A mountain fire monitoring system based on satellite remote sensing data

Abstract

The present invention relates to the field of forestry management technology, specifically a mountain fire monitoring system based on satellite remote sensing data, the system comprising: a plateau hotspot positioning and correction module obtains the hotspot coordinates of the target hotspot area monitored by satellite and the historical actual hotspot coordinates within the mountain fire monitoring range, and extracts the longitude and latitude, hotspot area intensity and elevation values ​​therefrom, calculates the longitude and latitude offset value between the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates, obtains the first longitude and latitude offset value, classifies the hotspot area intensity range and elevation interval of the historical actual hotspot coordinates in combination with the first longitude and latitude offset value, and generates a hotspot offset classification feature list. In the present invention, by introducing comparative analysis of hotspot coordinates and historical actual coordinates, accurate correction of satellite monitoring hotspot positions is achieved, combined with the classification of offset features of hotspot area intensity and elevation values, accurate correction of monitoring data under different terrain conditions is achieved, and positioning accuracy is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry management, and in particular to a mountain fire monitoring system based on satellite remote sensing data. Background Art

[0002] The forest fire monitoring system is a system that uses satellite remote sensing, ground sensor networks and drone technology to conduct real-time monitoring, hotspot identification and positioning of forest fires. Its main purpose is to quickly detect and warn of forest fires, provide accurate location information of fire points, help relevant departments formulate firefighting plans, and reduce fire losses.

[0003] Existing technologies are difficult to adapt to complex terrain environments during forest fire monitoring, resulting in significant deviations between hotspot positioning and actual fire locations. Taking plateau areas as an example, due to the influence of terrain undulations and altitude, satellite remote sensing data often have positioning offsets, resulting in unstable monitoring results. At the same time, the monitoring system has limited ability to identify surface features, and is prone to misjudgment in areas with dense water bodies, increasing the probability of false alarms. When conducting monitoring in mountainous and hilly areas, due to the large changes in terrain complexity, it is difficult for the system to accurately determine the location of the fire, especially in mountainous areas with dense vegetation, where the surface coverage is complex and changeable, and the positioning accuracy of the monitoring system is significantly reduced, delaying the fire handling time, including the frequent occurrence of monitoring blind spots in special terrain areas such as canyons, which will also affect the timeliness of fire detection. In addition, it is difficult for existing monitoring systems to identify the hotspot offset patterns under different terrain conditions, resulting in low accuracy of fire warnings, thereby reducing the efficiency of forest fire prevention. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a mountain fire monitoring system based on satellite remote sensing data.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A mountain fire monitoring system based on satellite remote sensing data comprises:

[0006] The plateau hotspot positioning and correction module obtains the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates of the target hotspot area within the wildfire monitoring range, and extracts the longitude and latitude, hotspot area intensity and elevation values ​​therefrom, and obtains the first longitude and latitude offset value by calculating the longitude and latitude offset value between the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates. Combined with the first longitude and latitude offset value, the hotspot area intensity range and elevation interval of the historical actual hotspot coordinates are classified to generate a hotspot offset classification feature list;

[0007] The offset difference adjustment module collects the water body underlying surface vector data corresponding to the hotspot area in the hotspot offset classification feature list, calculates the spatial offset between the water body underlying surface vector data and the historical actual hotspot coordinates in longitude and latitude, obtains the second longitude and latitude offset value, analyzes the offset difference between the first longitude and latitude offset value and the second longitude and latitude offset value, and generates an offset adjustment vector list;

[0008] The offset data analysis module performs rasterization processing on the corresponding hot spot area in the offset adjustment vector list, extracts the information of the spatial offset from the raster and obtains its statistical characteristics, calculates the correlation coefficient between the elevation value of the hot spot offset classification feature list and the information of the spatial offset in the raster, and analyzes the overall offset law of the hot spot area in combination with the statistical characteristics to generate a data offset analysis result;

[0009] The complex terrain error compensation module collects the terrain information of the hot spot area after rasterization, calculates the terrain complexity index according to the entropy value of the terrain information, analyzes the influence relationship between the terrain complexity index and the statistical characteristics in the data offset analysis results, and performs error compensation on the offset adjustment vector list with reference to the influence relationship to generate volcanic hot spot monitoring information.

[0010] As a further solution of the present invention, the step of obtaining the first latitude and longitude offset value is specifically:

[0011] Collect the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates of the target hotspot area within the wildfire monitoring range, extract the corresponding longitude and latitude, hotspot area intensity and elevation values ​​from them, and generate a data set of hotspot information;

[0012] Based on the data set of the hot spot information, the formula is used: ;

[0013] Calculate the longitude offset between the hotspot coordinates monitored by the satellite and the actual historical hotspot coordinates And the latitude offset value , generate the first longitude and latitude offset value;

[0014] in, It is the longitude coordinate of the hotspot monitored by the satellite in the hotspot information data set. is the latitude coordinate of the satellite monitoring hotspot in the hotspot information dataset, is the longitude coordinate of the actual historical hotspot in the hotspot information dataset, It is the latitude coordinate of the actual historical hotspot in the hotspot information dataset.

[0015] As a further solution of the present invention, the steps of obtaining the hotspot offset classification feature list are specifically as follows:

[0016] The first latitude and longitude offset value is matched with the intensity range of the hotspot area defined by the historical actual hotspot coordinates and the elevation interval divided according to the elevation value to generate structured classification data;

[0017] Based on the structured classification data, the offset direction is determined by judging whether the first longitude and latitude offset value is positive or negative, and the offset variation range is analyzed by extracting the maximum and minimum offset values ​​according to different offset directions. The offset features are divided according to each offset variation range to generate a hotspot offset classification feature list.

[0018] As a further solution of the present invention, the step of obtaining the second latitude and longitude offset value is specifically:

[0019] Obtaining the water body underlying surface vector data corresponding to the hotspot area in the hotspot offset classification feature list, extracting the water body coverage and area attributes at each time point from the water body underlying surface vector data, recording the water body change trend, and generating water body time series data for the hotspot area;

[0020] Based on the water body time series data of the hotspot area, the formula is used:

[0021] ;

[0022] Calculate the spatial offset of the water surface vector data and the historical actual hotspot coordinates in longitude The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates , generate a second latitude and longitude offset value;

[0023] in, is the longitude coordinate of the underlying surface of the water body obtained from the water body time series data of the hotspot area, It is the latitude coordinate of the underlying water surface obtained from the water body time series data of the hotspot area.

[0024] As a further solution of the present invention, the step of obtaining the offset adjustment vector list is specifically:

[0025] Compare the offset difference between the second longitude and latitude offset value and the first longitude and latitude offset value and the historical actual hotspot coordinates, select the offset value closest to the historical actual hotspot coordinates, and generate an offset accuracy analysis result;

[0026] Based on the above mentioned offset accuracy analysis results, the formula is adopted:

[0027] ;

[0028] Calculate the offset adjustment vector in the longitude direction respectively and the latitude offset adjustment vector , generate a list of offset adjustment vectors.

[0029] As a further solution of the present invention, the step of obtaining the data offset analysis result is specifically:

[0030] Rasterizing the corresponding hot spot area in the offset adjustment vector list, extracting the spatial offset information from the grid and obtaining its statistical features, the statistical features including covariance and standard deviation, and generating rasterized spatial offset distribution data;

[0031] Based on the gridded spatial offset distribution data, the formula is adopted:

[0032] ;

[0033] Calculate the elevation values ​​within the elevation interval respectively The spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates Correlation coefficient , and the elevation values ​​within the elevation interval The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates Correlation coefficient , get the correlation coefficient information;

[0034] in, is the elevation value within the elevation interval The spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates The covariance of is the elevation value within the elevation interval The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates The covariance of is the standard deviation of the elevation values ​​within the elevation interval, It is the spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates The standard deviation of It is the standard deviation of the spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates;

[0035] Based on the correlation coefficient information and statistical characteristics, by comparing the offsets in different elevation intervals, the concentration or dispersion state of the spatial offset information in the grid is determined, and the offset trend and distribution law are analyzed to generate data offset analysis results.

[0036] As a further solution of the present invention, the step of obtaining the terrain complexity index is specifically as follows:

[0037] Collect the terrain information of the hot spot area after rasterization, using the formula:

[0038] ;

[0039] Calculate the entropy value of the terrain information of the hotspot area , get terrain entropy information;

[0040] in, is the total number of grid cells in the hotspot area after rasterization, It is the first The NDVI value ratio of each grid cell is calculated as follows: , It is The NDVI value of each grid cell, All units in the area The sum of the NDVI values, express Logarithmic transformation in information entropy;

[0041] According to the terrain entropy information, the formula is adopted:

[0042] ;

[0043] Calculating the terrain complexity index , and obtain the terrain complexity analysis results;

[0044] in, It is the maximum entropy value of the terrain information in the hotspot area.

[0045] As a further solution of the present invention, the step of acquiring the volcanic hotspot monitoring information is specifically as follows:

[0046] Analyze the characteristics of the longitude and latitude offsets changing with the elevation interval under different terrain complexity indexes, establish the influence relationship between the statistical features in the terrain complexity analysis results and the data offset analysis results, and generate the longitude and latitude direction offset analysis results;

[0047] Based on the analysis result of the latitude and longitude offset, error compensation coefficients are dynamically set for the offset adjustment vectors of different areas in the offset adjustment vector list with reference to the influence relationship, and error compensation is performed. According to the error compensation result, volcanic hotspot monitoring information is generated.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, by introducing the comparative analysis of hotspot coordinates and historical actual coordinates, accurate correction of the satellite monitoring hotspot position is achieved, and combined with the classification of the offset characteristics of the hotspot area intensity and elevation value, accurate correction of monitoring data under different terrain conditions is achieved, effectively improving the positioning accuracy. The spatial offset is calculated based on the water body underlying surface vector data to construct an offset adjustment vector list so that the hotspot monitoring results are closer to the actual situation. The hotspot area is rasterized and the spatial offset information is extracted. The correlation between the elevation value and the spatial offset is analyzed to obtain the overall offset law, thereby improving the accuracy of fire warning. By calculating the terrain complexity index, the correlation between terrain features and offsets is established, and dynamic error compensation of monitoring data under complex terrain conditions is achieved. The entropy method is used to evaluate the complexity of the terrain, and differentiated error compensation coefficients are set for different areas, thereby further ensuring the monitoring accuracy, improving the reliability of forest fire monitoring, reducing false alarms, and shortening the response time. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a system flow chart of the present invention;

[0051] Figure 2 A flow chart for obtaining a first latitude and longitude offset value according to the present invention;

[0052] Figure 3 A flowchart for obtaining a hotspot offset classification feature list for the present invention;

[0053] Figure 4 A flow chart for obtaining a second latitude and longitude offset value according to the present invention;

[0054] Figure 5 A flowchart for obtaining an offset adjustment vector list for the present invention;

[0055] Figure 6 A flow chart for obtaining data deviation analysis results of the present invention;

[0056] Figure 7 A flow chart for calculating the terrain complexity index of the present invention;

[0057] Figure 8 This is a flow chart of the present invention for obtaining volcanic hotspot monitoring information. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0060] See also Figure 1 , a mountain fire monitoring system based on satellite remote sensing data includes:

[0061] The plateau hotspot positioning and correction module obtains the hotspot coordinates of the target hotspot area monitored by satellite within the wildfire monitoring range and the historical actual hotspot coordinates uploaded by drones or ground monitoring stations, and extracts the longitude and latitude, hotspot area intensity and elevation values ​​therefrom, calculates the longitude and latitude offset value between the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates, obtains the first longitude and latitude offset value, classifies the hotspot area intensity range and elevation interval of the historical actual hotspot coordinates in combination with the first longitude and latitude offset value, extracts the offset features in the classification results, such as the offset direction and range of variation, and generates a hotspot offset classification feature list;

[0062] The offset difference adjustment module collects the water body underlying surface vector data corresponding to the hotspot area in the hotspot offset classification feature list, calculates the spatial offset between the water body underlying surface vector data and the historical actual hotspot coordinates in longitude and latitude, obtains the second longitude and latitude offset value, analyzes the offset difference between the first longitude and latitude offset value and the second longitude and latitude offset value, and generates an offset adjustment vector list;

[0063] The offset data analysis module performs rasterization processing on the corresponding hotspot area in the offset adjustment vector list, extracts the spatial offset information from the raster and obtains its statistical characteristics, calculates the correlation coefficient between the elevation value of the hotspot offset classification feature list and the spatial offset information in the raster, and analyzes the overall offset law of the hotspot area in combination with the statistical characteristics to generate data offset analysis results;

[0064] The complex terrain error compensation module collects terrain information of the hotspot area after rasterization, including slope changes and slope direction vectors, calculates the terrain complexity index based on the entropy value of the terrain information, analyzes the influence relationship between the terrain complexity index and the statistical features in the data offset analysis results through polynomial curve fitting using the least squares method, and performs error compensation on the offset adjustment vector list with reference to the influence relationship to generate volcanic hotspot monitoring information;

[0065] See also Figure 2, the specific steps for obtaining the first latitude and longitude offset value are:

[0066] Collect the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates of the target hotspot area within the wildfire monitoring range, extract the corresponding longitude and latitude, hotspot area intensity and elevation values ​​from them, and generate a data set of hotspot information;

[0067] Obtain the coordinates of the satellite monitoring hotspots in the target area within the wildfire monitoring range, load the satellite data of the target area through remote sensing image analysis tools (such as ENVI or QGIS), use the spectral characteristics of the image to identify the wildfire hotspot area, extract the pixel position of the hotspot, convert the pixel position into geographic longitude and latitude coordinates through image projection coordinates, extract the radiant brightness value of the hotspot area as the hotspot intensity, and obtain the elevation value at the corresponding hotspot longitude and latitude based on the digital elevation model (DEM) data. At the same time, retrieve the historical hotspot records that match the time period of the satellite monitoring hotspot area from the database of the UAV monitoring equipment or ground monitoring station, format the hotspot records uploaded by the UAV or ground monitoring station through coordinate matching tools (such as ArcGIS), extract the longitude and latitude information of each historical hotspot record, the hotspot intensity value in the historical monitoring, and the terrain elevation data of the recorded point, compare the satellite monitoring data with the historical hotspot data item by item, check the integrity and consistency of the data format and timestamp, organize and output the structured data set, including longitude and latitude, hotspot area intensity and elevation values.

[0068] Based on the data set of hot spot information, the formula is used:

[0069] ;

[0070] Calculate the longitude offset between the hotspot coordinates monitored by the satellite and the actual historical hotspot coordinates And the latitude offset value , generate the first longitude and latitude offset value;

[0071] in, It is the longitude coordinate of the hotspot monitored by the satellite in the hotspot information data set. It is the latitude coordinates of the hotspots monitored by satellites in the hotspot information data set. The longitude and latitude coordinates of the hotspots are extracted through remote sensing image analysis software (such as ENVI), and the spatial reference information of the image is matched with the hotspot pixel position to obtain the longitude and latitude data. is the longitude coordinate of the actual historical hotspot in the hotspot information dataset, It is the latitude coordinates of the actual historical hot spots in the hot spot information dataset. Through the log files of drone monitoring equipment or ground monitoring station data, coordinate parsing tools (such as ArcGIS) are used to parse the plane coordinates of the historical records and convert them into geographic coordinates.

[0072] Assume that the latitude and longitude of the satellite monitoring hotspot is , the latitude and longitude of the historical hotspot is .

[0073] Enter the formula to calculate: ;

[0074] The results show that the first longitude offset value is , the first latitude offset value is .

[0075] See also Figure 3 , the steps for obtaining the hotspot offset classification feature list are as follows:

[0076] The first latitude and longitude offset value is matched with the intensity range of the hotspot area delineated by the historical actual hotspot coordinates and the elevation interval divided according to the elevation value to generate structured classification data;

[0077] Based on the calculated offset results, it is assumed that the hotspot intensity range recorded in the historical actual hotspot coordinates is divided into a low intensity range of 0-50W / m², a medium intensity range of 51-100W / m², and a high intensity range of 101-200W / m² based on the radiation intensity unit (W / m²). At the same time, the historical elevation data is divided into three intervals of 0-500 meters, 501-1000 meters, and 1001-1500 meters in meters (m). The classification process is as follows: According to the intensity value and elevation value of the historical actual hotspot coordinates, the offset value is extracted for each record and the corresponding intensity is matched. Range and elevation interval. For example, for a record with a hotspot intensity of 65W / m² and an elevation of 750 meters, the record is classified into the medium intensity range of 51-100W / m² and the elevation interval of 501-1000 meters. Combined with the offset value data, the longitude offset of 1.22 kilometers and the latitude offset of 3.89 kilometers are recorded in the above classification. All records are analyzed one by one, and the records that meet the intensity range and elevation interval are classified separately. For the classified data, subset files are created according to the intensity range and elevation interval respectively, and finally the corresponding offset value and original record in each classification are recorded;

[0078] Based on structured classification data, the offset direction is determined by judging the positive or negative of the first longitude and latitude offset value, and the offset variation range is analyzed by extracting the maximum and minimum offset values ​​according to different offset directions. The offset features are divided according to each offset variation range, and a hotspot offset classification feature list is generated.

[0079] Extract the offset features in the classification results, such as the offset direction and the range of variation. For the offset direction, the offset direction is determined by the positive and negative values ​​of the first longitude offset value and the first latitude offset value. When the longitude offset value is positive, it indicates an eastward offset, for example, when the first longitude offset value is 1.22 kilometers, the direction is eastward; when the first longitude offset value is negative, it indicates a westward offset, for example, when the first longitude offset value is -0.8 kilometers, the direction is westward; when the first latitude offset value is positive, it indicates a northward offset, for example, when the first latitude offset value is 3.89 kilometers, the direction is northward; when the first latitude offset value is negative, it indicates a southward offset, for example, when the first latitude offset value is -2.5 kilometers, the direction is southward. After summarizing the offset directions, record the main directions in combination with the classification data. For example, in the medium intensity range of 51-100W / m² and the elevation interval of 501-1000 meters, the offset directions are "eastward" and "northward". At the same time, the offset value range in each classification is extracted, and the variation range is calculated by the difference between the maximum and minimum values. For example, in the above medium intensity range, the variation range of the first longitude offset value is 1.5 kilometers (maximum 1.7 kilometers, minimum 0.2 kilometers), and the variation range of the first latitude offset value is 4.2 kilometers (maximum 4.6 kilometers, minimum 0.4 kilometers). The offset direction and variation range results of each intensity range and elevation interval are classified to generate a hotspot offset classification feature list containing the offset direction, variation range, and statistical values ​​of each category.

[0080] See also Figure 4 , the specific steps for obtaining the second latitude and longitude offset value are:

[0081] Obtain the water body underlying surface vector data corresponding to the hotspot area in the hotspot offset classification feature list, extract the water body coverage and area attributes at each time point from the water body underlying surface vector data, record the water body change trend, and generate the water body time series data of the hotspot area;

[0082] Remote sensing image analysis software (such as ENVI) and geographic information system tools (such as ArcGIS) are used to load multi-period satellite remote sensing image data in the hotspot area. Combined with the underlying surface vector data of the water body, the normalized difference water index (NDWI) is used to classify and process the images in the hotspot area. The NDWI index reflects the characteristics of the water body by calculating the green band and near-infrared band in the image. Generally, areas with NDWI values ​​greater than 0 can be identified as water bodies. The higher the value, the stronger the water body reflection. Non-water body areas (such as vegetation, soil or buildings) usually show negative values. NDWI is calculated for the multi-period images in the hotspot area one by one, the water body range is screened out according to the threshold, and the identification results are binarized to generate a water body coverage distribution map. For the image data at each time point, the spatial coverage range and area attributes of the water body in the hotspot area are extracted, and the water body change trend in the multi-period data is recorded, such as the increase or decrease of the water body area, the movement of the water body boundary shape, etc., and the extracted data are integrated to generate the water body time series data of the hotspot area.

[0083] Based on the water body time series data of the hotspot area, the formula is used:

[0084] ;

[0085] Calculate the spatial offset of the water surface vector data and the historical actual hotspot coordinates in longitude The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates , generate a second latitude and longitude offset value;

[0086] in, is the longitude coordinate of the underlying surface of the water body obtained from the water body time series data of the hotspot area, The latitude coordinates of the underlying surface of the water body are obtained from the water body time series data of the hotspot area. They all come from the clipped water body vector data. The specific acquisition method is as follows: based on the geographical scope of the hotspot coordinates, the water body vector data of the hotspot area is clipped, and the water body related attributes (such as area, shape and boundary) are extracted. The water body range is identified through image classification, and the longitude and latitude coordinates of the center point of the water body in the hotspot area are obtained by combining multi-time period image data, which are used as the basis for calculating the spatial offset.

[0087] Assume that the center coordinates of the water body underlying surface are: longitude ,latitude , historical actual hotspot coordinates: longitude ,latitude ;

[0088] Calculate the second latitude and longitude offset value:

[0089] ;

[0090] ;

[0091] The results show that the second longitude offset value is , the second latitude offset value is .

[0092] See also Figure 5 , the steps for obtaining the offset adjustment vector list are as follows:

[0093] Compare the offset difference between the second longitude and latitude offset value and the first longitude and latitude offset value and the historical actual hotspot coordinates, select the offset value closest to the historical actual hotspot coordinates, and generate an offset accuracy analysis result;

[0094] The first longitude and latitude offset value and the second longitude and latitude offset value are: (Based on the offset value between the satellite monitoring hotspot and the historical actual hotspot), (Based on the offset value between the water body underlying surface and the actual historical hotspot). Assume that the coordinates of the actual historical hotspot are: longitude ,latitude The coordinates of the satellite monitoring hotspot are: longitude ,latitude The coordinates of the center of the water body underlying surface are: longitude ,latitude The calculation of the first longitude and latitude offset value shows that the satellite monitoring hotspots are offset in the longitude direction compared with the actual historical hotspots. (eastward), latitude deviation (towards the north); the calculation of the second longitude and latitude offset value shows that the center of the water body underlying surface is offset in the longitude direction compared with the actual historical hotspot (west), latitude offset (Southward). By calculating the absolute value of the two sets of offset values ​​and the offset of the actual historical hotspots, the offset of the first longitude and latitude offset value in the longitude direction is , the latitude offset is ; The offset of the second longitude and latitude offset value in the longitude direction is , the latitude offset is Compared with the actual historical hotspot coordinates, the second longitude and latitude offset values ​​are closer to the actual historical hotspot coordinates, and their longitude and latitude offsets are smaller than the first longitude and longitude offset values. Therefore, the second longitude and longitude offset values ​​based on the water body underlying surface are more accurate, indicating that in the complex terrain scene of the plateau, the hotspot location may have a higher correlation with the water body characteristics. It is recommended to further correct it in combination with the second longitude and latitude offset values.

[0095] Based on the offset accuracy analysis results, the formula is used:

[0096] ;

[0097] Calculate the offset adjustment vector in the longitude direction respectively and the latitude offset adjustment vector , generate a list of offset adjustment vectors;

[0098] Substitute the first longitude and latitude offset value and the second longitude and latitude offset value into the formula to calculate:

[0099] ;

[0100] According to the offset adjustment vector result, the final adjustment vector offset in the longitude direction is (easting), the latitude deviation is (Northing), the generated offset adjustment vector list will be used as a reference for subsequent hotspot coordinate optimization and correction. The first longitude and latitude offset value is calculated by comparing the hotspot coordinates monitored by satellite with the actual historical hotspot coordinates. It provides a direct correction basis, but the error may be large under complex terrain conditions. The second longitude and latitude offset value is calculated based on the underlying surface vector data of the water body, reflecting the relationship between the hotspot and specific surface features (such as water body distribution), and can further capture the local correction information of the hotspot location. The offset adjustment vector combines the offset difference between the first longitude and latitude offset value and the second longitude and latitude offset value, with the aim of balancing the positioning error under different terrain and environmental conditions.

[0101] See also Figure 6 , the specific steps for obtaining the data offset analysis results are:

[0102] Rasterize the corresponding hot spot area in the offset adjustment vector list, extract the spatial offset information from the grid and obtain its statistical characteristics, including covariance and standard deviation, to generate rasterized spatial offset distribution data;

[0103] The hotspot areas corresponding to the offset adjustment vector list are rasterized, and the hotspot areas are divided into regular grids through geographic information system tools. The appropriate grid resolution (such as 1 km × 1 km) is set to ensure statistical accuracy and avoid data redundancy caused by too fine resolution. The spatial offset information of each hotspot coordinate is recorded through grid cells, including longitude offset and latitude offset. These offsets are attributed according to the grid cells where they are located. Then, the offset set in each grid cell is extracted, and the spatial offset average of the grid cell is obtained through the statistical analysis tool in the GIS software or by calling the Mean function. The variance is calculated through the Variance function, and the mean square error is calculated through the StandardDeviation function. Finally, the spatial offset statistics of each grid cell are generated and summarized to obtain the rasterized spatial offset distribution data.

[0104] Based on the rasterized spatial offset distribution data, the formula is used:

[0105] ;

[0106] Calculate the elevation values ​​within the elevation interval respectively The spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates Correlation coefficient , and the elevation values ​​within the elevation interval The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates Correlation coefficient , get the correlation coefficient information;

[0107] in, is the elevation value within the elevation interval The spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates The covariance of indicates the consistency of the changing trends of the two, and the calculation formula is: , Indicates The elevation values ​​of the sampling points are obtained from the digital elevation model (DEM) data. represents the average value of elevation. Indicates The spatial offset of the water surface vector data in longitude at each sampling point, is the average value of the spatial offset of the water body underlying surface vector data in longitude, represents the total number of sampling points, is the elevation value within the elevation interval The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates The covariance of indicates the consistency of the changing trends of the two, and the calculation formula is: , Indicates The spatial offset of the water surface vector data in latitude at each sampling point, is the average value of the spatial offset of the water body underlying surface vector data in latitude, It is the standard deviation of the elevation value within the elevation interval, which is used to measure the degree of dispersion of the elevation value distribution. The calculation formula is: , It is the spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates The standard deviation is calculated as: , It is the standard deviation of the spatial offset between the water body underlying surface vector data and the historical actual hotspot coordinates in latitude. The calculation formula is: .

[0108] Assuming the elevation range is 500~1000m, the sampling point data are as follows: The elevation value of sampling point 1 , longitude offset , latitude offset (Selected from the second latitude and longitude offset value); Elevation value of sampling point 2 , longitude offset , latitude offset (hypothesis).

[0109] Calculate the mean: ;

[0110] Compute the covariance:

[0111] longitude:

[0112] ;

[0113] ;

[0114] latitude:

[0115] ;

[0116] ;

[0117] Calculate the standard deviation:

[0118] ;

[0119] Calculate the correlation coefficient:

[0120] longitude:

[0121] ;

[0122] latitude:

[0123] ;

[0124] The results show that the elevation value is completely negatively correlated with the longitude offset (the correlation coefficient is -1), indicating that the higher the elevation, the smaller the longitude offset. The elevation value is completely positively correlated with the latitude offset (the correlation coefficient is 1), indicating that the higher the elevation, the larger the latitude offset.

[0125] Based on the correlation coefficient information and statistical characteristics, by comparing the offsets in different elevation intervals, the concentration or dispersion state of the spatial offset information in the grid is determined, and the offset trend and distribution law are analyzed to generate data offset analysis results;

[0126] First, according to the extracted elevation interval division and the correlation coefficient of longitude and latitude offset, the offset distribution pattern in different elevation intervals is clarified, and the offset standard deviation in the elevation interval is called (for example, the longitude direction is , the latitude direction is ), to judge whether the offset value is concentrated or dispersed. To this end, the "offset concentration threshold" is defined as the judgment standard. When the standard deviation of the longitude and latitude offset is less than the offset concentration threshold (for example ), the offset distribution is defined as "concentrated", otherwise it is defined as "dispersed". The specific implementation process is to use statistical tools such as Excel or statistical software such as SPSS to calculate the standard deviation of the offset in the elevation interval data, compare whether it meets the offset concentration threshold, and thus determine the offset distribution state. For example, when the standard deviation of the longitude offset in the 600~800m elevation interval is , which is less than the offset concentration threshold (0.001), is defined as concentrated distribution; if the standard deviation of the latitude offset in a certain interval is , is greater than the offset concentration threshold (0.001), it is defined as a dispersed distribution. In addition, the correlation coefficient (such as longitude direction , latitude direction ) Analyze the displacement trend, determine whether the displacement direction increases or decreases, and combine the average displacement size (such as the average value in the latitude direction) ) to evaluate the overall offset range of hotspots. For example, in the elevation range of 600~800m, the standard deviation of the offset in the longitude direction is small and the correlation coefficient is -1, indicating that the longitude offset distribution is concentrated and gradually decreases with increasing elevation; while the standard deviation of the offset in the latitude direction is large, and the correlation coefficient is 1, indicating that the latitude offset distribution is slightly dispersed, but gradually increases with increasing elevation. In order to illustrate the different offset distribution states, the concentrated and dispersed offsets are expanded: Concentrated offset (satisfying the offset concentration threshold): When the offset value is concentrated, such as the longitude offset corresponding to an elevation of 600m (standard deviation ) and latitude offset (standard deviation ), indicating that the match between the hotspot and the water body boundary is relatively stable, and the error correction range can be uniformly divided, which is suitable for the overall error compensation method. Dispersed offset (does not meet the offset concentration threshold): When the offset value is dispersed, such as assuming that the standard deviation of the latitude offset at an elevation of 800~1000m , indicating that the hotspot has inconsistent offset characteristics within this elevation range, and the error distribution is greatly affected by complex terrain or boundaries. At this time, it is necessary to refine the partitions and gradually correct the offset law accurately. Finally, through the standard of the offset concentration threshold, the concentrated or dispersed distribution state of the offset in the hotspot area is clarified, and the overall law is obtained by combining the offset correlation coefficient and statistical characteristics, that is, the impact of elevation change on longitude offset has the characteristics of "concentrated and decreasing", while the impact on latitude offset shows a "dispersed or concentrated, and increasing" trend.

[0127] See also Figure 7 , the steps to calculate the terrain complexity index are as follows:

[0128] First, the basic elevation information of the hotspot area is obtained using digital elevation model (DEM) data. The DEM data of the hotspot area is loaded through a geographic information system (such as ArcGIS or QGIS) and clipped to the target range. Then, the slope value of each grid cell is obtained. The slope value is calculated by analyzing the elevation difference and horizontal distance of adjacent grid cells. At the same time, the slope direction vector is obtained to indicate the slope change direction of each grid cell.

[0129] Collect the terrain information of the hot spot area after rasterization, using the formula:

[0130] ;

[0131] Calculate the entropy value of the terrain information of the hotspot area , get terrain entropy information;

[0132] in, is the total number of grid cells in the hotspot area after rasterization, indicating the total number of all grid cells in the area. It is the first The NDVI value ratio of each grid cell is calculated as follows: , It is The NDVI value of each grid cell is calculated from the satellite remote sensing image using the red band and near infrared band. The formula is: , and They are The near infrared band and red light band reflectance values ​​of each unit, All units in the area The sum of the NDVI values ​​in the region is calculated by counting the NDVI values ​​of the grid cells in the region. express The logarithmic transformation in information entropy is used to measure the contribution of the NDVI value of each grid cell to the overall complexity.

[0133] Assume that a hot spot area is divided into The NDVI values ​​of each grid cell are 0.3, 0.5 and 0.2 respectively. Calculate the proportion of NDVI values :

[0134] ;

[0135] ;

[0136] ;

[0137] Substitute the formula to calculate the entropy value :

[0138] ;

[0139] Use natural logarithms:

[0140] ;

[0141] ;

[0142] ;

[0143] The results show that the terrain entropy value of the current area is .

[0144] According to the terrain entropy information, the formula is used:

[0145] ;

[0146] Calculating the terrain complexity index , and obtain the terrain complexity analysis results;

[0147] in, It is the maximum entropy value of the terrain information in the hotspot area. It reaches its maximum value when the NDVI values ​​of all grid cells are completely evenly distributed. The calculation formula is: .

[0148] Assume that a region contains grid cells, entropy value , calculate the maximum entropy value and complexity index:

[0149] ;

[0150] ;

[0151] The results show that the current terrain complexity index is .

[0152] See also Figure 8,The specific steps for obtaining volcanic hotspot monitoring information are:

[0153] Analyze the characteristics of longitude and latitude offsets changing with elevation intervals under different terrain complexity indexes, establish the influence relationship between the statistical features in the terrain complexity analysis results and the data offset analysis results, and generate the longitude and latitude direction offset analysis results;

[0154] The relationship between the terrain complexity index and the statistical characteristics in the data offset analysis results was analyzed by polynomial curve fitting using the least squares method. First, the terrain complexity index was analyzed using statistical analysis software (such as Excel or SPSS). A data point set is established with the statistical features (including mean, variance and mean square error) in the data migration analysis results. The terrain complexity index is used as the independent variable, and the statistical features are used as the dependent variables. The fitting model is set as a quadratic polynomial, and the polynomial parameters are estimated by the least squares method to finally generate a fitting curve. It is a standardized index obtained through normalization processing. Its value range is [0, 1]. When the terrain complexity index is close to 1, it means that the terrain changes in the area are complex, and the distribution of vegetation coverage and slope information tends to be uneven; when the terrain complexity index is close to 0, it means that the terrain is relatively flat and the distribution pattern is simple. For the current terrain complexity index , indicating that the terrain complexity of the hotspot area is high, and there may be a variety of complex slope changes and vegetation distribution patterns, which have a great impact on the hotspot offset distribution. Combining the fitting results of the terrain complexity index and statistical characteristics, the influence of terrain complexity on the offset distribution state is analyzed, and it is found that the longitude offset shows a concentrated and decreasing distribution characteristic with the change of elevation, while the latitude offset shows a dispersed or concentrated and increasing trend. This influence is mainly reflected in the significant change in the standard deviation of longitude and latitude offsets in complex terrain areas. For example, the standard deviation of the longitude offset in the 600 to 800m elevation range is small, and the matching degree is high; while the standard deviation of the latitude offset in the 800 to 1000m elevation range is large, and the matching degree is reduced. This shows that the more complex the terrain is, the more unstable the hotspot offset law is.

[0155] Based on the analysis results of the offset in the longitude and latitude directions, the error compensation coefficients are dynamically set for the offset adjustment vectors in different areas of the offset adjustment vector list with reference to the influence relationship, and error compensation is performed. Based on the error compensation results, volcanic hotspot monitoring information is generated.

[0156] According to the influence of the terrain complexity index and the elevation range, the compensation coefficient is dynamically adjusted according to the interval range of the terrain complexity index and the distribution characteristics of the elevation change. For the compensation coefficient range of the terrain complexity index, [0, 0.4]: small, 0.7-0.8 in the longitude direction, 0.6-0.7 in the latitude direction, [0.4, 0.7]: medium to large, 0.8-0.9 in the longitude direction, 0.7-0.8 in the latitude direction, [0.7, 1.0]: large, 0.9-1.0 in the longitude direction, 0.75-0.85 in the latitude direction. For the compensation coefficient range of the elevation interval, 600-800 meters: 0.8 (upper limit) in the longitude direction, 0.7 (middle value) in the latitude direction, 800-1000 meters: 0.8-0.85 (upper limit) in the latitude direction, 0.85 (middle value) in the longitude direction. For flat areas with a terrain complexity index of [0, 0.4], since the offset rules in the longitude and latitude directions are relatively simple, the compensation coefficient is set to a smaller value (the compensation coefficient range in the longitude direction is 0.7-0.8, and the compensation coefficient range in the latitude direction is 0.6-0.7) to reduce excessive intervention in the offset adjustment vector; when the terrain complexity index is in a medium-complex area of ​​[0.4, 0.7], the distribution of longitude offset and latitude offset gradually becomes uneven, and the compensation coefficient range is set to 0.8-0.9 (longitude direction) and 0.7-0.8 (latitude direction) to balance the offset effects in different directions; when the terrain complexity index is in a complex area of ​​[0.7, 1.0], due to the drastic changes in the terrain, the longitude offset is more concentrated, and its offset needs to be reduced to a greater extent (the compensation coefficient is set to 0.9-1.0, and the latitude offset compensation coefficient is set to 0.75-0.85) to ensure that the latitude offset is appropriately retained and optimized. As for the influence of the elevation range, within the range of 600-800 meters, the matching degree in the longitude direction is relatively high, and the compensation coefficient selects the upper limit of the interval range to further reduce the offset, while the compensation coefficient for the latitude direction offset takes the middle value; within the elevation range of 800-1000 meters, the standard deviation of the offset in the latitude direction increases significantly. At this time, the compensation coefficient selects the upper limit of the interval range to increase the compensation intensity, and the compensation coefficient in the longitude direction selects the middle value of the interval for appropriate adjustment. Finally, the offset adjustment vector is optimized through the dynamically set compensation coefficient, so that its longitude and latitude offsets are closer to the actual historical hotspot coordinates. For example, according to the influence of terrain complexity index and elevation range: terrain complexity index: assuming 0.75 (complex area [0.7, 1.0]), longitude compensation coefficient: 0.9-1.0, latitude compensation coefficient: 0.75-0.85, elevation range: assuming 850 meters (800-1000 meters), longitude compensation coefficient: 0.85 (middle value), latitude compensation coefficient: 0.8-0.85 (upper limit), bring the compensation coefficient into the offset adjustment vector calculation, longitude direction: , latitude direction: The final longitude offset is: +0.00255° (east), and the final latitude offset is: +0.00144° (north). After adjustment, the offset errors in the longitude and latitude directions are significantly reduced, thereby effectively improving the accuracy and robustness of hotspot correction.

[0157] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A mountain fire monitoring system based on satellite remote sensing data, characterized in that: The system comprises: The plateau hotspot positioning and correction module obtains the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates of the target hotspot area within the wildfire monitoring range, and extracts the longitude and latitude, hotspot area intensity and elevation values ​​therefrom, and obtains the first longitude and latitude offset value by calculating the longitude and latitude offset value between the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates. Combined with the first longitude and latitude offset value, the hotspot area intensity range and elevation interval of the historical actual hotspot coordinates are classified to generate a hotspot offset classification feature list; The offset difference adjustment module collects the water body underlying surface vector data corresponding to the hotspot area in the hotspot offset classification feature list, calculates the spatial offset between the water body underlying surface vector data and the historical actual hotspot coordinates in longitude and latitude, obtains the second longitude and latitude offset value, analyzes the offset difference between the first longitude and latitude offset value and the second longitude and latitude offset value, and generates an offset adjustment vector list; The offset data analysis module performs rasterization processing on the corresponding hot spot area in the offset adjustment vector list, extracts the information of the spatial offset from the raster and obtains its statistical characteristics, calculates the correlation coefficient between the elevation value of the hot spot offset classification feature list and the information of the spatial offset in the raster, and analyzes the overall offset law of the hot spot area in combination with the statistical characteristics to generate a data offset analysis result; The complex terrain error compensation module collects the terrain information of the hot spot area after rasterization, calculates the terrain complexity index according to the entropy value of the terrain information, analyzes the influence relationship between the terrain complexity index and the statistical characteristics in the data offset analysis results, and performs error compensation on the offset adjustment vector list with reference to the influence relationship to generate volcanic hot spot monitoring information.

2. The mountain fire monitoring system based on satellite remote sensing data according to claim 1, characterized in that: The steps for obtaining the first latitude and longitude offset value are specifically as follows: Collect the hotspot coordinates monitored by satellite and the historical actual hotspot coordinates of the target hotspot area within the wildfire monitoring range, extract the corresponding longitude and latitude, hotspot area intensity and elevation values ​​from them, and generate a data set of hotspot information; Based on the data set of the hot spot information, the formula is used: Calculate the longitude offset between the hotspot coordinates monitored by the satellite and the actual historical hotspot coordinates And the latitude offset value , generate the first longitude and latitude offset value; in, It is the longitude coordinate of the hotspot monitored by the satellite in the hotspot information data set. is the latitude coordinate of the satellite monitoring hotspot in the hotspot information dataset, is the longitude coordinate of the actual historical hotspot in the hotspot information dataset, It is the latitude coordinate of the actual historical hotspot in the hotspot information dataset.

3. The mountain fire monitoring system based on satellite remote sensing data according to claim 1, characterized in that: The steps for obtaining the hotspot offset classification feature list are specifically as follows: The first latitude and longitude offset value is matched with the intensity range of the hotspot area defined by the historical actual hotspot coordinates and the elevation interval divided according to the elevation value to generate structured classification data; Based on the structured classification data, the offset direction is determined by judging whether the first longitude and latitude offset value is positive or negative, and the offset variation range is analyzed by extracting the maximum and minimum offset values ​​according to different offset directions. The offset features are divided according to each offset variation range to generate a hotspot offset classification feature list.

4. The mountain fire monitoring system based on satellite remote sensing data according to claim 2, characterized in that: The steps for obtaining the second latitude and longitude offset value are specifically as follows: Obtaining the water body underlying surface vector data corresponding to the hotspot area in the hotspot offset classification feature list, extracting the water body coverage and area attributes at each time point from the water body underlying surface vector data, recording the water body change trend, and generating water body time series data for the hotspot area; Based on the water body time series data of the hotspot area, the formula is used: Calculate the spatial offset of the water surface vector data and the historical actual hotspot coordinates in longitude The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates , generate a second longitude and latitude offset value; in, is the longitude coordinate of the underlying surface of the water body obtained from the water body time series data of the hotspot area, It is the latitude coordinate of the underlying surface of the water body obtained from the water body time series data of the hotspot area.

5. The mountain fire monitoring system based on satellite remote sensing data according to claim 4 is characterized in that: The steps of obtaining the offset adjustment vector list are specifically as follows: Compare the offset difference between the second longitude and latitude offset value and the first longitude and latitude offset value and the historical actual hotspot coordinates, select the offset value closest to the historical actual hotspot coordinates, and generate an offset accuracy analysis result; Based on the above mentioned offset accuracy analysis results, the formula is adopted: Calculate the offset adjustment vector in the longitude direction respectively and the latitude offset adjustment vector , generate a list of offset adjustment vectors.

6. The mountain fire monitoring system based on satellite remote sensing data according to claim 1, characterized in that: The steps for obtaining the data deviation analysis result are specifically as follows: The corresponding hot spot area in the offset adjustment vector list is rasterized, and the spatial offset information is extracted from the raster and its statistical characteristics are obtained. Statistical features include covariance and standard deviation, generating rasterized spatial offset distribution data; Based on the gridded spatial offset distribution data, the formula is adopted: Calculate the elevation values ​​within the elevation interval respectively The spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates Correlation coefficient , and the elevation values ​​within the elevation interval The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates Correlation coefficient , get the correlation coefficient information; in, is the elevation value within the elevation interval The spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates The covariance of is the elevation value within the elevation interval The spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates The covariance of is the standard deviation of the elevation values ​​within the elevation interval, It is the spatial offset in longitude between the water body underlying surface vector data and the historical actual hotspot coordinates The standard deviation of It is the standard deviation of the spatial offset in latitude between the water body underlying surface vector data and the historical actual hotspot coordinates; Based on the correlation coefficient information and statistical characteristics, by comparing the offsets in different elevation intervals, the concentration or dispersion state of the spatial offset information in the grid is determined, and the offset trend and distribution law are analyzed to generate data offset analysis results.

7. The mountain fire monitoring system based on satellite remote sensing data according to claim 1, characterized in that: The steps for obtaining the terrain complexity index are specifically as follows: Collect the terrain information of the hot spot area after rasterization, using the formula: Calculate the entropy value of the hotspot area terrain information , get terrain entropy information; in, is the total number of grid cells in the hotspot area after rasterization, It is the first The NDVI value ratio of each grid cell is calculated as follows: , It is The NDVI value of each grid cell, All units in the area The sum of the NDVI values, express Logarithmic transformation in information entropy; According to the terrain entropy information, the formula is adopted: Calculating the terrain complexity index , and obtain the terrain complexity analysis results; in, It is the maximum entropy value of the terrain information in the hotspot area.

8. The mountain fire monitoring system based on satellite remote sensing data according to claim 7, characterized in that: The steps for obtaining the volcanic hotspot monitoring information are specifically as follows: Analyze the characteristics of the longitude and latitude offsets changing with the elevation interval under different terrain complexity indexes, establish the influence relationship between the statistical features in the terrain complexity analysis results and the data offset analysis results, and generate the longitude and latitude direction offset analysis results; Based on the analysis result of the latitude and longitude offset, error compensation coefficients are dynamically set for the offset adjustment vectors of different areas in the offset adjustment vector list with reference to the influence relationship, and error compensation is performed. According to the error compensation result, volcanic hotspot monitoring information is generated.

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