Forest data analysis method and system based on satellite remote sensing technology

Abstract

The present application provides a kind of forest data analysis method and system based on satellite remote sensing technology, it is related to natural resource asset accounting method technical field, comprising: step S1: based on actual business demand, forest plot is outlined, the forest boundary that needs to be calculated is determined;Step S2: according to the forest boundary, the stock volume index of forest is analyzed by satellite remote sensing technology;Step S3: relevant forest parameters are obtained, and input satellite remote sensing system;Step S4: the forest data is calculated by the stock volume and forest parameters.The present application can solve a series of pain point problems such as long measurement period, large manpower consumption and poor measurement accuracy in the valuation and measurement of forest rights in forestry loans.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of natural resource asset accounting method, in particular to a forest data analysis method and system based on satellite remote sensing technology. BACKGROUND

[0002] Forestry resources are one of the most precious natural resources in China. Currently, in the process of converting forest ecological resources into assets and capital, the problem of how to effectively measure and monitor resources to reasonably assess their value still exists. SUMMARY

[0003] In view of the defects in the prior art, the present application provides a forest data analysis method and system based on satellite remote sensing technology.

[0004] According to the forest data analysis method and system based on satellite remote sensing technology provided by the present application, the scheme is as follows:

[0005] In the first aspect, a forest data analysis method based on satellite remote sensing technology is provided, which comprises:

[0006] Step S1: Based on actual business requirements, the forest plot is outlined, and the forest boundary to be calculated is determined;

[0007] Step S2: According to the forest boundary, the volume index of the forest is analyzed by satellite remote sensing technology;

[0008] Step S3: Obtain relevant forest parameters and input them into the satellite remote sensing system;

[0009] Step S4: Calculate the forest data by the volume and forest parameters.

[0010] Preferably, the step S1 comprises:

[0011] Step S1.1: Find the target forest range by manually dragging the map or searching, outline it on the satellite image map, and collect the plot range information;

[0012] Step S1.2: Outline the plot on the map, circle the forest boundary to be calculated in the target forest, and complete the frame selection collection of a single forest.

[0013] Preferably, the step S2 comprises:

[0014] Step S2.1: After determining the forest boundary, select a number of forest sample plots according to the set proportion, and obtain the volume of the selected sample plots;

[0015] Step S2.2: Radiometric calibration is performed on the remote sensing original image collected by remote sensing technology, and the non-dimensional value of the remote sensing original image is converted into a radiometric brightness value; atmospheric correction is performed on the radiometrically calibrated remote sensing image to obtain a reflectivity image representing real objects; then, based on the high-resolution image used in the second-class survey, relevant intersection points such as roads, rivers, ponds, and farmland are selected to perform geometric fine correction on the atmospheric corrected remote sensing image, and at the same time, based on the vector graph of the forest boundary, the geometrically fine corrected remote sensing image is cropped to obtain a forest boundary remote sensing image;

[0016] Step S2.3: Selecting forest-related remote sensing factors, analyzing the correlation coefficient between the stock volume and the remote sensing factors, and gradually selecting and determining the remote sensing factors by using the variance expansion factor method;

[0017] Step S2.4: Constructing multiple models such as multiple stepwise regression, partial least squares regression, and random forest model and performing estimation, using the above models to test the fitting relationship between the estimated value and the measured value, and performing precision test on the difference between the estimated value and the measured value to determine the model with the best fitting effect;

[0018] Step S2.5: Using the model with the best fitting effect for stock volume inversion of the entire forest.

[0019] Preferably, the step S3 comprises:

[0020] Step S3.1: Based on the growth condition data of the real forest stand obtained by field investigation, comparing the corresponding indicators of the real forest stand and the reference forest stand, determining the site quality and site class indicators item by item, and then using weighted average to obtain the value of the forest stand quality adjustment coefficient, and connecting the price of the real forest stand and the reference forest stand;

[0021] Step S3.2: According to the evaluated tree species, calculating the value of the price index adjustment coefficient;

[0022] Step S3.3: Querying the price value of the unit stock of the reference object;

[0023] Step S3.4: Inputting the relevant forest parameters into the satellite remote sensing system.

[0024] Preferably, in the step S4, the forest parameters include the forest stand quality adjustment coefficient K, the price index adjustment coefficient Kb, and the price G of the unit stock of the reference object, and according to the forest parameters and the stock volume M, the forest data is calculated by the formula En=K*Kb*G*M.

[0025] In a second aspect, a forest data analysis system based on satellite remote sensing technology is provided, and the system comprises:

[0026] Module M1: based on actual business needs, the forest plot is outlined, and the forest boundary to be calculated is determined;

[0027] Module M2: according to the forest boundary, the volume index of the forest is analyzed by satellite remote sensing technology;

[0028] Module M3: obtain relevant forest parameters and input them into the satellite remote sensing system;

[0029] Module M4: calculate the forest data based on the volume and forest parameters.

[0030] Preferably, the module M1 comprises:

[0031] Module M1.1: find the target forest range by manually dragging the map or searching, outline on the satellite image, and collect the plot range information;

[0032] Module M1.2: outline the plot on the map, circle the forest boundary to be calculated in the target forest, and complete the frame collection of a single forest.

[0033] Preferably, the module M2 comprises:

[0034] Module M2.1: after determining the forest boundary, select a plurality of forest sample plots according to the set proportion, and obtain the volume of the selected sample plots;

[0035] Module M2.2: radiometric calibration is performed on the raw remote sensing image collected by remote sensing technology, and the dimensionless value of the raw remote sensing image is converted into a radiometric brightness value; atmospheric correction is performed on the radiometrically calibrated remote sensing image to obtain a reflectivity image representing real objects; based on the high-resolution image used in the second-class survey, geometric fine correction is performed on the atmospheric corrected remote sensing image by selecting relevant intersection points such as roads, rivers, ponds and farmland, and based on the vector diagram of the forest boundary, the geometrically fine corrected remote sensing image is cropped to obtain the remote sensing image of the forest boundary;

[0036] Module M2.3: select forest-related remote sensing factors, analyze the correlation coefficient between the volume and the remote sensing factors, and gradually screen and determine the remote sensing factors by using the variance expansion factor method;

[0037] Module M2.4: construct a plurality of models including multiple stepwise regression, partial least squares regression, and random forest model and estimate them, use the above models to test the fitting relationship between the estimated values and the measured values, and perform precision test on the difference between the estimated values and the measured values to determine the model with the best fitting effect;

[0038] Module M2.5: use the model with the best fitting effect for volume inversion of the entire forest.

[0039] Preferably, the module M3 comprises:

[0040] Module M3.1: based on the growth data of the real forest obtained by the field investigation, comparing the corresponding indicators of the real forest with the reference forest, determining the site quality and the site class indicators item by item, and then using the weighted average to obtain the value of the forest quality adjustment coefficient, and connecting the price of the real forest with the reference forest;

[0041] Module M3.2: calculating the value of the price index adjustment coefficient according to the evaluated tree species;

[0042] Module M3.3: querying the price value of the reference unit volume;

[0043] Module M3.4: inputting the relevant forest parameters into the satellite remote sensing system.

[0044] Preferably, the forest parameters in the module M4 include the forest quality adjustment coefficient K, the price index adjustment coefficient Kb and the reference unit volume price G, and the forest data is calculated by the formula En=K*Kb*G*M according to the forest parameters and the volume M.

[0045] Compared with the prior art, the present application has the following beneficial effects:

[0046] The present application can engineer the detection process of forestry asset value by satellite remote sensing technology, reduce the cost and period of manual field measurement by using satellite remote sensing data with high reliability and strong real-time performance, and provide accurate and reliable forest right value reference for banks to develop forestry business, so as to better serve the drip irrigation forestry development. BRIEF DESCRIPTION OF DRAWINGS

[0047] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments with reference to the attached drawings:

[0048] Figure 1 It is a schematic diagram of the overall structure of the present application. DETAILED DESCRIPTION

[0049] The present application will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be pointed out that, for those skilled in the art, without departing from the concept of the present application, a number of changes and improvements can be made. These all belong to the protection scope of the present application.

[0050] The embodiment of the present application provides a forest data analysis method based on satellite remote sensing technology, as shown in the reference Figure 1 The method specifically comprises:

[0051] Step S1: Based on actual business needs, using the plot sketching function on the mobile end, complete the plot sketching of the forest farm, and determine the forest farm boundary for which the forest asset value needs to be calculated.

[0052] This step S1 specifically includes:

[0053] 1) Find the forest range of the forest farm customer through manual dragging of the map or search, and perform "sketching" on the satellite image map to collect information such as plot range.

[0054] 2) Perform plot sketching on the map by dotting or walking around the plot, and use the sketching method to circle the forest farm range of the forest farm customer to complete the frame selection collection of a single forest farm.

[0055] 3) After submitting the plot, re-enter the plot sketching function interface, which can support the echo of the frame selected plot.

[0056] Step S2: According to the forest farm boundary, use satellite remote sensing technology combined with auxiliary means to analyze the volume index of the forest farm.

[0057] This step S2 specifically includes:

[0058] 1) After determining the forest farm boundary, select a certain number of forest farm sample plots according to a certain proportion, and the sample plot data comes from the second-class investigation data of forest resources or manual offline forest farm point placement. The main data of sample plot investigation includes geographic coordinates, canopy density, tree species, stand type, diameter at breast height, and tree height of each sample plot. Through the commonly used single tree volume table of forest resource investigation, the cumulative volume of the selected sample plot is obtained.

[0059] 2) Radiometric calibration is performed on the original remote sensing image to convert the dimensionless value of the image into a radiance value. The remote sensing image after radiometric calibration is subjected to atmospheric correction to obtain a reflectance image representing the real object. Based on the high-resolution image used in the second-class investigation, geometric precise correction is performed on the remote sensing image used in the study by selecting road, river, pond, farmland, and other intersection points. At the same time, based on the forest farm range vector map framed by the customer manager, the geometric precise corrected remote sensing image is cropped to obtain the remote sensing image of the framed forest farm range.

[0060] 3) Select forest-related remote sensing factors, common ones include spectral information, vegetation index, texture factor, etc. Among them, spectral information includes blue band (band 1), green band (band 2), red band (band 3) and near-infrared band (band 4); vegetation index includes ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI); common texture factors include mean (Mean, ME), homogeneity (Homogeneity, HO), variance (Variance, VA), correlation (Correlation, CO), second moment (Second moment, SM), dissimilarity (Dissimilarity, DI), entropy (Entropy, EN), and contrast (Contrast, CT), etc. Analyze the correlation coefficient between stock volume and remote sensing factors, and use the variance expansion factor method to gradually select and determine the remote sensing factors.

[0061] 4) Build multiple models such as multiple stepwise regression, partial least squares regression, and random forest model, and perform estimation. Use the above models to test the fitting relationship between the estimated value and the measured value, and perform precision test on the difference between the estimated value and the measured value to determine the best fitting model.

[0062] 5) Based on the data obtained in the above steps, perform remote sensing image inversion, and use the best fitting model for stock volume inversion of the entire forest.

[0063] Step S3: Obtain data such as K (forest quality adjustment coefficient), Kb (price index adjustment coefficient), and G (transaction price of reference unit stock volume), and input them into the satellite remote sensing system.

[0064] This step S3 specifically includes:

[0065] 1) Based on the field survey, obtain the growth data of tree number, tree height, diameter at breast height, and stock volume of the forest stand. Then compare with the corresponding indicators of the reference forest stand, and determine the site quality and site class two indicators item by item, and then use weighted average to obtain the K (forest quality adjustment coefficient) value, which connects the price of the real forest stand with the price of the reference forest stand.

[0066] Step S3.2: Estimate the Kb (price index adjustment coefficient) value based on the evaluated tree species. For example, if the evaluated tree species is pine, and the price of pine can be obtained through public network channels, then directly refer to the price of pine; but if only the price of pine can be referred to, then estimate the ratio between pine and pine, and use the ratio as Kb (price index adjustment coefficient).

[0067] Step S3.3: Obtain the value of G (transaction price of reference unit stock) through the public network channel query.

[0068] Step S3.4: Record the data indicators such as forest stand quality adjustment coefficient K, price index adjustment coefficient Kb, and transaction price of reference unit stock G into the satellite remote sensing system.

[0069] Step S4: According to the "Technical Specification for Forest Resource Asset Assessment (Trial)", the current market price method is used to evaluate the value of forest assets. The stock volume M, forest stand quality adjustment coefficient K, price index adjustment coefficient Kb, and transaction price of reference unit stock G are automatically generated and output the forest asset value index through the formula En=K*Kb*G*M.

[0070] Step S5: The forest asset value index is transmitted from the satellite remote sensing system to the green financial business management system of the bank. By logging into the relevant business management system, the forest asset value index of the forest company applying for the loan business can be viewed, and the subsequent services will be determined based on the index.

[0071] The embodiment of the present application provides a forest data analysis method and system based on satellite remote sensing technology, which can solve a series of pain points such as long measurement period, large manpower consumption, and poor measurement accuracy in forest loan, and provide a basis for business pre-loan management, thereby better providing financial services for forest companies and "double carbon" construction.

[0072] Those skilled in the art know that, in addition to implementing the system and each device, module and unit thereof provided by the present application in the form of pure computer readable program code, the same function can also be realized by logically programming the method steps to make the system and each device, module and unit thereof provided by the present application in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, the system and each device, module and unit thereof provided by the present application can be considered as a hardware component, and the devices, modules and units included therein for realizing various functions can also be considered as structures within the hardware component. The devices, modules and units for realizing various functions can also be considered as both software modules realizing the method and structures within the hardware component.

[0073] The specific embodiments of the present application are described above. It should be understood that the present application is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essential content of the present application. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for analyzing forest data based on satellite remote sensing technology, characterized in that, include: Step S1: Based on actual business needs, delineate the forest land plots and determine the forest land boundaries that need to be measured; Step S2: Based on the forest farm boundary, analyze the forest farm's stock volume indicators using satellite remote sensing technology; Step S3: Obtain relevant forest tree parameters and input them into the satellite remote sensing system; Step S4: Calculate the forest data using the stock volume and forest parameters; Step S2 includes: Step S2.1: After determining the boundary of the forest farm, select several forest farm sample plots according to a set ratio and obtain the stock volume of the selected sample plots; Step S2.2: Radiometric calibration is performed on the original remote sensing images acquired through remote sensing technology, and the dimensionless values ​​of the original remote sensing images are converted into radiance values; atmospheric correction is performed on the radiometrically calibrated remote sensing images to obtain reflectance images representing real ground features; then, based on the high-resolution images used in the second-class survey, geometric fine correction is performed on the atmospherically corrected remote sensing images by selecting relevant intersections including roads, rivers, ponds, and farmland; at the same time, based on the vector map of the forest farm boundary, the geometrically fine-corrected remote sensing images are cropped to obtain the remote sensing images of the forest farm boundary; Step S2.3: Select relevant remote sensing factors for the forest farm, analyze the correlation coefficient between the stock volume and the remote sensing factors, and use the variance expansion factor method to gradually screen and determine the remote sensing factors; Step S2.4: Construct and estimate multiple related models, including multiple stepwise regression, partial least squares regression, and random forest model. Use the above models to test the fitting relationship between the model estimates and the measured values, and perform accuracy tests on the differences between the estimated values ​​and the measured values ​​to determine the model with the best fitting effect. Step S2.5: Use the model with the best fit to invert the stock volume of the entire forest farm; Step S3 includes: Step S3.1: Based on the field survey, obtain the growth status data of the actual forest stand, compare the corresponding indicators of the actual forest stand with those of the reference forest stand, determine the site quality and land use grade indicators one by one, and then use the weighted average to obtain the value of the forest stand quality adjustment coefficient, and link the price of the actual forest stand with that of the reference forest stand. Step S3.2: Calculate the price index adjustment coefficient based on the tree species being assessed; Step S3.3: Query the price value of the reference unit's storage volume; Step S3.4: Input the relevant forest parameters into the satellite remote sensing system.

2. The forest data analysis method based on satellite remote sensing technology according to claim 1, characterized in that, Step S1 includes: Step S1.1: Locate the target forest area by manually dragging the map or searching, delineate it on the satellite image, and collect the area information; Step S1.2: Draw plots on the map and circle the boundaries of the target forest farms that need to be measured to complete the selection and collection of individual forest farms.

3. The forest data analysis method based on satellite remote sensing technology according to claim 1, characterized in that, The forest parameters in step S4 include: stand quality adjustment coefficient K, price index adjustment coefficient Kb, and reference unit volume price G. Based on the forest parameters and volume M, the forest data is calculated using the formula En=K*Kb*G*M.

4. A forest data analysis system based on satellite remote sensing technology, characterized in that, include: Module M1: Based on actual business needs, delineate forest land plots and determine the forest land boundaries that need to be measured; Module M2: Based on the forest farm boundary, analyze the forest farm's stock volume indicators using satellite remote sensing technology; Module M3: Acquire relevant forest tree parameters and input them into the satellite remote sensing system; Module M4: Calculates forest data using the aforementioned stock volume and forest parameters; The module M2 includes: Module M2.1: After determining the forest farm boundary, select several forest farm sample plots according to a set ratio and obtain the stock volume of the selected sample plots; Module M2.2: Radiometrically calibrates the raw remote sensing images acquired through remote sensing technology and converts the dimensionless values ​​of the raw remote sensing images into radiance values; atmospherically corrects the radiometrically calibrated remote sensing images to obtain reflectance images representing real ground features; then, based on the high-resolution images used in the second-class survey, geometrically fine-corrects the atmospherically corrected remote sensing images by selecting relevant intersections including roads, rivers, ponds, and farmland; simultaneously, based on the vector map of the forest farm boundary, the geometrically fine-corrected remote sensing images are cropped to obtain the remote sensing images of the forest farm boundary; Module M2.3: Select relevant remote sensing factors for forest farms, analyze the correlation coefficient between the stock volume and the remote sensing factors, and use the variance expansion factor method to gradually screen and determine the remote sensing factors; Module M2.4: Construct and estimate multiple related models, including multiple stepwise regression, partial least squares regression, and random forest model. Use the above models to test the fitting relationship between the model estimates and the measured values, and perform accuracy tests on the differences between the estimated values ​​and the measured values ​​to determine the model with the best fitting effect. Module M2.5: Uses the model with the best fit to invert the stock volume of the entire forest farm; The module M3 includes: Module M3.1: Based on field surveys, the growth status data of actual forest stands are obtained. The corresponding indicators of the actual forest stands are compared with those of reference forest stands. Site quality and land use grade indicators are determined one by one. Then, the weighted average is used to obtain the value of the forest stand quality adjustment coefficient and link the price of the actual forest stands with that of the reference forest stands. Module M3.2: Calculate the price index adjustment coefficient based on the tree species being assessed; Module M3.3: Queries the price value per unit of storage for a reference object; Module M3.4: Input relevant forest parameters into the satellite remote sensing system.

5. The forest data analysis system based on satellite remote sensing technology according to claim 4, characterized in that, The module M1 includes: Module M1.1: Locate the target forest area by manually dragging the map or searching, delineate it on the satellite image, and collect the area information; Module M1.2: Delineate plots on the map, circle the boundaries of the target forest farm that need to be measured, and complete the selection and collection of individual forest farms.

6. The forest data analysis system based on satellite remote sensing technology according to claim 4, characterized in that, The forest parameters in module M4 include: stand quality adjustment coefficient K, price index adjustment coefficient Kb, and reference unit volume price G. Based on the forest parameters and volume M, forest data is calculated using the formula En=K*Kb*G*M.

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