A method and system for intelligently calculating forest carbon reserves

Abstract

The present invention is applicable to the technical field of forest carbon reserves, and provides an intelligent calculation method and system for forest carbon reserves. The present invention uses satellite remote sensing technology to perform remote sensing monitoring of a target forest area; performs secondary classification identification and regional division of altitude and plant community, and divides multiple partial forest sub-areas; selects multiple sampling units from multiple forest units, obtains multiple sampled carbon reserves; calculates sub-region carbon reserves of multiple partial forest sub-areas; performs summary processing, and calculates the total forest carbon reserves. Based on satellite remote sensing technology, the target forest area can be remotely sensed and monitored, divided into multiple partial forest sub-areas, and then the sampled carbon reserves of multiple sampling units are obtained, the sub-region carbon reserves of multiple partial forest sub-areas are calculated, and the total forest carbon reserves of the target forest area are summarized and calculated, thereby realizing accurate and efficient carbon reserve calculation for large forest areas, and can effectively promote the technical application of forest carbon reserve calculation.

Description

Technical Field

[0001] The present invention belongs to the technical field of forest carbon reserves, and in particular relates to an intelligent calculation method and system for forest carbon reserves. Background Art

[0002] Forest carbon storage refers to the amount of carbon in the forest carbon pool. It is the basis for assessing the carbon balance of forest ecosystems and reflects the ability of forests to absorb and store carbon dioxide. As a very important carbon sink, forests can absorb a large amount of carbon dioxide and fix it in plants and soil, which is of great significance to the study of global carbon cycle and climate change.

[0003] The existing technology has certain limitations in the measurement of forest carbon stocks. Specifically, it can accurately measure carbon stocks in small-scale and small-range forest areas. However, when faced with vast forest areas, it is difficult to achieve accurate and efficient carbon stock assessment, which seriously limits the technical application of forest carbon stock measurement and restricts the effective implementation of relevant policies. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for intelligently calculating forest carbon reserves, aiming to solve the technical problems existing in the prior art mentioned in the background technology.

[0005] The embodiment of the present invention is implemented as follows:

[0006] A method for intelligently calculating forest carbon reserves, the method specifically comprising the following steps:

[0007] Determine the target forest area, conduct remote sensing monitoring of the target forest area based on satellite remote sensing technology, and obtain remote sensing monitoring data;

[0008] Preprocessing the remote sensing monitoring data to generate remote sensing standard data, and performing secondary classification and regional division of altitude and plant community to divide the target forest area into a plurality of partial forest sub-areas;

[0009] Dividing the plurality of partial forest sub-areas into a plurality of forest units, and selecting a plurality of sampling units from the plurality of forest units to obtain the sampling carbon storage of the plurality of sampling units;

[0010] Calculating sub-region carbon stocks of a plurality of partial forest sub-regions based on a plurality of said sampled carbon stocks;

[0011] The carbon stocks of the plurality of sub-regions are aggregated and processed to calculate the total forest carbon stock of the target forest region.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the determining of the target forest area, performing remote sensing monitoring of the target forest area based on satellite remote sensing technology, and obtaining remote sensing monitoring data specifically include the following steps:

[0013] Identify target forest areas with forest carbon stock measurement needs;

[0014] Generate and send remote sensing image commands and lidar commands;

[0015] According to the remote sensing image instruction, remote sensing images of the target forest area are photographed to obtain remote sensing image data;

[0016] According to the laser radar instruction, the target forest area is detected by laser radar to obtain laser detection data;

[0017] The remote sensing image data and the laser detection data are integrated to obtain remote sensing monitoring data.

[0018] As a further limitation of the technical solution of the embodiment of the present invention, the preprocessing of the remote sensing monitoring data, generating remote sensing standard data, and performing secondary classification and regional division of altitude and plant community, and dividing the target forest area into a plurality of partial forest sub-areas specifically includes the following steps:

[0019] Performing geometric correction, radiation correction, image registration and enhancement processing on the remote sensing image data to generate standard image data;

[0020] Performing correction and registration, denoising filtering and feature extraction on the laser detection data to generate standard detection data;

[0021] According to the standard detection data and the standard image data, secondary classification and identification of altitude and vegetation and regional division are performed to divide the target forest area into a plurality of partial forest sub-areas.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the secondary classification and identification of altitude and vegetation and regional division are performed according to the standard detection data and the standard image data, and the target forest area is divided into a plurality of partial forest sub-areas, which specifically includes the following steps:

[0023] According to the standard detection data, regional altitude identification is performed on the target forest area, and regional altitude data is recorded;

[0024] Dividing the target forest area into a plurality of altitude classification areas according to the regional altitude data and a preset altitude classification standard;

[0025] According to the plurality of altitude classification areas, the standard image data is divided into a plurality of corresponding altitude classification image data;

[0026] Performing plant group classification and identification on the plurality of altitude classification image data respectively, and recording a plurality of plant group classification boundaries;

[0027] According to the plurality of plant community classification boundaries, the plurality of altitude classification areas are divided twice to obtain a plurality of partial forest sub-areas.

[0028] As a further limitation of the technical solution of the embodiment of the present invention, the dividing of the plurality of partial forest sub-areas into a plurality of forest units, and selecting a plurality of sampling units from the plurality of forest units, and obtaining the sampled carbon storage of the plurality of sampling units specifically comprises the following steps:

[0029] According to a preset unit standard, the plurality of partial forest sub-areas are divided into units, and the plurality of partial forest sub-areas are divided into a plurality of forest units;

[0030] Selecting a plurality of sampling units from the plurality of forest units;

[0031] The sampled carbon stocks of the plurality of sampling units are obtained.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, the calculation of the sub-region carbon storage of multiple partial forest sub-regions based on the multiple sampled carbon storages specifically includes the following steps:

[0033] Dividing the plurality of forest units into a plurality of sampling units and a plurality of unknown units;

[0034] In the plurality of partial forest sub-areas, measuring the cell distances between the plurality of unknown cells and the plurality of sampling cells to obtain a plurality of cell distance data;

[0035] The sub-region carbon stocks of the plurality of partial forest sub-regions are calculated based on the plurality of unit distance data and the plurality of sampled carbon stocks.

[0036] As a further limitation of the technical solution of the embodiment of the present invention, the calculation formula of the carbon storage of the sub-region is:

[0037] ;

[0038] in, is the carbon storage in the sub-region, Representative unknown units, some forest sub-areas have Unknown units, Representative sampling units, some forest sub-areas have Sampling units, For the The sampled carbon storage of each sampling unit, For the The unknown unit and The unit distance between sampling units, is the preset distance decomposition factor.

[0039] As a further limitation of the technical solution of the embodiment of the present invention, the aggregating the carbon stocks of the multiple sub-regions to calculate the total forest carbon stock of the target forest area specifically includes the following steps:

[0040] Aggregating the carbon stocks of the plurality of sub-regions to obtain an aggregated carbon stock;

[0041] Import preset calculation coefficients;

[0042] The total forest carbon stock of the target forest area is calculated by combining the calculation coefficient and the summary carbon stock.

[0043] An intelligent forest carbon storage calculation system, the system includes a regional remote sensing monitoring module, a secondary classification module, a sampling carbon storage acquisition module, a sub-region carbon storage calculation module and a total carbon storage calculation module, wherein:

[0044] The regional remote sensing monitoring module is used to determine the target forest area, conduct remote sensing monitoring of the target forest area based on satellite remote sensing technology, and obtain remote sensing monitoring data;

[0045] A secondary classification module is used to pre-process the remote sensing monitoring data, generate remote sensing standard data, and perform secondary classification identification and regional division of altitude and plant community, and divide the target forest area into multiple partial forest sub-areas;

[0046] A sampling carbon stock acquisition module is used to divide the plurality of partial forest sub-areas into a plurality of forest units, and select a plurality of sampling units from the plurality of forest units to acquire the sampling carbon stock of the plurality of sampling units;

[0047] A sub-region carbon storage calculation module, used to calculate the sub-region carbon storage of multiple partial forest sub-regions based on the multiple sampled carbon storages;

[0048] The total carbon stock calculation module is used to aggregate the carbon stocks of the multiple sub-regions and calculate the total forest carbon stock of the target forest area.

[0049] As a further limitation of the technical solution of the embodiment of the present invention, the secondary classification module specifically includes:

[0050] The first preprocessing submodule is used to perform geometric correction, radiation correction, image registration and enhancement processing on the remote sensing image data to generate standard image data;

[0051] A second preprocessing submodule is used to perform correction and registration, denoising filtering and feature extraction on the laser detection data to generate standard detection data;

[0052] The sub-region division sub-module is used to perform secondary classification and identification of altitude and vegetation and regional division according to the standard detection data and the standard image data, and divide the target forest area into a plurality of partial forest sub-regions.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The embodiment of the present invention uses satellite remote sensing technology to perform remote sensing monitoring of the target forest area; performs secondary classification identification and regional division of altitude and plant community, and divides the area into multiple partial forest sub-areas; selects multiple sampling units from multiple forest units, obtains multiple sampling carbon stocks; calculates the sub-region carbon stocks of multiple partial forest sub-areas; performs summary processing, and measures the total forest carbon stock. Based on satellite remote sensing technology, the target forest area can be remotely sensed and monitored, divided into multiple partial forest sub-areas, and then the sampling carbon stocks of multiple sampling units are obtained, the sub-region carbon stocks of multiple partial forest sub-areas are calculated, and the total forest carbon stock of the target forest area is summarized and calculated, thereby realizing accurate and efficient carbon stock calculation for large forest areas, and can effectively promote the technical application of forest carbon stock calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart showing a method for intelligently calculating forest carbon reserves provided by an embodiment of the present invention is shown;

[0056] Figure 2 A flow chart showing remote sensing monitoring of a target forest area in a method provided by an embodiment of the present invention is shown;

[0057] Figure 3 A flow chart showing the method provided by an embodiment of the present invention for dividing a target forest area into a plurality of partial forest sub-areas;

[0058] Figure 4 A flowchart of secondary classification identification and division in the method provided by an embodiment of the present invention is shown;

[0059] Figure 5 A flow chart of obtaining a plurality of sample carbon reserves in a method provided by an embodiment of the present invention is shown;

[0060] Figure 6 A flow chart of calculating carbon storage in multiple sub-regions in the method provided by an embodiment of the present invention is shown;

[0061] Figure 7A flow chart of calculating total forest carbon storage in the method provided in an embodiment of the present invention is shown;

[0062] Figure 8 An application architecture diagram of an intelligent forest carbon storage measurement system provided by an embodiment of the present invention is shown;

[0063] Fig. 9 A structural block diagram of a secondary classification module in a system provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] It is understandable that the existing technology has certain limitations in the measurement of forest carbon stocks. Specifically, it can accurately measure carbon stocks in small-scale and small-range forest areas. However, when faced with vast forest areas, it is difficult to achieve accurate and efficient carbon stock assessment, which seriously limits the technical application of forest carbon stock measurement and restricts the effective implementation of relevant policies.

[0066] To solve the above problems, an embodiment of the present invention discloses an intelligent forest carbon stock calculation method and system, which determines a target forest area, performs remote sensing monitoring on the target forest area based on satellite remote sensing technology, and obtains remote sensing monitoring data; pre-processes the remote sensing monitoring data to generate remote sensing standard data, and performs secondary classification and regional division of altitude and plant community to divide the target forest area into multiple partial forest sub-areas; divides the multiple partial forest sub-areas into multiple forest units, and selects multiple sampling units from the multiple forest units to obtain sampled carbon stocks of the multiple sampling units; calculates the sub-area carbon stocks of the multiple partial forest sub-areas based on the multiple sampled carbon stocks; summarizes the multiple sub-area carbon stocks to calculate the total forest carbon stocks of the target forest area. Based on satellite remote sensing technology, it is possible to conduct remote sensing monitoring of the target forest area, divide the forest into multiple partial sub-areas, obtain the sampling carbon storage of multiple sampling units, calculate the sub-region carbon storage of multiple partial forest sub-areas, and summarize and calculate the total forest carbon storage of the target forest area, thereby realizing accurate and efficient carbon storage measurement for large forest areas, which can effectively promote the technical application of forest carbon storage measurement.

[0067] Specifically, Figure 1 A flow chart of an intelligent method for calculating forest carbon reserves provided by an embodiment of the present invention is shown.

[0068] In a preferred embodiment of the present invention, a method for intelligently calculating forest carbon reserves comprises the following steps:

[0069] Step S101: determine a target forest area, perform remote sensing monitoring on the target forest area based on satellite remote sensing technology, and obtain remote sensing monitoring data.

[0070] In an embodiment of the present invention, a target forest area with a demand for forest carbon stock calculation is determined, remote sensing image instructions and lidar instructions are generated, and the remote sensing image instructions and lidar instructions are sent to a corresponding remote sensing satellite, so that the remote sensing satellite takes remote sensing images of the target forest area in accordance with the remote sensing image instructions to obtain remote sensing image data. At the same time, according to the lidar instructions, lidar detection is performed on the target forest area to obtain laser detection data, and then the remote sensing image data and the laser detection data are integrated to obtain remote sensing monitoring data of the target forest area.

[0071] Specifically, Figure 2 A flow chart of remote sensing monitoring of a target forest area in a method provided by an embodiment of the present invention is shown.

[0072] Among them, in the preferred embodiment provided by the present invention, the target forest area is determined, and remote sensing monitoring of the target forest area is performed based on satellite remote sensing technology, and obtaining remote sensing monitoring data specifically includes the following steps:

[0073] Step S1011: determine the target forest area with forest carbon stock measurement needs.

[0074] Step S1012: Generate and send remote sensing image instructions and laser radar instructions.

[0075] Step S1013: According to the remote sensing image instruction, remote sensing images of the target forest area are photographed to obtain remote sensing image data.

[0076] Step S1014: perform laser radar detection on the target forest area according to the laser radar instruction to obtain laser detection data.

[0077] Step S1015: Integrate the remote sensing image data and the laser detection data to obtain remote sensing monitoring data.

[0078] Furthermore, the intelligent calculation method of forest carbon storage also includes the following steps:

[0079] Step S102: pre-process the remote sensing monitoring data to generate remote sensing standard data, perform secondary classification and identification of altitude and plant community, and divide the target forest area into a plurality of partial forest sub-areas.

[0080] In the embodiment of the present invention, standard image data is generated by performing geometric correction (eliminating geometric distortion in remote sensing images, correcting image deformation caused by satellite orbit errors, earth rotation and other factors, so that the position of objects in the image is consistent with the actual position), radiation correction (correcting the brightness value of satellite images to eliminate the influence of factors such as atmosphere and terrain on image brightness, and improving the clarity and accuracy of images), image registration (joining multiple satellite images together to form a seamless geographic information image) and enhancement processing (performing contrast stretching, sharpening and other processing to enhance the features of objects in the image), and performing correction and registration (correcting the differences and errors between different sensors, and spatially aligning the data obtained by different sensors) and denoising filtering (removing noise and interference in the data, improving the reliability and accuracy of the data, and using filtering technology to smooth the data to reduce the influence of random errors) on the remote sensing image data. And feature extraction (including physical features such as reflectivity and scattering characteristics and geometric features such as elevation and slope) to generate standard detection data, and then based on the standard detection data, the altitude of the target forest area is identified in the area, the altitude of different locations in the target forest area is determined, and the regional altitude data is recorded. Then, according to the regional altitude data and the preset altitude classification standard, the target forest area is classified and divided into multiple altitude classification areas. After that, according to the multiple altitude classification areas, the standard image data is divided and processed accordingly, the standard image data is divided into multiple altitude classification image data corresponding to the multiple altitude classification areas, and then the multiple altitude classification image data are respectively classified and identified, and the multiple altitude classification boundaries in the multiple altitude classification areas are recorded. Then, according to the multiple altitude classification boundaries, the multiple corresponding altitude classification areas are divided for the second time to obtain multiple partial forest sub-areas.

[0081] It is understandable that the altitude classification standard can be an altitude range value. For example, when the altitude classification standard is 300m, the target forest area is divided into multiple altitude classification areas according to altitude classification of 0-300m, 300-600m, 600-900m, etc. Different altitude classification areas are in different altitude value ranges.

[0082] It can be understood that plant community classification and identification can be achieved by analyzing altitude classification image data to identify different plant community types according to vegetation coverage, crown width, leaf area, etc. in the forest. Different plant community types may include coniferous community, broad-leaved community, mixed-leaf community, grassland community, etc.

[0083] It can be understood that each altitude classification area is composed of multiple partial forest sub-areas, but the number of partial forest sub-areas in different altitude classification areas is different.

[0084] Specifically, Figure 3 A flow chart showing the division of a target forest area into a plurality of partial forest sub-areas in the method provided by an embodiment of the present invention.

[0085] Among them, in the preferred embodiment provided by the present invention, the remote sensing monitoring data is preprocessed to generate remote sensing standard data, and the secondary classification and regional division of altitude and plant community is performed to divide the target forest area into multiple partial forest sub-areas, which specifically includes the following steps:

[0086] Step S1021: Perform geometric correction, radiation correction, image registration and enhancement processing on the remote sensing image data to generate standard image data.

[0087] Step S1022: perform correction and registration, denoising filtering and feature extraction on the laser detection data to generate standard detection data.

[0088] Step S1023: perform secondary classification and identification of altitude and vegetation and regional division according to the standard detection data and the standard image data, and divide the target forest area into a plurality of partial forest sub-areas.

[0089] Specifically, Figure 4 A flow chart of secondary classification identification and division in the method provided by an embodiment of the present invention is shown.

[0090] Among them, in the preferred embodiment provided by the present invention, the secondary classification identification and regional division of altitude and vegetation based on the standard detection data and the standard image data, and the division of the target forest area into a plurality of partial forest sub-areas specifically include the following steps:

[0091] Step S10231: According to the standard detection data, the regional altitude of the target forest area is identified and the regional altitude data is recorded.

[0092] Step S10232: Divide the target forest area into a plurality of altitude classification areas according to the regional altitude data and a preset altitude classification standard.

[0093] Step S10233: Divide the standard image data into a plurality of corresponding altitude classification image data according to the plurality of altitude classification areas.

[0094] Step S10234: perform plant group classification and identification on the plurality of altitude classification image data respectively, and record a plurality of plant group classification boundaries.

[0095] Step S10235: According to the plurality of plant community classification boundaries, the plurality of altitude classification areas are divided twice to obtain a plurality of partial forest sub-areas.

[0096] Furthermore, the intelligent calculation method of forest carbon storage also includes the following steps:

[0097] Step S103: Divide the plurality of partial forest sub-areas into a plurality of forest units, select a plurality of sampling units from the plurality of forest units, and obtain the sampled carbon storage of the plurality of sampling units.

[0098] In an embodiment of the present invention, multiple partial forest sub-areas are divided into multiple forest units according to preset unit standards, and then multiple sampling units are selected from the multiple forest units according to preset sampling selection rules and sampling ratios, and the sampling carbon reserves of the multiple sampling units are obtained.

[0099] It is understandable that the unit standard may be an area. In the embodiment of the present invention, the unit standard may be set to 5m 2 .

[0100] It is understandable that the sampling selection rule can be simple random selection or interval sampling selection.

[0101] It is understandable that the sampling carbon storage of multiple sampling units can be calculated through direct measurement, biomass conversion factor method and drone remote sensing estimation method.

[0102] Specifically, Figure 5 A flow chart of obtaining carbon storage of multiple samples in the method provided by an embodiment of the present invention is shown.

[0103] Among them, in the preferred embodiment provided by the present invention, the dividing of the plurality of partial forest sub-areas into a plurality of forest units, and selecting a plurality of sampling units from the plurality of forest units, and obtaining the sampled carbon storage of the plurality of sampling units specifically comprises the following steps:

[0104] Step S1031: divide the plurality of partial forest sub-areas into units according to a preset unit standard, and divide the plurality of partial forest sub-areas into a plurality of forest units.

[0105] Step S1032: Select multiple sampling units from the multiple forest units.

[0106] Step S1033: Obtain the sampled carbon storage of the plurality of sampling units.

[0107] Furthermore, the intelligent calculation method of forest carbon storage also includes the following steps:

[0108] Step S104: Calculate sub-region carbon stocks of multiple partial forest sub-regions based on the multiple sampled carbon stocks.

[0109] In the embodiment of the present invention, all units other than the sampling units in the multiple forest units are marked as unknown units, so that the multiple sampling units and the multiple unknown units can be determined. In the multiple partial forest sub-areas, the unit distances between the multiple unknown units and the multiple sampling units are measured to obtain multiple unit distance data, and then the sub-area carbon stocks of the multiple partial forest sub-areas are calculated according to the multiple unit distance data and the multiple sampling carbon stocks. Specifically, the calculation formula of the sub-area carbon stock is:

[0110] ;

[0111] in, is the carbon storage in the sub-region, Representative unknown units, some forest sub-areas have Unknown units, Representative sampling units, some forest sub-areas have Sampling units, For the The sampled carbon storage of each sampling unit, For the The unknown unit and The unit distance between sampling units, is the preset distance decomposition factor.

[0112] Specifically, Figure 6 A flow chart of calculating carbon storage in multiple sub-regions in the method provided by an embodiment of the present invention is shown.

[0113] Among them, in the preferred embodiment provided by the present invention, the calculation of the sub-region carbon storage of multiple partial forest sub-regions based on the multiple sampled carbon storages specifically includes the following steps:

[0114] Step S1041, dividing the plurality of forest units into a plurality of sampling units and a plurality of unknown units.

[0115] Step S1042: In the plurality of partial forest sub-areas, the unit distances between the plurality of unknown units and the plurality of sampling units are measured to obtain a plurality of unit distance data.

[0116] Step S1043: Calculate sub-region carbon stocks of multiple partial forest sub-regions according to the multiple unit distance data and the multiple sampled carbon stocks.

[0117] Furthermore, the intelligent calculation method of forest carbon storage also includes the following steps:

[0118] Step S105: Aggregate the carbon stocks of the multiple sub-regions to calculate the total forest carbon stock of the target forest region.

[0119] In an embodiment of the present invention, the carbon reserves of multiple sub-regions are aggregated to obtain an aggregate carbon reserve, and then a preset calculation coefficient is introduced, and the calculation coefficient is multiplied by the aggregate carbon reserve to obtain the total forest carbon reserve of the target forest area.

[0120] It is understandable that the estimation coefficient may be an empirical correction coefficient obtained by analyzing the historical records of carbon storage estimation.

[0121] Specifically, Figure 7 A flow chart for calculating total forest carbon storage in the method provided in an embodiment of the present invention is shown.

[0122] Among them, in the preferred embodiment provided by the present invention, the aggregating the carbon stocks of the plurality of sub-regions to calculate the total forest carbon stock of the target forest area specifically comprises the following steps:

[0123] Step S1051: Aggregate the carbon stocks of the multiple sub-regions to obtain aggregated carbon stocks.

[0124] Step S1052: import the preset calculation coefficients.

[0125] Step S1053: Calculate the total forest carbon stock of the target forest area by combining the calculation coefficient and the summary carbon stock.

[0126] Furthermore, Figure 8 The application architecture diagram of the intelligent forest carbon storage calculation system provided by the embodiment of the present invention is shown.

[0127] Among them, in another preferred embodiment provided by the present invention, a smart forest carbon storage measurement system includes:

[0128] The regional remote sensing monitoring module 101 is used to determine the target forest area, perform remote sensing monitoring on the target forest area based on satellite remote sensing technology, and obtain remote sensing monitoring data.

[0129] In an embodiment of the present invention, the regional remote sensing monitoring module 101 determines a target forest area with forest carbon stock measurement needs, generates remote sensing image instructions and lidar instructions, and sends the remote sensing image instructions and lidar instructions to the corresponding remote sensing satellite, so that the remote sensing satellite takes remote sensing images of the target forest area according to the remote sensing image instructions and obtains remote sensing image data. At the same time, according to the lidar instructions, the target forest area is detected by lidar and laser detection data is obtained, and then the remote sensing image data and laser detection data are integrated to obtain remote sensing monitoring data of the target forest area.

[0130] The secondary classification module 102 is used to pre-process the remote sensing monitoring data, generate remote sensing standard data, and perform secondary classification identification and regional division of altitude and plant community, and divide the target forest area into multiple partial forest sub-areas.

[0131] In an embodiment of the present invention, the secondary classification module 102 generates standard image data by performing geometric correction on the remote sensing image data (eliminating geometric distortion in the remote sensing image, correcting image deformation caused by satellite orbit errors, earth rotation and other factors, so that the position of the ground objects in the image is consistent with the actual position), radiation correction (correcting the brightness value of the satellite image to eliminate the influence of factors such as atmosphere and terrain on the image brightness, and improving the clarity and accuracy of the image), image registration (splicing multiple satellite images together to form a seamless geographic information image) and enhancement processing (performing contrast stretching, sharpening and other processing to enhance the ground object features in the image), and performing correction and registration on the laser detection data (correcting the differences and errors between different sensors, and spatially aligning the data obtained by different sensors), denoising filtering (removing noise and interference in the data, improving the reliability and accuracy of the data, and using filtering technology to smooth the data and reduce random The influence of errors) and feature extraction (including physical features such as reflectivity and scattering characteristics and geometric features such as elevation and slope) are used to generate standard detection data, and then the altitude of the target forest area is identified according to the standard detection data, the altitude of different locations in the target forest area is determined, and the regional altitude data is recorded. Then, according to the regional altitude data and the preset altitude classification standard, the target forest area is classified and divided into multiple altitude classification areas. After that, according to the multiple altitude classification areas, the standard image data is divided and processed accordingly, the standard image data is divided into multiple altitude classification image data corresponding to the multiple altitude classification areas, and then the multiple altitude classification image data are respectively classified and identified, and the multiple altitude classification boundaries in the multiple altitude classification areas are recorded. Then, according to the multiple altitude classification boundaries, the multiple corresponding altitude classification areas are divided for the second time to obtain multiple partial forest sub-areas.

[0132] Specifically, Fig. 9 It shows a structural block diagram of the secondary classification module 102 in the system provided by an embodiment of the present invention.

[0133] Among them, in the preferred embodiment provided by the present invention, the secondary classification module 102 specifically includes:

[0134] The first pre-processing submodule 1021 is used to perform geometric correction, radiation correction, image registration and enhancement processing on the remote sensing image data to generate standard image data.

[0135] The second pre-processing submodule 1022 is used to perform correction and registration, denoising filtering and feature extraction on the laser detection data to generate standard detection data.

[0136] The sub-region division sub-module 1023 is used to perform secondary classification and identification of altitude and vegetation and regional division according to the standard detection data and the standard image data, and divide the target forest area into a plurality of partial forest sub-regions.

[0137] Furthermore, the intelligent forest carbon storage measurement system also includes:

[0138] The sampling carbon stock acquisition module 103 is used to divide the plurality of partial forest sub-areas into a plurality of forest units, select a plurality of sampling units from the plurality of forest units, and acquire the sampling carbon stock of the plurality of sampling units.

[0139] In an embodiment of the present invention, the sampling carbon stock acquisition module 103 divides the multiple partial forest sub-areas into units according to a preset unit standard, divides the multiple partial forest sub-areas into multiple forest units, and then selects multiple sampling units from the multiple forest units according to a preset sampling selection rule and sampling ratio, and obtains the sampling carbon stocks of the multiple sampling units.

[0140] The sub-region carbon storage calculation module 104 is used to calculate the sub-region carbon storage of multiple partial forest sub-regions based on the multiple sampled carbon storages.

[0141] In the embodiment of the present invention, the sub-region carbon stock calculation module 104 marks all units other than the sampling units in the multiple forest units as unknown units, so that multiple sampling units and multiple unknown units can be determined. In multiple partial forest sub-regions, the unit distances between the multiple unknown units and the multiple sampling units are measured to obtain multiple unit distance data, and then the sub-region carbon stocks of the multiple partial forest sub-regions are calculated based on the multiple unit distance data and the multiple sampling carbon stocks. Specifically, the calculation formula of the sub-region carbon stock is:

[0142] ;

[0143] in, is the carbon storage in the sub-region, Representative unknown units, some forest sub-areas have Unknown units, Representative sampling units, some forest sub-areas have Sampling units, For the The sampled carbon storage of each sampling unit, For the The unknown unit and The unit distance between sampling units, is the preset distance decomposition factor.

[0144] The total carbon storage calculation module 105 is used to aggregate the carbon storage of the multiple sub-regions to calculate the total forest carbon storage of the target forest area.

[0145] In the embodiment of the present invention, the total carbon stock calculation module 105 obtains the aggregated carbon stock by summarizing the carbon stocks of multiple sub-regions, and then imports a preset calculation coefficient, multiplies the calculation coefficient by the aggregated carbon stock, and obtains the total forest carbon stock of the target forest area.

[0146] 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.

[0147] 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).

[0148] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An intelligent method for calculating forest carbon reserves, characterized in that: The method specifically comprises the following steps: Determine the target forest area, conduct remote sensing monitoring of the target forest area based on satellite remote sensing technology, and obtain remote sensing monitoring data; Preprocessing the remote sensing monitoring data to generate remote sensing standard data, and performing secondary classification and regional division of altitude and plant community to divide the target forest area into a plurality of partial forest sub-areas; Dividing the plurality of partial forest sub-areas into a plurality of forest units, and selecting a plurality of sampling units from the plurality of forest units to obtain the sampling carbon storage of the plurality of sampling units; Calculating sub-region carbon stocks of a plurality of partial forest sub-regions based on a plurality of said sampled carbon stocks; Aggregating the carbon stocks of the plurality of sub-regions to calculate the total forest carbon stock of the target forest region; The calculating of the sub-region carbon stocks of the plurality of partial forest sub-regions based on the plurality of sampled carbon stocks specifically comprises the following steps: Dividing the plurality of forest units into a plurality of sampling units and a plurality of unknown units; In the plurality of partial forest sub-areas, measuring the cell distances between the plurality of unknown cells and the plurality of sampling cells to obtain a plurality of cell distance data; Calculating sub-region carbon stocks of a plurality of partial forest sub-regions based on the plurality of unit distance data and the plurality of sampled carbon stocks; The calculation formula for the carbon storage in the sub-region is: ； in, is the carbon storage in the sub-region, Representative unknown units, some forest sub-areas have Unknown units, Representative sampling units, some forest sub-areas have Sampling units, For the The sampled carbon storage of each sampling unit, For the The unknown unit and The unit distance between sampling units, is the preset distance decomposition factor.

2. The intelligent calculation method of forest carbon storage according to claim 1 is characterized in that: Determining the target forest area, performing remote sensing monitoring on the target forest area based on satellite remote sensing technology, and obtaining remote sensing monitoring data specifically include the following steps: Identify target forest areas with forest carbon stock measurement needs; Generate and send remote sensing image commands and lidar commands; According to the remote sensing image instruction, remote sensing images of the target forest area are photographed to obtain remote sensing image data; According to the laser radar instruction, the target forest area is detected by laser radar to obtain laser detection data; The remote sensing image data and the laser detection data are integrated to obtain remote sensing monitoring data.

3. The intelligent calculation method of forest carbon storage according to claim 2 is characterized in that: The preprocessing of the remote sensing monitoring data, generating remote sensing standard data, performing secondary classification and identification of altitude and plant community and regional division, and dividing the target forest area into a plurality of partial forest sub-areas specifically includes the following steps: Performing geometric correction, radiation correction, image registration and enhancement processing on the remote sensing image data to generate standard image data; Performing correction and registration, denoising filtering and feature extraction on the laser detection data to generate standard detection data; According to the standard detection data and the standard image data, secondary classification and identification of altitude and vegetation and regional division are performed to divide the target forest area into a plurality of partial forest sub-areas.

4. The intelligent calculation method of forest carbon storage according to claim 3 is characterized in that: The step of performing secondary classification and identification of altitude and vegetation and dividing the target forest area into a plurality of partial forest sub-areas according to the standard detection data and the standard image data specifically includes the following steps: According to the standard detection data, regional altitude identification is performed on the target forest area, and regional altitude data is recorded; Dividing the target forest area into a plurality of altitude classification areas according to the regional altitude data and a preset altitude classification standard; According to the plurality of altitude classification areas, the standard image data is divided into a plurality of corresponding altitude classification image data; Performing plant group classification and identification on the plurality of altitude classification image data respectively, and recording a plurality of plant group classification boundaries; According to the plurality of plant community classification boundaries, the plurality of altitude classification areas are divided twice to obtain a plurality of partial forest sub-areas.

5. The intelligent calculation method of forest carbon storage according to claim 1 is characterized in that: The step of aggregating the carbon stocks of the plurality of sub-regions to calculate the total forest carbon stock of the target forest region specifically comprises the following steps: Aggregating the carbon stocks of the plurality of sub-regions to obtain an aggregated carbon stock; Import preset calculation coefficients; The total forest carbon stock of the target forest area is calculated by combining the calculation coefficient and the summary carbon stock.

6. An intelligent forest carbon stock calculation system for executing the intelligent forest carbon stock calculation method as claimed in any one of claims 2 to 4, characterized in that: The system includes a regional remote sensing monitoring module, a secondary classification module, a sampling carbon storage acquisition module, a sub-region carbon storage calculation module and a total carbon storage calculation module, wherein: The regional remote sensing monitoring module is used to determine the target forest area, conduct remote sensing monitoring of the target forest area based on satellite remote sensing technology, and obtain remote sensing monitoring data; A secondary classification module is used to pre-process the remote sensing monitoring data, generate remote sensing standard data, and perform secondary classification identification and regional division of altitude and plant community, and divide the target forest area into multiple partial forest sub-areas; A sampling carbon stock acquisition module is used to divide the plurality of partial forest sub-areas into a plurality of forest units, and select a plurality of sampling units from the plurality of forest units to acquire the sampling carbon stock of the plurality of sampling units; A sub-region carbon storage calculation module, used to calculate the sub-region carbon storage of multiple partial forest sub-regions based on the multiple sampled carbon storages; A total carbon stock calculation module is used to aggregate the carbon stocks of the multiple sub-regions and calculate the total forest carbon stock of the target forest area; The calculation of the sub-region carbon storage of the plurality of partial forest sub-regions based on the plurality of sampled carbon storages is specifically as follows: Dividing the plurality of forest units into a plurality of sampling units and a plurality of unknown units; In the plurality of partial forest sub-areas, measuring the cell distances between the plurality of unknown cells and the plurality of sampling cells to obtain a plurality of cell distance data; Calculating sub-region carbon stocks of a plurality of partial forest sub-regions based on the plurality of unit distance data and the plurality of sampled carbon stocks; The calculation formula for the carbon storage in the sub-region is: ； in, is the carbon storage in the sub-region, Representative unknown units, some forest sub-areas have Unknown units, Representative sampling units, some forest sub-areas have Sampling units, For the The sampled carbon storage of each sampling unit, For the The unknown unit and The unit distance between sampling units, is the preset distance decomposition factor.

7. The intelligent forest carbon storage calculation system according to claim 6 is characterized in that: The secondary classification module specifically includes: The first preprocessing submodule is used to perform geometric correction, radiation correction, image registration and enhancement processing on the remote sensing image data to generate standard image data; A second preprocessing submodule is used to perform correction and registration, denoising filtering and feature extraction on the laser detection data to generate standard detection data; The sub-region division sub-module is used to perform secondary classification and identification of altitude and vegetation and regional division according to the standard detection data and the standard image data, and divide the target forest area into a plurality of partial forest sub-regions.

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