Forest volume monitoring and analysis method and system based on remote sensing technology

Abstract

The present application provides a forest stock monitoring and analysis method and system based on remote sensing technology. The forest stock monitoring and analysis method based on remote sensing technology includes: collecting forest images through unmanned aerial vehicles; fusing the forest images to generate a fused image; enhancing the fused image to generate an enhanced image, extracting forest features and spatial features in the enhanced image; determining the stock parameters corresponding to the forest area according to the forest features and the spatial features; and generating a monitoring and analysis report by comparing the stock parameters with historical stock parameters. The process uses unmanned aerial vehicles to efficiently collect forest images, extracts key forest features and spatial features through image fusion and enhancement technology, accurately calculates the stock parameters of the area, and compares them with historical data to generate a detailed monitoring and analysis report. It improves the accuracy and real-time performance of forest resource management, and provides a scientific basis for ecological protection and sustainable development.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically, to a forest stock monitoring and analysis method and system based on remote sensing technology. Background Art

[0002] As society's demand for forestry resources increases, forest resource management becomes increasingly important. As an important indicator for evaluating forest resources, accurate monitoring of forest stock is of great significance for forestry planning, management, determination of felling quotas, and resource supervision. Remote sensing technology, as an effective means of macro, comprehensive, dynamic, and rapid monitoring and evaluation of natural resources, has become an important tool for monitoring forest resources. Through remote sensing technology, forest resource-related data can be obtained continuously and quickly, greatly improving the efficiency of forest resource management and supervision.

[0003] Although the participation of multi-source data can improve classification accuracy, the data obtained by various sensors have differences in spatial, spectral and temporal resolution, which makes it difficult to meet the data distribution conditions required by traditional statistical classification methods. This may lead to errors and uncertainties in the data processing process, and thus cause inaccurate monitoring of forest stock. Summary of the invention

[0004] The embodiments of the present application provide a forest stock monitoring and analysis method and system based on remote sensing technology, which can solve the problem of inaccurate forest stock monitoring at least to a certain extent.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0006] According to one aspect of the present application, a forest stock monitoring and analysis method based on remote sensing technology is provided, including: collecting forest images through unmanned aerial vehicles; fusing the forest images to generate a fused image; enhancing the fused image to generate an enhanced image, and extracting forest features and spatial features in the enhanced image; determining the stock parameters corresponding to the forest area based on the forest features and the spatial features; and generating a monitoring and analysis report based on the comparison of the stock parameters with historical stock parameters.

[0007] In the present application, based on the aforementioned scheme, the forest image is fused to generate a fused image, including: obtaining the spatial positioning and attitude information of the drone when the forest image is collected; based on the spatial positioning and attitude information, geometrically correcting the forest image to generate a corrected image; and fusing based on the texture information of the corrected image to generate a fused image.

[0008] In the present application, based on the aforementioned scheme, the fused image is enhanced to generate an enhanced image, and the forest features and spatial features in the enhanced image are extracted, including: obtaining pixel distribution information of the fused image; enhancing the fused image based on the pixel distribution information to generate an enhanced image; and extracting forest features and spatial features in the enhanced image.

[0009] In the present application, based on the aforementioned scheme, the accumulation parameters corresponding to the forest area are determined according to the forest characteristics and the spatial characteristics, including: evaluating the biomass and carbon storage of the forest according to the forest characteristics; determining the accumulation parameters corresponding to the forest area according to the biomass, the carbon storage and the spatial characteristics.

[0010] In the present application, based on the aforementioned scheme, the accumulation parameters and historical accumulation parameters are compared to generate a monitoring and analysis report, including: calculating change parameters according to the accumulation parameters and the historical accumulation parameters; generating a monitoring and analysis report according to the change parameters, the accumulation parameters and the historical accumulation parameters.

[0011] In the present application, based on the above-mentioned scheme, it also includes: regularly taking aerial photos of the target forest area through a multi-spectral or high-resolution camera to obtain forest images from multiple angles and multiple time periods.

[0012] In the present application, based on the aforementioned scheme, it also includes: displaying the data of the monitoring and analysis report through charts and maps.

[0013] According to one aspect of the present application, a forest stock monitoring and analysis system based on remote sensing technology is provided, comprising:

[0014] A collection unit for collecting forest images via drones;

[0015] A fusion unit, used for fusing the forest image to generate a fused image;

[0016] An extraction unit, used for enhancing the fused image to generate an enhanced image, and extracting forest features and spatial features in the enhanced image;

[0017] A parameter unit, used for determining the accumulation parameter corresponding to the forest area according to the forest characteristics and the spatial characteristics;

[0018] The reporting unit is used to generate a monitoring and analysis report based on the comparison between the accumulation parameters and the historical accumulation parameters.

[0019] In the present application, based on the aforementioned scheme, the forest image is fused to generate a fused image, including: obtaining the spatial positioning and attitude information of the drone when the forest image is collected; based on the spatial positioning and attitude information, geometrically correcting the forest image to generate a corrected image; and fusing based on the texture information of the corrected image to generate a fused image.

[0020] In the present application, based on the aforementioned scheme, the fused image is enhanced to generate an enhanced image, and the forest features and spatial features in the enhanced image are extracted, including: obtaining pixel distribution information of the fused image; enhancing the fused image based on the pixel distribution information to generate an enhanced image; and extracting forest features and spatial features in the enhanced image.

[0021] In the present application, based on the aforementioned scheme, the accumulation parameters corresponding to the forest area are determined according to the forest characteristics and the spatial characteristics, including: evaluating the biomass and carbon storage of the forest according to the forest characteristics; determining the accumulation parameters corresponding to the forest area according to the biomass, the carbon storage and the spatial characteristics.

[0022] In the present application, based on the aforementioned scheme, the accumulation parameters and historical accumulation parameters are compared to generate a monitoring and analysis report, including: calculating change parameters according to the accumulation parameters and the historical accumulation parameters; generating a monitoring and analysis report according to the change parameters, the accumulation parameters and the historical accumulation parameters.

[0023] In the present application, based on the above-mentioned scheme, it also includes: regularly taking aerial photos of the target forest area through a multi-spectral or high-resolution camera to obtain forest images from multiple angles and multiple time periods.

[0024] In the present application, based on the aforementioned scheme, it also includes: displaying the data of the monitoring and analysis report through charts and maps.

[0025] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the forest stock monitoring and analysis method based on remote sensing technology as described in the above embodiment is implemented.

[0026] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the forest stock monitoring and analysis method based on remote sensing technology as described in the above-mentioned embodiments.

[0027] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the forest stock monitoring and analysis method based on remote sensing technology provided in the above various optional implementations.

[0028] In the technical solution of the present application, forest images are collected by drones; the forest images are fused to generate a fused image; the fused image is enhanced to generate an enhanced image, and forest features and spatial features in the enhanced image are extracted; based on the forest features and the spatial features, the accumulation parameters corresponding to the forest area are determined; based on the accumulation parameters and the historical accumulation parameters, a monitoring and analysis report is generated. This process uses drones to efficiently collect forest images, extracts key forest features and spatial features through image fusion and enhancement technology, accurately calculates the accumulation parameters of the area, and compares them with historical data to generate a detailed monitoring and analysis report. It improves the accuracy and real-time nature of forest resource management, and provides a scientific basis for ecological protection and sustainable development.

[0029] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 The flowchart of the forest stock monitoring and analysis method based on remote sensing technology in one embodiment of the present application is schematically shown.

[0032] Figure 2 The flowchart of generating a fused image in one embodiment of the present application is schematically shown.

[0033] Figure 3 The schematic diagram of a forest stock monitoring and analysis system based on remote sensing technology in one embodiment of the present application is schematically shown.

[0034] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0036] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0039] The implementation details of the technical solution of this application are described in detail below:

[0040] Figure 1 The flowchart of the forest stock monitoring and analysis method based on remote sensing technology according to an embodiment of the present application is shown. Figure 1 As shown, the forest stock monitoring and analysis method based on remote sensing technology at least includes steps S110 to S150, which are described in detail as follows:

[0041] In step S110 , a forest image is collected by a drone.

[0042] In one embodiment of the present application, a multi-spectral or high-resolution camera can be mounted on an unmanned aerial vehicle to regularly take aerial photos of the target forest area and obtain forest images from multiple angles and time periods.

[0043] Optionally, calibrate the camera before taking aerial photos to ensure consistency in color and image quality.

[0044] Optionally, design the drone's flight path based on the size and shape of the target forest area to ensure coverage of the area of ​​interest. Set appropriate flight altitude, speed, and shooting interval to ensure image overlap for subsequent stitching. Optionally, the image overlap range can be 70%-80%.

[0045] Optionally, establish an aerial photography schedule, such as quarterly or annually, to obtain data from multiple time periods. Take aerial photos under different weather and lighting conditions to obtain images from different angles to improve the comprehensiveness and reliability of the data.

[0046] Optionally, store the collected images on a reliable storage device, such as an external hard drive or cloud storage, and back them up. Classify and label the images, and record the time, location, and other relevant information of the collection for subsequent analysis. Compare the image analysis results with the ground-based measured data to verify the accuracy of the data. Through the above steps, multi-angle and multi-time images of forest areas can be efficiently acquired and managed, providing a solid data foundation for subsequent ecological monitoring and analysis.

[0047] In step S120, the forest images are fused to generate a fused image.

[0048] like Figure 2 As shown, in one embodiment of the present application, the forest image is fused to generate a fused image, including:

[0049] S210, obtaining the spatial positioning and attitude information of the drone when the forest image is collected;

[0050] S220, performing geometric correction on the forest image based on the spatial positioning and posture information to generate a corrected image;

[0051] S230: Perform fusion based on texture information of the corrected image to generate a fused image.

[0052] In one embodiment of the present application, when fusing forest images collected by a drone, it is first necessary to obtain the spatial positioning and attitude information of the drone, where the spatial positioning includes the position coordinates and the flight altitude, and the attitude information includes the heading angle, the pitch angle, and the roll angle, etc. Based on this information, geometric correction is performed to generate a corrected image, and then the texture information of the corrected image is used for fusion.

[0053] In the process of obtaining the spatial positioning and attitude information of the drone, the spatial positioning information can also include the latitude, longitude, and altitude of the drone, which are usually collected through GPS or GNSS modules. The attitude information includes the heading angle, pitch angle, and roll angle of the drone, which can be collected through an inertial measurement unit. In addition, time information can also be collected, specifically the timestamp of each image acquisition, to ensure synchronization with the positioning and attitude information during the fusion process.

[0054] In one embodiment of the present application, the geometric deformation of the image caused by the change of the flight path and attitude of the drone is eliminated so that all images are aligned in the same geographic coordinate system. The following steps are included: converting the spatial positioning information of the drone into coordinates in a geographic coordinate system or a projection coordinate system. Using the attitude information (heading angle, pitch angle, roll angle) of the drone, the image is rotated and deformed to eliminate the tilt effect. The image is reprojected to a unified ground coordinate system so that different images can be accurately aligned under the same coordinates. If there are multiple overlapping images, a large-area corrected image can be generated by stitching.

[0055] Optionally, performing fusion based on texture information of the corrected image to generate a fused image specifically includes the following steps:

[0056] Acquire pixel information from the corrected image, and determine an association parameter according to the pixel information, wherein the association parameter represents the number of pixel pairs having an association relationship between distance and direction in the corrected image;

[0057] extracting texture information from the corrected image according to the association parameter and the grayscale of the corrected image;

[0058] The fused image is generated based on the texture information of the corrected image.

[0059] Specifically, in one embodiment of the present application, pixel information is first obtained from the corrected image, and then it is determined whether there is a correlation relationship between the pixels in terms of distance and direction based on the pixel information, that is, whether the data difference corresponding to the distance and direction parameters between the pixels is less than a set threshold. Then, the number of pixel pairs with a correlation relationship is determined as the correlation parameter. Among them, i and j represent grayscale values.

[0060] Based on the association parameter and the gray level L of the corrected image, texture information is determined from the corrected image, wherein the texture information includes: a contrast parameter, an energy parameter, and an entropy parameter. for:

[0061]

[0062] Energy parameters for:

[0063]

[0064] Entropy parameter for:

[0065]

[0066] After the texture features are calculated, pixels with the same data texture information are merged according to the texture features of the corrected image, and the information of each pixel in the corrected image is weightedly fused to generate a fused image.

[0067] The above process resynthesizes the images fused based on texture information to generate the final fused image. The fused image not only retains high-resolution spatial detail information, but also retains the richness of texture. The final fused image can be used for tasks such as forest resource monitoring, vegetation status assessment, and forest stock estimation to improve monitoring accuracy and analysis results.

[0068] In step S130, the fused image is enhanced to generate an enhanced image, and forest features and spatial features in the enhanced image are extracted.

[0069] In one embodiment of the present application, the fused image is enhanced to generate an enhanced image, and forest features and spatial features in the enhanced image are extracted, including:

[0070] Acquiring pixel distribution information of the fused image;

[0071] Enhance the fused image based on the pixel distribution information to generate an enhanced image;

[0072] The forest features and the spatial features in the enhanced image are extracted.

[0073] In one embodiment of the present application, the pixel distribution information of the fused image is obtained as follows: , the gray level is Q, the fused image is enhanced based on the pixel distribution information, and the enhanced image is generated as follows:

[0074]

[0075] in, is the number of pixels at pixel level i, M and N are the width and height of the image, and x and y represent the pixel position; Indicates the maximum value of the pixel value. Indicates the minimum value Represents the pixel factor obtained by training based on historical data.

[0076] After the enhanced image is generated, the forest features and spatial features in the enhanced image are extracted. The forest features may include the normalized vegetation index or the enhanced vegetation index. The spatial features include the distribution of the forest in the image, including the shape, boundary, and distribution density of the region. Specifically, by calculating the gradient of the image and extracting the forest boundary, the edge features of the forest can be accurately obtained. The area and perimeter of the extracted forest area are calculated to quantify the distribution and morphological characteristics of the forest.

[0077] In step S140, the accumulation parameter corresponding to the forest area is determined according to the forest characteristics and the spatial characteristics.

[0078] In one embodiment of the present application, determining the accumulation parameter corresponding to the forest area according to the forest characteristics and the spatial characteristics includes:

[0079] Assess the biomass and carbon stock of the forest based on the described forest characteristics;

[0080] The accumulation parameters corresponding to the forest area are determined according to the biomass, the carbon storage and the spatial characteristics.

[0081] In one embodiment of the present application, biomass is an important parameter for evaluating forest accumulation. It can be estimated using the vegetation index NDVI and a known biomass relationship model. A first model based on NDVI or other vegetation indices is established:

[0082]

[0083] Among them, a and b are regression coefficients obtained based on field measurement data.

[0084] At the same time, for a specific tree species, the corresponding biomass is determined as:

[0085]

[0086] Among them, D is the diameter of the tree, and c and d are the biological factors corresponding to the tree species.

[0087] Through the above process, the biomass corresponding to the forest area is obtained as follows:

[0088]

[0089] Among them, n represents the type identification of a specific tree species, and N represents the total number of the specific tree species.

[0090] The above process can estimate the biomass of trees more accurately based on the growth characteristics and biomass accumulation patterns of specific tree species. This is crucial for forestry management and ecological monitoring. By monitoring the changes in the biomass of specific tree species, we can understand their status and role in the ecosystem, thus providing a reference for the protection and restoration of biodiversity.

[0091] In addition, carbon storage can be calculated by multiplying the biomass by the corresponding carbon content. Usually the proportion of carbon in forest biomass is about 50%.

[0092] Based on the extracted regional characteristics, the number of trees per unit area is calculated, and their biomass and carbon storage are further estimated, and then the average biomass and carbon storage of the forest area can be calculated. Then, combined with the spatial characteristics, the corresponding accumulation parameters of the forest area can be determined by weighting.

[0093] In addition, regional factors such as forest type, climate, and soil conditions can be considered and appropriate corrections can be made to improve the accuracy of the model.

[0094] The above process, combined with the above data, forms a set of stock assessment system to provide a scientific basis for forest management, protection and sustainable use. By combining these calculations, the stock parameters of the forest area can be accurately assessed and support can be provided for further ecological monitoring and management decisions.

[0095] In step S150, a monitoring and analysis report is generated based on a comparison between the accumulated parameters and historical accumulated parameters.

[0096] In one embodiment of the present application, a monitoring and analysis report is generated based on the comparison between the accumulation parameter and the historical accumulation parameter, including:

[0097] Calculating a change parameter according to the accumulation parameter and the historical accumulation parameter;

[0098] A monitoring and analysis report is generated according to the change parameters, the accumulation parameters and the historical accumulation parameters.

[0099] In one embodiment of the present application, by comparing recent and historical biomass changes, comparing recent and historical carbon storage changes, and calculating the rate of change. This embodiment generates a comprehensive monitoring and analysis report by comparing recent and historical accumulation parameters and calculating the rate of change, providing a scientific basis and decision support for forest management. This method has good applicability and practicality, and can effectively provide data support for forest ecological monitoring and management.

[0100] In one embodiment of the present application, the data in the monitoring and analysis report can be displayed through charts, for example: using a bar chart to compare the accumulation parameters of different time periods or regions, such as biomass and carbon storage, to intuitively display the change trend; using a line chart to display time series data to show the dynamic changes in forest characteristics; using a pie chart to show the coverage ratio of various types of forest vegetation to help understand biodiversity.

[0101] In addition, maps can be used to display data from monitoring and analysis reports. For example, heat maps can be used to display the distribution of biomass and carbon storage in different regions to facilitate identification of key protected areas. Colors are used on maps to represent biomass in different regions, with darker areas representing high biomass and lighter areas representing low biomass. Different sizes and colors of symbols in the graded symbol map represent the accumulation parameters of different regions, clearly showing spatial characteristics.

[0102] In the monitoring and analysis report, the above charts and maps are combined, and each visualization is combined with the corresponding text description to explain the meaning of the data and analysis results shown in the charts. Provide specific conclusions and suggestions to support subsequent decision-making based on the visualization results. Through the above methods, combined with visualization tools such as charts and maps, the readability of data analysis results can be effectively enhanced, helping relevant decision makers and managers to more intuitively understand the status quo and trends of forest monitoring.

[0103] In the technical solution of the present application, forest images are collected by drones; the forest images are fused to generate a fused image; the fused image is enhanced to generate an enhanced image, and forest features and spatial features in the enhanced image are extracted; based on the forest features and the spatial features, the accumulation parameters corresponding to the forest area are determined; based on the accumulation parameters and the historical accumulation parameters, a monitoring and analysis report is generated. This process uses drones to efficiently collect forest images, extracts key forest features and spatial features through image fusion and enhancement technology, accurately calculates the accumulation parameters of the area, and compares them with historical data to generate a detailed monitoring and analysis report. It improves the accuracy and real-time nature of forest resource management, and provides a scientific basis for ecological protection and sustainable development.

[0104] The following describes an embodiment of the device of the present application, which can be used to execute the forest stock monitoring and analysis method based on remote sensing technology in the above embodiment of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the forest stock monitoring and analysis method based on remote sensing technology in the above embodiment of the present application.

[0105] Figure 3A block diagram of a forest stock monitoring and analysis system based on remote sensing technology according to an embodiment of the present application is shown.

[0106] Reference Figure 3 As shown, according to an embodiment of the present application, a forest stock monitoring and analysis system based on remote sensing technology includes:

[0107] A collection unit 310 is used to collect forest images through a drone;

[0108] A fusion unit 320, configured to fuse the forest image to generate a fused image;

[0109] An extraction unit 330 is used to enhance the fused image to generate an enhanced image, and extract forest features and spatial features in the enhanced image;

[0110] A parameter unit 340, configured to determine an accumulation parameter corresponding to the forest area according to the forest characteristics and the spatial characteristics;

[0111] The reporting unit 350 is used to generate a monitoring and analysis report based on the comparison between the accumulation parameters and the historical accumulation parameters.

[0112] In the present application, based on the aforementioned scheme, the forest image is fused to generate a fused image, including: obtaining the spatial positioning and attitude information of the drone when the forest image is collected; based on the spatial positioning and attitude information, geometrically correcting the forest image to generate a corrected image; and fusing based on the texture information of the corrected image to generate a fused image.

[0113] In the present application, based on the aforementioned scheme, the fused image is enhanced to generate an enhanced image, and the forest features and spatial features in the enhanced image are extracted, including: obtaining pixel distribution information of the fused image; enhancing the fused image based on the pixel distribution information to generate an enhanced image; and extracting forest features and spatial features in the enhanced image.

[0114] In the present application, based on the aforementioned scheme, the accumulation parameters corresponding to the forest area are determined according to the forest characteristics and the spatial characteristics, including: evaluating the biomass and carbon storage of the forest according to the forest characteristics; determining the accumulation parameters corresponding to the forest area according to the biomass, the carbon storage and the spatial characteristics.

[0115] In the present application, based on the aforementioned scheme, the accumulation parameters and historical accumulation parameters are compared to generate a monitoring and analysis report, including: calculating change parameters according to the accumulation parameters and the historical accumulation parameters; generating a monitoring and analysis report according to the change parameters, the accumulation parameters and the historical accumulation parameters.

[0116] In the present application, based on the above-mentioned scheme, it also includes: regularly taking aerial photos of the target forest area through a multi-spectral or high-resolution camera to obtain forest images from multiple angles and multiple time periods.

[0117] In the present application, based on the aforementioned scheme, it also includes: displaying the data of the monitoring and analysis report through charts and maps.

[0118] In the technical solution of the present application, forest images are collected by drones; the forest images are fused to generate a fused image; the fused image is enhanced to generate an enhanced image, and forest features and spatial features in the enhanced image are extracted; based on the forest features and the spatial features, the accumulation parameters corresponding to the forest area are determined; based on the accumulation parameters and the historical accumulation parameters, a monitoring and analysis report is generated. This process uses drones to efficiently collect forest images, extracts key forest features and spatial features through image fusion and enhancement technology, accurately calculates the accumulation parameters of the area, and compares them with historical data to generate a detailed monitoring and analysis report. It improves the accuracy and real-time nature of forest resource management, and provides a scientific basis for ecological protection and sustainable development.

[0119] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0120] It should be noted that the computer system 400 of the electronic device shown in the figure is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0121] The computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 403. The CPU 401, the ROM 402 and the RAM 403 are connected to each other through the bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0122] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0123] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0124] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0125] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0127] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0128] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0129] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0130] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0131] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0132] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A forest stock monitoring and analysis method based on remote sensing technology, characterized in that: include: Collect forest images through drones; fusing the forest images to generate a fused image; enhancing the fused image to generate an enhanced image, and extracting forest features and spatial features in the enhanced image; Determining the accumulation parameter corresponding to the forest area according to the forest characteristics and the spatial characteristics; Compare the accumulation parameters with historical accumulation parameters to generate a monitoring and analysis report; The step of enhancing the fused image to generate an enhanced image and extracting forest features and spatial features from the enhanced image includes: Acquiring pixel distribution information of the fused image; The fused image is enhanced based on the pixel distribution information to generate an enhanced image: in, is the number of pixels at pixel level i, M and N are the width and height of the image, and x and y represent the pixel position; Represents the maximum value of the pixel value. Indicates the minimum value, represents the pixel factor obtained by training based on historical data, is the pixel distribution information, Q is the gray level; The forest features and the spatial features in the enhanced image are extracted.

2. The method according to claim 1, characterized in that The forest image is fused to generate a fused image, including: Acquiring spatial positioning and attitude information of the UAV when the forest image is collected; Based on the spatial positioning and posture information, geometrically correct the forest image to generate a corrected image; The fused image is generated based on the texture information of the corrected image.

3. The method according to claim 1, characterized in that Determining the accumulation parameters corresponding to the forest area according to the forest characteristics and the spatial characteristics includes: Assess the biomass and carbon stock of the forest based on the described forest characteristics; The accumulation parameters corresponding to the forest area are determined according to the biomass, the carbon storage and the spatial characteristics.

4. The method according to claim 1, characterized in that: By comparing the accumulation parameters with the historical accumulation parameters, a monitoring and analysis report is generated, including: Calculating a change parameter according to the accumulation parameter and the historical accumulation parameter; A monitoring and analysis report is generated according to the change parameters, the accumulation parameters and the historical accumulation parameters.

5. The method according to claim 1, characterized in that Also includes: Using multispectral or high-resolution cameras, regular aerial photography of target forest areas can be performed to obtain forest images from multiple angles and time periods.

6. The method according to claim 1, characterized in that Also includes: The data of the monitoring and analysis report are presented through charts and maps.

7. A forest stock monitoring and analysis system based on remote sensing technology, characterized in that: include: A collection unit for collecting forest images via drones; A fusion unit, used for fusing the forest image to generate a fused image; An extraction unit, used for enhancing the fused image to generate an enhanced image, and extracting forest features and spatial features in the enhanced image; A parameter unit, used for determining the accumulation parameter corresponding to the forest area according to the forest characteristics and the spatial characteristics; A reporting unit, used to generate a monitoring and analysis report based on the comparison between the accumulation parameters and the historical accumulation parameters; The step of enhancing the fused image to generate an enhanced image and extracting forest features and spatial features from the enhanced image includes: Acquiring pixel distribution information of the fused image; The fused image is enhanced based on the pixel distribution information to generate an enhanced image: in, is the number of pixels at pixel level i, M and N are the width and height of the image, and x and y represent the pixel position; Represents the maximum value of the pixel value. Indicates the minimum value, represents the pixel factor obtained by training based on historical data, is the pixel distribution information, Q is the gray level; The forest features and the spatial features in the enhanced image are extracted.

8. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the forest stock monitoring and analysis method based on remote sensing technology as described in any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the forest stock monitoring and analysis method based on remote sensing technology as described in any one of claims 1 to 6.

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