A method and system for measuring forest fuel load

Abstract

The present application discloses a method and system for measuring forest fuel load. The method includes: segmenting the images respectively corresponding to each region of the forest according to the RGB ranges of forest fuels at different levels to obtain the images respectively corresponding to different levels of each region of the forest; calculating the parameter information of different levels of each region of the forest based on the images respectively corresponding to different levels of each region of the forest; calculating the biomass information of different levels of each region of the forest by using biomass models of different levels based on the parameter information sets of different levels of each region of the forest; obtaining the fuel load of different levels of each region of the forest based on the biomass information of different levels of each region of the forest; and calculating the fuel load of each region of the forest based on the fuel load of different levels of each region of the forest. By using the solution of the present application, it is possible to be unaffected by environmental factors and the influence of the canopy layer on data acquisition of other levels, and it takes a short time and has a small workload.

Description

Technical Field

[0001] This application generally relates to the technical field of estimating combustible load. More specifically, this application relates to a method and system for calculating forest combustible load. Background Art

[0002] Forest combustible load refers to the oven-dry weight of all forest combustibles per unit area. Forest combustible load has a significant impact on the risk prevention and control of forest fires. When the combustible load in the forest reaches a relatively high level, major and extremely large forest fires may occur. Once too much combustible accumulates and catches fire, the fire will be extremely fierce and difficult to control. Moreover, high-load combustibles may also lead to the occurrence of crown fires, which are extremely destructive and cause devastating damage to the forest ecosystem. Therefore, it is necessary to effectively calculate the forest combustible load and timely clean up the combustibles according to the calculation results to reduce the risk of fire occurrence.

[0003] In the prior art, forest combustible load is mainly calculated by the plot survey method and the remote sensing estimation method. The plot survey method is to collect all forest combustibles in the selected forest plot and then take them to the laboratory to be dried to oven-dry and weighed manually or calculated using a mathematical model. However, using the plot survey method is extremely laborious, especially in large areas of forests. Collecting, drying, and weighing all combustibles is very time-consuming and laborious. Moreover, during the collection process, it is easy to miss or duplicate the collection, thus affecting the accuracy of the results. At the same time, the spatial representativeness is limited. The results of one plot are difficult to represent the situation of the entire forest area because the distribution of forest combustibles is highly heterogeneous in space. Even if multiple plots are set up, it is difficult to fully cover all the changing situations. The remote sensing estimation method is to use satellite or aerial optical sensors to obtain the spectral images of the forest and estimate the combustible load based on the differences in the reflection characteristics of different vegetation types and combustibles in the optical bands (such as visible light, near-infrared band). However, the data obtained by using the remote sensing estimation method has low accuracy, and the data acquisition is extremely affected by weather, time, etc.

[0004] In view of this, there is an urgent need to provide a forest combustible load calculation solution that is not affected by environmental factors during the forest combustible load calculation process, requires a short time, has a small workload, and is simple and convenient to operate. Summary of the Invention

[0005] To solve at least one or more of the above-mentioned technical problems, this application proposes a forest combustible load calculation solution in multiple aspects.

[0006] In a first aspect, the present application provides a method for measuring forest fuel load, including: segmenting the images respectively corresponding to each forest area according to the RGB ranges of forest fuels at different levels to obtain the images respectively corresponding to different levels of each forest area; calculating the parameter information of different levels of each forest area based on the images respectively corresponding to different levels of each forest area; calculating the biomass information of different levels of each forest area by using biomass models of different levels based on the parameter information sets of different levels of each forest area; obtaining the fuel load of different levels of each forest area based on the biomass information of different levels of each forest area; calculating the fuel load of each forest area based on the fuel load of different levels of each forest area.

[0007] In some embodiments, the different levels include the tree layer, the shrub layer, the herb layer, and the surface litter and humus layer.

[0008] In some embodiments, the parameter information of different levels includes: the average height of the tree layer, the average diameter at breast height of the tree layer, the number of trees in the tree layer, the canopy density of the tree layer, the average height of the shrub layer, the average basal diameter of the shrub layer, the canopy density of the shrub layer, the average height of the herb layer, and the canopy density of the herb layer.

[0009] In some embodiments, in the process of calculating the average height of the tree layer, the average height of the shrub layer, and the average height of the herb layer, the following steps are performed: successively performing grayscale processing and image binarization processing on the image corresponding to the level for which the average height needs to be calculated; finding all the contours in the image after grayscale processing and binarization processing; using a preset index to match the reference object contour among all the contours; obtaining the pixel height of the reference object contour; obtaining the average height corresponding to the corresponding level based on the pixel height of the reference object contour.

[0010] In some embodiments, obtaining the average height corresponding to the corresponding level based on the pixel height of the reference object contour includes: traversing the other contours except the reference object contour among all the contours to obtain the pixel heights of the other contours except the reference object contour; calculating the ratio between the pixel height of each other contour and the pixel height of the reference object contour; obtaining the actual height of each other contour through the actual height of the reference object and the ratio; obtaining the average height corresponding to the corresponding level by calculating the average value of the actual heights of each other contour.

[0011] In some embodiments, in the process of calculating the average diameter at breast height of the tree layer, the following steps are performed: obtaining all the contours in the image based on the image corresponding to the tree layer, where all the contours include a reference object contour and a plurality of non-reference object contours; obtaining the ratio between the pixel width of the reference object contour and the pixel width of each non-reference object contour; obtaining the actual diameter at breast height of each non-reference object contour through the actual diameter at breast height of the reference object and the ratio; and obtaining the average diameter at breast height of the tree layer by calculating the average value of the actual diameters at breast height of each non-reference object contour.

[0012] In some embodiments, in the process of calculating the canopy density of the tree layer, the canopy density of the shrub layer, and the canopy density of the herb layer, the following steps are performed: sequentially performing graying processing, contrast enhancement processing, and image binarization processing on the image corresponding to the layer for which the canopy density needs to be calculated; and calculating the ratio of the number of white pixels to the total number of pixels in the image that has undergone graying processing, contrast enhancement processing, and image binarization processing to obtain the canopy density of the corresponding layer.

[0013] In some embodiments, the parameter information of different layers further includes the moisture content of combustibles in different layers. In the process of calculating the moisture content of combustibles in different layers of each region of the forest, the following steps are performed: converting the images corresponding to different layers of each region of the forest into the HSV color space; extracting the regions in the HSV color space that satisfy the set HSV threshold range; calculating the ratio of the number of pixels in the extracted region to the total number of pixels in its corresponding image, and obtaining the moisture content of the combustibles in the corresponding layer based on this ratio.

[0014] In some embodiments, the biomass information of different layers includes: the biomass of the tree layer trunk, the biomass of the tree layer branches, the biomass of the tree layer leaves, the biomass of the shrub layer branches, the biomass of the shrub layer leaves, the biomass of the herb layer, and the biomass of the surface litter and humus layer; using a first calculation formula to obtain the fuel load of the tree layer in each region of the forest based on the biomass of the tree layer trunk, the biomass of the tree layer branches, and the biomass of the tree layer leaves. The first calculation formula is: M1 = [W 乔干 + W 乔枝 + W 乔叶 × n1 × (1 - P1), where W 乔干 is the biomass of the tree layer trunk, W 乔干 = 2.52 + 0.12(D 2 × h1), W 乔枝 is the biomass of the tree layer branches, W 乔枝 = 0.06(D 2 × h1) 0.73 ,W 乔叶 is the biomass of the tree layer leaves, W 乔叶 = 17.459 × h1 0.482, h1 is the average height of the tree layer, D is the average diameter at breast height of the tree layer, n1 is the number of trees in the tree layer, and P1 is the moisture content of the tree layer;

[0015] The second calculation formula is used to obtain the fuel load of the shrub layer in each area of the forest based on the branch biomass and leaf biomass of the shrub layer. The second calculation formula is: M2 = [W 灌枝 +W 灌叶 ×S 灌 ×U 灌 ×(1 - P2), where W 灌枝 is the branch biomass of the shrub layer, W 灌枝 = 0.28(B 2 ×h2) 0.74 , W 灌叶 is the leaf biomass of the shrub layer, W 灌叶 = 10.463×h2 0.476 , h2 is the average height of the shrub layer, B is the average base diameter of the shrub layer, S 灌 is the area of the shrub layer, U 灌 is the canopy density of the shrub layer, and P2 is the moisture content of the shrub layer; The third calculation formula is used to obtain the fuel load of the herb layer in each area of the forest based on the herb biomass. The third calculation formula is: M3 = W 草 ×(1 - P3), W 草 is the herb biomass, W 草 = 0.40×[(S 草 ×U 草 ) 2 ×h3] 0.56 , S 草 is the area of the herb layer, U 草 is the canopy density of the herb layer, h3 is the average height of the herb layer, and P3 is the moisture content of the herb layer; The fourth calculation formula is used to obtain the fuel load of the surface litter and humus layer in each area of the forest based on the surface litter and humus layer biomass. The fourth calculation formula is: M4 = W 总 ×S 总 ×P4, W 总 is the surface litter and humus layer biomass, W 总 = h 腐 +h 地 ×t, h 腐 is the thickness of the humus, h 地 is the thickness of the surface litter, t is the number of years, S 总 is the area of the surface litter and humus layer, and P4 is the moisture content of the surface litter and humus layer.

[0016] In a second aspect, the present application provides a system for measuring forest fuel load, which uses the method for measuring forest fuel load as described in any of the embodiments of the first aspect to measure the forest fuel load. The system includes: an image processing module, configured to perform segmentation processing on the images respectively corresponding to each region of the forest according to the RGB ranges of forest fuels at different levels, so as to obtain the images respectively corresponding to different levels of each region of the forest; a parameter acquisition module, configured to calculate the parameter information of different levels of each region of the forest based on the images respectively corresponding to different levels of each region of the forest; a biomass calculation module, configured to calculate the biomass information of different levels of each region of the forest by using biomass models of different levels based on the parameter information sets of different levels of each region of the forest; and a fuel load calculation module, configured to obtain the fuel loads of different levels of each region of the forest based on the biomass information of different levels of each region of the forest, and calculate the fuel load of each region of the forest based on the fuel loads of different levels of each region of the forest.

[0017] Through the above-provided solution for measuring forest fuel load, in the embodiments of the present application, by performing segmentation processing on the images respectively corresponding to each region of the forest to obtain the images respectively corresponding to different levels of each region of the forest, the weight of combustibles in each layer can be calculated according to the characteristics of different levels of plants, and the weight of forest combustibles in a certain region can be obtained through accumulation and averaging. It is not affected by environmental factors, nor is it affected by the acquisition of data of other levels by the canopy layer, and it requires short time and little workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present application will become readily understood. In the drawings, several embodiments of the present application are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0019] Figure 1 An exemplary flowchart of the method for measuring forest fuel load according to an embodiment of the present application is shown;

[0020] Figure 2 An exemplary flowchart of calculating the average heights of the tree layer, shrub layer, and herb layer according to an embodiment of the present application is shown;

[0021] Figure 3 An exemplary flowchart of obtaining the average height corresponding to the corresponding level based on the pixel height of the contour of the reference object according to an embodiment of the present application is shown;

[0022] Figure 4 An exemplary flowchart of calculating the average breast diameter of the tree layer according to an embodiment of the present application is shown;

[0023] Figure 5Shows an exemplary flowchart for calculating canopy density in an embodiment of the present application;

[0024] Figure 6 Shows an exemplary flowchart for calculating moisture content in an embodiment of the present application;

[0025] Figure 7A Shows an image of a forest area in an embodiment of the present application;

[0026] Figure 7B Shows an image corresponding to the tree layer obtained by segmenting the Figure 7A image in an embodiment of the present application;

[0027] Figure 7C Shows an image corresponding to the shrub layer obtained by segmenting the Figure 7A image in an embodiment of the present application;

[0028] Figure 7D Shows an image corresponding to the herb layer obtained by segmenting the Figure 7A image in an embodiment of the present application;

[0029] Figure 7E Shows an image corresponding to the surface litter and humus layer obtained by segmenting the Figure 7A image in an embodiment of the present application;

[0030] Figure 8 Shows an exemplary structural block diagram of a forest fuel load measurement system in an embodiment of the present application. Detailed implementation manners

[0031] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0032] It should be understood that the terms "including" and "comprising" used in the description and claims of the present application indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0033] It should also be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification and claims of this application, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms. It should be further understood that the term "and / or" used in the specification and claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0034] As used in this specification and the claims, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrases "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" depending on the context.

[0035] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0036] Figure 1 An exemplary flowchart of the forest fuel load measurement method 100 according to an embodiment of this application is shown.

[0037] As Figure 1 shown, in step S110, the images corresponding to each region of the forest are segmented according to the RGB ranges of the forest fuels at different levels, and the images corresponding to different levels of each region of the forest are obtained.

[0038] In the embodiments of this application, there are multiple images corresponding to each region of the forest, and the multiple images are taken from multiple different angles of the corresponding region of the forest. Specifically, the number of images corresponding to each region of the forest and the shooting angles can be set as needed, and this application does not limit this here.

[0039] After subsequent processing of the multiple images corresponding to the corresponding region of the forest, the average value of the fuel loads corresponding to each image is obtained to obtain the fuel load corresponding to this forest region.

[0040] By taking multiple images from multiple different angles of the corresponding region of the forest and obtaining the average value of the fuel loads corresponding to each image to obtain the fuel load corresponding to this forest region, the fuel load situation of this forest region can be more comprehensively reflected, and the error can be further reduced by calculating the average value, improving the accuracy of the fuel load estimation.

[0041] In the embodiments of the present application, before the images corresponding to each area of the forest are segmented, feature extraction processing is performed on the images corresponding to each area of the forest. Specifically, in the process of feature extraction processing, first, the deeplabv3 model preprocesses the images corresponding to each area of the forest. The preprocessing specifically includes adjusting the size and normalizing the pixel values, etc., so that the images corresponding to each area of the forest meet the input requirements of the deeplabv3 model. Subsequently, the initial convolutional layer of the deeplabv3 model extracts low-level features including color and edges corresponding to the images corresponding to each area of the forest, and then extracts multi-scale features corresponding to the images corresponding to each area of the forest through the dilated convolution in the spatial pyramid pooling module based on different sampling rates, capturing from the macroscopic overall structure of the forest to the microscopic local details. At the same time, the extracted low-level features and scale features are fused through multiple layers of convolution, so that the low-level features are gradually converted into high-level semantic features containing rich semantic information, enabling the model to understand the semantic categories of different objects in the image.

[0042] In the embodiments of the present application, the different layers include the tree layer, the shrub layer, the herb layer, and the surface litter and humus layer.

[0043] In the embodiments of the present application, in the process of segmenting the images corresponding to each area of the forest, based on the extracted and fused feature information, the RGB range of each pixel is judged through a classification algorithm, and finally the segmented image is output.

[0044] Specifically, the RGB range of the tree layer is: the range of the R (red) channel is 100 - 255, the range of the G (green) channel is 120 - 255, and the range of the B (blue) channel is 80 - 200. The RGB range of the shrub layer is: the range of the R (red) channel is 80 - 200, the range of the G (green) channel is 100 - 230, and the range of the B (blue) channel is 60 - 180. The RGB of the herb layer is: the range of the R (red) channel is 60 - 180, the range of the G (green) channel is 80 - 200, and the range of the B (blue) channel is 40 - 160. The RGB range of the surface litter and humus layer is: the range of the R (red) channel is 30 - 140, the range of the G (green) channel is 30 - 160, and the range of the B (blue) channel is 20 - 120.

[0045] Specifically, all image pixels within the RGB range of the tree layer are classified into the segmented tree layer image, all image pixels within the RGB range of the shrub layer are classified into the segmented shrub layer image, all image pixels within the RGB range of the herb layer are classified into the segmented herb layer image, and all image pixels within the RGB range of the surface litter and humus layer are classified into the segmented surface litter and humus layer image. Thus, by performing segmentation processing on the images corresponding to different regions of the forest, the corresponding tree layer image, shrub layer image, herb layer image, and surface litter and humus layer image are obtained.

[0046] In the embodiments of the present application, when redundant parts appear after segmentation processing on the images corresponding to different regions of the forest, the redundant parts can be manually removed by increasing the contrast. Specifically, during the process of manually removing the redundant parts by increasing the contrast, first, a suitable tool or algorithm (such as histogram equalization, adaptive contrast adjustment, etc.) is used to increase the contrast of the image to help strengthen the difference between the target object and the background or other non-related elements. Then, the image after contrast adjustment is segmented again. A suitable segmentation algorithm is selected according to the specific situation, such as threshold segmentation, edge detection, region growing method, watershed algorithm, etc. Post-processing is performed on the segmentation result to remove small and discontinuous regions by filling holes, smoothing boundaries, etc. Then, the above process is repeated until all the redundant parts are removed.

[0047] Increasing the contrast can enhance the brightness difference between different regions in the image, making the originally difficult-to-identify boundaries clearer. This helps to more accurately identify the boundaries between different layers of the corresponding regions of the forest during the image segmentation process and reduce the appearance of redundant parts. By manually removing these redundant parts, the accuracy of image segmentation can be further improved, providing more valuable information for subsequent image analysis and processing.

[0048] After step S110 is executed, in step S120, parameter information of different layers of different regions of the forest is calculated based on the images corresponding to different layers of different regions of the forest.

[0049] In the embodiments of the present application, the parameter information of different layers includes: the average height of the tree layer, the average breast diameter of the tree layer, the number of trees in the tree layer, the canopy density of the tree layer, the average height of the shrub layer, the average basal diameter of the shrub layer, the canopy density of the shrub layer, the average height of the herb layer and the canopy density of the herb layer, the area of the shrub layer, the area of the herb layer, the thickness of the humus, the thickness of the surface litter, the area of the surface litter and humus layer, etc.

[0050] In an embodiment of the present application, for the specific process of calculating the average height of the tree layer, the average height of the shrub layer, and the average height of the herb layer, reference can be made to Figure 2 .

[0051] Figure 2 FIG. shows an exemplary flowchart for calculating the average height of the tree layer, the shrub layer, and the herb layer in an embodiment of the present application.

[0052] As Figure 2 shown, in step S210, the images corresponding to the layers for which the average height needs to be calculated are sequentially subjected to grayscale processing and image binarization processing. In step S220, all the contours in the image after grayscale processing and binarization processing are found. In step S230, a preset index is used to match the reference object contour among all the contours. In step S240, the pixel height of the reference object contour is obtained. In step S250, the average height corresponding to the corresponding layer is obtained based on the pixel height of the reference object contour.

[0053] In an embodiment of the present application, during the grayscale processing, an image processing library (such as OpenCV) can be used to convert a color image into a grayscale image.

[0054] In an embodiment of the present application, after grayscale processing, the grayscale processed image can be subjected to Gaussian blur processing, and then the Gaussian blurred image is subjected to image binarization processing. Specifically, through Gaussian blur processing, the noise of the grayscale processed image can be reduced and the unnecessary details of the grayscale processed image can be removed. During the Gaussian blur processing, a convolution operation is performed on the image by using a weight matrix with a Gaussian distribution, so as to achieve a smoothing effect.

[0055] In an embodiment of the present application, during the image binarization processing, a threshold is selected to binarize the grayscale image, which will set each pixel point in the image to white (foreground) or black (background). Specifically, the aforementioned threshold can be set according to the application scenario to find the best separation effect, and the present application does not limit this here.

[0056] In an embodiment of the present application, existing image processing functions can be used to find all the contours in the image after grayscale processing and binarization processing, and the present application does not limit this here.

[0057] In an embodiment of the present application, the aforementioned preset index can be the position of the reference object, the shape of the reference object, the number of the reference object, etc., and the present application does not limit this here.

[0058] In an embodiment of the present application, the reference object may be a specially placed standard measuring rod, or may be an object at a corresponding level, for example, a tree in the tree layer, a shrub in the shrub layer, a herbaceous plant in the herbaceous layer, etc. The present application does not limit this here.

[0059] In an embodiment of the present application, for the specific process of obtaining the average height corresponding to the corresponding level based on the pixel height of the reference object contour, reference can be made to Figure 3 .

[0060] Figure 3 The exemplary flowchart of obtaining the average height corresponding to the corresponding level based on the pixel height of the reference object contour in the embodiment of the present application is shown.

[0061] As Figure 3 shown, in step S310, all contours other than the reference object contour among all contours are traversed to obtain the pixel heights of the contours other than the reference object contour. In step S320, the ratio between the pixel height of each other contour and the pixel height of the reference object contour is calculated. In step S330, the actual height of each other contour is obtained by dividing the actual height of the reference object by the ratio. In step S340, the average height corresponding to the corresponding level is obtained by calculating the average value of the actual heights of each other contour.

[0062] In an embodiment of the present application, for the specific process of calculating the average breast diameter of the tree layer, reference can be made to Figure 4 .

[0063] Figure 4 The exemplary flowchart of calculating the average breast diameter of the tree layer in the embodiment of the present application is shown.

[0064] As Figure 4 shown, in step S410, all contours in the image are obtained based on the image corresponding to the tree layer, where all contours include the reference object contour and a plurality of non-reference object contours. In step S420, the ratio between the pixel width of the reference object contour and the pixel width of each non-reference object contour is obtained. In step S430, the actual breast diameter of each non-reference object contour is obtained by dividing the actual breast diameter of the reference object by the ratio. In step S440, the average breast diameter of the tree layer is obtained by calculating the average value of the actual breast diameters of each non-reference object contour.

[0065] In an embodiment of the present application, before obtaining all contours in the image based on the image corresponding to the tree layer, grayscale processing and image binarization processing are performed on the image corresponding to the tree layer. For the specific grayscale processing process and image binarization processing process, reference can be made to the foregoing, and details are not described herein again.

[0066] In an embodiment of the present application, after all contours including a reference object contour and multiple non-reference object contours are acquired, the pixel width of the reference object contour and the pixel width of each non-reference object contour are acquired, and then the ratio between the pixel width of the reference object contour and the pixel width of each non-reference object contour is calculated.

[0067] In the embodiment of the present application, the actual diameter at breast height of each non-reference object outline is obtained by multiplying the actual diameter at breast height of the reference object by the ratio, that is, the actual diameter at breast height of each tree in the tree layer as a non-reference object is obtained.

[0068] In an embodiment of the present application, the calculation method of the average base diameter of the shrub layer is the same as the calculation method of the average breast diameter of the tree layer. As long as the pixel width of the reference object and the pixel width of the non-reference object contour in the image corresponding to the shrub layer are obtained, the average base diameter of the shrub layer can be obtained based on the actual base diameter of the reference object. This application will not go into details here.

[0069] In the embodiment of the present application, the specific process of calculating the canopy density of the tree layer, the canopy density of the shrub layer, and the canopy density of the herb layer can be found in Figure 5 .

[0070] Figure 5 An exemplary flow chart for calculating canopy density according to an embodiment of the present application is shown.

[0071] like Figure 5 As shown, in step S510, the image corresponding to the level for which the canopy density is to be calculated is subjected to grayscale processing, contrast enhancement processing and image binarization processing in sequence. In step S520, the ratio of the number of white pixels to the total number of pixels in the image subjected to grayscale processing, contrast enhancement processing and image binarization processing is calculated to obtain the canopy density of the corresponding level.

[0072] Specifically, the grayscale processing process and the image binarization processing process can be referred to in the previous text and will not be described in detail here.

[0073] In the embodiment of the present application, the parameter information at different levels also includes the moisture content of the combustibles at different levels. The specific process of calculating the moisture content of the combustibles at different levels in each forest area can be found in Figure 6 .

[0074] Figure 6 An exemplary flow chart for calculating the moisture content according to an embodiment of the present application is shown.

[0075] like Figure 6As shown, in step S610, the images corresponding to different levels of each area of the forest are converted to the HSV color space. In step S620, the areas in the HSV color space that meet the set HSV threshold range are extracted. In step S630, the proportion of the pixels of the extracted area in the total pixels of its corresponding image is calculated, and the moisture content of the combustibles at the corresponding level is obtained based on this proportion.

[0076] In an embodiment of the present application, the HSV threshold range is the HSV threshold range corresponding to green. When extracting the areas in the HSV color space that meet the set HSV threshold range, that is, the green areas in the images corresponding to different levels are extracted. After the green areas are extracted, the degree of greenness is measured by calculating the proportion of the green area pixels in the total pixels. Then, according to the relationship between the moisture content and the degree of greenness, the moisture content of the combustibles at the corresponding level is obtained.

[0077] In an embodiment of the present application, the relationship between the moisture content and the degree of greenness is: y = -0.6405x + 0.5372, where x is the degree of greenness and y is the moisture content.

[0078] After step S120 is executed, in step S130, different-level biomass models are used to calculate the biomass information of different levels of each area of the forest based on the parameter information sets of different levels of each area of the forest.

[0079] In an embodiment of the present application, the biomass information of different levels includes: the biomass of the tree layer trunk, the biomass of the tree layer branches, the biomass of the tree layer leaves, the biomass of the shrub layer branches, the biomass of the shrub layer leaves, the biomass of the herb layer, and the biomass of the surface litter and humus layer.

[0080] After obtaining the biomass information of different levels of each area of the forest, in step S140, the fuel load of different levels of each area of the forest is obtained based on the biomass information of different levels of each area of the forest.

[0081] In an embodiment of the present application, the first calculation formula is used to obtain the fuel load of the tree layer of each area of the forest based on the biomass of the tree layer trunk, the biomass of the tree layer branches, and the biomass of the tree layer leaves. The first calculation formula is: M1 = [W 乔干 +W 乔枝 +W 乔叶 ×n1×(1 - P1), where W 乔干 is the biomass of the tree layer trunk, W 乔干 = 2.52 + 0.12(D 2 ×h1), W 乔枝 is the biomass of the tree layer branches, W 乔枝 = 0.06(D 2 ×h1) 0.73 , W乔叶 is the leaf biomass of the tree layer, W 乔叶 = 17.459 × h1 0.482 , where h1 is the average height of the tree layer, D is the average diameter at breast height of the tree layer, n1 is the number of tree stems in the tree layer, and P1 is the moisture content of the tree layer.

[0082] Specifically, W 乔干 = 2.52 + 0.12(D 2 × h1), W 乔枝 = 0.06(D 2 × h1) 0.73 and W 乔叶 = 17.459 × h1 0.482 are the biomass models of the tree layer.

[0083] Specifically, the number of tree stems in the aforementioned tree layer can be obtained by performing object recognition on the image corresponding to the tree layer after segmentation, which is not elaborated in this application.

[0084] In the embodiments of this application, the second calculation formula is used to obtain the fuel load of the shrub layer in each region of the forest based on the branch biomass and leaf biomass of the shrub layer. The second calculation formula is: M2 = [W 灌枝 + W 灌叶 × S 灌 × U 灌 × (1 - P2), where W 灌枝 is the branch biomass of the shrub layer, W 灌枝 = 0.28(B 2 × h2) 0.74 , W 灌叶 is the leaf biomass of the shrub layer, W 灌叶 = 10.463 × h2 0.476 , h2 is the average height of the shrub layer, B is the average basal diameter of the shrub layer, S 灌 is the area of the shrub layer, U 灌 is the canopy density of the shrub layer, and P2 is the moisture content of the shrub layer.

[0085] Specifically, W 灌枝 = 0.28(B 2 × h2) 0.74 and W 灌叶 = 10.463 × h2 0.476 are the biomass models of the shrub layer.

[0086] Specifically, the area of the aforementioned shrub layer can be obtained by direct input from the user or by calling map information, etc., and this application does not limit it here.

[0087] In the embodiments of the present application, the third calculation formula is used to obtain the combustible load of the herbaceous layer in each area of the forest based on the herbaceous layer biomass. The third calculation formula is: M3 = W 草 ×(1 - P3), where W 草 is the herbaceous layer biomass, and W 草 = 0.40×[(S 草 ×U 草 ) 2 ×h3] 0.56 , where S 草 is the area of the herbaceous layer, U 草 is the canopy density of the herbaceous layer, h3 is the average height of the herbaceous layer, and P3 is the moisture content of the herbaceous layer.

[0088] Specifically, W 草 = 0.40×[(S 草 ×U 草 ) 2 ×h3] 0.56 is the biomass model of the herbaceous layer.

[0089] Specifically, the area of the aforementioned herbaceous layer can be obtained by direct input from the user or by calling map information, etc., and the present application does not limit this here.

[0090] In the embodiments of the present application, the fourth calculation formula is used to obtain the combustible load of the surface litter and humus layer in each area of the forest based on the surface litter and humus layer biomass. The fourth calculation formula is: M4 = W 总 ×S 总 ×P4, where W 总 is the surface litter and humus layer biomass, and W 总 = h 腐 + h 地 ×t, where h 腐 is the thickness of the humus, h 地 is the thickness of the surface litter, t is the number of years, S 总 is the area of the surface litter and humus layer, and P4 is the moisture content of the surface litter and humus layer.

[0091] Specifically, W 总 = h 腐 + h 地 ×t is the biomass model of the surface litter and humus layer.

[0092] Specifically, the area of the aforementioned surface litter and humus layer can be obtained by direct input from the user or by calling map information, etc., and the present application does not limit this here.

[0093] Specifically, the thickness of the aforementioned humus and the thickness of the surface litter can be obtained by actual measurement by the user, etc., and the present application does not limit this here.

[0094] After obtaining the fuel loads at different levels in each area of the forest, in step S150, the fuel load of each area of the forest is calculated based on the fuel loads at different levels in each area of the forest.

[0095] In an embodiment of the present application, the fuel loads at different levels in each area of the forest are added up to obtain the fuel load of each area of the forest.

[0096] In an embodiment of the present application, after obtaining the fuel load of each area of the forest, the forest fire danger level can be judged according to the fuel load of each area of the forest. For example, the floor area of each area of the forest is compared with 1 ha, and the floor area of each area of the forest is proportionally converted to 1 ha, so as to obtain the fuel load of forest combustibles in 1 ha of each area of the forest. Then, according to the range where the fuel load of forest combustibles in 1 ha is located, the corresponding forest fire danger level is obtained.

[0097] In an embodiment of the present application, in an area of the forest, at a height of 1.5 meters above the ground and with an elevation angle of 30 degrees for the tree top, so that the top of the tree is exactly at the top edge position of the photo, a first image is taken. A second image is taken at a position 5 - 10 degrees to the left of the first image shooting position, and a third image is taken at a position 5 - 10 degrees to the right of the first image shooting position. Then, the foregoing forest fuel load measurement method 100 is used to process the first image, the second image, and the third image in sequence, and the forest fuel load is measured based on the processing results.

[0098] Specifically, the taken first image is as Figure 7A shown. By performing segmentation processing on the first image, the images corresponding to the tree layer, Figure 7B as shown, the images corresponding to the shrub layer, Figure 7C as shown, the images corresponding to the herb layer, Figure 7D as shown, and the images corresponding to the surface litter and humus layer, Figure 7E as shown are obtained respectively.

[0099] Next, the reference object height, reference object breast diameter, reference object base diameter, and other data are input at the program command line. The data of each layer corresponding to the first image, the second image, and the third image are calculated as follows:

[0100] Tree layer: Average tree height: h1 = 3.892 m; Average breast diameter: D = 15.62 cm; Canopy density: U 乔 = 0.32; Green degree is: 0.32, Water content is: P1 = 0.30;

[0101] Shrub layer: Average tree height: h2 = 0.385 m; Average base diameter: B = 1.55 cm; Canopy density: U灌 = 0.29; The greenness level is: 0.43, and the moisture content is: P2 = 0.38;

[0102] Herb layer: Average height: h3 = 0.052 cm; Canopy density: U 草 = 0.71, the greenness level is: 0.26, and the moisture content is: P3 = 0.24;

[0103] Surface litter and humus layer: Biomass of surface litter and humus layer: W 总 = 0.02.

[0104] Then, the fuel loads of each layer corresponding to the first image are obtained: M1 = 776.032, M2 = 136.604, M3 = 7.648, and M4 = 1.49. And the fuel loads of each layer corresponding to the second image and the third image are obtained respectively.

[0105] Subsequently, the fuel load of the forest area obtained from the first image is: M 1 = M1 + M2 + M3 + M4 = 921.78. And the fuel load M of the forest area obtained from the second image is 2 = 921.56, and the fuel load M of the forest area obtained from the third image is 3 = 921.89.

[0106] According to the fuel load M of the forest area obtained = (M 1 + M 2 + M 3 ) / 3 = 921.74.

[0107] Finally, the occupied area of the forest area is compared with 1 ha, and the area of the shooting area is proportionally converted to 1 ha. The area in the figure is about 110 m 2 , then the fuel load M is converted to M' = 921.74 / 110 × 10000 = 8.37 t / ha, which corresponds to a low forest fire risk. Thus, this forest area is a low-risk area.

[0108] In summary, through the forest fuel load measurement scheme provided above, in the embodiments of the present application, by segmenting the images corresponding to different regions of the forest, the images corresponding to different levels of different regions of the forest are obtained, and the fuel weights of each layer can be calculated according to the characteristics of different plant levels. After accumulation and averaging, the fuel weight of a certain forest area can be obtained, which is not affected by environmental factors and is not affected by the canopy layer for obtaining data of other levels, and requires short time and small workload.

[0109] The embodiment of the present application also provides a forest fuel load measurement system, which can use the aforementioned forest fuel load measurement method 100 to measure the forest fuel load, or can use other forest fuel load measurement methods to measure the forest fuel load, and the present application does not limit this here.

[0110] Figure 8 The exemplary structural block diagram of the forest fuel load measurement system according to the embodiment of the present application is given.

[0111] As Figure 8 shown, the device 800 includes an image processing module 810, a parameter acquisition module 820, a biomass calculation module 830, and a fuel load calculation module 840. In the embodiment of the present application, the image processing module 810, the parameter acquisition module 820, the biomass calculation module 830, and the fuel load calculation module 840 can be separate units or integrated in an integrated circuit, and the present application does not limit this here.

[0112] Specifically, the image processing module 810 is used to perform segmentation processing on the images respectively corresponding to each region of the forest according to the RGB ranges of the forest fuels at different levels, so as to obtain the images respectively corresponding to different levels of each region of the forest.

[0113] Specifically, the parameter acquisition module 820 is used to calculate the parameter information of different levels of each region of the forest based on the images respectively corresponding to different levels of each region of the forest.

[0114] Specifically, the biomass calculation module 830 is used to calculate the biomass information of different levels of each region of the forest based on the parameter information sets of different levels of each region of the forest by using biomass models of different levels.

[0115] Specifically, the fuel load calculation module 840 is used to obtain the fuel loads of different levels of each region of the forest based on the biomass information of different levels of each region of the forest, and calculate the fuel loads of each region of the forest based on the fuel loads of different levels of each region of the forest.

[0116] When the device 800 uses the aforementioned forest fuel load measurement method 100 to measure the forest fuel load, the image processing module 810 executes the aforementioned step S110, the parameter acquisition module 820 executes the aforementioned step S120, the biomass calculation module 830 executes the aforementioned step S130, and the fuel load calculation module 840 executes the aforementioned step S140 and step S150. The specific execution process can refer to the foregoing, and will not be elaborated here.

[0117] Although multiple embodiments of the present application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art can conceive of many changes, alterations, and alternative ways without departing from the spirit and scope of the present application. It should be understood that various alternatives to the embodiments of the present application described herein may be employed in the practice of the present application. The appended claims are intended to define the scope of protection of the present application and thus cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for calculating forest combustible load, characterized in that Including: Segmenting the images corresponding to each region of the forest according to the RGB ranges of forest combustibles at different levels to obtain the images corresponding to different levels of each region of the forest; Calculating the parameter information of different levels of each region of the forest based on the images corresponding to different levels of each region of the forest; Calculating the biomass information of different levels of each region of the forest by using biomass models of different levels based on the parameter information sets of different levels of each region of the forest; Obtaining the fuel load of different levels of each region of the forest based on the biomass information of different levels of each region of the forest; Calculating the fuel load of each region of the forest based on the fuel load of different levels of each region of the forest; Wherein, the different levels include the tree layer, the shrub layer, the herb layer, and the surface litter and humus layer; The parameter information of different levels includes: the average height of the tree layer, the average breast diameter of the tree layer, the number of trees in the tree layer, the canopy density of the tree layer, the average height of the shrub layer, the average base diameter of the shrub layer, the canopy density of the shrub layer, the average height of the herb layer, and the canopy density of the herb layer; The parameter information of different levels also includes the moisture content of combustibles at different levels. In the process of calculating the moisture content of combustibles at different levels of each region of the forest, the following steps are performed: Converting the images corresponding to different levels of each region of the forest to the HSV color space; Extracting the regions in the HSV color space that meet the set HSV threshold range; Calculating the proportion of the pixels of the extracted region in the total pixels of its corresponding image, and obtaining the moisture content of the combustibles at the corresponding level based on this proportion.

2. The method for measuring forest fuel load according to claim 1, wherein, In the process of calculating the average height of the tree layer, the average height of the shrub layer, and the average height of the herb layer, the following steps are performed: Successively performing grayscale processing and image binarization processing on the image corresponding to the level for which the average height needs to be calculated; Searching for all contours in the image after grayscale processing and binarization processing; Using a preset index to match the reference object contour among all contours; Obtaining the pixel height of the reference object contour; Obtaining the average height corresponding to the corresponding level based on the pixel height of the reference object contour.

3. The method for measuring forest fuel load according to claim 2, wherein Obtaining the average height corresponding to the corresponding level based on the pixel height of the reference object contour includes: Traversing the other contours except the reference object contour among all contours to obtain the pixel heights of the other contours except the reference object contour; Calculating the ratio between the pixel height of each other contour and the pixel height of the reference object contour; Obtaining the actual height of each other contour through the actual height of the reference object and the ratio; Obtaining the average height corresponding to the corresponding level by calculating the average value of the actual heights of each other contour.

4. The method for measuring forest combustible load according to claim 1, characterized in that, In the process of calculating the average breast diameter of the tree layer, the following steps are performed: Obtaining all contours in the image based on the image corresponding to the tree layer, where all contours include the reference object contour and multiple non-reference object contours; Obtaining the ratio between the pixel width of the reference object contour and the pixel widths of each non-reference object contour; Obtaining the actual breast diameter of each non-reference object contour through the actual breast diameter of the reference object and the ratio; The average DBH of the tree layer is obtained by calculating the average of the actual DBHs of the contours of each non-reference object.

5. The method for measuring forest fuel load according to claim 1, characterized in that, In the process of calculating the canopy density of the tree layer, the canopy density of the shrub layer, and the canopy density of the herb layer, the following steps are performed: The images corresponding to the layers for which the canopy density needs to be calculated are sequentially subjected to grayscale processing, contrast enhancement processing, and image binarization processing; The ratio of the number of white pixels to the total number of pixels in the image that has undergone grayscale processing, contrast enhancement processing, and image binarization processing is calculated to obtain the canopy density of the corresponding layer.

6. The method for measuring forest fuel load according to claim 1, characterized in that, The biomass information of different layers includes: the biomass of the tree trunk in the tree layer, the biomass of the branches in the tree layer, the biomass of the leaves in the tree layer, the biomass of the branches in the shrub layer, the biomass of the leaves in the shrub layer, the biomass of the herb layer, and the biomass of the surface litter and humus layer; The combustible load of the tree layer in each area of the forest is obtained by using the first calculation formula based on the trunk biomass, branch biomass, and leaf biomass of the tree layer. The first calculation formula is: M1 = [W 乔干 +W 乔枝 +W 乔叶 ×n1×(1 - P1), where W 乔干 is the trunk biomass of the tree layer, W 乔干 = 2.52 + 0.12(D 2 ×h1), W 乔枝 is the branch biomass of the tree layer, W 乔枝 = 0.06(D 2 ×h1) 0.73 , W 乔叶 is the leaf biomass of the tree layer, W 乔叶 = 17.459×h1 0.482 , h1 is the average height of the tree layer, D is the average diameter at breast height of the tree layer, n1 is the number of trees in the tree layer, and P1 is the moisture content of the tree layer; The combustible load of the shrub layer in each area of the forest is obtained by using the second calculation formula based on the branch biomass and leaf biomass of the shrub layer. The second calculation formula is: M2 = [W 灌枝 + W 灌叶 × S 灌 × U 灌 × (1 - P2), where W 灌枝 is the branch biomass of the shrub layer, W 灌枝 = 0.28(B 2 × h2) 0.74 , W 灌叶 is the leaf biomass of the shrub layer, W 灌叶 = 10.463 × h2 0.476 , h2 is the average height of the shrub layer, B is the average base diameter of the shrub layer, S 灌 is the area of the shrub layer, U 灌 is the canopy density of the shrub layer, and P2 is the moisture content of the shrub layer; The combustible load of the herb layer in each area of the forest is obtained based on the herb layer biomass using the third calculation formula, and the third calculation formula is: M3 = W 草 ×(1 - P3), where W 草 is the herb layer biomass, and W 草 = 0.40×[(S 草 ×U 草 ) 2 ×h3] 0.56 , where S 草 is the area of the herb layer, U 草 is the canopy density of the herb layer, h3 is the average height of the herb layer, and P3 is the moisture content of the herb layer; Use the fourth calculation formula to obtain the combustible load of the litter and humus layer on the forest surface based on the biomass of the litter and humus layer. The fourth calculation formula is: M4 = W 总 ×S 总 ×P4, where W 总 is the biomass of the litter and humus layer, and W 总 = h 腐 + h 地 ×t, where h 腐 is the thickness of the humus, h 地 is the thickness of the litter, t is the number of years, and S 总 is the area of the litter and humus layer, and P4 is the water content of the litter and humus layer.

7. A forest fuel load measurement system, characterized in that, The forest fuel load is measured by using the forest fuel load measurement method described in any one of claims 1-6. The system includes: An image processing module for segmenting the images corresponding to the respective regions of the forest according to the RGB ranges of the forest fuels at different layers to obtain the images corresponding to different layers of the respective regions of the forest; A parameter acquisition module for calculating the parameter information of different layers of the respective regions of the forest based on the images corresponding to different layers of the respective regions of the forest; A biomass calculation module for calculating the biomass information of different layers of the respective regions of the forest by using biomass models of different layers based on the parameter information sets of different layers of the respective regions of the forest; A fuel load calculation module for obtaining the fuel loads of different layers of the respective regions of the forest based on the biomass information of different layers of the respective regions of the forest, and calculating the forest fuel load of the entire forest region based on the fuel loads of different layers of the respective regions of the forest.

Images