Forest terrain remote sensing mapping method and system for smart forestry

Abstract

The present disclosure provides a forest terrain remote sensing mapping method and system for smart forestry, which relates to the field of data processing. The method includes: obtaining a remote sensing image of the forest area to be measured; obtaining the elevation value of each monitoring station in the forest area to be measured and the grayscale value at the corresponding position in the remote sensing image, and establishing an elevation-grayscale relationship model; for each partition of the forest area to be measured, determining the vegetation coverage density of the partition based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition; determining the theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and correcting the measured elevation value based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value; determining the seasonal weight based on the temperature of the forest area to be measured, and determining the target elevation value of the partition based on the seasonal weight and the weighted elevation value. The present disclosure can improve the accuracy of forest elevation data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a forest terrain remote sensing mapping method and system for smart forestry. Background Art

[0002] With the development of information technology, particularly remote sensing, Geographic Information Systems (GIS), and Global Positioning Systems, the digital and intelligent management of forest resources is becoming more sophisticated. For example, smart forestry can manage, monitor, protect, and develop forest resources based on big data, the Internet of Things, artificial intelligence, and remote sensing. By leveraging modern remote sensing, data processing, and intelligent decision-making systems, it provides strong support for the precise monitoring, management, and protection of forest resources.

[0003] The digitalization and intelligent management of forest resources involves remote sensing and mapping of forest terrain. During this process, LiDAR (Light Detection and Ranging) technology is typically used to obtain elevation data for forest areas, allowing for terrain mapping based on this elevation data. However, because the forest floor is typically covered by trees, particularly in areas with dense and thick canopies, optical remote sensing equipment struggles to penetrate the treetops to reach the ground. This results in missing or inaccurate surface elevation information for the corresponding area, impacting the accuracy of forest terrain mapping. Summary of the Invention

[0004] In order to solve the problem in related technologies that the forest surface is generally covered by trees, especially in areas with dense and thick forest canopies, it is difficult for optical remote sensing equipment to penetrate the tree canopy to reach the ground, resulting in missing or inaccurate surface elevation information detection results for the corresponding area, affecting the accuracy of forest terrain mapping, the present invention provides a forest terrain remote sensing mapping method for smart forestry. The technical solutions adopted are as follows:

[0005] Acquire a remote sensing image of the forest area to be measured; the remote sensing image is an image obtained by photographing the forest area to be measured by partitioning the area using remote sensing technology;

[0006] Obtaining the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at a corresponding position in the remote sensing image, and establishing an elevation-grayscale relationship model based on the elevation value and the grayscale value of each monitoring station;

[0007] For each partition of the forest area to be measured, determining the vegetation coverage density of the partition based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition;

[0008] Determining a theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and correcting the measured elevation value based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value;

[0009] A seasonal weight is determined based on the temperature of the forest area to be measured, and a target elevation value of the partition is determined based on the seasonal weight and the weighted elevation value.

[0010] Correspondingly, the present invention also provides a forest terrain remote sensing mapping system for smart forestry, specifically comprising:

[0011] An image acquisition module is used to acquire a remote sensing image of the forest area to be measured; the remote sensing image is an image obtained by photographing the forest area to be measured by partitioning the area using remote sensing technology;

[0012] A model building module is used to obtain the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at the corresponding position of the remote sensing image, and to establish an elevation-grayscale relationship model based on the elevation value and the grayscale value of each monitoring station;

[0013] a data correction module, configured to determine, for each partition of the forest area to be measured, the vegetation coverage density of the partition based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition;

[0014] The data correction module is further configured to determine a theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and to correct the measured elevation value based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value;

[0015] The data correction module is further used to determine the seasonal weight based on the temperature of the forest area to be measured, and to determine the target elevation value of the partition based on the seasonal weight and the weighted elevation value.

[0016] The present invention may have some or all of the following beneficial effects:

[0017] In the forest terrain remote sensing mapping method for smart forestry provided by the present invention, a remote sensing image of the forest area to be measured is obtained; the remote sensing image is an image obtained by partitioning the forest area to be measured using remote sensing technology; the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at the corresponding position in the remote sensing image are obtained, and an elevation-grayscale relationship model is established based on the elevation value and grayscale value of each monitoring station; for each partition of the forest area to be measured, the vegetation coverage density of the partition is determined based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition; based on the elevation-grayscale relationship model, a theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition is determined, and the measured elevation value is corrected based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value; the seasonal weight is determined based on the temperature of the forest area to be measured, and the target elevation value of the partition is determined based on the seasonal weight and the weighted elevation value. The present invention establishes an elevation-grayscale relationship model based on the elevation values ​​of each monitoring station and the grayscale values ​​of the corresponding positions in the remote sensing image, so that the theoretical elevation value corresponding to the grayscale value of each pixel point can be obtained based on the elevation-grayscale relationship model; in addition, after obtaining the theoretical elevation value, the present invention also takes into account the influence of vegetation coverage and temperature on the elevation data, and preliminarily corrects the measured elevation value through the vegetation coverage and the theoretical elevation value to obtain a weighted elevation value, and further adjusts the weighted elevation value based on the seasonal weight corresponding to the temperature to obtain a target elevation value, which solves the problem of errors in the detection of surface elevation information due to the thick tree canopy in some areas making it difficult for optical remote sensing equipment to penetrate the tree canopy to reach the ground during forest remote sensing mapping, improves the accuracy of the elevation data, and thus helps to obtain more accurate terrain mapping results.

[0018] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of a remote sensing mapping method for forest terrain in smart forestry according to an exemplary embodiment of the present disclosure is shown;

[0021] Figure 2 A schematic diagram of a forest terrain remote sensing mapping system for smart forestry according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0022] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the remote sensing forest terrain mapping method for smart forestry proposed by the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0023] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0024] The specific scheme of the forest terrain remote sensing mapping method for smart forestry provided by the present invention is described in detail below with reference to the accompanying drawings.

[0025] See also Figure 1 , which shows a method flow chart of a forest terrain remote sensing mapping method for smart forestry provided by an embodiment of the present invention, such as Figure 1 As shown in the figure, the remote sensing mapping method of forest terrain for smart forestry specifically includes the following steps:

[0026] S110: Acquire a remote sensing image of the forest area to be measured; the remote sensing image is an image obtained by photographing the forest area to be measured by using remote sensing technology to divide the forest area into sections;

[0027] S120: Obtaining the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at the corresponding position in the remote sensing image, and establishing an elevation-grayscale relationship model based on the elevation value and grayscale value of each monitoring station;

[0028] S130: for each partition of the forest area to be measured, determining the vegetation coverage density of the partition based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition;

[0029] S140: Determine a theoretical elevation value corresponding to the grayscale value of each pixel in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and correct the measured elevation value based on the vegetation cover density and the theoretical elevation value to obtain a weighted elevation value;

[0030] S150: Determine a seasonal weight based on the temperature of the forest area to be measured, and determine a target elevation value of the partition based on the seasonal weight and the weighted elevation value.

[0031] The present invention establishes an elevation-grayscale relationship model based on the elevation values ​​of each monitoring station and the grayscale values ​​of the corresponding positions in the remote sensing image, so that the theoretical elevation value corresponding to the grayscale value of each pixel point can be obtained based on the elevation-grayscale relationship model; in addition, after obtaining the theoretical elevation value, the present invention also takes into account the influence of vegetation coverage and temperature on the elevation data, and preliminarily corrects the measured elevation value through the vegetation coverage and the theoretical elevation value to obtain a weighted elevation value, and further adjusts the weighted elevation value based on the seasonal weight corresponding to the temperature to obtain a target elevation value, which solves the problem of errors in the detection of surface elevation information due to the thick tree canopy in some areas making it difficult for optical remote sensing equipment to penetrate the tree canopy to reach the ground during forest remote sensing mapping, improves the accuracy of the elevation data, and thus helps to obtain more accurate terrain mapping results.

[0032] The following describes in detail the steps of the forest terrain remote sensing mapping method for smart forestry:

[0033] In step S110, a remote sensing image of the forest area to be measured is obtained; the remote sensing image is an image obtained by partitioning the forest area to be measured and photographing it using remote sensing technology.

[0034] In an embodiment of the present application, the above-mentioned forest area to be measured is an arbitrary forest area. The forest terrain remote sensing mapping method for smart forestry provided in the embodiment of the present application is used to correct the elevation value of the forest area to be measured obtained by remote sensing technology to improve the accuracy of terrain measurement and control of the forest area to be measured.

[0035] In the embodiments of the present application, the remote sensing images are images acquired through remote sensing technology that reflect objects and phenomena on the Earth's surface corresponding to the forest area being measured. Specifically, because the forest area being measured is generally large, and the resolution of aerial platform imaging equipment is limited, to ensure the quality of remote sensing images, the embodiments of the present application can be used to capture the forest area in sections by setting appropriate resolutions and altitudes. For example, during the sectioning process, the forest area can be divided into several relatively independent areas based on factors such as natural boundaries (e.g., rivers, ridgelines), vegetation type distribution, and management requirements (e.g., different logging areas, protected areas). The altitude can be selected based on the following: For larger areas with relatively flat terrain, a higher flight altitude can be selected to obtain a wider field of view and a larger range of images. For areas with complex terrain or special research needs, a lower flight altitude can be used to obtain clearer details. The resolution can be selected based on the following: Areas requiring macroscopic monitoring require lower resolution than areas requiring detailed analysis of vegetation species and tree health. The resolution of the former is sufficient to clearly display the general extent of the forest area and the overall vegetation cover, while the latter requires the ability to distinguish the morphology and subtle features of individual trees.

[0036] Specifically, the above-mentioned acquisition of remote sensing images of the forest area to be measured can be achieved as follows: using lidar technology, by emitting a laser beam and measuring the time it takes for it to be reflected back, three-dimensional information of objects on the ground and in the forest area to be measured is obtained, and high-precision point cloud data is generated; the point cloud data is denoised, resampled, triangulated, surface fitted, projected, and rasterized to convert it into a remote sensing image of the forest area to be measured.

[0037] In step S120, the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at the corresponding position in the remote sensing image are obtained, and an elevation-grayscale relationship model is established based on the elevation value and grayscale value of each monitoring station.

[0038] In the embodiment of the present application, the elevation value is the value of elevation data, which is important data used to describe the topography of the earth's surface and is the vertical distance from a ground point to the geoid or other reference surface.

[0039] In the embodiment of the present application, the grayscale value is a numerical value representing the grayscale level of the remote sensing image obtained in step S110.

[0040] In the embodiment of the present application, the height difference and vegetation coverage of the forest area to be measured at different locations will be reflected in the grayscale. For example, low-altitude areas usually have a warm and humid climate, which is suitable for the growth of various types of forests. Therefore, the vegetation grows lushly, the reflectivity of the forest is usually low, and the grayscale value of the remote sensing image is darker. As the altitude increases, the temperature decreases, the air becomes thinner, the reflectivity of the surface is relatively high, and the grayscale value of the remote sensing image is usually brighter. Therefore, the elevation value can be predicted by the grayscale value based on the relationship between the grayscale value and the elevation value. Specifically, the above-mentioned prediction of the elevation value based on the grayscale value can be achieved by establishing the above-mentioned elevation-grayscale relationship model.

[0041] For example, the elevation-grayscale relationship model can be established by setting up multiple monitoring stations in the forest area to be measured and obtaining the relationship between the elevation values ​​of each monitoring station and the grayscale values ​​of the corresponding positions in the remote sensing image. Specifically, the process can be implemented as follows: obtaining the elevation values ​​of each monitoring station and arranging the elevation values ​​in a preset order to obtain an elevation data sequence; determining the grayscale values ​​of each monitoring station at the corresponding positions in the remote sensing image to obtain a grayscale data sequence corresponding to the elevation data sequence; and determining a regression equation based on the elevation data sequence and the grayscale data sequence to obtain the elevation-grayscale relationship model.

[0042] The above-mentioned monitoring stations need to be set up at different heights as much as possible and ensure that the weather is clear during monitoring. In addition, it is also necessary to ensure that the monitoring stations are distributed in different locations of the forest area to be measured to avoid centralized setting in order to reduce measurement errors. Exemplarily, the setting of the above-mentioned monitoring stations can be implemented as follows: the entire forest area to be measured is divided into multiple blocks, and monitoring stations are set up at the lowest and highest points of each block. After the monitoring stations are set up, the elevation values ​​of each monitoring station can be further obtained by the following method: at the monitoring station, the height difference Δh between the monitoring station and the known reference point is measured by a level (theodolite) and a level rod; the sum of the elevation value H0 of the known reference point and the above-mentioned height difference H=H0+Δh is used as the elevation value of the monitoring station.

[0043] The above-mentioned process of obtaining the elevation values ​​of each monitoring station and arranging the elevation values ​​in a preset order to obtain an elevation data sequence can be specifically implemented as follows: After obtaining the elevation values ​​of each monitoring station, the elevation data of each monitoring station are arranged from low to high to obtain an elevation data sequence. .

[0044] The above determination of the grayscale value of each monitoring station at the corresponding position in the remote sensing image to obtain the grayscale data sequence corresponding to the elevation data sequence can be specifically implemented as follows: the position of the monitoring station is marked in the remote sensing image and the grayscale value of the remote sensing image at the position is obtained, and the grayscale values ​​are arranged according to the order of the monitoring stations corresponding to the elevation values ​​in the above elevation data series to obtain the elevation data sequence. The corresponding grayscale data sequence .

[0045] The above-mentioned determination of the regression equation based on the elevation data sequence and the grayscale data sequence to obtain the elevation-grayscale relationship model can be implemented as follows: calculating the slope and intercept of the regression equation by the least squares method based on the elevation data sequence and the grayscale data sequence; determining the local slope of the regression equation at different positions based on the elevation data sequence and the grayscale data sequence, determining the nonlinearity of the elevation data sequence and the grayscale data sequence based on the local slope, and taking the absolute value of the nonlinearity as the absolute value of the error term of the regression equation; determining the positive or negative of the error term based on the data distribution of the regression equation; substituting the slope, intercept and error term into the regression equation to obtain the elevation-grayscale relationship model.

[0046] With the above elevation data series And the above grayscale data sequence For example, the slope and intercept are calculated by the least squares method based on the elevation data sequence and the grayscale data sequence, and the corresponding regression equation is as follows:

[0047]

[0048] Among them, the above is the elevation value, is the grayscale value, Based on the above elevation data series And the above grayscale data sequence The slope of the regression equation is calculated. Based on the above elevation data series And the above grayscale data sequence Calculate the intercept of the regression equation, is the error term.

[0049] In addition, due to factors such as the randomness of the monitoring site locations and the irregular vegetation coverage at the same elevation, although there is a positive correlation between the above-mentioned elevation values ​​and the corresponding grayscale values, there may be nonlinear situations. In order to improve the accuracy of the above-mentioned elevation-grayscale relationship model, the embodiment of the present application introduces the above-mentioned error term to correct the regression equation so that the regression equation can better fit the relationship between grayscale and elevation.

[0050] Exemplarily, the above-mentioned determination of the local slope of the regression equation at different positions based on the elevation data sequence and the grayscale data sequence, and determination of the nonlinearity of the elevation data sequence and the grayscale data sequence based on the local slope can be achieved as follows: for adjacent elevation data in the elevation data sequence, calculate the first difference between adjacent elevation data, and the second difference between adjacent grayscale data corresponding to the elevation data sequence; calculate the ratio of the first difference and the second difference to obtain the local slope; calculate the third difference between each adjacent local slope, and determine the nonlinearity based on the sum of the third differences and the number of monitoring stations.

[0051] Specifically, the above elevation data series And the above grayscale data sequence For example, the specific calculation formula of the above local slope can be as follows:

[0052]

[0053] in, For the above elevation data series And the above grayscale data sequence In the The local slope at each location; and The above elevation data series are Middle elevation value and the i-1th elevation value, This is the first difference mentioned above; and are the above grayscale data sequences respectively The i-th grayscale value and the i-1-th grayscale value, This is the second difference mentioned above.

[0054] After obtaining the above local slope Then, based on the above elevation data sequence, And the above grayscale data sequence The local slopes at different positions determine the nonlinearity between the two data series. Specifically, the nonlinearity can be calculated by the following formula:

[0055]

[0056] in, For the above elevation data series And the above grayscale data sequence The nonlinearity between is the number of monitoring stations; By comparing the above elevation data series and grayscale data sequence All local slopes at each location are used to measure the degree of linearity between the two. If they are linearly related, then the elevation data series above and grayscale data sequence The corresponding local slopes are the same, and the value of U tends to 0; if the correlation is nonlinear, the larger the value of U, the higher the elevation data sequence. and grayscale data sequence The greater the nonlinearity between them.

[0057] The above elevation data series and grayscale data sequence The nonlinear relationship between ) All data points pass through the fitting curve corresponding to the regression equation. In order to make more data points fall on the fitting curve, the fitting curve can be moved in the direction of more data points that meet the regression equation to improve the fitting effect of the regression equation. Therefore, after the nonlinearity (that is, the size of the error term) is calculated by the above method, the embodiment of the present application also needs to determine the positive and negative of the error term through the data distribution to determine the moving direction of the fitting curve. Exemplarily, the process can be implemented as follows: determine the first data point of the non-overfitted curve located in the area above the fitting curve corresponding to the regression equation, and the second data point of the non-overfitted curve located in the area below the fitting curve; if the number of the first data points is greater than the number of the second data points, the error term takes a negative value; if the number of the first data points is less than the number of the second data points, the error term takes a positive value. Specifically, the positive and negative of the error term can be determined by the following formula:

[0058]

[0059] in, The difference in size of the data points that do not pass the regression equation is used to describe the size of the deviation of the data points from the trend or model represented by the regression equation; is the number of data points in the area above the fitting curve that does not pass the regression equation (i.e., the number of the first data points mentioned above); is the number of data points in the area below the fitting curve that does not pass the regression equation (i.e., the number of the second data points mentioned above); If is positive, then it proves , the regression equation should shift downward and the error term should be less than 0; If negative, then it proves , the regression equation should shift upward and the error term should be greater than 0.

[0060] Based on the above elevation data series and grayscale data sequence Calculate the slope of the regression equation and intercept , and the error term is determined by the above process After the magnitude and sign of ,intercept and error terms Substitute into the above regression equation: , a more accurate elevation-grayscale relationship model can be obtained.

[0061] In step S130, for each partition of the forest area to be measured, the vegetation coverage density of the partition is determined based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition.

[0062] In the embodiment of the present application, the above-mentioned vegetation cover density is used to measure the degree or density of vegetation cover in each sub-area of ​​the forest area to be measured. Since the vegetation cover density will affect the distribution of grayscale in the remote sensing image, for example, in areas with high vegetation cover density, the leaves fully cover the ground surface, the degree of surface exposure is small, and the corresponding remote sensing image grayscale distribution is deep and uniform; conversely, in areas with low vegetation cover density, the degree of surface exposure is large, and the corresponding remote sensing image grayscale distribution is shallow and uniform. Therefore, the vegetation cover density of the corresponding location can be determined based on the grayscale value of each pixel point in the remote sensing image.

[0063] Exemplarily, the above-mentioned determination of the vegetation coverage density of each partition of the forest area to be measured based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition can be achieved as follows: for each partition of the forest area to be measured, clustering processing is performed on the grayscale value of each pixel point in the remote sensing image corresponding to the partition to obtain multiple result cluster areas; based on the average grayscale value of each result cluster area, it is determined whether each result cluster area is a bare surface area; based on the area of ​​the bare surface area and the grayscale value of each pixel point in the non-bare surface area, the vegetation coverage density is determined.

[0064] Specifically, for each partition of the forest area to be tested, based on the relationship between the above-mentioned vegetation coverage density and the grayscale distribution in the remote sensing image, the grayscale in each partition can be clustered by a clustering method to obtain multiple result cluster areas, and the grayscale in the same result cluster area is more similar.

[0065] After obtaining multiple result cluster areas through clustering operations, illustratively, the above-mentioned determination of whether each result cluster area is a bare surface area based on the average grayscale value of each result cluster area can be implemented as follows: for each result cluster area, determine the first average grayscale value of the current result cluster area and the second average grayscale value of other result cluster areas except the current result cluster area; determine the surface exposure value of the current result cluster area based on the first average grayscale value, the second average grayscale value, the near-infrared band reflectivity of the current result cluster area, and the visible light band reflectivity of the current result cluster area; if the surface exposure value is less than a preset threshold, determine the current result cluster area as a non-bare surface area; otherwise, determine the current result cluster area as a bare surface area.

[0066] Specifically, the above-mentioned surface exposure value can be determined by the following formula:

[0067]

[0068] in, Used to describe a result cluster area The possibility of exposed surface area (i.e. the surface exposure value mentioned above); The result cluster area The average gray value of (that is, the first average gray value mentioned above), For any other result cluster area except this result cluster area The average gray value of (that is, the second average gray value mentioned above); The number of resulting cluster regions obtained for clustering. The result cluster area The near-infrared reflectivity, The result cluster area Reflectivity in the visible light band.

[0069] In the above formula, the above Represents the result cluster area The average grayscale difference with other result cluster areas is due to the fact that the overall grayscale of the bare surface is lighter than that of the vegetation cover (the grayscale value is larger). The larger the value of The higher the possibility of exposed surface areas; Represents the result cluster area Regarding the reflection characteristics of light, the reflectivity of vegetation in the red (visible) band and the near-infrared band is higher than that of the exposed surface area. The larger the value of The higher the probability of it being a bare surface area.

[0070] After determining the surface exposure value of any result cluster area through the above formula, the embodiment of the present disclosure can further normalize the calculated surface exposure value to the range of [0,1], and determine whether the corresponding result cluster area is a bare surface area by setting a preset threshold. Taking the preset threshold as 0.85 as an example, The resulting cluster area is identified as the bare surface area.

[0071] After determining the exposed surface area in any partition of the forest area to be measured, illustratively, the above-mentioned determination of the vegetation cover density based on the area of ​​the exposed surface area and the grayscale value of each pixel in the non-exposed surface area can be implemented as follows: determine the area ratio of the non-exposed surface area in the partition based on the area of ​​the partition and the area of ​​the bare surface area; calculate the fourth difference of the grayscale values ​​of any two pixels in the non-exposed surface area, and determine the uniformity of the grayscale value of the non-exposed surface area based on the sum of the fourth difference; determine the vegetation cover density based on the area ratio of the non-exposed surface area in the partition and the uniformity of the grayscale value of the non-exposed surface area.

[0072] Specifically, the vegetation coverage density of any subarea in the above-mentioned forest area to be tested can be determined by the following formula:

[0073]

[0074] in, Represents the vegetation coverage density of the current zone; is the actual area of ​​the current partition, is the area of ​​exposed surface in the current zone; Represents the grayscale value of the a-th pixel in the non-bare surface area of ​​the current partition; represents the grayscale value of the bth pixel in the non-bare surface area of ​​the current partition; A represents the total number of pixels in the non-bare surface area of ​​the current partition.

[0075] In the above formula, the above Indicates the area ratio of non-bare surface in the current partition. The larger the value of the area ratio, the smaller the area ratio of bare surface, and the higher the vegetation coverage density of the current partition. Represents the uniformity of the grayscale of all pixels in the non-bare surface area within the current partition. The smaller the value of , the more uniform the grayscale of all pixels in the non-bare surface area within the current partition, and the greater the corresponding vegetation coverage density; is a normalization function used to convert the calculated vegetation coverage density Normalized to the range [0,1].

[0076] In step S140, the theoretical elevation value corresponding to the grayscale value of each pixel in the remote sensing image corresponding to the partition is determined based on the elevation-grayscale relationship model, and the measured elevation value is corrected based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value.

[0077] In an embodiment of the present application, the above-mentioned theoretical elevation value is the elevation value obtained by inputting the grayscale value of any pixel point in the remote sensing image into the above-mentioned elevation-grayscale relationship model; the above-mentioned measured elevation value is the elevation value of the corresponding position of the forest area to be measured obtained by remote sensing technology; the above-mentioned weighted elevation value is the elevation value obtained by correcting the measured elevation value based on the above-mentioned vegetation cover density and the theoretical elevation value.

[0078] In an embodiment of the present application, since vegetation coverage will affect the accuracy of the elevation data measurement results of the forest area to be measured, that is, the greater the vegetation coverage density of a certain partition, the more seriously the elevation value of the partition is affected by the vegetation coverage, the greater the deviation of the corresponding elevation data, and the greater the degree of correction required. Therefore, when predicting the elevation value through the grayscale value, the vegetation coverage density can be used as an influencing factor. The grayscale conditions of the remote sensing images in different partitions of the forest area to be measured are compared through the above-mentioned elevation-grayscale model, and the vegetation coverage density of different partitions is calculated at the same time. The elevation correction factor is calculated according to the grayscale difference and vegetation coverage density of different partitions, and the measured elevation value is corrected by the calculated elevation correction factor.

[0079] Exemplarily, the above-mentioned determination of the theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and correction of the measured elevation value based on the vegetation cover density and the theoretical elevation value to obtain the weighted elevation value can be achieved as follows: inputting the grayscale value of each pixel point in the remote sensing image corresponding to the partition into the elevation-grayscale relationship model, and determining the theoretical elevation value corresponding to the input grayscale value based on the regression equation of the elevation-grayscale relationship model; obtaining the measured elevation value obtained by measurement corresponding to the theoretical elevation value; determining the weights of the theoretical elevation value and the measured elevation value based on the vegetation cover density, and determining the weighted elevation value based on the theoretical elevation value, the measured elevation value, and the weights of the theoretical elevation value and the measured elevation value.

[0080] Specifically, the weighted elevation value can be determined by the following formula:

[0081]

[0082] in, A pixel in the current partition The weighted elevation value of the corresponding position in the forest area to be measured, To use laser radar technology to detect pixel The measured elevation value obtained at the corresponding position of the forest area to be measured, is the pixel point obtained through the elevation-grayscale relationship model The theoretical elevation value of the corresponding position in the forest area to be measured; It is the vegetation coverage density of the current partition.

[0083] In the above formula, the density of vegetation coverage will affect the measured elevation value. Therefore, the greater the vegetation coverage density, the smaller the confidence level should be set for the measured elevation value. The larger the confidence level, the higher the confidence level. is a weight pair and Different weights are set to perform weighted correction on the elevation value of any pixel point at the corresponding position in the forest area to be measured, thereby improving the accuracy of the elevation data of the forest area to be measured.

[0084] In step S150, the seasonal weight is determined based on the temperature of the forest area to be measured, and the target elevation value of the partition is determined based on the seasonal weight and the weighted elevation value.

[0085] In an embodiment of the present application, the weighted elevation value determined by the above steps S110 to S140 is an elevation value calculated based on a remote sensing image in which trees and the ground surface are in a natural state under ideal weather conditions. However, in actual scenarios, there may be large differences in the vegetation coverage rate of the forest area to be measured in different seasons. Therefore, when the method of the above steps S110 to S140 does not specify the measurement season, the weighted elevation value obtained based on the vegetation coverage density correction needs to be further corrected to further improve the accuracy of the target elevation value finally obtained. The above seasonal weight is used for the above-mentioned further correction of the weighted elevation value.

[0086] Since the impact of seasons on vegetation cover is mainly reflected in temperature, the above-mentioned seasonal temperature can be set based on the temperature. For example, the above-mentioned determination of seasonal weights based on the temperature of the forest area to be measured, and determination of target elevation values ​​of the partitions based on seasonal weights and weighted elevation values ​​can be achieved as follows: obtain the average temperature of the forest area to be measured in the last month of each quarter, and determine the seasonal weights corresponding to each quarter based on the obtained average temperature; correct the weighted elevation values ​​of the forest area to be measured corresponding to the quarter based on the seasonal weights to obtain the target elevation values.

[0087] Specifically, the average temperature of the last month of each quarter in the past year in the forest area to be tested can be obtained. , and based on the average temperature of the last month of each season The seasonal weights are determined as follows:

[0088]

[0089] in, For the forest area to be tested Seasonal weights for each quarter; For the Average temperature in the last month of the quarter; For the The average temperature in the last month of the quarter.

[0090] Furthermore, the above-mentioned seasonal weight-based correction of the weighted elevation value of the forest area to be measured corresponding to the season can be achieved as follows:

[0091]

[0092] in, A pixel point in any partition of the forest area to be tested The target elevation value of the corresponding position; Pixel The corresponding position is Weighted elevation values ​​under each quarter; For the forest area to be tested Seasonal weights for each quarter.

[0093] In an embodiment of the present application, after obtaining the target elevation values ​​at various locations in the forest area to be measured through the above process, the embodiment of the present application can further perform the following processing on the obtained target elevation values ​​to accurately monitor, manage and protect forestry resources through the target elevation values: the target elevation values ​​at different locations in the forest area to be measured obtained through the above steps are represented in the form of a grid, and each grid unit (pixel) contains a height value; the grid units corresponding to each target elevation value are organized into a regular grid, illustratively, each grid can represent a 1m×1m area, and a corresponding digital elevation model is generated; for each target elevation point in the digital elevation model, a corresponding coordinate point (X, Y, Z) is generated in three-dimensional space, and the generated coordinate points are connected through modeling software to obtain a corresponding three-dimensional grid model, and the three-dimensional grid model is used to assist in the accurate monitoring, management and protection of forestry resources; wherein, the above-mentioned three-dimensional grid model can represent the terrain undulations of the forest area to be measured.

[0094] The present invention establishes an elevation-grayscale relationship model based on the elevation values ​​of each monitoring station and the grayscale values ​​of the corresponding positions in the remote sensing image, so that the theoretical elevation value corresponding to the grayscale value of each pixel point can be obtained based on the elevation-grayscale relationship model; in addition, after obtaining the theoretical elevation value, the present invention also takes into account the influence of vegetation coverage and temperature on the elevation data, and preliminarily corrects the measured elevation value through the vegetation coverage and the theoretical elevation value to obtain a weighted elevation value, and further adjusts the weighted elevation value based on the seasonal weight corresponding to the temperature to obtain a target elevation value, which solves the problem of errors in the detection of surface elevation information due to the thick tree canopy in some areas making it difficult for optical remote sensing equipment to penetrate the tree canopy to reach the ground during forest remote sensing mapping, improves the accuracy of the elevation data, and thus helps to obtain more accurate terrain mapping results.

[0095] The above mainly introduces the solution provided by the embodiment of the present invention from the perspective of method. In order to realize the above functions, it includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0096] The embodiment of the present disclosure provides a forest terrain remote sensing mapping system for smart forestry. Figure 2As shown, the forest terrain remote sensing mapping system 400 for smart forestry may include an image acquisition module 410, a model building module 420, and a data correction module 430, wherein:

[0097] The image acquisition module 410 can be used to acquire remote sensing images of the forest area to be measured; the remote sensing images are images obtained by partitioning the forest area to be measured using remote sensing technology;

[0098] The model building module 420 can be used to obtain the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at the corresponding position in the remote sensing image, and establish an elevation-grayscale relationship model based on the elevation value and grayscale value of each monitoring station;

[0099] The data correction module 430 can be used to determine the vegetation coverage density of each partition of the forest area to be measured based on the grayscale value of each pixel in the remote sensing image corresponding to the partition;

[0100] The data correction module 430 is further configured to determine the theoretical elevation value corresponding to the grayscale value of each pixel in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and to correct the measured elevation value based on the vegetation cover density and the theoretical elevation value to obtain a weighted elevation value.

[0101] The data correction module 430 is further used to determine the seasonal weight based on the temperature of the forest area to be measured, and to determine the target elevation value of the partition based on the seasonal weight and the weighted elevation value.

[0102] In an embodiment of the present application, the above-mentioned model building module is specifically used to: obtain the elevation values ​​of each monitoring station, and arrange the elevation values ​​in a preset order to obtain an elevation data sequence; determine the grayscale value of each monitoring station at the corresponding position of the remote sensing image to obtain a grayscale data sequence corresponding to the elevation data sequence; determine the regression equation based on the elevation data sequence and the grayscale data sequence to obtain an elevation-grayscale relationship model.

[0103] In an embodiment of the present application, the above-mentioned model building module is specifically used to: calculate the slope and intercept of the regression equation based on the elevation data sequence and the grayscale data sequence by the least squares method; determine the local slope of the regression equation at different positions based on the elevation data sequence and the grayscale data sequence, determine the nonlinearity of the elevation data sequence and the grayscale data sequence based on the local slope, and use the absolute value of the nonlinearity as the absolute value of the error term of the regression equation; determine the positive or negative sign of the error term based on the data distribution of the regression equation; substitute the slope, intercept and error term into the regression equation to obtain the elevation-grayscale relationship model.

[0104] In an embodiment of the present application, the above-mentioned model building module is specifically used to: calculate the first difference between adjacent elevation data in the elevation data sequence, and the second difference between adjacent grayscale data corresponding to the elevation data sequence; calculate the ratio of the first difference and the second difference to obtain the local slope; calculate the third difference between each adjacent local slope, and determine the nonlinearity based on the sum of the third differences and the number of monitoring stations.

[0105] In an embodiment of the present application, the above-mentioned data correction module is specifically used to: cluster the grayscale values ​​of each pixel in the remote sensing image corresponding to each partition of the forest area to be measured to obtain multiple result cluster areas; determine whether each result cluster area is a bare surface area based on the average grayscale value of each result cluster area; determine the vegetation coverage density based on the area of ​​the bare surface area and the grayscale value of each pixel in the non-bare surface area.

[0106] In an embodiment of the present application, the above-mentioned data correction module is specifically used to: determine, for each result cluster area, the first average grayscale value of the current result cluster area and the second average grayscale value of other result cluster areas except the current result cluster area; determine the surface exposure value of the current result cluster area based on the first average grayscale value, the second average grayscale value, the near-infrared band reflectivity of the current result cluster area and the visible light band reflectivity of the current result cluster area; if the surface exposure value is less than a preset threshold, determine the current result cluster area as a non-exposed surface area; otherwise, determine the current result cluster area as an exposed surface area.

[0107] In an embodiment of the present application, the above-mentioned data correction module is specifically used to: determine the area ratio of the non-bare surface area in the partition based on the area of ​​the partition and the area of ​​the bare surface area; calculate the fourth difference of the grayscale values ​​of any two pixels in the non-bare surface area, and determine the uniformity of the grayscale value of the non-bare surface area based on the sum of the fourth difference; determine the vegetation coverage density based on the area ratio of the non-bare surface area in the partition and the uniformity of the grayscale value of the non-bare surface area.

[0108] In an embodiment of the present application, the above-mentioned data correction module is specifically used to: input the grayscale value of each pixel point in the remote sensing image corresponding to the partition into the elevation-grayscale relationship model, and determine the corresponding theoretical elevation value based on the regression equation; obtain the measured elevation value corresponding to the theoretical elevation value; determine the weight of the theoretical elevation value and the measured elevation value based on the vegetation cover density, and determine the weighted elevation value based on the theoretical elevation value, the measured elevation value, and the weight of the theoretical elevation value and the measured elevation value. In an embodiment of the present application, the above-mentioned data correction module is specifically used to: obtain the average temperature of the forest area to be measured in the last month of each quarter, and determine the seasonal weight corresponding to each quarter based on the average temperature; correct the weighted elevation value of the forest area to be measured corresponding to the quarter based on the seasonal weight to obtain the target elevation value.

[0109] The embodiment of the present invention can divide the forest terrain remote sensing mapping system for smart forestry into functional modules according to the above-mentioned method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0110] In addition, the specific implementation details of the above-mentioned forest terrain remote sensing mapping system for smart forestry have been described in detail in the corresponding position of the forest terrain remote sensing mapping method for smart forestry, so they will not be repeated here.

[0111] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A remote sensing mapping method for forest terrain for smart forestry, characterized in that: The method comprises: Acquire a remote sensing image of the forest area to be measured; the remote sensing image is an image obtained by photographing the forest area to be measured by partitioning the area using remote sensing technology; Obtaining the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at a corresponding position in the remote sensing image, and establishing an elevation-grayscale relationship model based on the elevation value and the grayscale value of each monitoring station; For each partition of the forest area to be measured, determining the vegetation coverage density of the partition based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition; Determine the theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and correct the measured elevation value based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value; the measured elevation value is the elevation value of the corresponding position of the forest area to be measured obtained by remote sensing technology; A seasonal weight is determined based on the temperature of the forest area to be measured, and a target elevation value of the partition is determined based on the seasonal weight and the weighted elevation value.

2. The forest terrain remote sensing mapping method for smart forestry according to claim 1 is characterized in that: The step of obtaining the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at a corresponding position in the remote sensing image, and establishing an elevation-grayscale relationship model based on the elevation value and the grayscale value of each monitoring station, includes: Acquiring the elevation values ​​of each of the monitoring stations, and arranging the elevation values ​​in a preset order to obtain an elevation data sequence; Determine the grayscale value of each monitoring station at a corresponding position in the remote sensing image to obtain a grayscale data sequence corresponding to the elevation data sequence; A regression equation is determined based on the elevation data sequence and the grayscale data sequence to obtain the elevation-grayscale relationship model.

3. The forest terrain remote sensing mapping method for smart forestry according to claim 2, characterized in that: Determining a regression equation based on the elevation data sequence and the grayscale data sequence to obtain the elevation-grayscale relationship model includes: Calculating the slope and intercept of the regression equation based on the elevation data sequence and the grayscale data sequence by the least squares method; Determining local slopes of the regression equation at different positions based on the elevation data sequence and the grayscale data sequence, determining nonlinearity of the elevation data sequence and the grayscale data sequence based on the local slopes, and using the absolute value of the nonlinearity as the absolute value of the error term of the regression equation; Determining the sign of the error term based on the data distribution of the regression equation; Substituting the slope, the intercept and the error term into the regression equation, the elevation-grayscale relationship model is obtained.

4. The forest terrain remote sensing mapping method for smart forestry according to claim 3 is characterized in that: The determining of the local slopes of the regression equation at different positions based on the elevation data sequence and the grayscale data sequence, and determining the nonlinearity of the elevation data sequence and the grayscale data sequence based on the local slopes, comprises: For adjacent elevation data in the elevation data sequence, calculating a first difference between the adjacent elevation data and a second difference between adjacent grayscale data corresponding to the elevation data sequence; Calculating a ratio of the first difference to the second difference to obtain the local slope; A third difference between adjacent local slopes is calculated, and the nonlinearity is determined based on a sum of the third differences and the number of the monitoring stations.

5. The forest terrain remote sensing mapping method for smart forestry according to claim 3 is characterized in that: The determining, for each partition of the forest area to be measured, the vegetation coverage density of the partition based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition, includes: For each partition of the forest area to be measured, clustering the grayscale value of each pixel point in the remote sensing image corresponding to the partition to obtain a plurality of result cluster areas; determining whether each of the result cluster regions is a bare surface region based on an average grayscale value of each of the result cluster regions; The vegetation coverage density is determined based on the area of ​​the bare surface area and the grayscale value of each of the pixel points in the non-bare surface area.

6. The forest terrain remote sensing mapping method for smart forestry according to claim 5, characterized in that: The determining whether each of the result cluster areas is a bare surface area based on the average grayscale value of each of the result cluster areas includes: For each of the result cluster regions, determining a first average grayscale value of the current result cluster region and a second average grayscale value of other result cluster regions except the current result cluster region; Determine the surface exposure value of the current result cluster area based on the first average grayscale value, the second average grayscale value, the near-infrared band reflectance of the current result cluster area, and the visible light band reflectance of the current result cluster area; If the surface exposure value is less than a preset threshold, the current result cluster area is determined to be the non-exposed surface area; otherwise, the current result cluster area is determined to be the exposed surface area.

7. The forest terrain remote sensing mapping method for smart forestry according to claim 6, characterized in that: The determining of the vegetation coverage density based on the area of ​​the bare surface area and the grayscale value of each pixel point in the non-bare surface area includes: Determining an area ratio of the non-bare surface area in the partition based on the area of ​​the partition and the area of ​​the exposed surface area; Calculating a fourth difference between the grayscale values ​​of any two pixels in the non-bare surface area, and determining a uniformity of the grayscale value in the non-bare surface area based on a sum of the fourth differences; The vegetation coverage density is determined based on the area ratio of the non-bare surface area in the partition and the uniformity of the grayscale value of the non-bare surface area.

8. The forest terrain remote sensing mapping method for smart forestry according to claim 7, characterized in that: The method of determining a theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition based on the elevation-grayscale relationship model, and correcting the measured elevation value based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value includes: Inputting the grayscale value of each pixel point in the remote sensing image corresponding to the partition into the elevation-grayscale relationship model, and determining the corresponding theoretical elevation value based on the regression equation; Obtaining the measured elevation value corresponding to the theoretical elevation value; The weights of the theoretical elevation value and the measured elevation value are determined based on the vegetation coverage density, and the weighted elevation value is determined based on the theoretical elevation value, the measured elevation value, and the weights of the theoretical elevation value and the measured elevation value.

9. The forest terrain remote sensing mapping method for smart forestry according to claim 8, characterized in that: The step of determining a seasonal weight based on the temperature of the forest area to be measured, and determining a target elevation value of the partition based on the seasonal weight and the weighted elevation value, includes: Obtaining the average temperature of the forest area to be tested in the last month of each quarter, and determining the seasonal weight corresponding to each quarter based on the average temperature; The weighted elevation value of the forest area to be measured corresponding to the quarter is corrected based on the seasonal weight to obtain the target elevation value.

10. A forest terrain remote sensing mapping system for smart forestry, characterized by: The system comprises: An image acquisition module is used to acquire a remote sensing image of the forest area to be measured; the remote sensing image is an image obtained by photographing the forest area to be measured by partitioning the area using remote sensing technology; A model building module is used to obtain the elevation value of each monitoring station in the forest area to be measured and the grayscale value of each monitoring station at the corresponding position of the remote sensing image, and to establish an elevation-grayscale relationship model based on the elevation value and the grayscale value of each monitoring station; a data correction module, configured to determine, for each partition of the forest area to be measured, the vegetation coverage density of the partition based on the grayscale value of each pixel point in the remote sensing image corresponding to the partition; The data correction module is further configured to determine, based on the elevation-grayscale relationship model, a theoretical elevation value corresponding to the grayscale value of each pixel point in the remote sensing image corresponding to the partition, and to correct the measured elevation value based on the vegetation coverage density and the theoretical elevation value to obtain a weighted elevation value; the measured elevation value is the elevation value of the corresponding position of the forest area to be measured obtained by remote sensing technology; The data correction module is further used to determine the seasonal weight based on the temperature of the forest area to be measured, and to determine the target elevation value of the partition based on the seasonal weight and the weighted elevation value.

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