Carbon sink assessment method and apparatus for target forest region

Abstract

Embodiments of the present description provide a carbon sink assessment method and apparatus for a target forest region. The method comprises: on the basis of laser echo signals collected for a target forest region, predicting a first tree feature of the target forest region, wherein the first tree feature is used for indicating tree heights at sampling points in the target forest region; on the basis of optical images collected by a camera for a plurality of sampling areas in the target forest region, determining a second tree feature corresponding to each of the plurality of sampling areas, wherein the second tree feature is used for indicating the number, species, and diameter at breast height of trees included in the corresponding sampling area; on the basis of the first tree feature and the second tree feature corresponding to each of the plurality of sampling areas, predicting a first aboveground biomass of the target forest region; and on the basis of the first aboveground biomass, predicting a carbon sink stock of the target forest region.

Description

Carbon sink evaluation method and device for target forest area

[0001] The present application claims priority to the Chinese patent application No. 202410458372.2, filed on April 16, 2024, entitled "Carbon sink evaluation method and device for target forest area", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] One or more embodiments of the present specification relate to the technical field of carbon sink monitoring, in particular to a carbon sink evaluation method and device for target forest area. BACKGROUND

[0003] Forestry carbon sink development usually needs to follow a specific methodology, such as following the Chinese Certified Emission Reduction (CCER) or other institution approved methodology, which is based on manual field measurement or sample statistics. The diameter at breast height, tree height and tree species of trees can be manually observed on random sample plots in the target forest area; however, under the limited cost, it is often necessary to observe some discrete points every few years, which makes it difficult to achieve high spatial and temporal coverage of carbon sink evaluation, and it is difficult to accurately obtain the overall picture of carbon sink distribution and change trend. SUMMARY

[0004] One or more embodiments of the present specification provide a carbon sink evaluation method and device for target forest area.

[0005] In a first aspect, a carbon sink evaluation method for target forest area is provided, the method comprising: predicting a first tree feature of the target forest area according to laser echo signals collected from the target forest area, the first tree feature being used to indicate tree height of each sampling point in the target forest area; determining a second tree feature corresponding to each of a plurality of sampling regions in the target forest area according to optical images collected by a camera from the plurality of sampling regions respectively, the second tree feature being used to indicate the number, species and diameter at breast height of trees included in the corresponding sampling region; predicting a first aboveground biomass of the target forest area according to the first tree feature and the second tree feature corresponding to each of the plurality of sampling regions; and predicting a carbon sink stock of the target forest area according to the first aboveground biomass.

[0006] In a second aspect, a device for evaluating carbon sink of a target forest area is provided, which comprises: a laser signal processing unit configured to predict first tree characteristics of the target forest area according to laser echo signals collected from the target forest area, the first tree characteristics being used to indicate tree heights of sampling points in the target forest area; an optical image processing unit configured to determine second tree characteristics corresponding to a plurality of sampling regions in the target forest area respectively according to optical images collected from the plurality of sampling regions respectively by a camera, the second tree characteristics being used to indicate numbers, species and diameters at breast height of trees included in the corresponding sampling regions; a biomass calculation unit configured to predict first aboveground biomass of the target forest area according to the first tree characteristics and the second tree characteristics corresponding to the plurality of sampling regions respectively; and a carbon sink evaluation unit configured to predict a carbon sink stock of the target forest area according to the first aboveground biomass.

[0007] In a third aspect, a computer readable storage medium having stored thereon computer programs / instructions, which when executed in a computing device, cause the computing device to perform the method as described in the first aspect.

[0008] In a fourth aspect, a computing device is provided, which comprises a memory and a processor, the memory having stored thereon executable codes / instructions, and the processor, when executing the executable codes / instructions, implements the method as described in the first aspect.

[0009] By the method and device provided in one or more embodiments of the present specification, first, first tree characteristics of a target forest area can be predicted according to laser echo signals collected from the target forest area, wherein the first tree characteristics can more accurately indicate tree heights of sampling points in the target forest area; and second tree characteristics corresponding to a plurality of sampling regions in the target forest area can be determined according to optical images collected from the plurality of sampling regions respectively by a camera, wherein the second tree characteristics can more accurately indicate numbers, species and diameters at breast height of trees included in the corresponding sampling regions; and then, first aboveground biomass of the target forest area can be more accurately predicted according to the first tree characteristics and the second tree characteristics corresponding to the plurality of sampling regions respectively, and a carbon sink stock of the target forest area can be more accurately predicted according to the first aboveground biomass. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present specification, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0011] FIG. 1 is a technical scenario diagram of a technical method provided in an embodiment of the present specification;

[0012] FIG. 2 is a technical scenario diagram of a technical method provided in an embodiment of the present specification;

[0013] FIG. 3 is a process diagram of predicting the carbon sink stock of a target forest area based on multiple data sources;

[0014] FIG. 4 is a flowchart of a method for evaluating the carbon sink of a target forest area provided in an embodiment of the present specification;

[0015] FIG. 5 is a structural diagram of a device for evaluating the carbon sink of a target forest area provided in an embodiment of the present specification. DETAILED DESCRIPTION

[0016] The various non-limiting embodiments provided in the present specification will be described in detail below with reference to the accompanying drawings.

[0017] With the emphasis on climate change and environmental protection, accurate evaluation of the carbon sink of an ecological system is of great significance to promoting sustainable development and low-carbon economic transformation. Through effective spatio-temporal monitoring of the carbon sink of an ecological system, reliable data support is provided for decision-makers, which can effectively promote ecological protection and facilitate sustainable economic development.

[0018] A carbon sink refers to a process, activity or mechanism that achieves the absorption of carbon dioxide in the atmosphere through afforestation and vegetation restoration, thereby reducing the concentration of greenhouse gases in the atmosphere. The evaluation of the carbon sink of a target forest area refers to the evaluation of how much carbon dioxide is absorbed and stored by the target forest area, i.e., the evaluation of the ability of the target forest area to absorb and store carbon dioxide.

[0019] A method and device for evaluating the carbon sink of a target forest area are provided in an embodiment of the present specification. First, a first tree feature of a target forest area can be predicted based on laser echo signals collected from the target forest area, which can more accurately indicate the height of trees at each sampling point in the target forest area. Second, a second tree feature corresponding to each of a plurality of sampling regions in the target forest area can be determined based on optical images collected by a camera from the plurality of sampling regions, which can more accurately indicate the number, species and diameter at breast height of the trees included in the corresponding sampling region. Third, the first aboveground biomass of the target forest area can be more accurately predicted based on the first tree feature and the second tree feature corresponding to each of the plurality of sampling regions, and the carbon sink stock of the target forest area can be more accurately predicted based on the first aboveground biomass.

[0020] FIG. 1 is a technical scenario diagram of a technical method provided in an embodiment of the present specification.

[0021] Referring to FIG. 1, in order to more accurately implement the carbon sink evaluation of the target forest area, a spaceborne laser radar can be used to collect corresponding laser echo signals of the target forest area. The laser echo signals collected by the target forest area are used to predict first tree characteristics of the target forest area, and the first tree characteristics at least include tree heights corresponding to each sampling point in the target forest area. The laser radar can obtain spatially discrete laser echo signals of the target forest area, and by analyzing the vertical distribution of the intensity of the laser echo signals at each elevation, the tree height of each sampling point in the target forest area can be calculated in combination with the terrain data of the target forest area.

[0022] In some application scenarios, an unmanned aerial vehicle-mounted laser radar can also be used to collect corresponding laser echo signals of the target forest area.

[0023] Referring to FIG. 1, in order to more accurately implement the carbon sink evaluation of the target forest area, a spaceborne laser radar can be used to collect corresponding laser echo signals of the target forest area. The laser echo signals collected by the target forest area are used to predict first tree characteristics of the target forest area, and the first tree characteristics at least include tree heights corresponding to each sampling point in the target forest area. The laser radar can obtain spatially discrete laser echo signals of the target forest area, and by analyzing the vertical distribution of the intensity of the laser echo signals at each elevation, the tree height of each sampling point in the target forest area can be calculated in combination with the terrain data of the target forest area.

[0024] Correspondingly, different sampling regions in the aforementioned several sampling regions can be distributed in different management fragments of the target forest area. The management fragments described herein can correspond to the aforementioned forest blocks or small blocks, for example.

[0025] Referring to FIG. 3, if the laser echo signals used to predict the first tree characteristics and the several optical images used to predict the second tree characteristics are collected within the same time interval, the first aboveground biomass of the target forest area within the time interval can be more accurately predicted in combination with the first tree characteristics and the second tree characteristics.

[0026] Referring to FIG. 1, in order to more accurately implement the carbon sink evaluation of the target forest area, a spaceborne laser radar can be used to collect corresponding laser echo signals of the target forest area. The laser echo signals collected by the target forest area are used to predict first tree characteristics of the target forest area, and the first tree characteristics at least include tree heights corresponding to each sampling point in the target forest area. The laser radar can obtain spatially discrete laser echo signals of the target forest area, and by analyzing the vertical distribution of the intensity of the laser echo signals at each elevation, the tree height of each sampling point in the target forest area can be calculated in combination with the terrain data of the target forest area.

[0027] Referring to FIG. 1, in order to achieve more accurate implementation of the carbon sink evaluation of the target forest area, a spaceborne multispectral remote sensor can also be used to collect a multispectral remote sensing image of the target forest area; the multispectral remote sensing image is used to predict the leaf area index (LAI) of the target forest area in a corresponding time interval, and the leaf area index can be used to predict the gross primary productivity (GPP) of the target forest area in the corresponding time interval. The gross primary productivity of the target forest area refers to the total amount or fixed total energy of inorganic matter synthesized into organic matter per unit time and per unit area by the trees in the target forest area. It should be particularly noted that in some application scenarios, an unmanned aerial multispectral remote sensor can also be used to collect a corresponding multispectral remote sensing image of the target forest area.

[0028] The aboveground biomass of the target forest area in a certain time interval can be used to calculate the carbon sink present amount of the target forest area in the time interval; and the gross primary productivity of the target forest area in a certain time interval can be used to calculate the biological growth amount of the target forest area in the time interval, and further calculate the carbon sink growth amount of the target forest area in the time interval.

[0029] The revisit period of the spaceborne lidar, spaceborne microwave radar and spaceborne multispectral remote sensor to the target forest area is short, and the laser echo signal, microwave echo signal and multispectral remote sensor collected by each of them in a corresponding time interval can be used as needed to frequently calculate various parameters related to the carbon sink evaluation of the target forest area, such as one or more of the first tree feature, the second aboveground biomass and the gross primary productivity in the foregoing examples.

[0030] Taking the spaceborne multispectral remote sensor as an example, referring to FIG. 3, the time length T of a single time interval can be 1 day, 1 week or other relatively short preset time length as a prediction period to predict the leaf area index and other parameters of the target forest area corresponding to each of a plurality of consecutive time intervals. More specifically, for any ith time interval, a multispectral image of the target forest area can be collected in the ith time interval by the multispectral remote sensor, and the leaf area index of the target forest area in the ith time interval can be predicted according to the multispectral image. The total primary productivity of the target forest area in the ith time interval can be calculated by superimposing the leaf area index and the corresponding photosynthetically active radiation absorption ratio. In addition, the biological growth amount of the target forest area in the ith time interval can be calculated according to the area of the target forest area, the time length of the ith time interval, the total primary productivity of the target forest area in the ith time interval, and further the corresponding carbon sink growth amount can be converted from the calculated biological growth amount. In this way, the biological growth of the target forest area or the trend of the carbon sink capacity can be intuitively presented to the user through a plurality of biological growth amounts or carbon sink growth amounts corresponding to a plurality of time intervals.

[0031] The optical images collected by the camera on the target forest area have high resolution, but the manpower and time cost is relatively high. The optical images collected by the camera on the target forest area can be used to calculate the parameters related to the carbon sink evaluation of the target forest area in a large time range. For example, in a prediction period of P*T, P is greater than 1, the second tree feature of several sampling areas in the target forest area in the corresponding time interval is predicted; then combined with the first tree feature that meets the relevant time condition, the first aboveground biomass of the target forest area in the corresponding time interval can be predicted, and then based on the first aboveground biomass, the carbon sink stock of the target forest area is calculated alone or in combination with the corresponding second aboveground biomass and total primary productivity.

[0032] Next, in combination with the foregoing FIG. 1, FIG. 2 and FIG. 3, a carbon sink evaluation method of a target forest area is described in detail.

[0033] FIG. 4 is a flowchart of a carbon sink evaluation method of a target forest area provided in an embodiment of the present specification. The method exemplarily describes a process diagram of predicting the carbon sink stock of the target forest area in the K+N time interval on demand; wherein K is an integer greater than 0 and N is an integer not less than 0. The length of a single time interval T is, for example, 1 day, 1 week or other preset time length. The method can be executed by any device, platform, equipment or equipment cluster with computing / processing.

[0034] Referring to FIG. 4, the method can include, but is not limited to, some or all of the following steps S401-S411.

[0035] Step S401, according to the laser echo signals collected on the target forest area in the K+N time interval, the first tree feature of the target forest area is predicted, and the first tree feature is used to indicate the tree height of each sampling point in the target forest area.

[0036] Referring to the foregoing, the laser radar can obtain spatially discrete laser echo signals on the target forest area, and by analyzing the vertical distribution of the intensity of the laser echo signals at each elevation, combined with the terrain data of the target forest area, the tree height of each sampling point in the target forest area can be calculated. It can be understood that the aforementioned sampling point can correspond to a geographical area with a certain area in the target forest area.

[0037] Step S403, according to the optical images respectively collected by the camera on several sampling areas in the target forest area in the K+N time interval, the second tree feature corresponding to each of the several sampling areas is determined, and the second tree feature is used to indicate the number, species and diameter at breast height of the trees included in the corresponding sampling area.

[0038] Referring to the foregoing, the target forest area can be divided into a plurality of management patches, and one management patch can correspond to one forest plot or a small plot in the target forest area. In order to achieve sample coverage for management patches with different tree characteristics as much as possible, the number of the plurality of sampling areas can be multiple, and different sampling areas belong to different management patches; for example, the target forest area is divided into M management patches, and M sampling areas in the M management patches can be collected optical images respectively.

[0039] In step S405, the first aboveground biomass of the target forest area in the K+N time interval is predicted according to the first tree characteristic and the second tree characteristic corresponding to each sampling area.

[0040] When the target forest area is divided into a plurality of management patches, for any management patch in the plurality of management patches, the third aboveground biomass corresponding to the management patch can be predicted according to the tree height of each sampling point in the management patch and the second tree characteristic corresponding to the sampling area belonging to the management patch; and the first aboveground biomass is calculated according to the third aboveground biomass corresponding to each of the plurality of management patches, for example, the first aboveground biomass is obtained by summing the plurality of third aboveground biomasses.

[0041] For example, for any management patch, the cumulative number of trees included in the management patch can be predicted according to the number of trees included in the sampling area belonging to the management patch. In addition, the trees in the management patch usually belong to the same species, and the diameter at breast height of the trees in the management patch is similar or satisfies a certain distribution. Further, according to the species of the trees included in the sampling area belonging to the management patch, a corresponding growth equation can be selected to process the cumulative number of trees, the diameter at breast height, and the tree height of the trees included in the management patch, so as to obtain the aboveground biomass of the trees included in the management patch.

[0042] The foregoing manner is only exemplary. For example, the tree density of the target forest area can be relatively uniform, the diameter at breast height of the trees is consistent, and the species of the trees is single. In this case, for example, the number of trees in the target forest area, the average diameter at breast height of the trees in the target forest area, or the distribution satisfied by the diameter at breast height of all the trees in the target forest area can be estimated according to the second tree characteristics of a small number of sampling areas, and then a corresponding growth equation can be used to process the number, diameter at breast height, and tree height of all the trees included in the target forest area, so as to obtain the first aboveground biomass of the trees included in the target forest area.

[0043] In some embodiments, the method can further include the following steps S407 and / or step S409.

[0044] In step S407, the second aboveground biomass of the target forest area in the K+N time interval is predicted according to the microwave echo signal collected in the K+N time interval.

[0045] According to the microwave echo signal, the vegetation optical thickness of each sampling point in the target forest area can be predicted, and then the second aboveground biomass can be determined according to the vegetation optical thickness of each sampling point in the target forest area. For example, the microwave echo signal of the target forest area can be processed by a Water-Cloud model or other methods to retrieve the vegetation optical thickness of each sampling point in the target forest area, and then the vegetation optical thickness of each sampling point is processed by a corresponding conversion relationship to obtain the second aboveground biomass.

[0046] In step S409, the total primary productivity of the target forest area in each corresponding time interval from the K+1th to the K+Nth time interval is obtained, wherein the total primary productivity is predicted based on the multispectral remote sensing image collected in the corresponding time interval.

[0047] For any ith time interval, the leaf area index of the target forest area in the ith time interval can be predicted according to the multispectral remote sensing image collected in the ith time interval. The total primary productivity of the target forest area in the ith time interval can be obtained by processing the leaf area index of the target forest area in the ith time interval by a corresponding light energy utilization rate model. As described above, the total primary productivity of the target forest area in the ith time interval can also be combined with the area of the target forest area to calculate the biological growth of the target forest area in the ith time interval, and then the carbon sink growth of the target forest area in the ith time interval can be calculated based on the biological growth.

[0048] Finally, in step S411, the carbon sink stock of the target forest area in the K+Nth time interval is predicted according to at least the first aboveground biomass.

[0049] When the aforementioned step S407 is performed and the aforementioned step S409 is not performed, in the aforementioned step S411, the carbon sink stock of the target forest area in the K+Nth time interval can be calculated according to the first aboveground biomass and the second aboveground biomass. For example, the average of the first aboveground biomass and the second aboveground biomass is calculated, and the obtained aboveground biomass can more accurately represent the aboveground biomass of the target forest area in the K+Nth time interval. The carbon sink stock of the target forest area in the K+Nth time interval can be obtained by performing a corresponding proportional conversion on the obtained aboveground biomass.

[0050] When the step S409 is executed and the step S407 is not executed, in the step S411, the carbon sink stock of the target forest area in the K+Nth time interval can be calculated according to the first aboveground biomass, the predicted carbon sink stock of the target forest area in the Kth time interval, and the total primary productivity of the target forest area in the K+1th to K+Nth time intervals respectively. For example, the carbon sink increment of the target forest area in the K+1th to K+Nth time intervals respectively can be calculated according to the total primary productivity of the target forest area in the K+1th to K+Nth time intervals respectively, and the carbon sink increment of the target forest area in the K+1th to K+Nth time intervals respectively can be summed to obtain a total carbon sink increment. In addition, the first aboveground biomass can be converted into a corresponding carbon sink stock. Then, the carbon sink stock converted from the first aboveground biomass can be corrected according to the total carbon sink increment and the carbon sink stock of the target forest area in the Kth time interval, so as to obtain the carbon sink stock of the target forest area in the K+Nth time interval.

[0051] When the step S407 and the step S409 are executed, the step S411 specifically includes: calculating the carbon sink stock of the target forest area in the K+Nth time interval according to the first aboveground biomass, the second aboveground biomass, the predicted carbon sink stock of the target forest area in the Kth time interval, and the total primary productivity of the target forest area in the K+1th to K+Nth time intervals respectively. For example, the first aboveground biomass and the second aboveground biomass can be converted into a first carbon sink stock and a second carbon sink stock respectively. In addition, the carbon sink increment of the target forest area in the K+1th to K+Nth time intervals respectively can be calculated according to the total primary productivity of the target forest area in the K+1th to K+Nth time intervals respectively, and the carbon sink increment of the target forest area in the K+1th to K+Nth time intervals respectively can be summed to obtain a total carbon sink increment. Then, the average of the first carbon sink stock and the second carbon sink stock can be corrected according to the total carbon sink increment and the carbon sink stock of the target forest area in the Kth time interval, so as to obtain the carbon sink stock of the target forest area in the K+Nth time interval.

[0052] In summary, the various methods provided in the embodiments of the present specification can comprehensively use laser radar collected laser echo signals of the target forest area, microwave radar collected microwave echo signals of the target forest area, multispectral remote sensing collected multispectral smoke sensing images of the target forest area, and optical images collected by cameras in the sampling area of the target forest area, and other multi-modal data sources for collaborative analysis, i.e., one or more of the first aboveground biomass, the second aboveground biomass, and the total primary productivity that meet the corresponding time conditions are collaboratively analyzed, and the carbon sink evaluation of the target forest area can achieve higher time and spatial coverage, thereby facilitating more accurate implementation of the carbon sink evaluation of the target forest area.

[0053] In some embodiments, some features of the trees in the target forest area can also be observed by the staff to some extent, such as observing the species, diameter at breast height, and number of trees in some sampling areas, so as to correct the relevant data predicted in the calculation process according to the observed values, so as to more accurately achieve the carbon sink evaluation of the target forest area.

[0054] In some embodiments, multispectral remote sensing images of the target forest area can be collected at a high frequency, so as to analyze the vegetation change of the target forest area through a large number of multispectral remote sensing images, including land use types, forest disturbances such as fires or diseases, so as to correct the predicted carbon sink stock according to the vegetation change as an environmental factor.

[0055] In some embodiments, for the multispectral remote sensing images collected by the multispectral remote sensor, the relevant vegetation physiological indicators of the target forest area can also be retrieved using each spectral band, such as vegetation evapotranspiration and dry plant content, and then the retrieved vegetation physiological indicators are used to evaluate the health status, water supply pressure, and flammable risk of the vegetation in the target forest area, so as to facilitate the staff to better manage and maintain the target forest area to some extent.

[0056] Based on the same idea as the foregoing method embodiments, the present specification embodiments also provide a carbon sink evaluation device 50 for a target forest area. Referring to FIG. 5, the device 50 comprises: a laser signal processing unit 501 configured to predict first tree features of the target forest area according to laser echo signals collected from the target forest area, the first tree features being used to indicate tree heights of each sampling point in the target forest area; an optical image processing unit 503 configured to determine second tree features corresponding to a plurality of sampling regions in the target forest area respectively according to optical images collected from the plurality of sampling regions by a camera, the second tree features being used to indicate numbers, species and diameters at breast height of trees included in the corresponding sampling regions; a biomass calculation unit 505 configured to predict first aboveground biomasses of the target forest area according to the first tree features and the second tree features corresponding to the plurality of sampling regions respectively; and a carbon sink evaluation unit 507 configured to predict a carbon sink stock of the target forest area according to the first aboveground biomasses.

[0057] Those skilled in the art should be aware that, in one or more examples described above, the functions described in the present specification can be implemented in hardware, software, firmware or any combination thereof. When implemented in software, the computer program corresponding to the functions described in the present specification can be stored in a computer readable medium or transmitted as one or more instructions / code on a computer readable medium, so that when the computer program corresponding to the functions described in the present specification is executed by a computer, the method described in any one of the embodiments of the present specification is implemented by the computer.

[0058] The present specification embodiments also provide a computer readable storage medium having stored thereon a computer program / instruction, when the computer program / instruction is executed in a computing device, the computing device executes the carbon sink evaluation method for a target forest area provided in any one of the embodiments of the present specification.

[0059] The present specification embodiments also provide a computing device comprising a memory and a processor, the memory having stored executable code / instructions, when the processor executes the executable code / instructions, the carbon sink evaluation method for a target forest area provided in any one of the embodiments of the present specification is implemented.

[0060] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts in each of the embodiments can be referred to each other, and the difference from other embodiments is mainly described in each of the embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0061] The above describes particular embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous or necessary.

[0062] The above describes particular embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous or necessary.

[0062] The above describes particular embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order in order to achieve the desired results. In some implementations, multitasking and parallel processing can be advantageous or necessary.

Claims

1. A method for evaluating carbon sink of a target forest area, the method comprising: predicting first tree characteristics of the target forest area according to laser echo signals collected from the target forest area, the first tree characteristics being indicative of tree heights of sampling points in the target forest area; determining second tree characteristics corresponding to a plurality of sampling regions in the target forest area respectively according to optical images collected from the plurality of sampling regions respectively by a camera, the second tree characteristics being indicative of numbers, species and diameters at breast height of trees included in the corresponding sampling regions; predicting first aboveground biomass of the target forest area according to the first tree characteristics and the second tree characteristics corresponding to the plurality of sampling regions respectively; predicting carbon sink stock of the target forest area according to the first aboveground biomass. 2.The method of claim 1, wherein the target forest area is divided into a plurality of management patches, and different sampling areas belong to different management patches. The management patches correspond to compartments or sub-compartments. 3.The method of claim 2, wherein the predicting the first aboveground biomass of the target forest area according to the first tree characteristics and the second tree characteristics corresponding to the plurality of sampling regions respectively comprises: for any management patch in the plurality of management patches, predicting third aboveground biomass corresponding to the any management patch according to tree heights of sampling points in the any management patch and the second tree characteristics corresponding to sampling regions belonging to the any management patch; and calculating the first aboveground biomass according to the third aboveground biomass corresponding to the plurality of management patches respectively.

4. The method of claim 1, further comprising: predicting second aboveground biomass of the target forest area according to microwave echo signals collected from the target forest area; wherein the predicting the carbon sink stock of the target forest area according to the first aboveground biomass comprises predicting the carbon sink stock of the target forest area according to the first aboveground biomass and the second aboveground biomass. 5.The method of claim 4, wherein the predicting the second aboveground biomass of the target forest area according to the microwave echo signals collected from the target forest area comprises: predicting vegetation optical thicknesses of sampling points in the target forest area according to the microwave echo signals; and determining the second aboveground biomass according to the vegetation optical thicknesses of the sampling points in the target forest area.

6. The method of claim 1, said laser return signal and said optical image being collected within a K+Nth time interval; wherein, The method further comprises: obtaining total primary productivity corresponding to K+1th to K+Nth time intervals respectively of the target forest area, the total primary productivity being predicted based on multispectral remote sensing images collected from the target forest area in the corresponding time intervals; the predicting the carbon sink stock of the target forest area according to the first aboveground biomass comprises predicting the carbon sink stock of the target forest area in the K+Nth time interval according to the first aboveground biomass, the carbon sink stock of the target forest area in the Kth time interval which has been predicted, and the total primary productivity corresponding to the K+1th to K+Nth time intervals respectively of the target forest area. 7.The method of claim 6, wherein the total primary productivity corresponding to any ith time interval of the target forest area is obtained by: predicting a leaf area index of the target forest region in the i-th time interval according to multispectral remote sensing images collected in the i-th time interval from the target forest region; calculating a total primary productivity of the target forest region in the i-th time interval according to the leaf area index of the target forest region in the i-th time interval.

8. The method of claim 6, further comprising: calculating a carbon sink increment of the target forest region in the K+N-th time interval according to the total primary productivity of the target forest region in the K+N-th time interval. 9.The method of claim 6, wherein the multispectral remote sensing images are collected by a spaceborne multispectral remote sensor. 10.A carbon sink evaluation device for a target forest region, the device comprising: a laser signal processing unit configured to predict a first tree feature of the target forest region according to laser echo signals collected from the target forest region, the first tree feature being indicative of a tree height of each sampling point in the target forest region; an optical image processing unit configured to determine a second tree feature of each of a plurality of sampling regions in the target forest region according to optical images respectively collected by a camera from the plurality of sampling regions, the second tree feature being indicative of a number, a species, and a diameter at breast height of trees included in the corresponding sampling region; a biomass calculation unit configured to predict a first aboveground biomass of the target forest region according to the first tree feature and the second tree feature of each of the plurality of sampling regions; a carbon sink evaluation unit configured to predict a carbon sink stock of the target forest region according to the first aboveground biomass. 11.A computer readable storage medium having stored thereon a computer program, which when executed in a computing device, causes the computing device to perform the method of any one of claims 1-9. 12.A computing device comprising a memory and a processor, the memory having stored therein executable code, which when executed by the processor, implements the method of any one of claims 1-9.

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