Method and device for evaluating spatial representativeness of forest aboveground biomass plots

Abstract

A method and device for evaluating spatial representativeness of forest aboveground biomass plots, the method comprising: an index construction and calculation step, a weight setting step and an evaluation step: obtaining a spatial representativeness comprehensive evaluation value and an evaluation grade of a plot, wherein in the index construction and calculation step, the constructed spatial representativeness evaluation index of the plot comprises: a relative mean absolute deviation RMAD for evaluating surface spatial heterogeneity within a biomass product pixel based on high-resolution NDVI image pixels, a relative spatial sampling error RSSE for evaluating the difference between a plot observation value and the mean of multiple high-resolution NDVI image pixels, and a vegetation type ratio DVTP for quantitatively describing spatial heterogeneity within a biomass product pixel based on a high-resolution land cover map. In this way, the correlation and conflict between spatial representativeness evaluation indicators can be comprehensively considered, the objectivity of indicator weight setting can be enhanced, and an objective and comprehensive spatial representativeness evaluation result can be obtained.

Description

Technical Field

[0001] The present invention relates to the application field of remote sensing technology, and more specifically to a method and device for evaluating spatial representativeness of forest aboveground biomass plots. Background Art

[0002] The core of forest above-ground biomass (AGB) remote sensing product verification (hereinafter referred to as product or biomass product, such as various tools and equipment used to measure and monitor biomass) is to obtain the relative true value of the pixel scale that is consistent with the scale of the forest above-ground biomass remote sensing product to be verified. In current practical applications, sample plot data is usually used directly as the verification data of the forest above-ground biomass product to be verified. The size of forest resource survey sample plot data is usually 25m×25m, while the spatial resolution of large-area forest above-ground biomass remote sensing products is usually at the hundred-meter and kilometer level. The spatial scales of the sample plot and the product to be verified are often inconsistent. Secondly, the natural landform types are complex and diverse, and the terrain undulations are obviously different. At the same time, the forest types are rich and colorful, and the forest growth is uneven. There is widespread spatial heterogeneity on the surface. Affected by the scale effect, when the sample plot data is directly applied to the verification of remote sensing products, it will bring uncertainty to the verification results. In order to obtain the relative true value of the pixel scale, the sample plot data needs to undergo consistency analysis with the scale of the product to be verified, spatial representativeness evaluation of the sample plot, and spatial scale conversion. Therefore, the evaluation of the spatial representativeness of forest aboveground biomass plots is an important step in the verification of forest aboveground biomass remote sensing products, and is also a key application technology.

[0003] In current practical applications, there are relatively few studies on the spatial representativeness of sample plots for forest aboveground biomass products. The spatial representativeness evaluation of ground observation data for other products generally establishes spatial representativeness indicators for qualitative evaluation, or establishes spatial representativeness evaluation indicators for quality grading, and removes ground data with low spatial representativeness at the pixel scale of the corresponding product.

[0004] However, these spatial representativeness evaluation methods have limitations in terms of quantification, objectivity and comprehensive evaluation. How to effectively obtain scientific spatial representativeness evaluation results is an urgent problem to be solved. Summary of the invention

[0005] In order to solve the above-mentioned problems, the object of the present invention is to provide a method and device for evaluating the spatial representativeness of forest aboveground biomass plots.

[0006] According to one aspect of the present invention, a method for evaluating spatial representativeness of forest aboveground biomass plots is provided, comprising the following steps:

[0007] Indicator construction and calculation steps: construct a spatial representativeness evaluation index system for sample plots and calculate each index;

[0008] Weight setting step: setting the evaluation index weight of each index; and

[0009] Evaluation steps: Obtain the spatial representativeness comprehensive evaluation value and evaluation grade of the sample plot,

[0010] In the indicator construction and calculation steps, the constructed sample plot spatial representativeness evaluation indicators include: the relative mean absolute deviation RMAD based on high-resolution NDVI image pixels to evaluate the surface spatial heterogeneity within the biomass product pixel, the relative spatial sampling error RSSE used to evaluate the difference between the sample plot observation value and the mean of multiple high-resolution NDVI image pixels, and the vegetation type proportion DVTP based on the high-resolution land cover map to quantitatively describe the spatial heterogeneity within the biomass product pixel.

[0011]

[0012] Where: RMAD represents relative mean absolute deviation;

[0013] Indicates the number of high-resolution NDVI image pixels in the biomass product pixel corresponding to the sample plot;

[0014] Indicates the biomass product pixel corresponding to the sample plot. High-resolution NDVI pixel values;

[0015] It represents the arithmetic mean of all high-resolution NDVI pixel values ​​in the biomass product pixel corresponding to the sample plot.

[0016] RSSE represents relative spatial sampling error;

[0017] Indicates High-resolution NDVI pixel value corresponding to each sample plot;

[0018] DVTP represents the proportion of vegetation types;

[0019] Indicates the vegetation type of the plot The area in the high-resolution land cover map that falls within the corresponding biomass product pixel;

[0020] Represents the biomass product pixel area.

[0021] Preferably, in the indicator construction and calculation steps, the relative mean absolute deviation RMAD and the relative spatial sampling error index RSSE of the spatial representativeness of the sample plot are determined based on the high-resolution NDVI image, and the vegetation type proportion index DVTP of the spatial representativeness of the sample plot is determined based on the high-resolution land cover map.

[0022] Preferably, in the weight setting step, the indicator correlation weight method is applied to set the evaluation indicator weight, wherein the decision matrix is ​​constructed , standardize the decision matrix to get the standardized matrix Z=( ), find the standard deviation of each indicator , using the Pearson correlation coefficient to calculate the correlation between the indicators , using the standard deviation Correlation coefficient Calculate contrast intensity , so as to obtain the weight of each indicator ,in,

[0023] ,

[0024] in, For the A sample in The evaluation value of the indicators,

[0025] and Respectively The maximum and minimum values ​​of the indicators,

[0026] For the A sample in The standardized evaluation value of each indicator is For the The arithmetic mean of the indicators, is the number of sample plots,

[0027] Is an indicator and indicators The correlation between and Respectively A sample in indicators and The standardized evaluation value of each indicator is and Respectively indicators and The arithmetic mean of the indicators,

[0028] Is an indicator The standard deviation of is the number of indicators, 1- Indicators and indicators The difference between

[0029] For the The weight of an indicator.

[0030] Preferably, in the evaluation step, a comprehensive evaluation value of the spatial representativeness of each plot is obtained, and the range of the evaluation score is (0, 1). The closer the evaluation value is to 1, the greater the spatial representativeness of the plot. The evaluation level is set according to the concentrated distribution, discrete distribution and natural breakpoints of the comprehensive evaluation value of the spatial representativeness of the plot. Calculation of the comprehensive evaluation value of the spatial representativeness of the plot:

[0031]

[0032] in, For the Comprehensive evaluation value of spatial representativeness of each sample plot.

[0033] Preferably, the method for evaluating the spatial representativeness of forest aboveground biomass plots further comprises a product verification and comparison step: using all plots and medium- and high-space representative plots to verify the accuracy of biomass products:

[0034] ,

[0035] Where: R 2 is the coefficient of determination, RMSE is the root mean square error, rRMSE is the relative root mean square error, MAE is the mean absolute error, MAPE is the mean absolute percentage error, Bias is the bias, SD is the standard deviation, is the biomass value of the sample plot, is the arithmetic mean of biomass of all plots, is the biomass product pixel value corresponding to the sample plot, It is the arithmetic mean of the biomass product pixels corresponding to all sample plots.

[0036] According to another aspect of the present invention, a device for evaluating spatial representativeness of forest aboveground biomass plots is provided, which is used to implement the above-mentioned method for evaluating spatial representativeness of forest aboveground biomass plots, and includes the following modules:

[0037] Index construction and calculation module: configured to construct a spatial representativeness evaluation index system for sample plots and calculate each index;

[0038] A weight setting module: configured to set the evaluation indicator weights of each indicator; and

[0039] Evaluation module: configured to obtain the spatial representativeness comprehensive evaluation value and evaluation grade of the sample plot,

[0040] In the indicator construction and calculation module, the constructed sample plot spatial representativeness evaluation indicators include: the relative mean absolute deviation RMAD based on high-resolution NDVI image pixels to evaluate the surface spatial heterogeneity within the biomass product pixels, the relative spatial sampling error RSSE used to evaluate the difference between the sample plot observation value and the mean of multiple high-resolution NDVI image pixels, and the vegetation type proportion DVTP based on the high-resolution surface cover map to quantitatively describe the spatial heterogeneity within the biomass product pixels.

[0041] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the above method.

[0042] According to another aspect of the present invention, there is provided an electronic device, comprising: a processor for executing a plurality of instructions; and a memory for storing the plurality of instructions; wherein the plurality of instructions are used to be stored in the memory and loaded by the processor to execute the method as described above.

[0043] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method described above is implemented.

[0044] According to the present invention, a spatial representativeness index system for forest aboveground biomass plots can be constructed, including three indicators: relative mean absolute deviation, relative spatial sampling error and proportion of main vegetation types, to form a comprehensive evaluation method for spatial representativeness of plots. The indicator correlation weight method is applied to comprehensively consider the correlation and conflict between spatial representativeness evaluation indicators, enhance the objectivity of indicator weight setting, and thus obtain objective and comprehensive spatial representativeness evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The overall flow chart of the method for evaluating spatial representativeness of forest aboveground biomass plots according to an embodiment of the present invention is schematically shown.

[0046] Figure 2 The frequency distribution diagram of the spatial representativeness evaluation values ​​of the sample plots is shown.

[0047] Figure 3 Results of the validation of biomass products using all plots are shown.

[0048] Figure 4 Results of validation of biomass products using medium to high spatially representative plots are shown.

[0049] Figure 5Another example of the results of the validation of biomass products using all plots is shown.

[0050] Figure 6 Another example of the validation of biomass products using medium to high spatial representative plots is shown.

[0051] Figure 7 It is a schematic diagram of the device structure of the present invention.

[0052] Figure 8 It is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0053] Below, the example embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. It should be noted that unless otherwise specifically stated, the relative arrangement of the parts and steps, the numerical expressions and the numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0054] Those skilled in the art can understand that the terms "first", "second", "1", "2" and the like in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and do not represent any specific technical meaning, nor do they represent the necessary logical order between them. It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present invention, in the absence of explicit limitation or contrary revelation given in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship. It should also be understood that the description of each embodiment of the present invention emphasizes the differences between the embodiments, and the same or similar parts can refer to each other. For the sake of brevity, they will not be repeated one by one. At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention and its application or use. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, they should be considered part of the specification. It should be noted that similar reference numerals and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0055] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc. Terminal devices, computer systems, servers, etc. Electronic devices can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, target programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0056] Exemplary Methods

[0057] According to an exemplary embodiment of the present invention, the operation flow of the method for evaluating the spatial representativeness of forest aboveground biomass plots is as follows: Figure 1 As shown, it can be roughly divided into five steps:

[0058] The first step is to construct and calculate the evaluation index system of spatial representativeness of forest aboveground biomass plots;

[0059] The second step is to set the evaluation index weights by applying the index correlation weight method, for example;

[0060] The third step is to obtain the spatial representativeness comprehensive evaluation value and evaluation grade of the sample plot;

[0061] The fourth step is the verification and comparative study of forest aboveground biomass products.

[0062] In some embodiments, the spatial resolution of the biomass product used in the experiment is 1 km, the forest resource sample plot size is 24.5 m × 24.5 m, the reference data are NDVI (normalized vegetation index based on remote sensing image data) images with a spatial resolution of 30 m and a surface cover map with a spatial resolution of 30 m, and all data are from the same year.

[0063] The following is a detailed introduction to the evaluation method of spatial representativeness of forest aboveground biomass plots. Here, the "high resolution" in high-resolution NDVI images and high-resolution land cover maps refers to the high spatial resolution compared to biomass products. High-resolution data is used to capture the spatial heterogeneity characteristics within the relatively low biomass product pixels.

[0064] The first step is to construct and calculate the evaluation index system of spatial representativeness of forest aboveground biomass plots:

[0065] The constructed spatial representativeness evaluation indicators of sample plots include three indicators: relative mean absolute deviation, relative spatial sampling error and proportion of vegetation types.

[0066] (1) The relative mean absolute deviation is used to evaluate the surface spatial heterogeneity within the biomass product pixel based on the high-resolution NDVI image pixel. The formula is as follows:

[0067]

[0068] Where:

[0069] RMAD – relative mean absolute deviation;

[0070] ——The number of high-resolution NDVI image pixels within the biomass product pixel corresponding to the sample plot;

[0071] ——The first High-resolution NDVI pixel values;

[0072] ——The arithmetic mean of all high-resolution NDVI pixel values ​​within the biomass product pixel corresponding to the sample plot.

[0073] (2) Relative spatial sampling error is used to evaluate the difference between the sample plot observation value and the mean value of multiple high-resolution NDVI image pixels, and is used to characterize whether the sample plot observation value falls into the abnormal value. The formula is as follows:

[0074]

[0075] Where:

[0076] RSSE – relative spatial sampling error;

[0077] ——No. High-resolution NDVI pixel value corresponding to each sample plot;

[0078] ——The arithmetic mean of all high-resolution NDVI pixel values ​​within the biomass product pixel corresponding to the sample plot.

[0079] (3) The vegetation type ratio is used to calculate the vegetation type ratio index of the sample plot within the biomass product pixel scale with the help of high-resolution land cover maps, and quantitatively describe the spatial heterogeneity within the biomass product pixel. The formula is as follows:

[0080] DVTP=

[0081] Where:

[0082] DVTP – proportion of vegetation types;

[0083] ——Vegetation type of the sample plot The area in the high-resolution land cover map that falls within the corresponding biomass product pixel;

[0084] ——Biomass product pixel area.

[0085] The relative mean absolute deviation and relative spatial sampling error indices of the spatial representativeness of the sample plots were calculated based on the NDVI image with a spatial resolution of 30 m, and the vegetation type proportion index of the spatial representativeness of the sample plots was calculated based on the surface cover map with a spatial resolution of 30 m.

[0086] The second step is to set the weights using the indicator correlation weight method:

[0087] 1. Build a decision matrix

[0088]

[0089] In the formula, For the A sample in Evaluation scores on each indicator.

[0090] 2. Standardized decision matrix

[0091] In order to eliminate the dimensional differences between different evaluation indicators, the decision matrix is ​​standardized. Standardized matrix Z = ( ) is obtained by the following formula:

[0092]

[0093] In the formula, For the A sample in The evaluation scores on the indicators are and Respectively The maximum and minimum values ​​of the indicators.

[0094] 3. Calculate indicator variability using standard deviation

[0095] The standard deviation of each indicator Calculate the degree of dispersion of the indicator between sample plots. The larger the standard deviation, the greater the variability of the indicator.

[0096]

[0097] In the formula, For the A sample in The evaluation scores on the indicators are For the The arithmetic mean of the indicators, is the number of sample plots.

[0098] 4. Calculate the correlation coefficient matrix

[0099] The Pearson correlation coefficient was used to calculate the correlation between the indicators and construct a correlation coefficient matrix ( ).

[0100]

[0101] In the formula, Is an indicator and indicators The correlation between and Respectively A sample in indicators and The evaluation scores on the indicators are and Respectively indicators and The arithmetic mean of the indicators, is the number of sample plots.

[0102] 5. Calculate contrast intensity

[0103] The contrast strength of an indicator depends not only on its standard deviation but also on its correlation with other indicators. The contrast strength of the indicators The calculation formula is as follows:

[0104]

[0105] in, Is an indicator The standard deviation of is the number of indicators, 1- Indicators and indicators The difference between.

[0106] 6. Calculate weights

[0107] The weight of each indicator Its contrast intensity The weight reflects the relative contribution of each indicator to the overall decision-making process. The calculation formula is as follows:

[0108]

[0109] According to the above research, the weights of the three spatial representative indicators RMAD, RSSE and DVTP for biomass products in this study are 23.83%, 30.43% and 45.74% respectively.

[0110] The third step is to obtain the spatial representativeness comprehensive evaluation value and evaluation level of the sample site:

[0111] Calculation of comprehensive evaluation value of spatial representativeness of sample plots:

[0112]

[0113] in, For the The comprehensive evaluation value of spatial representativeness of each plot, For the A sample in The standardized evaluation value of each indicator is For the is the weight of an indicator, and m is the number of indicators.

[0114] The comprehensive evaluation value of spatial representativeness of each plot was obtained. The range of the evaluation value is (0, 1). The larger the value, the better the spatial representativeness of the plot, and the smaller the value, the worse the spatial representativeness of the plot. The frequency distribution of the spatial representativeness evaluation value of the plot is shown in Figure 2 According to the concentrated distribution, discrete distribution and natural breakpoints of the comprehensive evaluation value of the spatial representativeness of the sample plots, the spatial representativeness evaluation level of the sample plots is set. For example, the threshold is set to 0.46. A value less than 0.46 is a low spatial representative sample plot, and a value greater than or equal to 0.46 is a medium-high spatial representative sample plot.

[0115] Step 4: Comparative verification of biomass products:

[0116] The following indicators are applied to biomass products, and the accuracy of biomass products is verified using all sample plots and medium and high spatial representative sample plots.

[0117]

[0118] Where: R2 is the coefficient of determination, RMSE is the root mean square error, rRMSE is the relative root mean square error, MAE is the mean absolute error, MAPE is the mean absolute percentage error, Bias is the bias, SD is the standard deviation, is the biomass value of the sample plot, is the arithmetic mean of biomass of all plots, is the biomass product pixel value corresponding to the sample plot, is the arithmetic mean of the biomass product pixels corresponding to all plots, The number of sample plots.

[0119] Comparison and verification results are as follows Figure 3 , Figure 4 As shown in the figure, the R 2 The results show that the medium- and high-representative spatial plots are closer to the true pixel value of the biomass product, and the reliability of product verification using medium- and high-representative spatial plots is higher. Therefore, the accuracy of product verification has been improved, which confirms the effectiveness and necessity of this method in evaluating the spatial representativeness of forest aboveground biomass plots.

[0120] also, Figure 5 , Figure 6 Another example of verification results is shown. The difference from the above is that the same forest resource sample plot and reference data were used to conduct the same sample plot spatial representativeness evaluation for biomass products with a spatial resolution of 100m. Compared with the verification of the biomass product using all sample plots, the verification of biomass products using medium- and high-spatial representative sample plots also shows that the verification of biomass products using medium- and high-spatial representative sample plots has higher accuracy.

[0121] Exemplary Devices

[0122] like Figure 7 As shown, the spatial representativeness evaluation device for forest aboveground biomass plots of this embodiment includes the following modules:

[0123] Index construction and calculation module: configured to construct a spatial representativeness evaluation index system for sample plots and calculate each index;

[0124] A weight setting module: configured to set the evaluation indicator weights of each indicator; and

[0125] Evaluation module: configured to obtain the spatial representativeness comprehensive evaluation value and evaluation grade of the sample plot,

[0126] In the indicator construction and calculation module, the constructed sample plot spatial representativeness evaluation indicators include: the relative mean absolute deviation RMAD for evaluating the surface spatial heterogeneity within the biomass product pixel based on the high-resolution NDVI image pixel, the relative spatial sampling error RSSE for evaluating the difference between the sample plot observation value and the mean of multiple high-resolution NDVI image pixels, and the vegetation type proportion index DVTP for calculating the spatial representativeness of the sample plot based on the high-resolution surface cover map.

[0127] Exemplary Electronic Devices

[0128] Figure 8 This is the structure of an electronic device 1 provided by an exemplary embodiment of the present invention. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signals from them. Figure 8 FIG. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure. Figure 8 As shown, the electronic device includes one or more processors 2 and a memory 4. The processor 2 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory 4 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 2 may run the program instructions to implement the methods of the software programs of the various embodiments of the present disclosure described above and / or other desired functions.

[0129] In one example, the electronic device may further include: an input device 3 and an output device 5, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). In addition, the input device 3 may also include, for example, a keyboard, a mouse, etc. The output device 5 may output various information to the outside. The output device 5 may include, for example, a display, a speaker, a printer, and a communication network and a remote output device connected thereto, etc. Of course, for simplicity, Figure 8 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0130] Exemplary computer program products and computer-readable storage media

[0131] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present disclosure described in the above-mentioned "Exemplary Method" section of this specification.

[0132] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0133] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present disclosure described in the above “Exemplary Method” section of this specification.

[0134] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0135] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0136] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0137] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0138] The method and apparatus of the present disclosure may be implemented in many ways. For example, the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0139] It should also be noted that in the apparatus, equipment and method of the present disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present disclosure. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present disclosure. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown here, but to the widest scope consistent with the principles and novel features disclosed herein.

[0140] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for evaluating the spatial representativeness of forest aboveground biomass plots, characterized in that: The steps include: Indicator construction and calculation steps: construct a spatial representativeness evaluation index system for sample plots and calculate each index; Weight setting steps: set the evaluation index weight of each indicator; and Evaluation steps: Obtain the spatial representativeness comprehensive evaluation value and evaluation grade of the sample plot, In the indicator construction and calculation steps, the constructed sample plot spatial representativeness evaluation indicators include: relative mean absolute deviation RMAD, which is used to evaluate the surface spatial heterogeneity within the biomass product pixel based on the high-resolution NDVI image pixel, relative spatial sampling error RSSE, which is used to evaluate the difference between the sample plot observation value and the mean of multiple high-resolution NDVI image pixels, and vegetation type proportion DVTP, which is used to quantitatively describe the spatial heterogeneity within the biomass product pixel based on the high-resolution land cover map. ， ， DVTP= , Where: RMAD represents relative mean absolute deviation; Indicates the number of high-resolution NDVI image pixels within the biomass product pixel corresponding to the sample plot; Indicates the first High-resolution NDVI pixel values; It represents the arithmetic mean of all high-resolution NDVI pixel values ​​within the biomass product pixel corresponding to the sample plot; RSSE represents relative spatial sampling error; Indicates High-resolution NDVI pixel value corresponding to each sample plot; DVTP represents the proportion of vegetation types; Indicates the vegetation type of the plot The area in the high-resolution land cover map that falls within the corresponding biomass product pixel; represents the biomass product pixel area, In the weight setting step, the indicator correlation weight method is used to set the evaluation indicator weights, where the decision matrix is ​​constructed , standardize the decision matrix to get the standardized matrix Z=( ), find the standard deviation of each indicator , using the Pearson correlation coefficient to calculate the correlation between the indicators , using the standard deviation Correlation coefficient Calculate contrast intensity , so as to obtain the weight of each indicator ,in, ， in, For the A sample in The evaluation value of the indicators, and Respectively The maximum and minimum values ​​of the indicators, For the A sample in The standardized evaluation value of each indicator is For the The standardized arithmetic mean of the indicators, is the number of sample plots, Is an indicator and indicators The correlation between and Respectively A sample in indicators and The standardized evaluation value of each indicator is and Respectively indicators and The standardized arithmetic mean of the indicators, Is an indicator The standard deviation of is the number of indicators, 1- Indicator and indicators The difference between For the The weight of an indicator.

2. The method for evaluating spatial representativeness of forest aboveground biomass plots according to claim 1, characterized in that: In the evaluation step, the comprehensive evaluation value of the spatial representativeness of each plot is obtained. The value range of the evaluation value is (0, 1). The closer the evaluation value is to 1, the greater the spatial representativeness of the plot. The evaluation level is set according to the concentrated distribution, discrete distribution and natural breakpoints of the comprehensive evaluation value of the spatial representativeness of the plot. Among them, the calculation of the comprehensive evaluation value of the spatial representativeness of the sample plot is: ， in, For the The comprehensive evaluation value of the spatial representativeness of each sample plot.

3. The method for evaluating spatial representativeness of forest aboveground biomass plots according to claim 2, characterized in that: It also includes product verification and comparison steps: the following indicators are applied to biomass products, and the accuracy of biomass products is verified using all sample plots and medium and high spatial representative sample plots: ， Where: R 2 is the coefficient of determination, RMSE is the root mean square error, rRMSE is the relative root mean square error, MAE is the mean absolute error, MAPE is the mean absolute percentage error, Bias is the bias, SD is the standard deviation, is the biomass value of the sample plot, is the arithmetic mean of biomass of all plots, is the biomass product pixel value corresponding to the sample plot, It is the arithmetic mean of the biomass product pixels corresponding to all sample plots.

4. A device for evaluating spatial representativeness of forest aboveground biomass plots, characterized in that: The method for evaluating spatial representativeness of forest aboveground biomass plots according to any one of claims 1 to 3 comprises the following modules: Index construction and calculation module: configured to construct a sample plot spatial representativeness evaluation index system and calculate each index; Weight setting module: configured to set the evaluation index weight of each index; and Evaluation module: configured to obtain the spatial representativeness comprehensive evaluation value and evaluation grade of the sample plot, In the indicator construction and calculation module, the constructed sample plot spatial representativeness evaluation indicators include: relative mean absolute deviation RMAD for evaluating the surface spatial heterogeneity within the biomass product pixel based on high-resolution NDVI image pixels, relative spatial sampling error RSSE for evaluating the difference between the sample plot observation value and the mean of multiple high-resolution NDVI image pixels, and vegetation type proportion DVTP for quantitatively describing the spatial heterogeneity within the biomass product pixel based on the high-resolution surface cover map.

5. A computer-readable storage medium, characterized in that: The storage medium stores a computer program; the computer program is used to be loaded and executed by a processor to implement the method according to any one of claims 1 to 3.

6. An electronic device, characterized in that: The electronic device comprises a processor, a memory and a computer program stored in the memory, wherein the processor loads and executes the computer program to implement the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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