Method for continuously detecting stress level of phyllostachys pubescens in phyllostachys pubescens forest phyllostachys pubescens based on change

Abstract

The invention provides a phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest phyllostachys pubescens forest Based on the NDVI time sequence data, the growth season initial stage SOS and the growth season final stage EOS of the moso bamboo forest are extracted; the moso bamboo forest pest stress level is judged through a normalized pest stress index NPSI, and a calculation method of the normalized pest stress index NPSI comprises the steps that the phenological weight Wp is dynamically adjusted according to the dispersion degree of NDVI time sequence data, so that the Wp value is reduced along with the increase of the dispersion degree, and natural phenological interference is inhibited; and coupling normalized values of NDVI, EVI and LAI, and calculating NPSI by combining Wp and a phenological option weight coefficient.

Description

Technical Field

[0001] This invention belongs to the technical fields of forestry, geography, ecology, remote sensing science and technology, and pest and disease monitoring. Specifically, it relates to a method for continuous detection of the stress level of the Phyllostachys nigra moth in moso bamboo forests based on changes in remote sensing phenological characteristics. Background Technology

[0002] Moso bamboo (Phyllostachys pubescens) is one of the most representative economic bamboo species in my country, widely distributed in provinces such as Fujian, Zhejiang, Jiangxi, and Hunan. As a typical fast-growing evergreen plant of the Bambusoideae subfamily of the Poaceae family, moso bamboo not only has significant carbon sequestration potential but also plays a key role in the "bamboo-for-plastic" and "bamboo-for-wood" transformation of the bamboo industry, promoting green economic development and ecological construction. However, moso bamboo is often severely threatened during its growth by the bamboo tussock moth (Pantana phyllostachysae Chao). The larvae of this pest damage the bamboo leaf tissue by feeding on it, causing holes, curling, and even leaf drop. This not only directly leads to a large loss of chlorophyll and a significant decrease in photosynthetic efficiency but also weakens the overall growth of the bamboo forest. According to statistics, the annual economic losses caused by pests to bamboo forests in my country reach hundreds of millions of yuan, among which the bamboo tussock moth is one of the most destructive pests. Traditional pest monitoring mainly relies on manual field surveys, which has significant limitations: (1) the data collection cycle is long and the timeliness is poor, making it difficult to capture the dynamic changes of pest outbreaks in a timely manner; (2) the monitoring range is limited by manpower and terrain conditions, making it difficult to cover large areas of bamboo forests; (3) the cost is high and the subjectivity is strong, making it easy to be influenced by the experience of the surveyors. In addition, although existing remote sensing technology has made some progress in forest pest monitoring, it mostly focuses on the identification of static pest surfaces, which usually relies on single-phase or short-series images, making it difficult to capture early signals and continuous changes in pest stress. At the same time, vegetation phenological characteristics (such as the beginning and end of the growing season and changes in leaf area index) can sensitively reflect the impact of environmental stress on plant physiology. Studies have shown that pest stress can significantly change the phenological time sequence of vegetation. However, existing research still faces challenges in phenological monitoring of evergreen vegetation (such as moso bamboo) because it lacks a clear leaf fall period, and traditional threshold methods are difficult to accurately extract key phenological parameters. Therefore, the current monitoring technology for the bamboo tussock moth faces two major bottlenecks: first, traditional methods are inefficient and have a narrow coverage; second, existing remote sensing models mostly rely on static features and lack dynamic capture of the spatiotemporal continuity of pest stress. Summary of the Invention

[0003] To address the shortcomings and deficiencies of existing technologies—such as poor timeliness of traditional pest monitoring, difficulty in capturing early stress signals, and inaccurate extraction of phenological parameters from evergreen vegetation—this invention provides a method and system for continuous detection of the stress level of the bamboo tussock moth in moso bamboo forests based on changes in remote sensing phenological characteristics. A dynamic phenological weight adjustment mechanism is creatively constructed. By analyzing the discrete characteristics (such as fluctuation intensity) of NDVI time-series data in real time, the phenological weight value adaptively decreases as the degree of dispersion increases, effectively suppressing the interference of natural factors such as cloud changes and light interference on pest signals, and significantly improving detection sensitivity. This mechanism has demonstrated superior performance in validation during the rapid growth period of moso bamboo forests.

[0004] In terms of pest quantification models, this invention achieves, for the first time, a three-dimensional synergistic analysis of chlorophyll activity (NDVI), canopy structure (EVI), and biomass (LAI). By eliminating dimensional differences between vegetation indices through normalization and combining dynamic weighting coefficients from different phenological stages, a Normalized Normalized Index (NPSI) comprehensively reflects the degree of pest stress. This index overcomes the limitations of traditional qualitative identification, enabling quantitative grading of pest severity—when the NPSI value is below the health threshold, it is considered a healthy bamboo forest; exceeding the threshold indicates a damaged state. The grading results have been validated using field samples.

[0005] To ensure the accuracy of phenological parameter extraction, this invention employs SG filtering technology to smoothly reconstruct the original NDVI curve, effectively filtering out noise interference. Based on the reconstructed growth trend curve, a specific proportion of seasonal amplitude is innovatively used as a dynamic threshold to accurately pinpoint key nodes at the beginning and end of the bamboo forest growing season. This technological breakthrough solves the problem of phenological monitoring in evergreen vegetation due to the lack of a distinct leaf fall period.

[0006] The resulting continuous monitoring system dynamically tracks pest evolution through multi-temporal NPSI values. When significant changes occur continuously in a specific area, the system automatically triggers an early warning mechanism. The entire technical solution forms a complete closed loop of "data acquisition → phenological analysis → stress quantification → decision support," providing full-chain support from theory to practice for bamboo forest pest and disease control. Statistical verification of the technical effectiveness has shown that it exhibits superior pest identification capabilities during the core growth stage.

[0007] The specific technical solution adopted by this invention to solve its technical problem is as follows:

[0008] A method for continuous detection of the stress level of the tussock moth in moso bamboo forests based on changes in remote sensing phenological characteristics:

[0009] Acquire temporal multispectral satellite remote sensing images of moso bamboo forests and extract temporal data of vegetation indices including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI).

[0010] Extract the start-of-season SOS and end-of-season EOS of moso bamboo forest based on NDVI time series data;

[0011] The level of pest stress in moso bamboo forests was determined by the Normalized Normalized Pest Index (NPSI). The calculation method for the NPSI is as follows:

[0012] The phenological weight W is dynamically adjusted based on the dispersion of NDVI time-series data. p , making W p The value decreases as the dispersion increases, in order to suppress natural phenological disturbances;

[0013] The normalized values ​​of coupled NDVI, EVI, and LAI, combined with W p NPSI is calculated based on the phenological weighting factor.

[0014] Furthermore, the degree of dispersion is determined by the variance or standard deviation of the NDVI time-series data; the phenological weight W p It is negatively correlated with the variance or standard deviation of NDVI time series data.

[0015] Furthermore, the phenological weighting coefficient is dynamically allocated according to the growing season stages; the growing season stages include the bamboo shoot emergence period, the rapid growth period, the leaf expansion period, the nutrient accumulation period, and the dormancy period.

[0016] Furthermore, the normalized value is (current value - minimum value during the growing season) / (maximum value during the growing season - minimum value).

[0017] Furthermore, the Normalized Pest Stress Index (NPSI) is: phenological weight W p The product of the time-series vegetation index data with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI).

[0018] Furthermore, the pest stress level corresponding to the NPSI value is set as follows: when NPSI≤0.25, it represents healthy pests, and when NPSI>0.25, it represents pests that are damaged.

[0019] Furthermore, the SG filtering method was used to smooth and reconstruct the NDVI time series data, and the remote sensing phenological parameters of the moso bamboo forest, including the SOS at the beginning of the growing season and the EOS at the end of the growing season, were extracted using the dynamic threshold method.

[0020] Furthermore, the dynamic threshold method uses 20% of the seasonal amplitude of NDVI as the threshold.

[0021] And, a continuous detection system for the stress level of the Phyllostachys nigra tussock moth in moso bamboo forests based on changes in remote sensing phenological characteristics, comprising:

[0022] Data acquisition module: Acquires time-series multispectral remote sensing images;

[0023] Phenological analysis module: Perform the method described above;

[0024] Early warning output module: Generates a spatial distribution map of stress levels.

[0025] And a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described above.

[0026] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0027] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0028] 1. The dynamic phenological anti-interference mechanism significantly improves detection accuracy.

[0029] By establishing a dynamic response mechanism between phenological weights and NDVI temporal fluctuations, the system can automatically reduce weight values ​​during periods of strong natural phenological fluctuations (such as cloud cover changes and light interference) and enhance the influence of weights during periods of calm fluctuations, effectively suppressing the interference of environmental noise on pest signals. This design fundamentally solves the industry pain point of "difficulty in distinguishing between natural fluctuations and pest stress" in traditional methods, significantly improving the sensitivity of early pest identification.

[0030] 2. Multispectral coupling enables precise quantification of pest infestation severity.

[0031] This innovative approach integrates three key indicators—chlorophyll activity (NDVI), canopy structure (EVI), and biomass (LAI)—and eliminates dimensional differences through normalization. It then constructs a comprehensive stress index (NPSI) by combining phenological stage-specific weighting coefficients. This three-dimensional synergistic analysis overcomes the limitations of single-index monitoring, achieving for the first time a continuous quantitative grading system from a binary "presence or absence of pests" to a "healthy-mild-moderate-severe" approach, providing a scientific basis for precise pest control.

[0032] 3. Full-cycle continuous detection overcomes the limitations of static detection.

[0033] Based on SG filtering reconstruction of phenological curves and dynamic threshold extraction technology, the system accurately captures key nodes (SOS / EOS) of evergreen vegetation's growing season, overcoming the limitation of traditional threshold methods that rely on the leaf fall period. Through dynamic tracking of multi-temporal NPSI values, the system can capture the complete evolution of pests from occurrence and development to outbreak. When significant changes occur continuously in a specific area, early warnings are automatically triggered, forming a closed-loop management system of "monitoring-early warning-decision".

[0034] 4. Tiered output supports the entire chain of prevention and control decision-making.

[0035] A three-tiered output system is constructed, consisting of a data layer (multispectral acquisition), a model layer (NPSI calculation), and an application layer (spatial distribution mapping). The stress level spatial distribution map visually displays pest hotspots, while the UAV verification module enables collaborative "sky-ground" verification. This system transforms complex algorithms into control guidelines that forestry management departments can directly implement, promoting the efficient transformation of scientific research results into industrial applications. Attached Figure Description

[0036] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0037] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0038] Figure 2 The following is a map showing the research area and data acquisition in an embodiment of the present invention: (a) is the research area and field sampling points; (b) is a topographic profile; and (c) is a display of the field survey.

[0039] Figure 3 This is a comparison chart of the NDVI timing curve before and after smooth reconstruction in an embodiment of the present invention.

[0040] Figure 4 The figures show the spatiotemporal distribution of SOS and EOS in a bamboo forest under pest stress according to an embodiment of the present invention. In the figures: (a) is the spatiotemporal distribution of SOS, and (b) is the spatiotemporal distribution of EOS.

[0041] Figure 5 This is a diagram showing the results of remote sensing information extraction of pests based on NPSI in an embodiment of the present invention.

[0042] Figure 6 This is a comparison chart of the pest continuity detection results based on NPSI and the measured data in the embodiments of the present invention. Detailed Implementation

[0043] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in detail:

[0044] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] To address the shortcomings and deficiencies of existing technologies, this invention proposes a continuous detection method for pest stress levels based on changes in remote sensing phenological characteristics. By coupling the temporal changes in phenology of bamboo forests (such as the dynamic responses of NDVI, EVI, and LAI) with the spectral characteristics of pests, a normalized pest stress index (NPSI) is constructed, achieving high-precision monitoring from "point" to "area" and from "static" to "dynamic." This method can not only identify early pest stress signals but also quantify the degree of damage at different phenological stages, providing a scientific basis for the ecological security and sustainable management of bamboo forests. It can identify early pest stress signals and quantify the degree of damage at different phenological stages.

[0047] Its basic design concept includes:

[0048] (1) Data acquisition and preprocessing: Time series data of vegetation indices such as NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and LAI (Leaf Area Index) were acquired using satellite remote sensing images; combined with field measured samples (healthy, mild, moderate and severe affected sample points), a dataset was constructed, and the pest stress level was divided by indicators such as leaf loss rate per plant, insect population density, and number of eggs (healthy: leaf loss rate 0%; mild: leaf loss rate 0-20%; moderate: 20-50%; severe: >50%), a total of 220 sample points were acquired.

[0049] (2) Extraction of remote sensing phenological features of moso bamboo forest: The SG filtering method was used to smooth and reconstruct the NDVI time series curve. The key phenological parameters of moso bamboo forest were extracted by the dynamic threshold method, mainly including the start of the growing season (SOS) and the end of the growing season (EOS). Combined with UAV aerial photography and ground phenological observation data, the accuracy of the extraction results was verified, and the spatiotemporal variation characteristics of phenological parameters of moso bamboo forest under pest stress were analyzed.

[0050] (3) Construction of the normalized pest stress index based on remote sensing phenology of moso bamboo forest: introducing phenological weights (W p The concept of W is used to enhance the sensitivity of detection indicators to changes in pests, suppress the interference of natural phenological fluctuations in bamboo forests, and adjust W according to different phenological stages of bamboo forests (shoot emergence period, rapid growth period, leaf expansion period, nutrient accumulation period, and dormancy period). pIn addition, based on different phenological stages of bamboo forests, weighting coefficients (λ) were constructed. p μ p To enhance the sensitivity of pest response to the phenology of moso bamboo forests, the Normalized Pest Stress Index (NPSI) was constructed by coupling NDVI, EVI, and LAI.

[0051] (4) Continuous detection and verification of pest infestation: The detection capability of NPSI was verified by detecting the damage based on the NPSI threshold and combined with ANOVA and Fisher discriminant analysis. It was found that the detection effect of NPSI was good, and the overall trend was consistent with the true law that it gradually increases with the aggravation of pest infestation. It was also good when judging healthy bamboo forests and could be judged as a low value at different times for reference.

[0052] Based on the above design, its basic implementation process is as follows: Figure 1 As shown, it includes the following steps:

[0053] (1) Data acquisition and preprocessing: such as Figure 2 As shown, temporal multispectral satellite remote sensing images of bamboo forests were acquired, and combined with ground-measured data, NDVI (Normalized Difference Vegetation Index) and NDVI were analyzed and extracted. re Key vegetation indices such as the red-edge normalized difference vegetation index (NDMI), normalized difference moisture index (BI), bamboo forest index (EVI), enhanced vegetation index (EVI), and leaf area index (LAI) are used to obtain remote sensing information on moso bamboo forests, assess the health status of moso bamboo forests, and obtain remote sensing phenology of moso bamboo forests.

[0054] (2) Phenological feature extraction: The SG filtering method is used to smooth and reconstruct the NDVI time series data, such as Figure 3 As shown, the remote sensing phenological parameters of moso bamboo forests, including the start of the growing season (SOS) and the end of the growing season (EOS), were extracted using the dynamic threshold method.

[0055] (3) Pest stress response analysis: The spatiotemporal variation characteristics of SOS and EOS under different pest stress levels were analyzed by ANOVA method to quantify the impact of pests on remote sensing phenology of moso bamboo forest.

[0056] (4) Constructing the Phenology-Based Normalized Pest Stress Index (NPSI): Coupled with NDVI, EVI, LAI and phenological weights (W p Construct the NPSI using the following formula:

[0057] (1)

[0058] In the formula: λ p and μ pThe weighting coefficients represent the different phenological stages of the bamboo forest.

[0059] (5) Continuous pest detection: Based on the NPSI threshold, determine whether the bamboo forest is damaged, and realize dynamic monitoring of the pest range.

[0060] In step (2), the dynamic threshold method uses 20% of the seasonal amplitude of NDVI as the threshold to extract SOS and EOS of bamboo forest.

[0061] In step (4), the W p Dynamic adjustments are made based on the NDVI time-series fluctuation characteristics; when the fluctuation is small, W... p When W is relatively high; when it fluctuates significantly, p Lower.

[0062] In step (5), based on the field measured sample points in the study area (covering healthy, mild, moderate and severe pest stress levels), the distribution characteristics of NPSI values ​​are analyzed as shown in Equation 1. Combined with the natural fluctuation range of the phenology of moso bamboo forest, the threshold of NPSI is determined. The larger the value, the higher the degree of damage to the moso bamboo forest.

[0063] The following is a more specific application example to illustrate and describe the above solutions of the present invention in more detail:

[0064] (1) Data acquisition and preprocessing: 100 30 m × 30 m quadrats were evenly distributed in the study area. In May, July and October 2021, 53 healthy, 53 mild, 50 moderate and 39 severe samples of bamboo moth were collected. The coordinates, pest level and phenological stage of bamboo forest were recorded at the same time. Based on the GEE platform and combined with ENVI software, image processing was performed to obtain Sentinel-2 time series data in 2021. Various detection indicators were calculated, multi-band datasets were synthesized, remote sensing information of bamboo forest was obtained, the health status of bamboo forest was assessed and remote sensing phenology of bamboo forest was obtained.

[0065] (2) Remote sensing phenological feature extraction of moso bamboo forest: In this embodiment, the SG filtering method is used to smooth and reconstruct the NDVI time series curve, remove noise and retain the main growth trend. When the NDVI value exceeds the dynamic threshold, it is determined to be SOS, and the SOS of moso bamboo forest is approximately day 80 (mid-February); when the NDVI value drops below the threshold, it is determined to be EOS, approximately day 521 (early May of the following year); the growing season length (LOS) of moso bamboo forest is defined as the difference between EOS and SOS, approximately 441 days, as shown below. Figure 4 As shown.

[0066] (3) NPSI construction: Combining different phenological stages of moso bamboo forest, based on empirical assignment and / or temporal characteristics, the phenological weights of each phenological period of moso bamboo forest are determined: Shoot emergence period W p=0.65, W during rapid growth period p =0.85, leaf expansion period W p =0.80, Nutrient accumulation period W p =0.50, Dormant period W p =0.75. NPSI calculation:

[0067] (1)

[0068] In the formula: NDVI, EVI, and LAI are the vegetation index values ​​within the current phenological stage; NDVI min With NDVI max (And the minimum and maximum values ​​corresponding to EVI and LAI) are obtained from statistical data of each period; W p As a phenological weight, during periods of relatively small fluctuations in NDVI, W p Higher, during periods of significant NDVI fluctuation, W p Lower; λ p and μ p The influence weights of EV1 and LAI under different phenological stages are respectively.

[0069] (4) Pest detection and verification: such as Figure 5 , Figure 6 As shown, the pest stress level corresponding to the NPSI value is set as follows: when NPSI ≤ 0.25, it represents a healthy bamboo forest, and the closer the NPSI is to 0, the healthier the bamboo forest; when NPSI > 0.25, it represents a bamboo forest suffering from pests, and the further the NPSI is from 0.25, the more severe the pest infestation. Based on the ANVOA method, the analysis shows that NPSI can effectively distinguish between the healthy and damaged states of bamboo forests, but its discrimination effect varies with the phenological stage. Based on Fisher discriminant analysis, the results showed that NPSI performed best overall during the rapid growth period to the leaf expansion stage (detection accuracy was 76.42% and 74.52%, Kappa values ​​were 0.4762 and 0.4662, and R² values ​​were 0.5041 and 0.4684, respectively). After entering the nutrient accumulation period, although the discriminant ability improved (detection accuracy was 68.31%, Kappa value was 0.4214, R² = 0.4222), the discriminant ability remained at a moderate or low level during the shoot emergence and dormancy periods (detection accuracy was 74.75% during shoot emergence, Kappa value was 0.4672, R² = 0.3825; detection accuracy was 72.08% during dormancy, Kappa value was 0.4086, R² = 0.3616). Overall, the NPSI can distinguish between healthy and damaged states in all phenological stages of bamboo forests, but its discrimination accuracy and reliability (reflected in indicators such as detection accuracy, Kappa value and R²) vary in different phenological stages.

[0070] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0071] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0072] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0074] This invention is not limited to the above-described preferred embodiments. Anyone inspired by this invention can derive various other methods for detecting the continuous stress level of the Phyllostachys edulis moth in moso bamboo forests based on changes in remote sensing phenological characteristics. All equivalent changes and modifications made within the scope of the patent applications of this invention shall fall within the scope of this invention.

Claims

1. A method for continuous detection of the stress level of the tussock moth in moso bamboo forests based on changes in remote sensing phenological characteristics, characterized in that: Acquire temporal multispectral satellite remote sensing images of moso bamboo forests and extract temporal data of vegetation indices including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI). Extract the start-of-season SOS and end-of-season EOS of moso bamboo forest based on NDVI time series data; The level of pest stress in moso bamboo forests was determined by the Normalized Normalized Pest Index (NPSI). The calculation method for the NPSI is as follows: The phenological weight W is dynamically adjusted based on the dispersion of NDVI time-series data. p , making W p The value decreases as the dispersion increases, in order to suppress natural phenological disturbances; The normalized values ​​of coupled NDVI, EVI, and LAI, combined with W p NPSI is calculated based on the phenological weighting factor.

2. The method for continuous detection of the stress level of the Phyllostachys edulis moth in bamboo forests based on changes in remote sensing phenological characteristics, as described in claim 1, is characterized in that: The degree of dispersion is determined by the variance or standard deviation of the NDVI time-series data; the phenological weight W p It is negatively correlated with the variance or standard deviation of NDVI time series data.

3. The method for continuous detection of the stress level of the Phyllostachys nigra forest based on changes in remote sensing phenological characteristics, as described in claim 1, is characterized in that: The phenological weighting coefficient is dynamically allocated according to the growing season stages; the growing season stages include the shoot emergence period, rapid growth period, leaf expansion period, nutrient accumulation period, and dormancy period.

4. The method for continuous detection of the stress level of the Phyllostachys edulis moth in bamboo forests based on changes in remote sensing phenological characteristics, as described in claim 1, is characterized in that: The normalized value is (current value - minimum value during the growing season) / (maximum value during the growing season - minimum value).

5. The method for continuous detection of the stress level of the tussock moth in moso bamboo forest based on changes in remote sensing phenological characteristics, as described in claim 1, is characterized in that: The Normalized Pest Stress Index (NPSI) is defined as follows: phenological weight W p The product of the time-series vegetation index data with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Leaf Area Index (LAI).

6. The method for continuous detection of the stress level of the Phyllostachys nigra forest by the tussock moth based on changes in remote sensing phenological characteristics, as described in claim 1, is characterized in that: The pest stress level corresponding to the NPSI value is set as follows: when NPSI≤0.25, it represents healthy pests; when NPSI>0.25, it represents pests that are damaged.

7. The method for continuous detection of the stress level of the tussock moth in bamboo forests based on changes in remote sensing phenological characteristics, as described in claim 1, is characterized in that: The SG filtering method was used to smooth and reconstruct the NDVI time series data, and the remote sensing phenological parameters of the moso bamboo forest, including the SOS at the beginning of the growing season and the EOS at the end of the growing season, were extracted using the dynamic threshold method.

8. The method for continuous detection of the stress level of the tussock moth in moso bamboo forest based on changes in remote sensing phenological characteristics, as described in claim 1, is characterized in that: The dynamic threshold method uses 20% of the seasonal amplitude of NDVI as the threshold.

9. A continuous detection system for the stress level of the tussock moth in bamboo forests based on remote sensing phenological characteristics, characterized in that, include: Data acquisition module: Acquires time-series multispectral remote sensing images; Phenological analysis module: Performs the method described in any one of claims 1-8; Early warning output module: Generates a spatial distribution map of stress levels.

10. A computer-readable storage medium storing a computer program, characterized in that... When executed by a processor, the program implements the steps of the method described in any one of claims 1-8.

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