Method for predicting phenology of long leaves of trees based on activity accumulated temperature
Through the prediction method based on the accumulated temperature of activity, standardize observation data and establish nonlinear models, the shortcomings of tree long-leaf phenology observation and prediction in the existing technology are solved, and high-precision and applicable long-leaf phenology prediction are achieved, supporting forest ecosystem research and climate change response.
Patent Information
- Application Number
- CN202411985616.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-31
- Publication Date
- 2025-05-30
AI Technical Summary
The prior art has problems such as insufficient data, low model accuracy and lack of standardized processing in tree long leaf phenological observation and prediction, resulting in the lack of robustness and applicability of the model under interannual climate fluctuations and extreme weather conditions.
Using a prediction method based on the active accumulation temperature, a sample tree is randomly selected for long-leaf phenology observation, the leaf area and leaf quantity data are standardized, and combined with the active accumulation temperature calculation, a nonlinear model is established to fit the long-leaf phenology curve to achieve accurate long-leaf phenology prediction.
It significantly improves the accuracy and applicability of long-leaf phenology prediction, can adapt to long-leaf dynamic research in different tree species and regions, and supports forest ecosystem functional assessment and climate change research.
Smart Images
Figure CN120070085A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to forest ecological modeling and plant phenology research, and more specifically, to a method for predicting the leafing phenology of trees based on active accumulated temperature, which is used to analyze the growth dynamics of trees, support ecosystem function assessment, forest resource management, and scientific research on climate change response. Background Art
[0002] In forest ecosystems, the leafing phenology of trees is an important indicator reflecting the response of plants to environmental changes, directly affecting photosynthesis, carbon absorption, and ecosystem function dynamics. The research on leafing phenology is not only of great significance in the field of ecology but also crucial for climate change response, predicting forest productivity, and formulating forest management strategies. However, there are significant limitations in the observation and prediction of tree leafing phenology in the existing technologies. Traditional methods often rely on fixed-point manual observations, with a long data collection period and limited spatial resolution, making it difficult to comprehensively reflect the leafing dynamics under different tree species and environmental conditions. Especially against the background of interannual climate fluctuations and frequent extreme weather, the phenology models constructed based on single-year data lack robustness and applicability. In addition, the existing heat accumulation models insufficiently consider the setting of the base temperature and the dynamic changes in accumulated temperature calculation, resulting in low model accuracy and difficulty in distinguishing the phenological differences between different tree species. The lack of unified standardized processing of observational data makes it difficult to integrate and apply data at different scales, regions, or time intervals, further restricting the cross-regional promotion of the models.
[0003] Therefore, there is an urgent need for a method for predicting leafing phenology based on long-term observational data, combined with active accumulated temperature calculation and standardized processing, to improve the accuracy and applicability of the model and comprehensively support the research and management of forest ecosystems. Summary of the Invention
[0004] Aiming at the problems of insufficient observational data and low model accuracy in the prediction of tree leafing phenology in the existing technologies, the present invention provides a method for predicting tree leafing phenology based on active accumulated temperature. This method aims to establish an accurate leafing phenology model through active accumulated temperature calculation and standardized data processing, providing support for forest ecosystem research and management.
[0005] To achieve the above object, the present invention provides the following technical solutions:
[0006] A method for predicting tree leafing phenology based on active accumulated temperature, comprising the following steps:
[0007] Step 1, Observation of the leafing phenology of the target tree species:
[0008] Before the onset of the leafing phenology of the target tree species, randomly select several sample trees, and mark several branches in the south, north, and east directions of each sample tree for leafing phenology observation; during the leafing phenology of the target tree species, record the number of leaves on the marked branches every 2-4 days, and measure the area of each leaf until the leafing phenology of the target tree species is completed;
[0009] Step 2, Standardization of leaf area and leaf number of the target tree species:
[0010] Standardize the observed leaf area and calculate the relative leaf area. The calculation formula is:
[0011]
[0012] In the formula: LA T is the relative leafing area at time T, A T is the absolute value corresponding to the leafing area observed at time T, A max is the maximum leafing area observed during the leafing phenology;
[0013] Standardize the observed leaf emergence number and calculate the relative leaf emergence number. The calculation formula is:
[0014]
[0015] In the formula: LN T is the relative leaf emergence number at time T, N T is the absolute value corresponding to the leaf emergence number observed at time T, N max is the maximum leaf emergence number observed during the leafing phenology;
[0016] Step 3, Calculation of the active accumulated temperature of the target tree species:
[0017] Describe the heat accumulation during the leafing phenology of the target tree species through the active accumulated temperature. The calculation formula is:
[0018]
[0019] In the formula: AAT is the active accumulated temperature during the leafing phenology of the target tree species, T m is the daily average temperature, T b is the base temperature, DOY 1 is the date when the daily average temperature continuously exceeds T b since January 1st, DOY 2 is the end date of the leafing phenology observation of the target tree species;
[0020] Step 4, Establishment of the leafing phenology prediction model of the target tree species:
[0021] Using the standardized observed data obtained in Step 2 and combining with the active accumulated temperature data in Step 3, a non-linear model is used to fit the leafing phenology curve of the target tree species, and a prediction model for the leafing phenology of the target tree species is obtained. The expression of the prediction model for the leafing phenology of the target tree species is:
[0022]
[0023] In the formula: L is the leafing phenology process of the target tree species, A is the asymptote of the maximum value of the leafing phenology process, k is the leafing phenology change rate, b is the active accumulated temperature at the start of leafing phenology, and e is the natural constant;
[0024] Step 5, predicting the leafing phenology of the tree by using the prediction model for the leafing phenology of the target tree species.
[0025] Furthermore, in Step 1, color plant hanging tags are used to label the branches.
[0026] Furthermore, in Step 1, the start of the leafing phenology of the tree species means that more than a certain ratio of bud scales crack and expose the lower leaf tissues; the completion of the leafing phenology of the tree species means that more than a certain ratio of leaves reach full size and at least three consecutive leafing phenology observation results show that the number and area of the leaves remain unchanged.
[0027] Furthermore, in Step 1, the start of the leafing phenology of the tree species means that more than 5% of the bud scales crack and expose the lower leaf tissues; the completion of the leafing phenology of the tree species means that more than 95% of the leaves reach full size.
[0028] Furthermore, in Step 1, a leaf area meter is used to measure the leaf area, or the leaf area is calculated based on the leaf area model of leaf length and leaf width.
[0029] In Step 2, the standardization process ensures the comparability of data between different observation times and scales, and provides high-quality data input for model fitting.
[0030] Furthermore, in Step 3, the reference temperature T b is set to 0 - 5 °C.
[0031] Furthermore, in Step 3, the reference temperature T b is set to 5 °C.
[0032] Furthermore, in Step 4, the establishment of the prediction model for the leafing phenology of the target tree species uses the observation data of three consecutive years or more to ensure the robustness and applicability of the model.
[0033] Furthermore, the standardized data can be extended to the scale of single plants or communities for dynamic analysis of the changes in the total leaf number and area.
[0034] Compared with the prior art, the beneficial effects of the present invention are:
[0035] The present invention combines active accumulated temperature and standardization processing, significantly improving the accuracy of long-leaf phenology prediction; through the establishment of a phenological curve model, it can adapt to the long-leaf dynamic research of different tree species and regions. The present invention has high operability and versatility, is applicable to a variety of forest ecological research scenarios; and provides a scientific basis for studying the changes in ecosystem functions and coping with climate change. Description of the Drawings
[0036] Figure 1 It is a schematic flowchart of a method for predicting the long-leaf phenology of trees based on active accumulated temperature provided by an embodiment of the present invention;
[0037] Figure 2 It is a scatter plot of the relative leaf emergence quantity and relative long-leaf area of the target tree species Tilia amurensis changing with time provided by an embodiment of the present invention;
[0038] Figure 3 It is a scatter plot of the active accumulated temperature of the target tree species Tilia amurensis changing with time during the long-leaf phenology provided by an embodiment of the present invention;
[0039] Figure 4 It is a curve graph of the relative leaf emergence quantity of the long-leaf phenology of the target tree species Tilia amurensis predicted by fitting based on active accumulated temperature provided by an embodiment of the present invention;
[0040] Figure 5 It is a curve graph of the relative long-leaf area of the long-leaf phenology of the target tree species Tilia amurensis predicted by fitting based on active accumulated temperature provided by an embodiment of the present invention. Detailed Embodiments
[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0042] Please refer to Figures 1 - 5 , a method for predicting the long-leaf phenology of trees based on active accumulated temperature, includes the following steps:
[0043] Step 1, Observation of the Long-Leaf Phenology of the Target Tree Species
[0044] Select sample trees:
[0045] The target tree species is Tilia amurensis. Before the start of the long-leaf phenology of the target tree species, randomly select 3 sample trees, and mark 1 branch in each of the south, north, and east directions of each tree. The marking method uses colored plant tag signs to ensure stable marking and easy identification.
[0046] Observation Content and Frequency:
[0047] During the phenological period of leaf emergence of the target tree species, record the number of leaves on the marked branches every two days, and measure the area of each leaf using a leaf area meter or a single-leaf area model based on leaf length and width.
[0048] Leaf Area Model Formula:
[0049] LA = a × (L × W) b
[0050] In the formula, LA is the leaf area, L is the leaf length, W is the leaf width, and a and b are model parameters.
[0051] For the target tree species Tilia amurensis, the leaf area model is:
[0052] LA = 0.948 × (L × W) 0.969
[0053] Definition of Key Phenological Nodes:
[0054] Beginning of Leaf Emergence: More than a certain ratio of bud scales split, revealing the lower leaf tissue, and this ratio is preferably 5%;
[0055] End of Leaf Emergence: More than a certain ratio of leaves reach full size, and this ratio is preferably 95%, and it remains stable for at least three consecutive observations.
[0056] Step 2, Standardization of Leaf Area and Leaf Quantity
[0057] Data Standardization Formula:
[0058] Standardize the observed leaf area to calculate the relative leaf area, and its calculation formula is:
[0059]
[0060] In the formula: LA T is the relative leaf emergence area at time T, A T is the absolute value corresponding to the leaf emergence area observed at time T, A max is the maximum value of the leaf emergence area observed during the leaf emergence phenology;
[0061] Standardize the observed number of emerging leaves to calculate the relative number of emerging leaves, and its calculation formula is:
[0062]
[0063] In the formula: LN T is the relative number of emerging leaves at time T, N T is the absolute value corresponding to the number of emerging leaves observed at time T, N maxIt is the maximum value of the leaf emergence quantity observed in the phenology of long leaves.
[0064] Standardization significance:
[0065] The standardized data can eliminate the differences between different samples, ensure the comparability of data at different time and space scales, and provide a unified input format for the construction of subsequent models.
[0066] Such as Figure 2 shown, the changes in the relative leaf emergence quantity and relative long leaf area of the target tree species Tilia amurensis over time.
[0067] Step 3, Calculation of active accumulated temperature
[0068] Setting of reference temperature:
[0069] The reference temperature for active accumulated temperature is set to 5°C because the physiological activities of most target tree species are significantly enhanced when the temperature exceeds 5°C.
[0070] Formula for accumulated temperature calculation:
[0071]
[0072] In the formula: AAT is the active accumulated temperature during the long leaf phenology of the target tree species, T m is the daily average temperature, T b is the reference temperature, DOY 1 is the date when the daily average temperature continuously exceeds T since January 1st. Here, it refers to the date when, starting from January 1st, the daily average temperature first continuously exceeds T b For example, if the daily average temperature continuously exceeds T from January 7th to January 10th b , then DOY b is January 7th, DOY 1 is January 7th, DOY 2 is the end date of the long leaf phenology observation of the target tree species.
[0073] Data recording method:
[0074] The daily average temperature data is recorded by an automatic weather station to ensure the continuity and accuracy of the temperature data.
[0075] Such as Figure 3 shown, the changes in the active accumulated temperature during the long leaf phenology of the target tree species Tilia amurensis.
[0076] Step 4, Establishment of the long leaf phenology model
[0077] Data input and model formula:
[0078] According to the standardized leaf data obtained in Step 1 and Step 2 and the active accumulated temperature data in Step 3, the following non-linear model is used to fit the long leaf phenology curve of the target tree species:
[0079]
[0080] In the formula: L is the phenological process of leaf emergence of the target tree species, A is the asymptote of the maximum value of the phenological process of leaf emergence, k is the rate of change of the phenological process of leaf emergence, b is the active accumulated temperature at the start of the phenological process of leaf emergence, and e is the natural constant.
[0081] Steps for model fitting:
[0082] Utilize long-term observation data (3 years or more) to enhance the robustness of the model;
[0083] Use the cross-validation method to evaluate the fitting accuracy and prediction performance of the model.
[0084] Scope of model application:
[0085] The model is not only applicable to the prediction of the phenology of leaf emergence at the single-tree scale, but can also be extended to the community or regional scale for ecosystem function assessment and climate change research.
[0086] The observation data of the phenology of leaf emergence of the target tree species Tilia amurensis is from 2017 to 2020, for a total of 4 years. The model of the relative leaf emergence quantity of its phenology of leaf emergence is:
[0087]
[0088] As Figure 4 shown, the relative leaf emergence quantity of the phenology of leaf emergence of the target tree species Tilia amurensis fitted and predicted based on the active accumulated temperature.
[0089] Its model of the relative leaf area of the phenology of leaf emergence is:
[0090]
[0091] As Figure 5 shown, the relative leaf area of the phenology of leaf emergence of the target tree species Tilia amurensis fitted and predicted based on the active accumulated temperature.
[0092] Step 5, predict the phenology of leaf emergence of the tree through the phenology prediction model of the target tree species.
[0093] This embodiment can directly be applied to forest ecology and canopy structure research. Through the method for estimating the light intensity at different canopy heights of the forest, it can not only estimate the light intensity of each canopy in real time, but also help to understand the forest canopy structure, plant photosynthesis, and the light energy utilization of the ecosystem.
[0094] Although embodiments of the present invention have been shown and described, those of ordinary skill in the art will appreciate that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for predicting tree leafing phenology based on active accumulated temperature, characterized in that: The steps include: Step 1: Observation of leaf growth phenology of target tree species: Before the leafing phenology of the target tree species begins, several sample trees are randomly selected, and several branches are marked in the south, north, and east directions of each sample tree for leafing phenology observation; During the leafing phenology of the target tree species, the number of leaves on the marked branches was recorded every 2-4 days, and the area of each leaf was measured until the leafing phenology of the target tree species was completed; Step 2: Standardization of leaf area and leaf number of target tree species: The observed leaf area was standardized and the relative leaf area was calculated using the following formula: Where: LA T is the relative long leaf area at time T, A T is the absolute value of the long leaf area observed at time T, A max is the maximum leaf area observed during the leaf growth phenology; The observed number of leaves is standardized and the relative number of leaves is calculated. The calculation formula is: In the formula: LN T is the relative number of leaves at time T, N T is the absolute value of the number of leaves observed at time T, N max It is the maximum number of leaves observed during the leaf growth phenology; Step 3: Calculation of active accumulated temperature of target tree species: The heat accumulation during the leafing phenology of the target tree species is described by the active accumulated temperature, and the calculation formula is: Where: AAT is the active accumulated temperature during the leafing period of the target tree species, T m is the daily average temperature, T b is the base temperature, and DOY1 is the period from January 1 when the daily average temperature exceeds T b DOY2 is the date when the leaf-out phenology observation of the target tree species ends; Step 4: Establishment of leafing phenology prediction model for target tree species: Using the standardized observation data obtained in step 2 and the active accumulated temperature data in step 3, a nonlinear model is used to fit the leaf growth phenology curve of the target tree species to obtain the leaf growth phenology prediction model of the target tree species. The expression of the leaf growth phenology prediction model of the target tree species is: Where: L is the leafing phenology process of the target tree species, A is the asymptote of the maximum value of the leafing phenology process, k is the leafing phenology change rate, b is the active accumulated temperature when the leafing phenology starts, and e is the natural constant; Step 5, predicting the leafing phenology of trees using the leafing phenology prediction model of the target tree species.
2. The method for predicting tree leafing phenology based on active accumulated temperature according to claim 1, characterized in that: In step 1, use colorful plant tags to mark the branches.
3. The method for predicting tree leafing phenology based on active accumulated temperature according to claim 1, characterized in that: In step 1, the beginning of leaf growth phenology of tree species refers to the bud scales splitting exceeding a certain ratio, exposing the lower leaf tissue, and the ratio is preferably 5%; the completion of leaf growth phenology of tree species refers to the leaves reaching full size exceeding a certain ratio, preferably 95%, and at least three consecutive leaf growth phenology observations show that the number of leaves and the leaf area remain unchanged.
4. The method for predicting tree leafing phenology based on active accumulated temperature according to claim 3, characterized in that: In step 1, the start of leafing phenology of a tree species means that more than 5% of the bud scales have split, exposing the lower leaf tissue; the completion of leafing phenology of a tree species means that more than 95% of the leaves have reached full size.
5. The method for predicting tree leafing phenology based on active accumulated temperature according to claim 1, characterized in that: In step 1, the leaf area is measured using a leaf area meter, or the leaf area is calculated using a leaf area model based on leaf length and leaf width.
6. The method for predicting tree leafing phenology based on active accumulated temperature according to claim 1, characterized in that: In step 3, the reference temperature T b Set to 0-5℃.
7. The method for predicting tree leafing phenology based on active accumulated temperature according to claim 6, characterized in that: In step 3, the reference temperature T b Set to 5°C.
8. The method for predicting tree leafing phenology based on active accumulated temperature according to claim 1, characterized in that: In step 4, the leaf-out phenology prediction model for the target tree species is established using observation data from three consecutive years or more.