of the same forest area , Personnel activity parameters , Tree value parameters After normalization, it is inserted into the formula Calculate and obtain the regional value parameters of the forest area ,in All of them are preset weight factors greater than 0. In addition, there are also preset strict control intervals, key intervals, and ordinary intervals, according to the regional value parameters of the forest area. The forest area and its affiliated areas are marked as strictly controlled areas, key areas or ordinary areas according to the intervals they belong to. When located within the strict control area, the forest area and its affiliated areas will be marked as a strict control area.

[0054] According to the different situation parameters of each forest area, the regional value parameters of each forest area are calculated comprehensively, which are used to establish an intuitive measurement standard for the value of each forest area and make quantitative judgments on it, so as to facilitate the regional division and measurement of the entire forest and formulate relevant monitoring and protection strategies. The larger the regional value parameter, the higher the value of the forest area and the easier it is to become a target for illegal loggers. Therefore, the level of monitoring and the intensity of control should be higher.

[0055] The monitoring and control module regularly monitors the strictly controlled areas, key areas and ordinary areas and uniformly records them as monitoring areas. The supervision levels of the strictly controlled areas, key areas and ordinary areas are marked as level three, level two and level one respectively. Different monitoring cycles are set according to different levels to monitor them. In one monitoring cycle, each monitoring area of ​​the same level is cyclically monitored. The monitoring cycles of level three, level two and level one are 3T, 2T and T respectively, and T is the preset cycle length. The cyclic monitoring process of the monitoring and control module is as follows:

[0056] Infrared images of all monitoring areas at the same supervision level are obtained in sequence, and fire monitoring and analysis is performed on each monitoring area based on the infrared images. Fire remote sensing monitoring belongs to the existing technology and will not be described in detail here. When the infrared image has fire image features, a fire alarm signal is generated and the location of the monitoring area is sent to the monitoring center;

[0057] The radar monitoring images of all monitoring areas of the same supervision level are obtained in sequence, and the similarity between the radar monitoring image currently collected in the same monitoring area and the radar monitoring image collected in the previous monitoring cycle is calculated. When the similarity is less than or equal to the preset similarity threshold, a felling signal is generated and the location of the monitoring area is sent to the monitoring center.

[0058] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote monitoring system for forest and grass coverage based on satellite remote sensing images, including a radar remote sensing sensor and a multispectral remote sensing sensor, characterized in that: Also includes: Image acquisition module, image analysis module, key marking module, monitoring and control module; The image acquisition module obtains the radar remote sensing image of the ground, obtains the optical remote sensing image of the ground, sets the monitoring area, and intercepts the monitoring area of ​​the radar remote sensing image and the optical remote sensing image to obtain the radar monitoring image and the optical monitoring image; The image analysis module includes a standard acquisition unit and an image processing unit. The standard acquisition unit is used to extract the standard optical image in the optical monitoring image. The image processing unit extracts the three most recently acquired standard optical images and performs grayscale processing to obtain a first standard grayscale image, a second standard grayscale image, and a third standard grayscale image. The standard acquisition unit extraction process is as follows: The standard acquisition unit evenly sets up multiple meteorological monitoring points in the monitoring area, regularly obtains the optical monitoring images of the monitoring area and the meteorological data of each meteorological monitoring point at that time, and when the meteorological data of each meteorological monitoring point in the same optical monitoring image meet the cloud cover below U cloudiness , atmospheric transmittance is higher than U transmittance And the solar altitude angle is within the interval [U′ sun , U′ s ' un ], the optical monitoring image is recorded as the standard optical image, where U cloudiness , U′ sun , U′ s ' un All are preset values, U transmittance It depends on the local air pollution index, latitude and altitude. The calculation formula is U transmittance =u′+λ1u HB -λ2(u WR +u WD ), where u′ is a constant preset value, λ1 and λ2 are preset weight coefficients, and u HB Indicates altitude, u WR Indicates the local average air pollution index in the last month, u WD represents the latitude value, and u WR with u WD After normalization; The processing process of the image processing unit is specifically as follows: The number of pixels whose grayscale values ​​are within the green grayscale value range in the first standard grayscale image, the second standard grayscale image, and the third standard grayscale image is divided by the total number of pixels to obtain the green proportion value in each standard grayscale image, which are respectively recorded as the first green proportion value α1, the second green proportion value α2, and the third green proportion value α3. Substitute them into the formula α′=β1α1+β2α2+β3α3 for calculation to obtain the forest and grass coverage rate α′ of the monitoring area, where the evaluation formulas of β1, β2, and β3 are respectively Wherein T1, T2, and T3 represent the time difference between the first standard grayscale image acquisition time and the current time, the time difference between the second standard grayscale image acquisition time and the current time, and the time difference between the third standard grayscale image acquisition time and the current time, respectively; The proportion of pixels in the three standard grayscale images that belong to the preset green grayscale value interval is analyzed to obtain the forest and grass coverage rate of the monitoring area. A leaf-falling time interval is preset. If the current time is in the leaf-falling time interval, the forest and grass coverage rate α′ collected last time before the leaf-falling time interval is extracted as the pending comparison value. The standard optical image collected last time before the leaf-falling time interval is extracted as the comparison image, which is grayed and processed. The pixels in the image whose grayscale values ​​are in the green grayscale value interval are marked to obtain multiple marked areas composed of marked pixels. The first standard grayscale image, the second standard grayscale image, and the third standard grayscale image are compared with the comparison image in the marked area. The comparison process is as follows: Obtain a grayscale distribution bar graph in the marked area of ​​the third standard grayscale image, preset a dead leaf grayscale value interval, record the grayscale values ​​that are neither in the dead leaf grayscale value interval nor in the green grayscale value interval as special grayscale values, and count the total proportion of characteristic grayscale values. When it is less than or equal to the preset characteristic proportion threshold, use the to-be-determined comparison value as the current forest and grass coverage rate. When it is greater than the preset characteristic proportion threshold, substitute it into the formula α″=α′*(1-Z) for calculation to obtain the current forest and grass coverage rate α″, where Z represents the total proportion of characteristic grayscale values; The key marking module divides the third standard grayscale image into multiple square unit areas, and marks them as forest areas, affiliated areas of forest areas or scattered areas according to the proportion of pixels belonging to the green grayscale value interval and their center positions in the square unit areas. The regional value parameters of the forest areas are obtained according to the transportation distance parameters, personnel activity parameters and tree value parameters of the forest areas, and the forest areas and their affiliated areas are marked as strictly controlled areas, key areas or ordinary areas according to the regional value parameters. The monitoring and control module divides the supervision levels into strict control areas, key areas and ordinary areas and records them uniformly as monitoring areas. Different monitoring cycles are set according to the monitoring levels to conduct fire monitoring and felling monitoring in the monitoring areas.

2. The remote monitoring system for forest and grass coverage based on satellite remote sensing images according to claim 1 is characterized in that: The marking process of the key marking module is as follows: Extract the third standard grayscale image and perform greening processing on the pixels whose grayscale values ​​belong to the green grayscale value interval to obtain green pixels. Segment the third standard grayscale image with grid lines whose unit grid length is n to obtain multiple square unit areas, where n is a preset value and is greater than or equal to 50m. Calculate the proportion of green pixels in each square unit area, which is recorded as the regional green proportion value. When the regional green percentage value is greater than or equal to the preset green percentage threshold, the square unit area is recorded as a forest area, and the center point of each forest area is used as its measurement point. When the regional green percentage value is less than the preset green percentage threshold, the distance from the center point of the square unit area to the nearest measurement point is calculated. If the distance is equal to 50m, the square unit area is recorded as a subsidiary area of ​​the corresponding forest area. If the distance is greater than 50m, the square unit area is recorded as a scattered area. According to the satellite remote sensing map, the roads with a width greater than or equal to 5m are marked in the third standard grayscale map as transportation roads, and the shortest distance from each metering point to the nearest transportation road is calculated and recorded as the transportation distance parameter of the metering point; A circular area is drawn with the measurement point as the center and the preset length value R as the radius, recorded as the activity range area, and the population number in the activity range area is calculated and recorded as the personnel activity parameter of the measurement point; The types and proportions of trees with a diameter greater than or equal to 10CM in the forest area corresponding to the measurement point are counted. A tree type value table is preset, which contains multiple tree types and each tree type corresponds to a price value. The proportion of each tree type in the forest area corresponding to the measurement point is multiplied by its price value to obtain the regional reference value of the tree type, and then the regional reference values ​​of all tree types in the forest area are summed to obtain the tree value parameters of the forest area; The transport distance parameter of the same forest area Personnel activity parameters Tree value parameters After normalization, it is inserted into the formula Calculation is performed to obtain the regional value parameter θ, where μ1, μ2, and μ3 are all preset weight factors greater than 0. There are preset strict control intervals, key intervals, and ordinary intervals. According to the interval corresponding to the regional value parameter θ of the forest area, the forest area and its affiliated areas are marked as strict control areas, key areas, or ordinary areas.

3. The remote monitoring system for forest and grass coverage based on satellite remote sensing images according to claim 2 is characterized in that: The monitoring and control module supervision process is as follows: Regularly carry out key monitoring of strictly controlled areas, key areas and ordinary areas and record them as monitoring areas. The supervision levels of strictly controlled areas, key areas and ordinary areas are marked as level 3, level 2 and level 1 respectively. Different monitoring cycles are set according to different levels to monitor them. In one monitoring cycle, various monitoring areas of the same level are monitored cyclically. The monitoring cycles of level 3, level 2 and level 1 are 3T, 2T and T respectively, where T is the preset cycle length. Sequentially obtain infrared images of all monitoring areas at the same supervision level, and when the infrared images contain fire image features, generate a fire alarm signal and send the location of the monitoring area to the monitoring center; The radar monitoring images of all monitoring areas of the same supervision level are obtained in sequence, and the similarity between the radar monitoring image currently collected in the same monitoring area and the radar monitoring image collected in the previous monitoring cycle is calculated. When the similarity is less than or equal to the preset similarity threshold, a felling signal is generated and the location of the monitoring area is sent to the monitoring center.

Images