A method for determining the degree of rodent-damaged pine based on hyperspectral and physiological and biochemical parameters
By combining hyperspectral and physiological and biochemical parameter methods, using spectral radiometers and random forest models, the problem of inaccurate assessment of rodent damage in Chinese pine was solved, the degree of damage to Chinese pine was accurately determined, and the scientific nature and efficiency of rodent damage monitoring were improved.
Patent Information
- Application Number
- CN202411658006.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-20
- Publication Date
- 2025-10-03
- Estimated Expiration
- 2044-11-20
AI Technical Summary
The existing technology is inaccurate in assessing the extent of pine squirrel damage and consumes a lot of manpower and material resources, making it difficult to achieve accurate assessment and effective prevention and control.
A method based on hyperspectral and physiological and biochemical parameters was used to obtain the spectral reflectance data of Pinus tabulaeformis needles through a spectroradiometer. The chlorophyll content and moisture content were processed. Spectral differentiation technology and correlation analysis were used to construct a random forest model to accurately determine the degree of damage to Pinus tabulaeformis.
It improves the scientificity and accuracy of rodent damage assessment, reduces random errors, provides an efficient means of rodent damage monitoring, and provides a scientific basis for forest management and prevention.
Smart Images
Figure CN119595557B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of forestry pest monitoring and prevention, and in particular to a method for determining the damage degree of Chinese pine based on ground object hyperspectral and physiological parameters. Background Art
[0002] Pinus tabulaeformis is a major tree species planted in the returning farmland to forest areas of the Loess Plateau. Its growth and health are crucial to the stability and sustainability of the region's ecosystem. However, the Gansu zokor, by gnawing on the roots of Pinus tabulaeformis, severely hinders its growth and even kills it, thereby causing degradation of the forest ecosystem. Effective rodent monitoring is key to preventing and controlling rodent infestations. Accurately categorizing the extent of damage to Pinus tabulaeformis and implementing appropriate management measures is crucial for maintaining the ecological environment.
[0003] Currently, assessments of damage caused by zokors to Chinese pine trees in Gansu Province primarily rely on manual field surveys. However, this method not only struggles to accurately identify even mildly affected pines but also requires significant manpower and material resources. Therefore, a more efficient method is urgently needed. With the rapid development of remote sensing technology, its rapidity, efficiency, and low cost have led to its increasing application in forest pest monitoring. This paper proposes a rodent infestation monitoring method based on remote sensing technology to address the limitations of traditional monitoring methods and provide technical support for the precise assessment and prevention of rodent infestation in Chinese pine trees. Summary of the Invention
[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for determining the degree of damage to Chinese pine caused by rodents based on hyperspectral and physiological and biochemical parameters, which solves the problem of inaccurate assessment of the degree of damage to Chinese pine caused by rodents in the prior art.
[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: a method for determining the degree of damage caused by rodents to Chinese pine based on hyperspectral and physiological and biochemical parameters, comprising the following steps:
[0006] S1. Determine the target area of rodent-infested pine trees based on the characteristics of rodent damage, divide the target area into sections, and collect pine needles in the target area;
[0007] S2. Based on the Chinese pine needles, using a spectroradiometer to obtain spectral reflectance data of the Chinese pine needles, and processing the Chinese pine needles to obtain the chlorophyll content and moisture content of the Chinese pine needles;
[0008] S3. Using a convolution smoothing method to process outliers in the spectral reflectance data to obtain smoothed spectral reflectance data, and removing outliers in the chlorophyll content and moisture content to obtain processed chlorophyll content and moisture content;
[0009] S4. performing first-order derivative processing on the smoothed spectral reflectance data using a spectral differential technique to obtain first-order differential spectral reflectance data of Chinese pine needles;
[0010] S5. Determine a candidate vegetation index and a candidate red edge parameter based on the smoothed spectral reflectance data and the first-order differential spectral reflectance data, and determine a target vegetation index and a target red edge parameter using a correlation calculation formula based on the candidate vegetation index and the candidate red edge parameter and the processed chlorophyll content and water content;
[0011] S6, using the target vegetation index and target red edge parameter as input and the processed chlorophyll content and moisture content as output, training the random forest model to obtain a Pinus tabulaeformis damage degree estimation model;
[0012] S7. Input the target vegetation index and target red edge parameter of the pine needles to be tested into the pine damage degree estimation model, output the chlorophyll content and moisture content of the pine needles to be tested through the pine damage degree estimation model, and determine the damage degree of the pine needles to be tested based on the chlorophyll content and moisture content of the pine needles to be tested.
[0013] The beneficial effects of the above scheme are as follows: the present invention uses hyperspectral remote sensing to obtain the spectral reflectance of Chinese pine needles, calculates the spectral index, and analyzes the spectral characteristics of damaged and healthy plants. It also measures the chlorophyll content and moisture content of the needles, establishes an inversion model that correlates the chlorophyll and moisture content with the spectral characteristics, and determines the extent of damage to the tested Chinese pine needles based on the spectral characteristics and chlorophyll content. Using hyperspectral remote sensing to classify damage caused by Chinese pine zokors provides basic data for large-scale remote sensing monitoring of zokors, improving the scientificity, accuracy, and efficiency of rodent control.
[0014] Furthermore, in step S2, based on the Chinese pine needles, a spectroradiometer is used to obtain spectral reflectance data of the Chinese pine needles, which specifically includes:
[0015] Based on the Chinese pine needles, the Chinese pine needles are measured at least 20 times using a spectroradiometer, and the average value of the measurement results of at least 20 measurements is taken as the spectral reflectance data.
[0016] The beneficial effect of the above further solution is that slight changes in environmental conditions or differences in operator techniques may cause fluctuations in measurement results. Multiple measurements help smooth out these fluctuations, obtain a more stable average value, reduce the impact of random errors, and improve data reliability.
[0017] Furthermore, in step S2, the pine needles are processed to obtain the chlorophyll content of the pine needles, which specifically includes:
[0018] S21. Treating Pinus tabulaeformis needles according to the test method;
[0019] S22. Use a UV2600 spectrophotometer to measure the absorbance at 645 nm and 663 nm of the treated Chinese pine needles;
[0020] S23. Determine the chlorophyll content according to the chlorophyll content calculation formula based on the absorbance value at 645 nm and the absorbance value at 663 nm.
[0021] Furthermore, in step S23, the chlorophyll content calculation formula is:
[0022] Ca=12.7A 663 -2.69A 645
[0023] Cb=22.9A 645 -4.68A 663
[0024] C 总 =Ca+Cb
[0025] Among them, Ca represents the chlorophyll a content, A 663 Indicates the absorbance at 663 nm, A 645 represents the absorbance value at 645nm, Cb represents the chlorophyll b content, C 总 Indicates chlorophyll content.
[0026] The above-mentioned further solution has the following beneficial effects: Chlorophyll is a key pigment for plant photosynthesis, and its content directly reflects the photosynthetic capacity and health of the plant. By measuring chlorophyll content, the growth status of the plant can be effectively assessed, and the extent of rodent damage to Chinese pine needles can be promptly detected.
[0027] Furthermore, in step S5, there are 15 vegetation indices to be selected and 8 target red edge parameters to be selected.
[0028] Furthermore, in step S5, the correlation calculation formula is:
[0029]
[0030] Where r represents the correlation, x represents the measured value of any candidate vegetation index or any candidate red edge parameter, y represents the measured value of chloroplast pigment content or water content, and n represents the number of samples.
[0031] The beneficial effect of this further approach is that, because the spectral curve of the Chinese pine canopy exhibits significant differences in different wavelengths depending on the level of rodent damage, the differential spectral curves of the pines under different health conditions exhibit distinct patterns in reflectance variation across these wavelengths. These specific variations in spectral reflectance are primarily due to differences in chlorophyll content and moisture content in the needles. Correlation analysis can be used to select candidate vegetation indices and red edge parameters that are highly correlated with the severity of Chinese pine damage, thereby improving the ability to discern the severity of Chinese pine damage.
[0032] Furthermore, the method further comprises:
[0033] The determination coefficient, root mean square error and mean relative error were used to evaluate the damage extent estimation model of Pinus tabulaeformis.
[0034] The beneficial effects of the above further scheme are: the comprehensive use of evaluation indicators can comprehensively evaluate the performance of the Pinus tabulaeformis damage estimation model, guide model optimization, ensure that the model can accurately and reliably predict the damage status of Pinus tabulaeformis in practical applications, provide a scientific basis for forest management and rodent control, and promote the sustainable management and protection of forestry resources. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] Figure 1 The figure is a flow chart of a method for determining the degree of damage to Chinese pine caused by rodent infestation based on hyperspectral and physiological and biochemical parameters. DETAILED DESCRIPTION
[0036] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0037] like Figure 1 As shown, a method for determining the degree of damage to Chinese pine caused by rodent infestation based on hyperspectral and physiological and biochemical parameters includes the following steps:
[0038] S1. Determine the target area for rodent-infested pine according to the characteristics of rodent damage, divide the target area into sections, and collect pine needles in the target area.
[0039] For example, the target area could be the Pinus tabulaeformis plantation in Maotao Village, Yuanzhou District, Guyuan City, Ningxia. The target area could be divided into ten 30m x 30m plots. A systematic random sampling method was used to collect needles from 30 healthy and damaged Pinus tabulaeformis trees (mild, moderate, severe, and dead) using a random sampling method. The needles were collected from each tree, 6 each, for a total of 30 trees. Each tree was sampled from the top, middle, and bottom to ensure that the damage status of the entire plant was reflected.
[0040] S2. Based on the Chinese pine needles, a spectroradiometer is used to obtain the spectral reflectance data of the Chinese pine needles, and the Chinese pine needles are processed to obtain the chlorophyll content and moisture content of the Chinese pine needles.
[0041] In this embodiment, in step S2, spectral reflectance data of the Chinese pine needles is obtained using a spectroradiometer based on the Chinese pine needles, which specifically includes:
[0042] Based on the Chinese pine needles, the Chinese pine needles are measured at least 20 times using a spectroradiometer, and the average value of the measurement results of at least 20 measurements is taken as the spectral reflectance data.
[0043] In this embodiment, in step S2, the pine needles are processed to obtain the chlorophyll content of the pine needles, which specifically includes:
[0044] S21. Treating Pinus tabulaeformis needles according to the test method;
[0045] S22. Use a UV2600 spectrophotometer to measure the absorbance at 645 nm and 663 nm of the treated Chinese pine needles;
[0046] S23. Determine the chlorophyll content according to the chlorophyll content calculation formula based on the absorbance value at 645 nm and the absorbance value at 663 nm.
[0047] In this embodiment, the chlorophyll content calculation formula is:
[0048] Ca=12.7A 663 -2.69A 645
[0049] Cb=22.9A 645 -4.68A 663
[0050] C 总 =Ca+Cb
[0051] Among them, Ca represents the chlorophyll a content, A 663 Indicates the absorbance at 663 nm, A 645 represents the absorbance value at 645nm, Cb represents the chlorophyll b content, C 总 Indicates chlorophyll content.
[0052] For example, step S21 may specifically include:
[0053] Cut all the needles collected from the sealed bag, wash and dry them, and then chop them into small pieces (no larger than 1.5 mm). Weigh 0.3 g of the sample into a test tube, add 25 ml of 95% ethanol, and seal the tube to protect from light. After 36 hours of extraction, filter the solution into a volumetric flask, dilute to 25 ml, shake well, and number the sample. Pour the solution into a centrifuge tube, protect from light, and use 95% ethanol as a control.
[0054] Secondly, a UV2600 spectrophotometer can be used to measure the 645nm absorbance value and the 663nm absorbance value of the treated Chinese pine needles respectively, and the average value can be taken.
[0055] For example, the moisture content of the Chinese pine needles can be obtained by processing the Chinese pine needles as follows:
[0056] Step 1: Weigh the fresh weight (FW) of all collected samples.
[0057] Step 2: Place the weighed sample in a drying oven and incubate at 105°C for 30 minutes. Then adjust the temperature to 80°C and dry to a constant weight, and then weigh the dry weight (DW).
[0058] Step 3: Calculate the moisture content of needles. The calculation formula can be: leaf moisture content = (FW-DW) / FW×100%.
[0059] For example, using a spectroradiometer to obtain spectral reflectance data for Chinese pine needles can be done as follows: A numbered sample of pine needles is brought indoors for spectrum measurement. The needles are placed on a black screen. During the measurement, reflectance correction is performed to ensure that the reflectance of the whiteboard is always 1. To ensure accuracy, 20 spectrum measurements are taken for each sample, and the average value is used as the spectral reflectance for that sample.
[0060] The spectra were collected using the ASD Field Spec 4 spectroradiometer, manufactured by ASD Corporation in the United States. The ASD spectroradiometer covers a wavelength range of 350 to 2500 nm, with a total of 2151 bands.
[0061] S3. Use the convolution smoothing method to process the outliers of the spectral reflectance data to obtain smoothed spectral reflectance data, and remove the outliers in the chlorophyll content and moisture content to obtain the processed chlorophyll content and moisture content.
[0062] In this embodiment, during the acquisition of hyperspectral data, some abnormal data will be generated due to the influence of environmental and equipment noise, so it is very important to remove the abnormal data. Before using the data, the data needs to be removed for abnormal points.
[0063] S4. Use spectral differentiation technology to perform first-order derivative processing on the smoothed spectral reflectance data to obtain first-order differential spectral reflectance data of Chinese pine needles.
[0064] In the present embodiment, the SG (Savitzky-Golay) convolution smoothing method is adopted to process outliers, and a quadratic polynomial fit is used with 7 smoothing points, thereby obtaining smooth spectral reflectance data. Spectral differentiation technology is to reduce the interference of baseline drift, background interference, and other components in the sample by performing differential processing on the spectral data, thereby improving the accuracy of detection. Therefore, based on the smoothed spectral data, first-order derivative processing is performed to obtain the first-order differential spectral reflectance of each sample of Pinus tabulaeformis.
[0065] S5. Determine the candidate vegetation index and the candidate red-edge parameter based on the smoothed spectral reflectance data and the first-order differential spectral reflectance data, and determine the target vegetation index and the target red-edge parameter using a correlation calculation formula based on the candidate vegetation index and the candidate red-edge parameter and the processed chlorophyll content and moisture content.
[0066] In this embodiment, in step S5, there are 15 vegetation indices to be selected and 8 target red edge parameters to be selected.
[0067] Furthermore, in step S5, the correlation calculation formula is:
[0068]
[0069] Where r represents the correlation, x represents the measured value of any candidate vegetation index or any candidate red edge parameter, y represents the measured value of chloroplast pigment content or water content, and n represents the number of samples.
[0070] S6. Using the target vegetation index and target red edge parameter as input and the processed chlorophyll content and moisture content as output, the random forest model is trained to obtain a Pinus tabulaeformis damage degree estimation model.
[0071] In the random forest model, multiple decision trees are constructed, using ensemble learning to improve prediction accuracy. During training, model performance can be optimized by setting the number of decision trees in the random forest (e.g., the ntree parameter) and the minimum number of samples required for each tree to split a node (e.g., the nodesize parameter). To ensure model generalization and avoid overfitting, tree depth and other parameters can also be adjusted. During the prediction phase, the trained random forest model can be used to predict the test dataset and compare the actual values with the predicted values to evaluate model performance.
[0072] S7. Input the target vegetation index and target red edge parameter of the pine needles to be tested into the pine damage degree estimation model, output the chlorophyll content and moisture content of the pine needles to be tested through the pine damage degree estimation model, and determine the damage degree of the pine needles to be tested based on the chlorophyll content and moisture content of the pine needles to be tested.
[0073] For example, the damage degree of the tested Chinese pine needles can be divided into healthy, mild, moderate, severe, and dead according to actual conditions, as shown in Table 1. No specific limitation is imposed here.
[0074] Table 1 Grade of damage degree of Pinus tabulaeformis
[0075]
[0076] In this embodiment, the method further includes:
[0077] The determination coefficient, root mean square error and mean relative error were used to evaluate the damage extent estimation model of Pinus tabulaeformis.
[0078] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the invention.
Claims
1. A method for determining the extent of rodent-damaged pine trees based on hyperspectral and physiological and biochemical parameters, characterized in that: The method comprises: S1. Determine a target area of rodent-infested pine trees based on rodent-infested characteristics, divide the target area into sections, and collect pine needles in the target area; S2. Based on the Chinese pine needles, using a spectroradiometer to obtain spectral reflectance data of the Chinese pine needles, and processing the Chinese pine needles to obtain chlorophyll content and moisture content of the Chinese pine needles; Based on the Chinese pine needles, using a spectroradiometer to obtain spectral reflectance data of the Chinese pine needles, specifically comprising: based on the Chinese pine needles, using the spectroradiometer to measure the Chinese pine needles at least 20 times, and taking an average value of the measurement results of the at least 20 measurements as the spectral reflectance data; S3, using the SG convolution smoothing method to process outliers, using quadratic polynomial fitting with 7 smoothing points to obtain smoothed spectral reflectance data, and removing outliers in the chlorophyll content and the moisture content to obtain processed chlorophyll content and moisture content; S4. performing first-order derivative processing on the smoothed spectral reflectance data using a spectral differential technique to obtain first-order differential spectral reflectance data of the Chinese pine needles; S5. Determine a candidate vegetation index and a candidate red-edge parameter based on the smoothed spectral reflectance data and the first-order differential spectral reflectance data, and determine a target vegetation index and a target red-edge parameter using a correlation calculation formula based on the candidate vegetation index and the candidate red-edge parameter and the processed chlorophyll content and moisture content; there are 15 candidate vegetation indices. S6. Using the target vegetation index and target red edge parameter as input and the processed chlorophyll content and moisture content as output, training a random forest model to obtain a Pinus tabulaeformis damage degree estimation model; S7. Input the target vegetation index and target red edge parameter of the pine needles to be tested into the pine damage degree estimation model, output the chlorophyll content and moisture content of the pine needles to be tested through the pine damage degree estimation model, and determine the damage degree of the pine needles to be tested based on the chlorophyll content and moisture content of the pine needles to be tested.
2. The method according to claim 1, characterized in that In S2, the processing of the Chinese pine needles to obtain the chlorophyll content of the Chinese pine needles specifically includes: S21. treating the Chinese pine needles according to the test method; S22. Use a UV2600 spectrophotometer to measure the absorbance at 645 nm and 663 nm of the treated Chinese pine needles; S23. Determine the chlorophyll content according to the 645nm absorbance value and the 663nm absorbance value and the chlorophyll content calculation formula.
3. The method according to claim 2, characterized in that In S23, the chlorophyll content calculation formula is: in, Indicates the chlorophyll a content, Indicates the absorbance value at 663nm. Indicates the absorbance value at 645nm. Indicates the chlorophyll b content, Indicates chlorophyll content.
4. The method according to claim 1, wherein In S5, the correlation calculation formula is: in, Indicates the relevance, represents the measured value of any candidate vegetation index or any candidate red edge parameter, Indicates the measured value of chloroplast pigment content or water content, Indicates the sample size.
5. The method according to claim 1, wherein The method further comprises: The coefficient of determination, root mean square error, and mean relative error were used to evaluate the damage severity estimation model for Pinus tabulaeformis.