Vegetation growth evaluation method, device, equipment and storage medium

Abstract

This invention relates to the field of remote sensing technology, and provides a method, apparatus, device, and storage medium for assessing vegetation growth. The method includes: acquiring a first remote sensing image of the vegetation to be assessed, and current climate factors of the area where the vegetation is located; determining the current vegetation index and current phenological stage of the vegetation to be assessed based on the first remote sensing image; acquiring a corresponding phenological growth characteristic space based on the current phenological stage, and determining a target vegetation index of the vegetation to be assessed based on the phenological growth characteristic space and the current climate factors; and assessing the growth of the vegetation to be assessed based on the target vegetation index and the current vegetation index. This invention accurately assesses the growth of the vegetation to be assessed by combining a pre-constructed phenological growth characteristic space of each phenological zone with current climate factors.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, and in particular to a method, apparatus, equipment and storage medium for assessing vegetation growth. Background Technology

[0002] Vegetation growth monitoring and assessment are of great significance in agriculture, forestry, and ecological conservation. It can reflect the real-time status of vegetation growth, providing a scientific basis for crop yield forecasting, pest and disease early warning, and precision agriculture, thus contributing to food security. In forestry, growth monitoring aids in forest resource management and ecological restoration assessment. Furthermore, vegetation growth is an important indicator of ecosystem health, enabling the assessment of the impacts of climate change and natural disasters, and providing data support for ecological conservation and policy formulation.

[0003] Early vegetation growth monitoring relied primarily on ground observations and farmers' experience, with ground observations mainly consisting of manual surveys. While manual surveys are accurate, they are labor-intensive, time-consuming, and have limited coverage, making them unsuitable for large-scale regional monitoring. Furthermore, manual observations are easily influenced by subjective judgment, resulting in low repeatability and consistency of the results.

[0004] With the introduction of remote sensing technology, vegetation indices have become an important means of monitoring vegetation growth, enabling qualitative and quantitative analysis of vegetation growth over large areas using medium- and low-resolution satellite imagery. Therefore, methods based on spectral index anomalies at the same time over the years have gradually become a crucial tool for monitoring crop growth. This method assesses growth by comparing the current vegetation index with the multi-year average. However, when planting habits or growth cycles change, anomalies at the same time may correspond to differences in growth at different phenological stages, leading to inaccurate or even erroneous monitoring results. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for assessing vegetation growth, thereby addressing the shortcomings of poor accuracy in existing growth assessments and improving the accuracy of vegetation growth assessment.

[0006] This invention provides a method for assessing vegetation growth, comprising:

[0007] Acquire the first remote sensing image of the vegetation to be assessed, as well as the current climate factors of the area where the vegetation to be assessed is located;

[0008] Based on the first remote sensing image, determine the current vegetation index and current phenological period of the vegetation to be evaluated;

[0009] Based on the current phenological period, the corresponding phenological growth characteristic space is obtained, and the target vegetation index of the vegetation to be evaluated is determined according to the phenological growth characteristic space and the current climate factors.

[0010] The growth of the vegetation to be evaluated is assessed based on the target vegetation index and the current vegetation index.

[0011] According to the vegetation growth assessment method provided by the present invention, before obtaining the corresponding phenological growth characteristic space based on the current phenological period, the method further includes:

[0012] Multiple second remote sensing images of the vegetation to be evaluated within a historical time frame are acquired, along with historical climate factors of the area where the vegetation to be evaluated is located; wherein, the historical climate factors include historical accumulated temperature and historical cumulative precipitation.

[0013] Based on the second remote sensing image, determine the vegetation index of the vegetation to be evaluated in each of the phenological stages;

[0014] Based on the vegetation index and the historical climate factors, a three-dimensional feature space of the vegetation to be evaluated in each of the phenological periods is constructed.

[0015] Based on the three-dimensional feature space, the phenological growth characteristic space of the vegetation to be evaluated in each of the phenological stages is obtained.

[0016] According to the vegetation growth assessment method provided by the present invention, obtaining the phenological growth characteristic space of the vegetation to be assessed in each phenological stage based on the three-dimensional feature space includes:

[0017] Based on the historical climate factors, the three-dimensional feature space is meshed.

[0018] Extract the maximum and minimum values ​​of vegetation index from the grids in each of the three-dimensional feature spaces;

[0019] By fitting high-order polynomials, the maximum and minimum values ​​of vegetation indices in each of the three-dimensional feature spaces are respectively fitted with surfaces to obtain the first and second surfaces of each of the phenological periods.

[0020] Based on the first surface and the second surface, the phenological growth characteristic space of each phenological period is obtained.

[0021] According to a vegetation growth assessment method provided by the present invention, the step of determining the current vegetation index and current phenological stage of the vegetation to be assessed based on the first remote sensing image includes:

[0022] Based on the first remote sensing image, determine the current vegetation index of the vegetation to be evaluated;

[0023] Obtain the vegetation index time series curve of the vegetation to be evaluated, and determine the current phenological period based on the current vegetation index and the vegetation index time series curve.

[0024] According to a vegetation growth assessment method provided by the present invention, the current vegetation index includes the normalized difference vegetation index, the ratio vegetation index, or the enhanced vegetation index.

[0025] According to a vegetation growth assessment method provided by the present invention, the step of determining the current vegetation index and current phenological stage of the vegetation to be assessed based on the first remote sensing image includes:

[0026] The first remote sensing image is preprocessed using at least one of radiometric correction, atmospheric correction, and geometric correction.

[0027] Based on the preprocessed first remote sensing image, the current vegetation index and current phenological period of the vegetation to be evaluated are determined.

[0028] According to the vegetation growth assessment method provided by the present invention, the target vegetation index includes a first vegetation index determined by a first surface in the phenological growth characteristic space and a second vegetation index determined by a second surface in the phenological growth characteristic space.

[0029] The assessment of the growth of the vegetation to be evaluated based on the target vegetation index and the current vegetation index includes:

[0030] Based on the current vegetation index, the first vegetation index, and the second vegetation index, calculate the vegetation growth assessment index.

[0031] The growth of the vegetation to be evaluated is assessed based on the vegetation growth assessment index.

[0032] The present invention also provides a vegetation growth assessment device, comprising:

[0033] The first acquisition module is configured to acquire a first remote sensing image of the vegetation to be evaluated, as well as the current climate factors of the area where the vegetation to be evaluated is located.

[0034] The first determining module is configured to determine the current vegetation index and current phenological period of the vegetation to be evaluated based on the first remote sensing image.

[0035] The second determining module is configured to obtain the corresponding phenological growth characteristic space based on the current phenological period, and determine the target vegetation index of the vegetation to be evaluated based on the phenological growth characteristic space and the current climate factors.

[0036] The evaluation module is configured to evaluate the growth of the vegetation to be evaluated based on the target vegetation index and the current vegetation index.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vegetation growth assessment method as described above.

[0038] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vegetation growth assessment method as described above.

[0039] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the vegetation growth assessment method as described above.

[0040] The vegetation growth assessment method, apparatus, equipment, and storage medium provided by this invention acquire a first remote sensing image of the vegetation to be assessed, as well as the current climate factors of the area where the vegetation is located. Based on the first remote sensing image, the current vegetation index and current phenological stage of the vegetation to be assessed are determined. A phenological growth characteristic space for each phenological zone is pre-constructed, which comprehensively depicts the complex relationship between vegetation growth status and climate factors. Then, a target vegetation index is obtained based on the current climate factors. Combining the target vegetation index and the current vegetation index, the growth of the vegetation to be assessed is comprehensively evaluated, significantly improving the accuracy and scientific rigor of vegetation growth assessment. This invention overcomes the limitations of traditional methods that rely solely on the absolute value or anomaly of the vegetation index, providing a more comprehensive and accurate analysis for vegetation growth monitoring. It can be widely applied in agriculture, ecological monitoring, and resource management. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the vegetation growth assessment method provided by the present invention.

[0043] Figure 2 This is a schematic diagram of the vegetation growth characteristic space provided by the present invention.

[0044] Figure 3 This is a schematic diagram of the vegetation growth assessment device provided by the present invention.

[0045] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0047] In recent years, methods based on spectral index anomalies at the same time in previous years have gradually become an important means of monitoring crop growth. This method assesses growth by comparing the deviation of the current vegetation index from the multi-year average. However, when planting habits or growth cycles change (such as earlier or later sowing time, differences in climatic conditions, or changes in vegetation varieties), anomalies at the same time may correspond to differences in growth under different phenological stages, which can lead to inaccurate or even erroneous monitoring results. Furthermore, remote sensing monitoring of vegetation growth is also limited by its actual phenological stage. The canopy spectral reflectance of vegetation varies significantly at different growth stages; therefore, comparisons of growth between different phenological stages may lead to unreasonable or even erroneous conclusions. Monitoring under conditions where the phenological stage is clearly defined is crucial.

[0048] In summary, existing methods for assessing vegetation growth have the following technical problems:

[0049] (1) The differences in vegetation growth period are not fully considered: There are significant differences in the phenological period or growth period (such as the growth start period, peak period, etc.) of vegetation between different fields, and even within a single field, there are differences. Existing methods fail to effectively combine phenological characteristics, resulting in inaccurate assessment results.

[0050] (2) Ignoring the influence of meteorological conditions: Meteorological factors have an important impact on vegetation growth, but existing remote sensing monitoring methods for vegetation growth do not fully integrate meteorological characteristics, which limits the accuracy and applicability of vegetation growth assessment;

[0051] (3) Single evaluation model: Existing methods mostly rely on single remote sensing spectral features (such as NDVI), lack comprehensive analysis of multi-dimensional features, and are difficult to fully reflect the vegetation growth status.

[0052] Therefore, the present invention provides a method, apparatus, device and storage medium for assessing vegetation growth to solve the above problems.

[0053] The remote sensing images involved in this invention are all data that have been fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0054] Figure 1This is a flowchart illustrating a method for assessing vegetation growth according to an exemplary embodiment. Figure 1 As shown in an exemplary embodiment, the vegetation growth assessment method includes steps 110 to 140, which are described in detail below.

[0055] Step 110: Obtain the first remote sensing image of the vegetation to be evaluated, as well as the current climate factors of the area where the vegetation to be evaluated is located.

[0056] In this embodiment of the invention, a first remote sensing image of the vegetation to be evaluated at the current moment is acquired, as well as the current climate factors of the area where the vegetation to be evaluated is located, such as accumulated temperature and cumulative precipitation.

[0057] Step 120: Based on the first remote sensing image, determine the current vegetation index and current phenological period of the vegetation to be evaluated.

[0058] In this embodiment of the invention, vegetation growth is a core indicator for measuring crop health and growth status, but it is often influenced by multiple factors, presenting ambiguity and quantification challenges. However, vegetation indices extracted using remote sensing technology, such as the Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), and Enhanced Vegetation Index (EVI), can accurately capture the dynamics of vegetation growth during specific phenological stages. These indices demonstrate extremely high application value, especially in the vast field of agricultural and forestry monitoring. Therefore, based on the first remote sensing image, the current vegetation index and current phenological stage of the vegetation to be evaluated are determined.

[0059] Step 130: Obtain the corresponding phenological growth characteristic space based on the current phenological period, and determine the target vegetation index of the vegetation to be evaluated based on the phenological growth characteristic space and the current climate factors.

[0060] In this embodiment of the invention, a phenological growth characteristic space of the vegetation to be evaluated in each phenological period is pre-constructed. Based on the climate factors of the indicator to be evaluated at the current moment, the theoretical target vegetation index that the indicator to be evaluated can reach at the current moment is obtained from the corresponding phenological growth characteristic space.

[0061] Step 140: Assess the growth of the vegetation to be evaluated based on the target vegetation index and the current vegetation index.

[0062] In this embodiment of the invention, a first remote sensing image of the vegetation to be evaluated and the current climate factors of the area where the vegetation is located are acquired. Based on the first remote sensing image, the current vegetation index and current phenological stage of the vegetation to be evaluated are determined. A phenological growth characteristic space for each phenological zone is pre-constructed, which comprehensively depicts the complex relationship between vegetation growth status and climate factors. Then, a target vegetation index is obtained based on the current climate factors. Combining the target vegetation index and the current vegetation index, the growth of the vegetation to be evaluated is comprehensively assessed, significantly improving the accuracy and scientific rigor of vegetation growth assessment. This invention overcomes the limitations of traditional methods that rely solely on the absolute value or anomaly value of the vegetation index, providing a more comprehensive and accurate analysis for vegetation growth monitoring, and can be widely applied in agriculture, ecological monitoring, and resource management.

[0063] In this embodiment of the invention, the growth of the vegetation to be evaluated is assessed based on the target vegetation index and the current vegetation index.

[0064] In an exemplary embodiment of the present invention, before obtaining the corresponding phenological growth characteristic space based on the current phenological period, the method further includes:

[0065] Multiple second remote sensing images of the vegetation to be evaluated within a historical time frame are acquired, along with historical climate factors of the area where the vegetation to be evaluated is located; wherein, the historical climate factors include historical accumulated temperature and historical cumulative precipitation.

[0066] Based on the second remote sensing image, determine the vegetation index of the vegetation to be evaluated in each of the phenological stages;

[0067] Based on the vegetation index and the historical climate factors, a three-dimensional feature space of the vegetation to be evaluated in each of the phenological periods is constructed.

[0068] Based on the three-dimensional feature space, the phenological growth characteristic space of the vegetation to be evaluated in each of the phenological stages is obtained.

[0069] In this embodiment of the invention, precipitation, as another key climatic factor for vegetation growth, directly regulates the water supply to vegetation. Adequate precipitation maintains soil moisture, promotes vigorous root growth and efficient nutrient absorption, thereby enhancing photosynthetic efficiency. However, excessive precipitation can easily lead to soil waterlogging, root hypoxia, and even induce pests and diseases, inhibiting vegetation growth. In arid regions, insufficient precipitation directly leads to water shortages in vegetation, affecting growth and yield.

[0070] Accumulated temperature and precipitation are two intertwined factors that jointly shape the growth trajectory of vegetation. In warm climates, sufficient precipitation provides strong support for vegetation growth; however, in arid regions, even with high accumulated temperature, insufficient precipitation can still lead to water scarcity. When accumulated temperature is too high and precipitation is insufficient, water evaporation intensifies, and vegetation may suffer from drought stress; conversely, when precipitation is sufficient but accumulated temperature is too low, vegetation growth may slow down. Therefore, achieving a balance between accumulated temperature and precipitation is crucial, especially during the critical growth stages of vegetation—sowing, flowering, and maturity—as the suitability of climatic conditions directly determines the final yield.

[0071] To maximize the representation of vegetation growth potential, second remote sensing imagery over a historical timeframe, such as the past five years or longer, was used to extract all vegetation indices and corresponding historical accumulated temperature and historical cumulative precipitation for each phenological stage. This historical timeframe for second remote sensing imagery emphasizes the temporal continuity of phenological stages, capturing dynamic changes in vegetation growth and avoiding assessment biases caused by neglecting differences in growth stages in traditional methods. Vegetation indices, historical accumulated temperature, and historical cumulative precipitation were used as the three dimensions of a three-dimensional feature space. For each phenological stage, a three-dimensional feature space of accumulated temperature, precipitation, and vegetation indices was constructed. After the three-dimensional feature space was constructed, polynomial regression fitting was used to obtain the first and second surfaces of vegetation growth for each phenological stage, thus forming the phenological growth feature space, which reflects the vegetation growth potential range under specific accumulated temperature and precipitation conditions under dynamic phenological characteristics.

[0072] Accumulated temperature and precipitation are key interacting environmental factors that jointly determine the growth and development of vegetation. To more accurately assess the impact of environmental factors on vegetation growth, this invention deeply integrates remote sensing data (such as vegetation indices) with meteorological data (such as temperature and precipitation) to construct a three-dimensional feature space of accumulated temperature, precipitation, and vegetation indices. Based on this three-dimensional feature space, the theoretical potential range of vegetation growth is clarified by fitting the first and second surfaces of vegetation growth. Further coupling temporal phenological characteristics with the three-dimensional feature space forms a phenological growth feature space, which can dynamically reflect the difference between the actual state and theoretical potential of vegetation growth under different phenological stages. This invention overcomes the limitations of traditional vegetation growth monitoring, providing more scientific and reliable data support for smart agroforestry management, and can be widely applied in fields such as crop growth monitoring, yield prediction, and precision agriculture management.

[0073] In this embodiment of the invention, the phenological growth characteristic space not only reflects the growth status of vegetation (such as health level, growth rate, etc.), but also, through the dynamic changes of phenological periods, more accurately assesses the growth process of vegetation and its relationship with climatic factors. Through this multi-dimensional characteristic space, the present invention can more comprehensively and accurately describe and analyze the complex relationships of vegetation growth, providing a more scientific basis for monitoring vegetation growth.

[0074] In an exemplary embodiment of the present invention, obtaining the phenological growth characteristic space of the vegetation to be evaluated in each phenological stage based on the three-dimensional feature space includes:

[0075] Based on the historical climate factors, the three-dimensional feature space is meshed.

[0076] Extract the maximum and minimum values ​​of vegetation index from the grids in each of the three-dimensional feature spaces;

[0077] By fitting high-order polynomials, the maximum and minimum values ​​of vegetation indices in each of the three-dimensional feature spaces are respectively fitted with surfaces to obtain the first and second surfaces of each of the phenological periods.

[0078] Based on the first surface and the second surface, the phenological growth characteristic space of each phenological period is obtained.

[0079] In this embodiment of the invention, the historical accumulated temperature and historical cumulative precipitation of each phenological period are divided into fixed step sizes, and then the three-dimensional feature space is divided into multiple grids. The fixed step sizes of historical accumulated temperature and historical cumulative precipitation are respectively taken as 10% of their respective interval ranges, and then the maximum and minimum values ​​of vegetation index in each grid are extracted.

[0080] Then, at each phenological stage, a high-order polynomial fitting method was used to perform surface fitting on the maximum and minimum values ​​of the extracted vegetation indices, respectively, to obtain the first surface and the second surface, as shown below. Figure 2 The maximum and minimum surfaces for phenological periods 1-i are shown. The space between the first and second surfaces represents the potential growth space of the vegetation to be evaluated. The order of the polynomial is controlled between 1 and 3. After fitting, the first and second surfaces represent the theoretical maximum and minimum values ​​of the vegetation index under specific accumulated temperature and rainfall conditions, respectively.

[0081] In an exemplary embodiment of the present invention, determining the current vegetation index and current phenological period of the vegetation to be evaluated based on the first remote sensing image includes:

[0082] Based on the first remote sensing image, determine the current vegetation index of the vegetation to be evaluated;

[0083] Obtain the vegetation index time series curve of the vegetation to be evaluated, and determine the current phenological period based on the current vegetation index and the vegetation index time series curve.

[0084] In this embodiment of the invention, the current vegetation index is calculated based on the first remote sensing image, such as the Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), or Enhanced Vegetation Index (EVI). The vegetation index time series curve of the vegetation to be evaluated throughout its entire life cycle is pre-constructed, and the current phenological stage of the vegetation to be evaluated, such as the budding stage, leaf unfolding stage, flowering stage, maturity stage, and leaf fall stage, is identified through time series analysis.

[0085] In an exemplary embodiment of the present invention, the current vegetation index includes the normalized difference vegetation index, the ratio vegetation index, or the enhanced vegetation index.

[0086] In this embodiment of the invention, the Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), or Enhanced Vegetation Index (EVI) is calculated based on the first remote sensing image.

[0087] Specifically, taking advantage of the strong absorption of red light and strong reflection of near-infrared light by vegetation, the normalized difference vegetation index, ratio vegetation index, and enhanced vegetation index are calculated using the following formulas:

[0088] ;

[0089] ;

[0090] ;

[0091] Where NIR represents the surface reflectance in the near-infrared band, R represents the surface reflectance in the red band, B represents the surface reflectance in the blue band, and G represents the gain factor. , , where represents the atmospheric aerosol resistance coefficient, and L represents the background adjustment parameter.

[0092] In an exemplary embodiment of the present invention, determining the current vegetation index and current phenological period of the vegetation to be evaluated based on the first remote sensing image includes:

[0093] The first remote sensing image is preprocessed using at least one of radiometric correction, atmospheric correction, and geometric correction.

[0094] Based on the preprocessed first remote sensing image, the current vegetation index and current phenological period of the vegetation to be evaluated are determined.

[0095] In this embodiment of the invention, the collected raw remote sensing image data suffers from poor data quality due to sensor errors, atmospheric interference, and terrain effects. Therefore, preprocessing of the acquired remote sensing images is necessary to ensure their applicability and effectiveness. To this end, this embodiment implements a preprocessing procedure for the raw remote sensing images to guarantee the quality of the data itself and its practicality for subsequent analysis.

[0096] Image quality and cloud cover significantly affect the calculation of vegetation indices. This invention employs a preliminary screening of raw remote sensing images from different sources to ensure that only high-quality images are retained for further analysis. When remote sensing images are captured by different sensors at different times, a standardization problem often arises, resulting in significant differences in grayscale values ​​between two images. To address this issue, this invention performs radiometric correction on the collected remote sensing images, thereby improving the readability and reliability of the data.

[0097] Although the atmosphere generally has a relatively small impact on visible light, aerosols and water vapor can still affect light propagation. Therefore, embodiments of the present invention perform atmospheric correction on remote sensing images to further optimize image quality.

[0098] During the imaging process, remote sensing images are deformed due to factors such as photographic material distortion, objective lens distortion, atmospheric refraction, Earth curvature, Earth rotation, and topographic relief. As a result, the geometric position, shape, size, orientation, and other characteristics of various objects on the original remote sensing images are distorted. Therefore, geometric correction is used to eliminate or correct the geometric errors of remote sensing images.

[0099] In an exemplary embodiment of the present invention, the target vegetation index includes a first vegetation index determined by a first surface in the phenological growth characteristic space and a second vegetation index determined by a second surface in the phenological growth characteristic space;

[0100] The assessment of the growth of the vegetation to be evaluated based on the target vegetation index and the current vegetation index includes:

[0101] Based on the current vegetation index, the first vegetation index, and the second vegetation index, calculate the vegetation growth assessment index.

[0102] The growth of the vegetation to be evaluated is assessed based on the vegetation growth assessment index.

[0103] In embodiments of the present invention, such as Figure 2 As shown, the first vegetation index is determined on the largest surface (i.e., the first curved surface), and the second vegetation index is determined on the smallest surface (i.e., the second curved surface). Figure 2 The actual observed value is the current vegetation index determined based on the first remote sensing image.

[0104] Based on the first and second surfaces obtained from the aforementioned fitting, a Vegetation Growth Comprehensive Index (VGCI) based on phenological growth characteristic space is proposed. This index assesses the gap between actual vegetation growth and the theoretical maximum value under dynamic phenological characteristics. The formula for calculating VGCI is as follows:

[0105] ;

[0106] Where i represents the phenological stage of the vegetation to be assessed, j represents the accumulated temperature value, and k represents the cumulative precipitation. This represents the theoretical maximum value of vegetation growth under the conditions of phenological period i, accumulated temperature j, and cumulative precipitation k, i.e., the first vegetation index. The second vegetation index represents the theoretical minimum of vegetation growth under the conditions of phenological period i, accumulated temperature j, and cumulative precipitation k. This represents the actual value of vegetation growth under the conditions of phenological period i, accumulated temperature j, and cumulative precipitation k, i.e., the current vegetation index. and The VGCI values ​​are derived from the first and second surfaces of the spatial fitting of phenological growth characteristics, respectively. The VGCI value varies between 0 and 1. The closer the value is to 1, the closer the actual growth of the vegetation to be evaluated is to the theoretical maximum value, and the better the growth condition. Conversely, the closer the value is to 0, the worse the growth condition is.

[0107] The vegetation growth assessment device provided by this invention is described below. The vegetation growth assessment device described below can be referred to in correspondence with the vegetation growth assessment method described above. It should be noted that the device provided in the following embodiments and the method provided in the above embodiments belong to the same concept, and the specific way in which each module and unit performs its operation has been described in detail in the method embodiments, and will not be repeated here.

[0108] In one exemplary embodiment of the present invention, please refer to Figure 3 , Figure 3 A vegetation growth assessment device is shown according to an exemplary embodiment, comprising the following modules.

[0109] The first acquisition module 310 is configured to acquire a first remote sensing image of the vegetation to be evaluated, as well as the current climate factors of the area where the vegetation to be evaluated is located.

[0110] The first determining module 320 is configured to determine the current vegetation index and current phenological period of the vegetation to be evaluated based on the first remote sensing image.

[0111] The second determining module 330 is configured to obtain the corresponding phenological growth characteristic space based on the current phenological period, and determine the target vegetation index of the vegetation to be evaluated based on the phenological growth characteristic space and the current climate factors.

[0112] The evaluation module 340 is configured to evaluate the growth of the vegetation to be evaluated based on the target vegetation index and the current vegetation index.

[0113] In an exemplary embodiment of the present invention, the vegetation growth assessment device further includes:

[0114] The second acquisition module is configured to acquire multiple second remote sensing images of the vegetation to be evaluated within a historical time range, as well as historical climate factors of the area where the vegetation to be evaluated is located; wherein, the historical climate factors include historical accumulated temperature and historical cumulative precipitation.

[0115] The third determining module is configured to determine the vegetation index of the vegetation to be evaluated in each of the phenological stages based on the second remote sensing image;

[0116] The construction module is configured to construct a three-dimensional feature space of the vegetation to be evaluated in each of the phenological periods based on the vegetation index and the historical climate factors.

[0117] The module is configured to obtain the phenological growth characteristic space of the vegetation to be evaluated in each of the phenological stages based on the three-dimensional feature space.

[0118] In one exemplary embodiment of the present invention, the module includes:

[0119] The gridding processing submodule is configured to perform gridding processing on the three-dimensional feature space based on the historical climate factors;

[0120] The extraction submodule is configured to extract the maximum and minimum values ​​of vegetation indices from the grids of each of the three-dimensional feature spaces, respectively.

[0121] The fitting submodule is configured to perform surface fitting on the maximum and minimum values ​​of vegetation indices in each of the three-dimensional feature spaces using high-order polynomial fitting, to obtain the first and second surfaces of each of the phenological periods.

[0122] The submodule is obtained and configured to obtain the phenological growth characteristic space of each phenological period based on the first surface and the second surface.

[0123] In an exemplary embodiment of the present invention, the first determining module 320 includes:

[0124] The first determining submodule is configured to determine the current vegetation index of the vegetation to be evaluated based on the first remote sensing image;

[0125] The second determining submodule is configured to acquire the vegetation index time series curve of the vegetation to be evaluated, and determine the current phenological period based on the current vegetation index and the vegetation index time series curve.

[0126] In an exemplary embodiment of the present invention, the current vegetation index includes the normalized difference vegetation index, the ratio vegetation index, or the enhanced vegetation index.

[0127] In an exemplary embodiment of the present invention, the first determining module 320 includes:

[0128] The preprocessing submodule is configured to perform at least one of the following preprocessing operations on the first remote sensing image: radiometric correction, atmospheric correction, and geometric correction.

[0129] The third determination submodule is configured to determine the current vegetation index and current phenological period of the vegetation to be evaluated based on the preprocessed first remote sensing image.

[0130] In an exemplary embodiment of the present invention, the target vegetation index includes a first vegetation index determined by a first surface in the phenological growth characteristic space and a second vegetation index determined by a second surface in the phenological growth characteristic space;

[0131] Evaluation module 340 includes:

[0132] The calculation submodule is configured to calculate a vegetation growth assessment index based on the current vegetation index, the first vegetation index, and the second vegetation index.

[0133] The evaluation submodule is configured to evaluate the growth of the vegetation to be evaluated based on the vegetation growth evaluation index.

[0134] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a vegetation growth assessment method, which includes: acquiring a first remote sensing image of the vegetation to be assessed, and the current climate factors of the area where the vegetation to be assessed is located;

[0135] Based on the first remote sensing image, determine the current vegetation index and current phenological period of the vegetation to be evaluated;

[0136] Based on the current phenological period, the corresponding phenological growth characteristic space is obtained, and the target vegetation index of the vegetation to be evaluated is determined according to the phenological growth characteristic space and the current climate factors.

[0137] The growth of the vegetation to be evaluated is assessed based on the target vegetation index and the current vegetation index.

[0138] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being able to be stored on a non-transitory computer-readable storage medium, the computer program being executed by a processor, the computer being able to execute the vegetation growth assessment method provided by the above methods, the method including: acquiring a first remote sensing image of the vegetation to be assessed, and the current climate factors of the area where the vegetation to be assessed is located.

[0140] Based on the first remote sensing image, determine the current vegetation index and current phenological period of the vegetation to be evaluated;

[0141] Based on the current phenological period, the corresponding phenological growth characteristic space is obtained, and the target vegetation index of the vegetation to be evaluated is determined according to the phenological growth characteristic space and the current climate factors.

[0142] The growth of the vegetation to be evaluated is assessed based on the target vegetation index and the current vegetation index.

[0143] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the vegetation growth assessment method provided by the above methods, the method comprising: acquiring a first remote sensing image of the vegetation to be assessed, and current climate factors of the area where the vegetation to be assessed is located.

[0144] Based on the first remote sensing image, determine the current vegetation index and current phenological period of the vegetation to be evaluated;

[0145] Based on the current phenological period, the corresponding phenological growth characteristic space is obtained, and the target vegetation index of the vegetation to be evaluated is determined according to the phenological growth characteristic space and the current climate factors.

[0146] The growth of the vegetation to be evaluated is assessed based on the target vegetation index and the current vegetation index.

[0147] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0148] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing vegetation growth, characterized in that, include: Acquire the first remote sensing image of the vegetation to be assessed, as well as the current climate factors of the area where the vegetation to be assessed is located; Based on the first remote sensing image, determine the current vegetation index and current phenological period of the vegetation to be evaluated; Based on the current phenological period, the corresponding phenological growth characteristic space is obtained, and based on the phenological growth characteristic space and the current climate factors, the target vegetation index of the vegetation to be evaluated is determined. The growth of the vegetation to be evaluated is assessed based on the target vegetation index and the current vegetation index. Before obtaining the corresponding phenological growth characteristic space based on the current phenological period, the method further includes: Multiple second remote sensing images of the vegetation to be evaluated within a historical time frame are acquired, along with historical climate factors of the area where the vegetation to be evaluated is located; wherein, the historical climate factors include historical accumulated temperature and historical cumulative precipitation. Based on the second remote sensing image, determine the vegetation index of the vegetation to be evaluated in each of the phenological stages; Based on the vegetation index and the historical climate factors, a three-dimensional feature space of the vegetation to be evaluated in each of the phenological periods is constructed. Based on the three-dimensional feature space, the phenological growth characteristic space of the vegetation to be evaluated in each phenological stage is obtained; The process of obtaining the phenological growth characteristic space of the vegetation to be evaluated at each of the phenological stages based on the three-dimensional feature space includes: The historical accumulated temperature and historical cumulative precipitation of each phenological period are divided into fixed step sizes, and the three-dimensional feature space is then gridded. Extract the maximum and minimum values ​​of vegetation index from the grids in each of the three-dimensional feature spaces; By fitting high-order polynomials with the order of polynomials controlled between 1 and 3, surface fitting is performed on the maximum and minimum values ​​of vegetation indices in each of the three-dimensional feature spaces to obtain the first and second surfaces of each of the phenological periods. Based on the first surface and the second surface, the phenological growth characteristic space of each phenological period is obtained; The target vegetation index includes a first vegetation index determined by a first surface in the phenological growth characteristic space and a second vegetation index determined by a second surface in the phenological growth characteristic space; The assessment of the growth of the vegetation to be evaluated based on the target vegetation index and the current vegetation index includes: based on Calculate the vegetation growth assessment index; among which, Indicates the first vegetation index, This indicates the second vegetation index. The current vegetation index is represented; the vegetation growth assessment index is used to assess the gap between the actual vegetation growth and the theoretical maximum value under dynamic phenological characteristics. Its value varies between 0 and 1. The closer the value is to 1, the better the growth condition. The closer the value is to 0, the worse the growth condition. The growth of the vegetation to be evaluated is assessed based on the vegetation growth assessment index.

2. The vegetation growth assessment method according to claim 1, characterized in that, The step of determining the current vegetation index and current phenological stage of the vegetation to be evaluated based on the first remote sensing image includes: Based on the first remote sensing image, determine the current vegetation index of the vegetation to be evaluated; Obtain the vegetation index time series curve of the vegetation to be evaluated, and determine the current phenological period based on the current vegetation index and the vegetation index time series curve.

3. The vegetation growth assessment method according to claim 1, characterized in that, The current vegetation index includes the normalized difference vegetation index, the ratio vegetation index, or the enhanced vegetation index.

4. The vegetation growth assessment method according to claim 1, characterized in that, The step of determining the current vegetation index and current phenological stage of the vegetation to be evaluated based on the first remote sensing image includes: The first remote sensing image is preprocessed using at least one of radiometric correction, atmospheric correction, and geometric correction. Based on the preprocessed first remote sensing image, the current vegetation index and current phenological period of the vegetation to be evaluated are determined.

5. A vegetation growth assessment device, characterized in that, include: The first acquisition module is configured to acquire a first remote sensing image of the vegetation to be evaluated, as well as the current climate factors of the area where the vegetation to be evaluated is located. The first determining module is configured to determine the current vegetation index and current phenological period of the vegetation to be evaluated based on the first remote sensing image. The second determining module is configured to obtain the corresponding phenological growth characteristic space based on the current phenological period, and determine the target vegetation index of the vegetation to be evaluated based on the phenological growth characteristic space and the current climate factors. The evaluation module is configured to evaluate the growth of the vegetation to be evaluated based on the target vegetation index and the current vegetation index. The vegetation growth assessment device also includes: The second acquisition module is configured to acquire multiple second remote sensing images of the vegetation to be evaluated within a historical time range, as well as historical climate factors of the area where the vegetation to be evaluated is located; wherein, the historical climate factors include historical accumulated temperature and historical cumulative precipitation. The third determining module is configured to determine the vegetation index of the vegetation to be evaluated in each of the phenological stages based on the second remote sensing image; The construction module is configured to construct a three-dimensional feature space of the vegetation to be evaluated in each of the phenological periods based on the vegetation index and the historical climate factors. The module is configured to obtain the phenological growth characteristic space of the vegetation to be evaluated in each phenological stage based on the three-dimensional feature space. The module obtained includes: The gridding submodule is configured to divide the historical accumulated temperature and historical cumulative precipitation of each phenological period into fixed step sizes and perform gridding processing on the three-dimensional feature space. The extraction submodule is configured to extract the maximum and minimum values ​​of vegetation indices from the grids of each of the three-dimensional feature spaces, respectively. The fitting submodule is configured to perform surface fitting on the maximum and minimum values ​​of vegetation indices in each of the three-dimensional feature spaces by controlling the order of the polynomial between 1 and 3, so as to obtain the first surface and the second surface of each of the phenological periods. The submodule is obtained and configured to obtain the phenological growth characteristic space of each phenological period based on the first surface and the second surface; The target vegetation index includes a first vegetation index determined by a first surface in the phenological growth characteristic space and a second vegetation index determined by a second surface in the phenological growth characteristic space; The evaluation module includes: The computation submodule is configured to be based on Calculate the vegetation growth assessment index; among which, Indicates the first vegetation index, This indicates the second vegetation index. The current vegetation index is represented; the vegetation growth assessment index is used to assess the gap between the actual vegetation growth and the theoretical maximum value under dynamic phenological characteristics. Its value varies between 0 and 1. The closer the value is to 1, the better the growth condition. The closer the value is to 0, the worse the growth condition. The evaluation submodule is configured to evaluate the growth of the vegetation to be evaluated based on the vegetation growth evaluation index.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the vegetation growth assessment method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the vegetation growth assessment method as described in any one of claims 1 to 4.

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