Forest biomass change monitoring method and system

Abstract

The invention is suitable for the technical field of forest monitoring, and provides a forest biomass change monitoring method and system, and the method comprises the following steps: collecting aerial photography image information and aerial photography influence information, the aerial photography influence information comprises weather factors, light intensity and white balance parameters, and carrying out the color restoration of a forest aerial photography image based on the aerial photography influence information; the forest aerial image is identified based on the color features and the texture features, a plurality of pieces of forest region information are obtained, and each piece of forest region information corresponds to the tree type, the region area and the growth stage. The method can eliminate the influence of weather and light on the image quality, and enables the subsequent feature recognition to be more accurate. The forest change information can be obtained by comparing the forest region information in the aerial image information with the forest region information in the historical image information, the forest change information comprises the coverage area change condition and the growth information change condition of each tree type, and the change condition of the forest biomass can be accurately reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of forest monitoring, and in particular to a method and system for monitoring changes in forest biomass. Background Art

[0002] Traditional forest biomass monitoring methods mainly rely on ground surveys. Although this method can provide relatively accurate data, it has many limitations. For example, ground surveys require a lot of manpower, material resources and time, and are limited by natural conditions such as terrain and climate, making it difficult to conduct continuous and comprehensive monitoring of large areas of forests. In addition, the data update speed of ground surveys is slow, and it is difficult to reflect the dynamic changes of forest biomass in a timely manner. With the popularization of drone aerial photography technology, new technical means have been provided for forest biomass monitoring. Drone aerial photography technology has the advantages of wide field of view, high flexibility, and fast data acquisition speed. It can achieve large-scale, rapid, and continuous monitoring of forest areas. However, monitoring based solely on aerial images still faces some problems. For example, the quality of aerial images is easily affected by factors such as weather and light, resulting in image color distortion, which in turn leads to inaccurate subsequent feature analysis; and there is no more in-depth analysis of aerial image information. Therefore, it is necessary to provide a forest biomass change monitoring method and system to solve the above problems. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a forest biomass change monitoring method and system to solve the problems existing in the above-mentioned background technology.

[0004] The present invention is achieved by providing a forest biomass change monitoring method, the method comprising the following steps: Collecting aerial image information, the aerial image information including forest aerial images, coordinate positions and timestamps; Collecting aerial photography impact information, wherein the aerial photography impact information includes weather factors, light intensity, and white balance parameters, and performing color restoration on the forest aerial image based on the aerial photography impact information; Based on color and texture features, the aerial images of forests are identified to obtain information about several forest regions, each of which corresponds to tree species, area, and growth stage. Receiving a change monitoring time period, retrieving corresponding historical image information from a historical aerial image library, the historical image information including a forest aerial image, a coordinate position, a timestamp, and information of a plurality of forest regions; Comparing the forest area information in the aerial image information with the forest area information in the historical image information to obtain forest change information, wherein the forest change information includes changes in the coverage area and growth information of each tree species; Retrieve climate information and soil conditions during the change monitoring period, analyze forest change information based on the climate information and soil conditions, and generate change warning information.

[0005] As a further solution of the present invention: the step of performing color restoration on the forest aerial image based on the aerial photography impact information specifically includes: Determine the true color temperature Tc according to the weather factors, determine the compensated color temperature Ts according to the white balance parameters, and calculate the color temperature difference ΔT=Tc-Ts; The color temperature correction factor CF is calculated according to the color temperature difference ΔT and the reference color temperature value Tb, CF=Tb / (Tb+ΔT), and the color value of each pixel in the forest aerial image is adjusted using the color temperature correction factor to achieve color restoration; The brightness of the forest aerial image is adjusted based on the histogram equalization algorithm and the light intensity.

[0006] As a further solution of the present invention: the step of identifying the forest aerial image based on color features and texture features to obtain information of several forest areas specifically includes: Retrieving color features and texture features from a tree feature library, wherein the tree feature library includes tree species, each tree species includes several growth stages, and each growth stage corresponds to a color feature and a texture feature; The forest aerial images are identified and matched based on color features and texture features, and the forest aerial images are divided into regions according to the identification and matching results to obtain information on several forest regions.

[0007] As a further solution of the present invention: the step of comparing the forest area information in the aerial image information with the forest area information in the historical image information to obtain the forest change information specifically includes: Matching the forest area information in the aerial image information with the forest area information in the historical image information one by one according to the coordinate positions; The information of each pair of forest areas was analyzed according to tree species, regional area and growth stage, and the changes in tree species, regional area and growth stage were obtained; Summarize the changes in all tree species, regional area and growth stages to obtain the changes in coverage area and growth information of each tree species.

[0008] As a further solution of the present invention: the step of analyzing forest change information based on climate information and soil conditions to generate change warning information specifically includes: Get predicted growth change information for each tree species based on climate information, soil conditions, and monitoring time period; Compare the predicted growth change information with the corresponding growth stage changes to generate growth change warning information; Analyze the changes in coverage area of ​​each tree species and generate early warning information on area changes.

[0009] As a further solution of the present invention: the step of obtaining the predicted growth change information of each tree species based on the climate information, soil conditions and monitoring time period specifically includes: Determine each tree species and corresponding growth stage based on historical image information, and input tree species, growth stage, climate information, soil conditions, and monitoring time period into the growth prediction model; Output the predicted growth change information of each tree species, climate information including temperature, humidity, rainfall and light information, soil conditions including soil type, pH, nutrient content and moisture status.

[0010] Another object of the present invention is to provide a forest biomass change monitoring system, the system comprising: An aerial image acquisition module is used to acquire aerial image information, wherein the aerial image information includes a forest aerial image, a coordinate position, and a timestamp; An image color restoration module is used to collect aerial photography impact information, wherein the aerial photography impact information includes weather factors, light intensity and white balance parameters, and perform color restoration on the forest aerial image based on the aerial photography impact information; Aerial image recognition module, used to recognize forest aerial images based on color features and texture features, and obtain information of several forest areas, each of which corresponds to tree species, area and growth stage; A historical image retrieval module is used to receive a change monitoring time period and retrieve corresponding historical image information from a historical aerial image library, wherein the historical image information includes a forest aerial image, a coordinate position, a timestamp, and information about a number of forest areas; A forest change information module is used to compare the forest area information in the aerial image information with the forest area information in the historical image information to obtain forest change information, wherein the forest change information includes the change of the coverage area and the change of the growth information of each tree species; The change warning information module is used to retrieve the climate information and soil conditions within the change monitoring period, analyze the forest change information based on the climate information and soil conditions, and generate change warning information.

[0011] As a further solution of the present invention: the image color restoration module includes: A color temperature difference calculation unit, used to determine the real color temperature Tc according to the weather factor, determine the compensation color temperature Ts according to the white balance parameter, and calculate the color temperature difference ΔT=Tc-Ts; A color temperature correction factor unit is used to calculate a color temperature correction factor CF according to the color temperature difference ΔT and the reference color temperature value Tb, CF=Tb / (Tb+ΔT), and use the color temperature correction factor to adjust the color value of each pixel in the forest aerial image to achieve color restoration; The brightness adjustment unit is used to adjust the brightness of the forest aerial image based on the histogram equalization algorithm and the light intensity.

[0012] As a further solution of the present invention: the aerial image recognition module includes: A feature information retrieving unit, used to retrieve color features and texture features from a tree feature library, wherein the tree feature library contains tree species, each tree species contains several growth stages, and each growth stage corresponds to a color feature and a texture feature; The forest area information unit is used to identify and match the forest aerial image based on color features and texture features, and divide the forest aerial image into regions according to the identification and matching results to obtain a number of forest area information.

[0013] As a further solution of the present invention: the forest change information module includes: A region information pairing unit, used to pair the forest region information in the aerial image information with the forest region information in the historical image information one by one according to the coordinate positions; a change determination unit, for analyzing each pair of forest area information according to tree species, regional area and growth stage, and obtaining tree species change, regional area change and growth stage change; The change summary unit is used to summarize the changes in all tree species, regional area changes and growth stage changes to obtain the changes in coverage area and growth information of each tree species.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects aerial photography impact information, which includes weather factors, light intensity and white balance parameters, and automatically restores the color of forest aerial images according to the aerial photography impact information to eliminate the impact of weather and light on image quality, improve the color accuracy of the image, and make subsequent feature recognition more accurate. The forest area information in the aerial image information can be compared with the forest area information in the historical image information to obtain forest change information, which includes the changes in coverage area and growth information of each tree species, so that the changes in forest biomass can be accurately reflected; the climate information and soil conditions within the change monitoring period are also retrieved, the forest change information is analyzed, and change warning information is generated to provide timely warnings and references for decision makers. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a forest biomass change monitoring method.

[0016] Figure 2 This is a flow chart for color restoration of forest aerial images in a forest biomass change monitoring method.

[0017] Figure 3 The present invention is a flowchart for identifying forest aerial images in a forest biomass change monitoring method.

[0018] Figure 4 The figure is a flow chart for obtaining forest change information in a forest biomass change monitoring method.

[0019] Figure 5 A flow chart for generating change warning information in a forest biomass change monitoring method.

[0020] Figure 6 A flow chart for obtaining predicted growth change information in a forest biomass change monitoring method.

[0021] Figure 7 This is a structural diagram of a forest biomass change monitoring system. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a forest biomass change monitoring method, the method comprising the following steps: S100, collecting aerial image information, where the aerial image information includes a forest aerial image, a coordinate position, and a timestamp; S200, collecting aerial photography impact information, where the aerial photography impact information includes weather factors, light intensity, and white balance parameters, and performing color restoration on the forest aerial photography image based on the aerial photography impact information; S300, identifying the forest aerial image based on color features and texture features to obtain a number of forest area information, each forest area information corresponding to tree species, area and growth stage; S400, receiving a change monitoring time period, and retrieving corresponding historical image information from a historical aerial image library, wherein the historical image information includes a forest aerial image, a coordinate position, a timestamp, and information of a plurality of forest regions; S500, comparing the forest area information in the aerial image information with the forest area information in the historical image information to obtain forest change information, wherein the forest change information includes a change in the coverage area and a change in the growth information of each tree species; S600, retrieve climate information and soil conditions within the change monitoring period, analyze forest change information based on the climate information and soil conditions, and generate change warning information.

[0025] In the embodiment of the present invention, aerial image information is first obtained through a drone, and the aerial image information includes forest aerial images, coordinate positions and timestamps. At the same time, aerial photography impact information is collected, and the aerial photography impact information includes weather factors (such as sunny days, cloudy days, rainy days, etc.), light intensity and white balance parameters. The light intensity can be measured by a light intensity meter, and the white balance parameters are directly read from the camera settings. Then, the embodiment of the present invention will perform color restoration on the forest aerial image according to the aerial photography impact information to eliminate the impact of weather and light on image quality, improve the color accuracy of the image, and make subsequent feature recognition more accurate. Then, the forest aerial image will be identified based on color features and texture features of leaves to obtain several forest area information. Each forest area information corresponds to tree species, area and growth stage. The growth stage includes seedlings, saplings, strong forests, middle forests, forbidden mature forests, mature forests and over-mature forests. When the user wants to compare with the previous forest to obtain change information, he needs to enter the change monitoring time period, for example, three months. At this time, the corresponding historical image information will be automatically retrieved from the historical aerial image library. The historical image information includes forest aerial images, coordinate positions, timestamps and several forest area information. Then, the forest area information in the aerial image information will be compared with the forest area information in the historical image information to obtain forest change information. The forest change information includes the changes in coverage area and growth information of each tree species, which can accurately reflect the changes in forest biomass. Finally, the climate information and soil conditions within the change monitoring period will be retrieved. The climate information and soil conditions need to be regularly collected and stored by relevant personnel. Then, the forest change information will be analyzed based on the climate information and soil conditions to generate change warning information to provide timely warnings and references for decision makers.

[0026] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of performing color restoration on the forest aerial image based on the aerial photography impact information specifically includes: S201, determining the real color temperature Tc according to the weather factor, determining the compensated color temperature Ts according to the white balance parameter, and calculating the color temperature difference ΔT=Tc-Ts; S202, calculating a color temperature correction factor CF according to the color temperature difference ΔT and the reference color temperature value Tb, and using the color temperature correction factor to adjust the color value of each pixel in the forest aerial image to achieve color restoration; S203, adjusting the brightness of the forest aerial image based on a histogram equalization algorithm and the light intensity.

[0027] In an embodiment of the present invention, in order to restore the color of an image, it is first necessary to determine the real color temperature Tc according to the weather factors. Here, a predefined color temperature table is used for determination. A sunny day corresponds to a higher color temperature (such as 6500K), and a cloudy day corresponds to a lower color temperature (such as 5500K). Then, the compensation color temperature Ts is determined according to the white balance parameter. The compensation color temperature is set by the camera itself, and the color temperature difference ΔT=Tc-Ts is calculated. Next, the color temperature correction factor CF is calculated according to the color temperature difference ΔT and the reference color temperature value Tb. The reference color temperature value is a standard color temperature value determined in advance, such as 5800K, CF=Tb / (Tb+ΔT). After obtaining the color temperature correction factor, the color value of each pixel in the forest aerial image is adjusted using the color temperature correction factor to achieve color restoration. Finally, the forest aerial image is also adjusted for brightness according to the light intensity and an adaptive brightness adjustment algorithm (such as histogram equalization).

[0028] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of identifying the forest aerial image based on color features and texture features to obtain information of several forest areas specifically includes: S301, retrieving color features and texture features from a tree feature library, wherein the tree feature library includes tree species, each tree species includes several growth stages, and each growth stage corresponds to a color feature and a texture feature; S302, identifying and matching the forest aerial image based on color features and texture features, and dividing the forest aerial image into regions according to the identification and matching results to obtain information of several forest regions.

[0029] In the embodiment of the present invention, it is necessary to construct a tree feature library in advance, and the tree feature library contains a large number of tree species, each tree species contains several growth stages, and each growth stage corresponds to a color feature and a texture feature. In this way, each part of the forest aerial image can be identified and matched according to the color features and texture features in the tree feature library, and the forest aerial image can be divided into regions according to the identification and matching results to obtain information on several forest regions. Among them, the area of ​​the region can be accurately measured using the area measurement tool in the image processing software.

[0030] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of comparing the forest area information in the aerial image information with the forest area information in the historical image information to obtain the forest change information specifically includes: S501, matching the forest area information in the aerial image information with the forest area information in the historical image information one by one according to the coordinate positions; S502, analyzing each pair of forest area information according to tree species, regional area and growth stage to obtain changes in tree species, regional area and growth stage; S503, summarizing the changes in all tree species, regional area changes, and growth stage changes to obtain changes in coverage area and growth information of each tree species.

[0031] In the embodiment of the present invention, in order to determine the change, the forest area information in the aerial image information is matched with the forest area information in the historical image information according to the coordinate position and the tree type. When matching, the regional center coordinates of each forest area information in the historical image information are used as a reference to determine the forest area information corresponding to the regional center coordinates in the aerial image information. After all the matching is completed, each pair of forest area information is analyzed according to the tree type, regional area and growth stage to obtain the change of tree type, regional area and growth stage. Finally, all the changes of tree type, regional area and growth stage are summarized to obtain the change of coverage area and growth information of each tree type. In this way, the change of forest biomass can be accurately reflected.

[0032] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of analyzing forest change information based on climate information and soil conditions to generate change warning information specifically includes: S601, obtaining predicted growth change information of each tree species based on climate information, soil conditions and monitoring time period; S602, comparing the predicted growth change information with the corresponding growth stage change situation to generate growth change warning information; S603, analyzing the changes in the coverage area of ​​each tree type and generating area change warning information.

[0033] In an embodiment of the present invention, predicted growth change information of each tree species is obtained based on climate information, soil conditions and monitoring time period, and then the predicted growth change information is compared with the corresponding growth stage changes to generate growth change warning information, such as whether the growth rate is too slow, the growth rate is too fast or the growth rate is normal. Finally, the coverage area changes of each tree species are analyzed to generate area change warning information, such as a significant decrease or increase in the coverage area.

[0034] like Figure 6 As shown, as a preferred embodiment of the present invention, the step of obtaining the predicted growth change information of each tree species based on climate information, soil conditions and monitoring time period specifically includes: S6011, determining each tree species and corresponding growth stage based on historical image information, and inputting the tree species, growth stage, climate information, soil conditions, and monitoring time period into a growth prediction model; S6012, output the predicted growth change information of each tree species.

[0035] In the embodiment of the present invention, it is necessary to construct a growth prediction model in advance. Here, a large number of data sets are collected and machine learning models, such as decision trees, random forests, support vector machines, etc., are trained to obtain the growth prediction model. When predicting growth change information, each tree species and the corresponding growth stage are first determined based on historical image information, and the tree species, growth stage, climate information, soil conditions and monitoring time period are input into the growth prediction model, and the predicted growth change information of each tree species will be automatically output, wherein the climate information includes temperature, humidity, rainfall and light information, and the soil conditions include soil type, pH, nutrient content and moisture status.

[0036] like Figure 7 As shown, an embodiment of the present invention further provides a forest biomass change monitoring system, the system comprising: An aerial image acquisition module 100 is used to acquire aerial image information, wherein the aerial image information includes a forest aerial image, a coordinate position, and a timestamp; The image color restoration module 200 is used to collect aerial photography impact information, wherein the aerial photography impact information includes weather factors, light intensity and white balance parameters, and perform color restoration on the forest aerial image based on the aerial photography impact information; The aerial image recognition module 300 is used to recognize the forest aerial image based on color features and texture features to obtain a number of forest area information, each forest area information corresponding to tree species, area and growth stage; The historical image retrieval module 400 is used to receive the change monitoring time period and retrieve the corresponding historical image information from the historical aerial image library, wherein the historical image information includes the forest aerial image, coordinate position, time stamp and a plurality of forest area information; The forest change information module 500 is used to compare the forest area information in the aerial image information with the forest area information in the historical image information to obtain forest change information, wherein the forest change information includes the coverage area change and growth information change of each tree species; The change warning information module 600 is used to retrieve the climate information and soil conditions within the change monitoring period, analyze the forest change information based on the climate information and soil conditions, and generate change warning information.

[0037] As a preferred embodiment of the present invention, the image color restoration module 200 includes: A color temperature difference calculation unit, used to determine the real color temperature Tc according to the weather factor, determine the compensation color temperature Ts according to the white balance parameter, and calculate the color temperature difference ΔT=Tc-Ts; A color temperature correction factor unit is used to calculate a color temperature correction factor CF according to the color temperature difference ΔT and the reference color temperature value Tb, CF=Tb / (Tb+ΔT), and use the color temperature correction factor to adjust the color value of each pixel in the forest aerial image to achieve color restoration; The brightness adjustment unit is used to adjust the brightness of the forest aerial image based on the histogram equalization algorithm and the light intensity.

[0038] As a preferred embodiment of the present invention, the aerial image recognition module 300 includes: A feature information retrieving unit, used to retrieve color features and texture features from a tree feature library, wherein the tree feature library contains tree species, each tree species contains several growth stages, and each growth stage corresponds to a color feature and a texture feature; The forest area information unit is used to identify and match the forest aerial image based on color features and texture features, and divide the forest aerial image into regions according to the identification and matching results to obtain a number of forest area information.

[0039] As a preferred embodiment of the present invention, the forest change information module 500 includes: A region information pairing unit, used to pair the forest region information in the aerial image information with the forest region information in the historical image information one by one according to the coordinate positions; a change determination unit, for analyzing each pair of forest area information according to tree species, regional area and growth stage, and obtaining tree species change, regional area change and growth stage change; The change summary unit is used to summarize the changes in all tree species, regional area changes and growth stage changes to obtain the changes in coverage area and growth information of each tree species.

[0040] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0041] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0042] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0043] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A method for monitoring forest biomass changes, characterized in that: The method comprises the following steps: Collecting aerial image information, the aerial image information including forest aerial images, coordinate positions and timestamps; Collecting aerial photography impact information, wherein the aerial photography impact information includes weather factors, light intensity, and white balance parameters, and performing color restoration on the forest aerial image based on the aerial photography impact information; Based on color and texture features, the aerial images of forests are identified to obtain information about several forest regions, each of which corresponds to tree species, area, and growth stage. Receiving a change monitoring time period, retrieving corresponding historical image information from a historical aerial image library, the historical image information including a forest aerial image, a coordinate position, a timestamp, and information of a plurality of forest regions; Comparing the forest area information in the aerial image information with the forest area information in the historical image information to obtain forest change information, wherein the forest change information includes changes in the coverage area and growth information of each tree species; Retrieve climate information and soil conditions during the change monitoring period, analyze forest change information based on the climate information and soil conditions, and generate change warning information.

2. The forest biomass change monitoring method according to claim 1, characterized in that: The step of performing color restoration on the forest aerial image based on the aerial photography impact information specifically includes: Determine the true color temperature Tc according to the weather factors, determine the compensated color temperature Ts according to the white balance parameters, and calculate the color temperature difference ΔT=Tc-Ts; The color temperature correction factor CF is calculated according to the color temperature difference ΔT and the reference color temperature value Tb, CF=Tb / (Tb+ΔT), and the color value of each pixel in the forest aerial image is adjusted using the color temperature correction factor to achieve color restoration; The brightness of the forest aerial image is adjusted based on the histogram equalization algorithm and the light intensity.

3. The forest biomass change monitoring method according to claim 1, characterized in that: The step of identifying the forest aerial image based on color features and texture features to obtain information of several forest areas specifically includes: Retrieving color features and texture features from a tree feature library, wherein the tree feature library includes tree species, each tree species includes several growth stages, and each growth stage corresponds to a color feature and a texture feature; The forest aerial images are identified and matched based on color features and texture features, and the forest aerial images are divided into regions according to the identification and matching results to obtain information on several forest regions.

4. The forest biomass change monitoring method according to claim 1, characterized in that: The step of comparing the forest area information in the aerial image information with the forest area information in the historical image information to obtain the forest change information specifically includes: Matching the forest area information in the aerial image information with the forest area information in the historical image information one by one according to the coordinate positions; The information of each pair of forest areas was analyzed according to tree species, regional area and growth stage, and the changes in tree species, regional area and growth stage were obtained; Summarize the changes in all tree species, regional area and growth stages to obtain the changes in coverage area and growth information of each tree species.

5. The forest biomass change monitoring method according to claim 4, characterized in that: The step of analyzing forest change information based on climate information and soil conditions to generate change warning information specifically includes: Get predicted growth change information for each tree species based on climate information, soil conditions, and monitoring time period; Compare the predicted growth change information with the corresponding growth stage changes to generate growth change warning information; Analyze the changes in coverage area of ​​each tree species and generate early warning information on area changes.

6. The forest biomass change monitoring method according to claim 5, characterized in that: The step of obtaining predicted growth change information of each tree species based on climate information, soil conditions and monitoring time period specifically includes: Determine each tree species and corresponding growth stage based on historical image information, and input tree species, growth stage, climate information, soil conditions, and monitoring time period into the growth prediction model; Output the predicted growth change information of each tree species, climate information including temperature, humidity, rainfall and light information, soil conditions including soil type, pH, nutrient content and moisture status.

7. A forest biomass change monitoring system, characterized in that: The system comprises: An aerial image acquisition module is used to acquire aerial image information, wherein the aerial image information includes a forest aerial image, a coordinate position, and a timestamp; An image color restoration module is used to collect aerial photography impact information, wherein the aerial photography impact information includes weather factors, light intensity and white balance parameters, and perform color restoration on the forest aerial image based on the aerial photography impact information; Aerial image recognition module, used to recognize forest aerial images based on color features and texture features, and obtain information of several forest areas, each of which corresponds to tree species, area and growth stage; A historical image retrieval module is used to receive a change monitoring time period and retrieve corresponding historical image information from a historical aerial image library, wherein the historical image information includes a forest aerial image, a coordinate position, a timestamp, and information about a number of forest areas; A forest change information module, used to compare the forest area information in the aerial image information with the forest area information in the historical image information to obtain forest change information, wherein the forest change information includes the change of the coverage area and the change of the growth information of each tree species; The change warning information module is used to retrieve the climate information and soil conditions within the change monitoring period, analyze the forest change information based on the climate information and soil conditions, and generate change warning information.

8. The forest biomass change monitoring system according to claim 7, characterized in that: The image color restoration module comprises: A color temperature difference calculation unit, used to determine the real color temperature Tc according to the weather factor, determine the compensation color temperature Ts according to the white balance parameter, and calculate the color temperature difference ΔT=Tc-Ts; A color temperature correction factor unit is used to calculate a color temperature correction factor CF according to the color temperature difference ΔT and the reference color temperature value Tb, CF=Tb / (Tb+ΔT), and use the color temperature correction factor to adjust the color value of each pixel in the forest aerial image to achieve color restoration; The brightness adjustment unit is used to adjust the brightness of the forest aerial image based on the histogram equalization algorithm and the light intensity.

9. The forest biomass change monitoring system according to claim 7, characterized in that: The aerial image recognition module includes: A feature information retrieving unit, used to retrieve color features and texture features from a tree feature library, wherein the tree feature library contains tree species, each tree species contains several growth stages, and each growth stage corresponds to a color feature and a texture feature; The forest area information unit is used to identify and match the forest aerial image based on color features and texture features, and divide the forest aerial image into regions according to the identification and matching results to obtain a number of forest area information.

10. The forest biomass change monitoring system according to claim 7, characterized in that: The forest change information module includes: A region information pairing unit, used to pair the forest region information in the aerial image information with the forest region information in the historical image information one by one according to the coordinate positions; a change determination unit, for analyzing each pair of forest area information according to tree species, regional area and growth stage, and obtaining tree species change, regional area change and growth stage change; The change summary unit is used to summarize the changes in all tree species, regional area changes and growth stage changes to obtain the changes in coverage area and growth information of each tree species.

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