Forest land soil data management method and system based on unmanned aerial vehicle multispectral inversion

Abstract

The invention discloses a forest land soil data management method and system based on unmanned aerial vehicle multispectral inversion, and particularly relates to the technical field of soil ecology, and the method comprises the steps: S1, management region determination: determining target forest land soil data as a target management region, and then dividing the target management region into sub-regions according to an equal-time division mode, the sub-regions in the target management region are sequentially numbered as 1, 2,..., i,..., n according to equal time; s2, unmanned aerial vehicle image data acquisition, which is used for acquiring unmanned aerial vehicle image data of a target management area; the system not only comprises soil physical and chemical indexes, but also covers unmanned aerial vehicle image data so as to obtain spectrum related data, inversion is carried out after multi-source data fusion, the soil ecological function is comprehensively described, and a more comprehensive management result is provided.

Description

Technical Field

[0001] The present invention relates to the field of soil ecological technology, and more specifically, to a forest soil data management method and system based on unmanned aerial vehicle multispectral inversion. Background Art

[0002] Soil is an important component of the ecosystem, and its ecological function is crucial to maintaining ecological balance and human survival and development; evaluating and managing its data can guide agricultural production, support land resource management, protect the ecological environment, and promote sustainable development; therefore, it is particularly urgent to develop a method that can manage soil data intelligently and efficiently.

[0003] Traditional soil management systems include sampling modules, analysis modules, management modules, and generation modules. The sampling module collects soil samples at target locations and times for subsequent laboratory analysis. The analysis module performs physical, chemical, and biological analyses on the collected soil samples to obtain detailed characteristic data of the soil. The management module combines the results obtained from the data processing module to conduct a comprehensive evaluation of the soil's quality, fertility, and pollution status to determine whether the soil is suitable for specific purposes. The generation module presents the results in the form of a report.

[0004] However, in actual use, it still has some shortcomings, such as poor real-time performance. The sampling and analysis process of the traditional system is mainly based on manual collection, which is relatively time-consuming and cannot obtain soil information in real time; the data dimension is single, and the traditional system mainly focuses on the physical and chemical indicators of the soil analyzed in the laboratory, and the portrayal of the soil ecological functions is not comprehensive enough; the predictive ability is insufficient. Traditional systems mostly use simple statistical methods, which are difficult to accurately analyze the nonlinear relationships and dynamic changes of complex soil ecosystems, and cannot effectively predict the future changes in soil ecological functions.

[0005] Therefore, there is an urgent need to provide a forest soil data management method based on UAV multispectral inversion to solve the problems of poor real-time performance, single data dimension and insufficient prediction ability of the existing soil management system. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a forest soil data management method based on drone multispectral inversion, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a forest soil data management method based on UAV multispectral inversion, comprising:

[0008] S1, management area determination, for determining the target forest land soil data as the target management area, and then dividing it into sub-areas according to the equal time division method, and numbering each sub-area in the target management area in sequence as 1, 2, ..., i, ..., n according to the equal time;

[0009] S2, UAV image data acquisition, used to obtain UAV image data of the target management area;

[0010] S3, UAV image data processing, for analyzing and processing the UAV image data to obtain the spectral reflectance data influence coefficient value, the spectral feature data influence coefficient value and the spectral texture feature data influence coefficient value;

[0011] S4. Soil property data collection, used to obtain soil property data of the target management area;

[0012] S5, soil property data processing, for analyzing and processing the soil property data to obtain the soil microenvironment data influence coefficient value, the soil structure data influence coefficient value and the soil thermal property data influence coefficient value;

[0013] S6. Comprehensive data analysis, for importing the influence coefficient value of spectral reflectance data, the influence coefficient value of spectral feature data, the influence coefficient value of spectral texture feature data, the influence coefficient value of soil microenvironment data, the influence coefficient value of soil structure data, and the influence coefficient value of soil biological metabolism data into the soil inversion method rationality index mathematical model to obtain the soil inversion method rationality index value;

[0014] S7, soil inversion method judgment, for comparing the soil inversion method rationality index value with preset soil inversion method rationality index values ​​for multiple different types of soil, calculating the minimum value of the difference between the soil inversion method rationality index value and the preset soil inversion method rationality index values ​​for multiple different types of soil, and obtaining a soil judgment result for the corresponding type based on the minimum value of the difference;

[0015] S8. Data interaction transmission: used to transmit the difference value, judgment result and target forest soil data to the information terminal.

[0016] Preferably, the drone image data includes spectral reflectance data influencing parameters, spectral feature data influencing parameters and spectral texture feature data influencing parameters;

[0017] The influencing parameters of the spectral reflectance data include the spectral near-infrared band, denoted as NIR; the red light band reflectance, denoted as R; the blue light band reflectance, denoted as B; the green light band reflectance, denoted as G; the influencing parameters of the spectral feature data include the spectral absorption depth, denoted as D; the spectral absorption width, denoted as W; the spectral reflectance slope, denoted as s; the influencing parameters of the spectral texture feature data include the spectral grayscale co-occurrence matrix contrast, denoted as c; the spectral grayscale co-occurrence matrix entropy, denoted as h; the spectral grayscale co-occurrence matrix uniformity, denoted as UNI; and the spectral grayscale co-occurrence matrix energy, denoted as E.

[0018] Preferably, the S3 includes a step of calculating the influence coefficient of spectral reflectance data, a step of calculating the influence coefficient of spectral feature data, and a step of calculating the influence coefficient of spectral texture feature data;

[0019] The spectral reflectance data influence coefficient calculation step is used to import the spectral reflectance data influence parameters into the spectral reflectance data influence coefficient mathematical model to obtain the spectral reflectance data influence coefficient value; the spectral feature data influence coefficient calculation step is used to import the spectral feature data influence parameters into the spectral feature data influence coefficient mathematical model to obtain the spectral feature data influence coefficient value; the spectral texture feature data influence coefficient calculation step is used to import the spectral texture feature data influence parameters into the spectral texture feature data influence coefficient mathematical model to obtain the spectral texture feature data influence coefficient value.

[0020] Preferably, the mathematical model of the influence coefficient of the spectral reflectance data is specifically:

[0021]

[0022] The mathematical model of the influence coefficient of spectral characteristic data is as follows:

[0023]

[0024] The mathematical model of the influence coefficient of spectral texture feature data is as follows:

[0025]

[0026] Among them, NIR i represents the near-infrared band of the spectrum in the i-th time period; R i represents the red light band reflectivity in the i-th time period; B i represents the reflectivity of the blue light band in the i-th time period; G i represents the reflectivity of the green light band in the i-th time period; D i represents the spectral absorption depth of the i-th time period; W i represents the spectral absorption width of the i-th time period; s i represents the spectral reflectance slope of the i-th time period; spectral texture ci represents the spectral gray-level co-occurrence matrix contrast of the i-th time period; h i Represents the spectral gray-level co-occurrence matrix entropy of the i-th time period; UNI i represents the uniformity of the spectral gray-level co-occurrence matrix in the i-th time period; E i Represents the spectral gray-level co-occurrence matrix energy of the i-th time period.

[0027] Preferably, the soil property data include soil microenvironment data influencing parameters, soil structure data influencing parameters and soil biological metabolism data influencing parameters;

[0028] The influencing parameters of the soil microenvironment data include soil moisture content, denoted as M; soil oxygen diffusion rate, denoted as O; soil temperature, denoted as T; soil water holding capacity, denoted as H; soil moisture infiltration rate, denoted as v; the influencing parameters of soil structure data include soil porosity, denoted as P; soil bulk density, denoted as D; soil pore connectivity, denoted as Cr; soil infiltration rate, denoted as IR; soil sand content, denoted as S; soil silt content, denoted as Si; soil clay content, denoted as C; the influencing parameters of soil biological metabolism data include soil respiration rate, denoted as r; soil biomass carbon, denoted as MBC; soil biomass nitrogen, denoted as SBN; microbial carbon source utilization rate, denoted as U; soil organic matter content, denoted as SOM; and soil moisture content, denoted as SMC.

[0029] Preferably, the S5 includes a step of calculating the influence coefficient of soil microenvironment data, a step of calculating the influence coefficient of soil structure data, and a step of calculating the influence coefficient of soil biological metabolism data;

[0030] The soil microenvironment data influence coefficient calculation step is used to import the soil microenvironment data influence parameters into the soil microenvironment data influence coefficient mathematical model to obtain the soil microenvironment data influence coefficient value; the soil structure data influence coefficient calculation step is used to import the soil structure data influence parameters into the soil structure data influence coefficient mathematical model to obtain the soil structure data influence coefficient value; the soil biological metabolism data influence coefficient calculation step is used to import the soil biological metabolism data influence parameters into the soil biological metabolism data influence coefficient mathematical model to obtain the soil biological metabolism data influence coefficient value.

[0031] Preferably, the soil microenvironment data influence coefficient mathematical model is specifically:

[0032]

[0033] The mathematical model of soil structure data influence coefficient is as follows:

[0034]

[0035] The mathematical model of the influence coefficient of soil biological metabolism data is as follows:

[0036]

[0037] Among them, M i represents the soil moisture content in the i-th time period; O i represents the soil oxygen diffusion rate in the i-th time period; T i represents the soil temperature in the i-th time period; H i represents the soil water holding capacity in the i-th time period; VI represents the soil water infiltration rate in the i-th time period; P i represents the soil porosity in the i-th time period; D i represents the soil bulk density in the i-th time period; Cr i represents the soil pore connectivity rate in the i-th time period; IR i represents the soil infiltration rate in the i-th time period; S i represents the soil sand content in the i-th time period; Si i represents the soil silt content in the i-th time period; C i represents the soil clay content in the i-th time period; R i represents the soil respiration rate in the i-th time period; MBC i represents the soil biomass carbon in the i-th time period; SBN i represents the soil biomass nitrogen in the i-th time period; U i represents the carbon source utilization rate of microorganisms in the i-th time period; SOM i represents the soil organic matter content in the i-th time period; SMC i represents the soil moisture content in the i-th time period.

[0038] Preferably, the mathematical model of the soil inversion method rationality index is specifically:

[0039]

[0040] Preferably, a forest soil data management system based on drone multispectral inversion specifically includes:

[0041] Management area determination module: used to determine the target forest soil data as the target management area, and then divide it into sub-areas according to the equal time division method, and number each sub-area in the target management area in sequence as 1, 2, ..., i, ..., n according to the equal time;

[0042] UAV image data acquisition module: used to obtain UAV image data of the target management area and transmit soil property data to the UAV image data processing module;

[0043] UAV image data processing module: used to analyze and process the UAV image data collected by the UAV image data acquisition module to obtain the spectral reflectance data influence coefficient value, spectral feature data influence coefficient value and spectral texture feature data influence coefficient value, and transmit them to the comprehensive analysis module;

[0044] Soil property data acquisition module: used to obtain soil property data of the target management area and transmit it to the soil property data processing module;

[0045] Soil property data processing module: used to analyze and process the soil property data collected by the soil property data acquisition module to obtain the soil microenvironment data influence coefficient value, soil structure data influence coefficient value and soil thermal characteristic data influence coefficient value, and transmit them to the comprehensive analysis module;

[0046] Comprehensive data analysis module: used to import the spectral reflectance data influence coefficient value, spectral feature data influence coefficient value, spectral texture feature data influence coefficient value, soil microenvironment data influence coefficient value, soil structure data influence coefficient value, and soil biological metabolism data influence coefficient value obtained by the UAV image data processing module and the soil property data processing module into the soil inversion method rationality index mathematical model to obtain the soil inversion method rationality index value, and transmit it to the soil inversion method judgment module;

[0047] Soil inversion method judgment module: used to compare the soil inversion method rationality index value obtained by the comprehensive data analysis module with the preset soil inversion method rationality index values ​​of multiple different types of soil, calculate the minimum value of the difference between the soil inversion method rationality index value and the preset soil inversion method rationality index values ​​of multiple different types of soil, obtain the soil judgment result of the corresponding type according to the minimum value of the difference value, and transmit the judgment result and the target forest land soil data to the data interaction transmission module;

[0048] Data interaction and transmission module: used to transmit the difference value, judgment result and target forest land soil data obtained by the soil inversion method judgment module to the information terminal to provide reference data.

[0049] The technical effects and advantages of the present invention are as follows:

[0050] 1. This invention uses sensor networks and devices to achieve real-time collection and transmission of soil data, enabling dynamic management of changes in soil ecological functions, providing immediate feedback, and supporting rapid monitoring of sudden soil problems.

[0051] 2. This invention not only includes soil physical and chemical indicators, but also covers drone image data to obtain spectral-related data. By fusing and inverting multi-source data, it can comprehensively depict soil ecological functions and provide more comprehensive management results.

[0052] 3. Based on ecological principles, the present invention constructs a nonlinear model to describe the dynamic changes of soil ecosystems. Through historical data and real-time data, it predicts the future trend of changes in soil ecological functions and provides problem warnings based on the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the overall structure of the present invention.

[0054] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] As attached Figure 1 The forest soil data management method based on UAV multispectral inversion shown in the figure includes:

[0057] S1. Management area determination is used to determine the target forest soil data as the target management area, and then divide it into sub-areas according to the equal time division method, and number each sub-area in the target management area in sequence as 1, 2, ..., i, ..., n according to the equal time.

[0058] S2. UAV image data collection is used to obtain UAV image data of the target management area.

[0059] In this embodiment, it should be specifically explained that the UAV image data includes spectral reflectance data influencing parameters, spectral feature data influencing parameters, and spectral texture feature data influencing parameters.

[0060] In this embodiment, it should be specifically explained that the spectral reflectance data influencing parameters include the spectral near-infrared band, denoted as NIR; the red light band reflectance, denoted as R; the blue light band reflectance, denoted as B; the green light band reflectance, denoted as G; the spectral feature data influencing parameters include the spectral absorption depth, denoted as D; the spectral absorption width, denoted as W; the spectral reflectance slope, denoted as s; the spectral texture feature data influencing parameters include the spectral grayscale co-occurrence matrix contrast, denoted as c; the spectral grayscale co-occurrence matrix entropy, denoted as h; the spectral grayscale co-occurrence matrix uniformity, denoted as UNI; and the spectral grayscale co-occurrence matrix energy, denoted as E.

[0061] In this embodiment, it is specifically necessary to explain that S2 controls the drone to fly according to the planned route. During the flight, the multispectral sensor continuously collects multispectral image data of the forest land, performs radiation correction, geometric correction and image fusion preprocessing operations on the collected drone multispectral image data to obtain a multispectral image; directly extracts the pixel value of the corresponding band image from the multispectral image, and the original data obtained by the sensor is a digital quantization value, that is, the DN value, and calculates the actual reflectance value to obtain the spectral reflectance data influencing parameters; uses spectral analysis software to determine the position of a specific absorption peak for the spectral curve of the target forest land soil area; obtains the spectral absorption depth by measuring the vertical distance from the bottom of the absorption peak to the line connecting the shoulders on both sides; the wavelength range corresponding to the half-height width on both sides of the absorption peak is measured and calculated by the spectral curve analysis tool. The slope of the spectral reflectance changing with wavelength is calculated by the linear regression method; the multispectral image is converted into a grayscale image, and the grayscale co-occurrence matrix is ​​calculated using the image processing library, and the spectral texture feature data influencing parameters are obtained at the same time.

[0062] S3, UAV image data processing, is used to analyze and process the UAV image data to obtain the spectral reflectance data influence coefficient value, the spectral feature data influence coefficient value and the spectral texture feature data influence coefficient value.

[0063] In this embodiment, it should be specifically explained that S3 includes a step of calculating the influence coefficient of spectral reflectance data, a step of calculating the influence coefficient of spectral feature data, and a step of calculating the influence coefficient of spectral texture feature data.

[0064] In this embodiment, it is specifically necessary to explain that the spectral reflectance data influence coefficient calculation step is used to import the spectral reflectance data influence parameters into the spectral reflectance data influence coefficient mathematical model to obtain the spectral reflectance data influence coefficient value; the spectral feature data influence coefficient calculation step is used to import the spectral feature data influence parameters into the spectral feature data influence coefficient mathematical model to obtain the spectral feature data influence coefficient value; the spectral texture feature data influence coefficient calculation step is used to import the spectral texture feature data influence parameters into the spectral texture feature data influence coefficient mathematical model to obtain the spectral texture feature data influence coefficient value.

[0065] In this embodiment, it should be specifically explained that the mathematical model of the spectral reflectance data influence coefficient is specifically:

[0066]

[0067] The mathematical model of the influence coefficient of spectral characteristic data is as follows:

[0068]

[0069] The mathematical model of the influence coefficient of spectral texture feature data is as follows:

[0070]

[0071] Among them, NIR i represents the near-infrared band of the spectrum in the i-th time period; R i represents the red light band reflectivity in the i-th time period; B i represents the reflectivity of the blue light band in the i-th time period; G i represents the reflectivity of the green light band in the i-th time period; D i represents the spectral absorption depth of the i-th time period; W i represents the spectral absorption width of the i-th time period; s i represents the spectral reflectance slope of the i-th time period; spectral texture c i represents the spectral gray-level co-occurrence matrix contrast of the i-th time period; h i Represents the spectral gray-level co-occurrence matrix entropy of the i-th time period; UNI i represents the uniformity of the spectral gray-level co-occurrence matrix in the i-th time period; E i Represents the spectral gray-level co-occurrence matrix energy of the i-th time period.

[0072] S4. Soil property data collection is used to obtain soil property data of the target management area.

[0073] In this embodiment, it should be specifically explained that the soil property data includes soil microenvironment data influencing parameters, soil structure data influencing parameters and soil biological metabolism data influencing parameters.

[0074] In this embodiment, it should be specifically noted that the soil microenvironment data influencing parameters include soil moisture content, denoted as M; soil oxygen diffusion rate, denoted as O; soil temperature, denoted as T; soil water holding capacity, denoted as H; soil moisture infiltration rate, denoted as v; soil structure data influencing parameters include soil porosity, denoted as P; soil bulk density, denoted as D; soil pore connectivity, denoted as Cr; soil infiltration rate, denoted as IR; soil sand content, denoted as S; soil silt content, denoted as Si; soil clay content, denoted as C; soil biological metabolism data influencing parameters include soil respiration rate, denoted as r; soil biomass carbon, denoted as MBC; soil biomass nitrogen, denoted as SBN; microbial carbon source utilization rate, denoted as U; soil organic matter content, denoted as SOM; soil moisture content, denoted as SMC.

[0075] In this embodiment, it should be specifically explained that the S4 uses a soil moisture sensor to be inserted into the soil to directly read the moisture content; an oxygen diffusor is used to measure the diffusion rate of oxygen in the soil; a soil temperature sensor is inserted into the soil to record temperature data; soil samples are taken, and the field water holding capacity and wilting point are determined in the laboratory using a pressure plate instrument to calculate the water holding capacity; a double-ring infiltrator is used to determine the water penetration rate in the field; soil samples are taken, and the total porosity is measured using a porosity meter in the laboratory; soil samples are taken using the ring knife method, and the bulk density is calculated after determining the dry weight and volume of the soil; tomography technology is used to analyze the soil pore structure and calculate the pore connectivity; soil samples are taken, and the proportion of sand, silt and clay is determined using screening and sedimentation methods; the CO2 release rate is measured using a soil respirometer to obtain the soil respiration rate; microbial biomass carbon and microbial biomass nitrogen are determined using a chloroform fumigation extraction method; the carbon utilization efficiency of microorganisms is determined using an isotope labeling method; and the organic matter content is determined using a potassium dichromate oxidation method.

[0076] In this embodiment, it should be specifically noted that soil moisture content, soil oxygen diffusion rate and soil temperature are interrelated. Higher soil moisture content will limit the diffusion of oxygen, but suitable temperature will promote the biological and chemical processes in the soil; soil water holding capacity reflects the ability of soil to retain moisture; higher water holding capacity will lead to poor soil aeration, thereby affecting the ecological function of the soil; soil water infiltration rate affects soil moisture distribution and plant root growth; higher porosity provides storage space for air and water, while good pore connectivity ensures smooth flow of air and water in the soil; soil bulk density reflects the degree of soil compaction; soil infiltration rate; soil texture affects water retention, aeration and nutrient supply; soil respiration rate intuitively reflects the overall metabolic activity of organisms in the soil; soil biomass carbon and soil biomass carbon and nitrogen are important material bases for the metabolic activities of soil organisms; the proportion of carbon source utilization by microorganisms reflects the efficiency of external carbon source utilization in the microbial metabolic process; soil organic matter content reflects the effect of organic matter on soil biological metabolism.

[0077] S5. Soil property data processing is used to analyze and process the soil property data to obtain the soil microenvironment data influence coefficient value, the soil structure data influence coefficient value and the soil thermal characteristic data influence coefficient value.

[0078] In this embodiment, it should be specifically explained that S5 includes a step of calculating the influence coefficient of soil microenvironment data, a step of calculating the influence coefficient of soil structure data, and a step of calculating the influence coefficient of soil biological metabolism data.

[0079] In this embodiment, it should be specifically explained that the soil microenvironment data influence coefficient calculation step is used to import the soil microenvironment data influence parameters into the soil microenvironment data influence coefficient mathematical model to obtain the soil microenvironment data influence coefficient value; the soil structure data influence coefficient calculation step is used to import the soil structure data influence parameters into the soil structure data influence coefficient mathematical model to obtain the soil structure data influence coefficient value; the soil biological metabolism data influence coefficient calculation step is used to import the soil biological metabolism data influence parameters into the soil biological metabolism data influence coefficient mathematical model to obtain the soil biological metabolism data influence coefficient value.

[0080] In this embodiment, it should be specifically noted that the mathematical model of the soil microenvironment data influence coefficient is specifically:

[0081]

[0082] The mathematical model of soil structure data influence coefficient is as follows:

[0083]

[0084] The mathematical model of the influence coefficient of soil biological metabolism data is as follows:

[0085]

[0086] Among them, M i represents the soil moisture content in the i-th time period; O i represents the soil oxygen diffusion rate in the i-th time period; T i represents the soil temperature in the i-th time period; H i represents the soil water holding capacity in the i-th time period; VI represents the soil water infiltration rate in the i-th time period; P i represents the soil porosity in the i-th time period; D i represents the soil bulk density in the i-th time period; Cr i represents the soil pore connectivity rate in the i-th time period; IR i represents the soil infiltration rate in the i-th time period; S i represents the soil sand content in the i-th time period; Si i represents the soil silt content in the i-th time period; C i represents the soil clay content in the i-th time period; R i represents the soil respiration rate in the i-th time period; MBC i represents the soil biomass carbon in the i-th time period; SBN i represents the soil biomass nitrogen in the i-th time period; U i represents the carbon source utilization rate of microorganisms in the i-th time period; SOM irepresents the soil organic matter content in the i-th time period; SMC i represents the soil moisture content in the i-th time period.

[0087] S6. Comprehensive data analysis is used to import the influence coefficient values ​​of spectral reflectance data, spectral feature data, spectral texture feature data, soil microenvironment data, soil structure data and soil biological metabolism data into the mathematical model of the rationality index of the soil inversion method to obtain the rationality index value of the soil inversion method.

[0088] In this embodiment, it should be specifically explained that the mathematical model of the soil inversion method rationality index is specifically:

[0089]

[0090] S7. Soil inversion method judgment, which is used to compare the soil inversion method rationality index value with the preset soil inversion method rationality index values ​​of multiple different types of soil, calculate the minimum value of the difference between the soil inversion method rationality index value and the preset soil inversion method rationality index values ​​of multiple different types of soil, and obtain the soil judgment result of the corresponding type based on the minimum value of the difference value.

[0091] In this embodiment, it should be specifically noted that the preset soil inversion method rationality index value of the multiple different types of soil is the average of the historical rationality index values ​​of the inversion methods of different types of soil; the preset difference value is the average of the difference values ​​between the historical soil inversion method rationality index value and the corresponding preset soil inversion method rationality index value.

[0092] S8. Data interaction transmission: used to transmit the difference value, judgment result and target forest soil data to the information terminal.

[0093] As attached Figure 2 As shown, this embodiment provides a forest soil data management system based on drone multispectral inversion, including the following steps:

[0094] Management area determination module: used to determine the target forest soil data as the target management area, and then divide it into sub-areas according to the equal time division method, and number each sub-area in the target management area in sequence as 1, 2, ..., i, ..., n according to the equal time;

[0095] UAV image data acquisition module: used to obtain UAV image data of the target management area and transmit soil property data to the UAV image data processing module;

[0096] UAV image data processing module: used to analyze and process the UAV image data collected by the UAV image data acquisition module to obtain the spectral reflectance data influence coefficient value, spectral feature data influence coefficient value and spectral texture feature data influence coefficient value, and transmit them to the comprehensive analysis module;

[0097] Soil property data acquisition module: used to obtain soil property data of the target management area and transmit it to the soil property data processing module;

[0098] Soil property data processing module: used to analyze and process the soil property data collected by the soil property data acquisition module to obtain the soil microenvironment data influence coefficient value, soil structure data influence coefficient value and soil thermal characteristic data influence coefficient value, and transmit them to the comprehensive analysis module;

[0099] Comprehensive data analysis module: used to import the spectral reflectance data influence coefficient value, spectral feature data influence coefficient value, spectral texture feature data influence coefficient value, soil microenvironment data influence coefficient value, soil structure data influence coefficient value, and soil biological metabolism data influence coefficient value obtained by the UAV image data processing module and the soil property data processing module into the soil inversion method rationality index mathematical model to obtain the soil inversion method rationality index value, and transmit it to the soil inversion method judgment module;

[0100] Soil inversion method judgment module: used to compare the soil inversion method rationality index value obtained by the comprehensive data analysis module with the preset soil inversion method rationality index values ​​of multiple different types of soil, calculate the minimum value of the difference between the soil inversion method rationality index value and the preset soil inversion method rationality index values ​​of multiple different types of soil, obtain the soil judgment result of the corresponding type according to the minimum value of the difference value, and transmit the judgment result and the target forest land soil data to the data interaction transmission module;

[0101] Data interaction and transmission module: used to transmit the difference value, judgment result and target forest land soil data obtained by the soil inversion method judgment module to the information terminal to provide reference data.

[0102] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict.

[0103] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A forest soil data management method based on UAV multispectral inversion, characterized in that: include: S1, management area determination, for determining the target forest land soil data as the target management area, and then dividing it into sub-areas according to the equal time division method, and numbering each sub-area in the target management area in sequence as 1, 2, ..., i, ..., n according to the equal time; S2, UAV image data acquisition, used to obtain UAV image data of the target management area; S3, UAV image data processing, for analyzing and processing the UAV image data to obtain the spectral reflectance data influence coefficient value, the spectral feature data influence coefficient value and the spectral texture feature data influence coefficient value; S4. Soil property data collection, used to obtain soil property data of the target management area; S5, soil property data processing, for analyzing and processing the soil property data to obtain the soil microenvironment data influence coefficient value, the soil structure data influence coefficient value and the soil thermal property data influence coefficient value; S6. Comprehensive data analysis, for importing the influence coefficient value of spectral reflectance data, the influence coefficient value of spectral feature data, the influence coefficient value of spectral texture feature data, the influence coefficient value of soil microenvironment data, the influence coefficient value of soil structure data, and the influence coefficient value of soil biological metabolism data into the soil inversion method rationality index mathematical model to obtain the soil inversion method rationality index value; S7, soil inversion method judgment, for comparing the soil inversion method rationality index value with preset soil inversion method rationality index values ​​for multiple different types of soil, calculating the minimum value of the difference between the soil inversion method rationality index value and the preset soil inversion method rationality index values ​​for multiple different types of soil, and obtaining a soil judgment result for the corresponding type based on the minimum value of the difference; S8. Data interaction transmission: used to transmit the difference value, judgment result and target forest soil data to the information terminal.

2. The forest soil data management method based on UAV multispectral inversion according to claim 1 is characterized by: The UAV image data includes spectral reflectance data influencing parameters, spectral feature data influencing parameters and spectral texture feature data influencing parameters; The influencing parameters of the spectral reflectance data include the spectral near-infrared band, denoted as NIR; the red light band reflectance, denoted as R; the blue light band reflectance, denoted as B; the green light band reflectance, denoted as G; the influencing parameters of the spectral feature data include the spectral absorption depth, denoted as D; the spectral absorption width, denoted as W; the spectral reflectance slope, denoted as s; the influencing parameters of the spectral texture feature data include the spectral grayscale co-occurrence matrix contrast, denoted as c; the spectral grayscale co-occurrence matrix entropy, denoted as h; the spectral grayscale co-occurrence matrix uniformity, denoted as UNI; and the spectral grayscale co-occurrence matrix energy, denoted as E.

3. The forest soil data management method based on UAV multispectral inversion according to claim 1 is characterized by: Said S3 includes the steps of calculating the influence coefficient of spectral reflectance data, calculating the influence coefficient of spectral feature data and calculating the influence coefficient of spectral texture feature data; The spectral reflectance data influence coefficient calculation step is used to import the spectral reflectance data influence parameters into the spectral reflectance data influence coefficient mathematical model to obtain the spectral reflectance data influence coefficient value; the spectral feature data influence coefficient calculation step is used to import the spectral feature data influence parameters into the spectral feature data influence coefficient mathematical model to obtain the spectral feature data influence coefficient value; the spectral texture feature data influence coefficient calculation step is used to import the spectral texture feature data influence parameters into the spectral texture feature data influence coefficient mathematical model to obtain the spectral texture feature data influence coefficient value.

4. The forest soil data management method based on UAV multispectral inversion according to claim 3 is characterized by: The mathematical model of the influence coefficient of the spectral reflectance data is specifically: The mathematical model of the influence coefficient of spectral characteristic data is as follows: The mathematical model of the influence coefficient of spectral texture feature data is as follows: Among them, NIR i represents the near-infrared band of the spectrum in the i-th time period; R i represents the red light band reflectivity in the i-th time period; B i represents the reflectivity of the blue light band in the i-th time period; G i represents the reflectivity of the green light band in the i-th time period; D i represents the spectral absorption depth of the i-th time period; W i represents the spectral absorption width of the i-th time period; s i represents the spectral reflectance slope of the i-th time period; spectral texture c i represents the spectral gray-level co-occurrence matrix contrast of the i-th time period; h i Represents the spectral gray-level co-occurrence matrix entropy of the i-th time period; UNI i represents the uniformity of the spectral gray-level co-occurrence matrix in the i-th time period; E i Represents the spectral gray-level co-occurrence matrix energy of the i-th time period.

5. The forest soil data management method based on UAV multispectral inversion according to claim 1 is characterized by: The soil property data include soil microenvironment data influencing parameters, soil structure data influencing parameters and soil biological metabolism data influencing parameters; The influencing parameters of the soil microenvironment data include soil moisture content, denoted as M; soil oxygen diffusion rate, denoted as O; soil temperature, denoted as T; soil water holding capacity, denoted as H; soil moisture infiltration rate, denoted as v; the influencing parameters of soil structure data include soil porosity, denoted as P; soil bulk density, denoted as D; soil pore connectivity, denoted as Cr; soil infiltration rate, denoted as IR; soil sand content, denoted as S; soil silt content, denoted as Si; soil clay content, denoted as C; the influencing parameters of soil biological metabolism data include soil respiration rate, denoted as r; soil biomass carbon, denoted as MBC; soil biomass nitrogen, denoted as SBN; microbial carbon source utilization rate, denoted as U; soil organic matter content, denoted as SOM; and soil moisture content, denoted as SMC.

6. The forest soil data management method based on UAV multispectral inversion according to claim 1 is characterized by: Said S5 includes the steps of calculating the influence coefficient of soil microenvironment data, the influence coefficient of soil structure data and the influence coefficient of soil biological metabolism data; The soil microenvironment data influence coefficient calculation step is used to import the soil microenvironment data influence parameters into the soil microenvironment data influence coefficient mathematical model to obtain the soil microenvironment data influence coefficient value; the soil structure data influence coefficient calculation step is used to import the soil structure data influence parameters into the soil structure data influence coefficient mathematical model to obtain the soil structure data influence coefficient value; the soil biological metabolism data influence coefficient calculation step is used to import the soil biological metabolism data influence parameters into the soil biological metabolism data influence coefficient mathematical model to obtain the soil biological metabolism data influence coefficient value.

7. The method for managing forest soil data based on multispectral inversion using unmanned aerial vehicle (UAV) according to claim 6, characterized in that: The mathematical model of the soil microenvironment data influence coefficient is specifically: The mathematical model of soil structure data influence coefficient is as follows: The mathematical model of the influence coefficient of soil biological metabolism data is as follows: Among them, M i represents the soil moisture content in the i-th time period; O i represents the soil oxygen diffusion rate in the i-th time period; T i represents the soil temperature in the i-th time period; H i represents the soil water holding capacity in the i-th time period; VI represents the soil water infiltration rate in the i-th time period; P i represents the soil porosity in the i-th time period; D i represents the soil bulk density in the i-th time period; Cr i represents the soil pore connectivity rate in the i-th time period; IR i represents the soil infiltration rate in the i-th time period; S i represents the soil sand content in the i-th time period; Si i represents the soil silt content in the i-th time period; C i represents the soil clay content in the i-th time period; R i represents the soil respiration rate in the i-th time period; MBC i represents the soil biomass carbon in the i-th time period; SBN i represents the soil biomass nitrogen in the i-th time period; U i represents the carbon source utilization rate of microorganisms in the i-th time period; SOM i represents the soil organic matter content in the i-th time period; SMC i represents the soil moisture content in the i-th time period.

8. The forest soil data management method based on UAV multispectral inversion according to claim 1 is characterized by: The mathematical model of the rationality index of the soil inversion method is specifically:

9. A forest soil data management system based on UAV multispectral inversion, used to implement the forest soil data management method based on UAV multispectral inversion according to any one of claims 1 to 8, characterized in that: include: Management area determination module: used to determine the target forest soil data as the target management area, and then divide it into sub-areas according to the equal time division method, and number each sub-area in the target management area in sequence as 1, 2, ..., i, ..., n according to the equal time; UAV image data acquisition module: used to obtain UAV image data of the target management area; UAV image data processing module: used to analyze and process UAV image data to obtain the influence coefficient value of spectral reflectance data, the influence coefficient value of spectral feature data and the influence coefficient value of spectral texture feature data; Soil property data acquisition module: used to obtain soil property data of the target management area; Soil property data processing module: used to analyze and process soil property data to obtain soil microenvironment data influence coefficient value, soil structure data influence coefficient value and soil thermal property data influence coefficient value; Comprehensive data analysis module: used to import the influence coefficient value of spectral reflectance data, the influence coefficient value of spectral feature data, the influence coefficient value of spectral texture feature data, the influence coefficient value of soil microenvironment data, the influence coefficient value of soil structure data and the influence coefficient value of soil biological metabolism data into the soil inversion method rationality index mathematical model to obtain the soil inversion method rationality index value; Soil inversion method judgment module: used to compare the soil inversion method rationality index value with the preset soil inversion method rationality index values ​​of multiple different types of soil, calculate the minimum value of the difference between the soil inversion method rationality index value and the preset soil inversion method rationality index values ​​of multiple different types of soil, and obtain the soil judgment result of the corresponding type according to the minimum value of the difference value; Data interaction and transmission module: used to transmit the difference value, judgment result and target forest land soil data obtained by the soil inversion method judgment module to the information terminal to provide reference data.

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