Plateau greening nursery stock variety screening method based on leaf life span

Abstract

The invention relates to the technical field of plant screening, in particular to a plateau greening seedling type screening method based on leaf life, which comprises the following steps: acquiring climate data and leaf life data of a plateau area, and establishing a relation model of leaf life and climate conditions based on the climate data and the leaf life data; verifying a relation model of leaf life and climate conditions based on specific region data of the plateau region; using the verified model to calculate the leaf life range of the suitable nursery stock variety in the target area, and screening and determining the nursery stock variety in the target area based on the leaf life range of the suitable nursery stock variety. According to the method, efficient screening of the greening seedlings in the plateau area can be realized through rapid calculation based on the model.

Description

Technical Field

[0001] The present application relates to the technical field of plant screening, and in particular to a method for screening plateau greening seedling species based on leaf lifespan. Background Art

[0002] With the global climate change and the improvement of ecological environmental protection awareness, greening projects have become an important means of ecological restoration in various places, especially in plateau areas. Greening projects are of great significance for preventing soil erosion and improving the ecological environment. However, due to the special climatic conditions and soil environment in plateau areas, the selection of greening seedlings has become one of the key factors for the successful implementation of plateau greening projects. The strong sunshine, low temperature, dry climate and large temperature difference in plateau areas make the selection of seedlings with strong adaptability a complex and challenging task. Therefore, the study of screening methods for seedling species adapted to plateau environments has become an important topic in the field of ecological greening.

[0003] At present, in plateau greening projects, the selection of seedling species is usually based on previous planting experience and actual project data. For example, engineering personnel select suitable seedling species based on indicators such as the survival rate and preservation rate of past afforestation tree species, combined with local climate conditions and soil characteristics.

[0004] However, existing technologies generally have a significant defect: the screening process is time-consuming and difficult to generalize. Existing methods rely on a large number of planting tests to screen suitable seedling species, a process that takes months or even years to obtain clear results. In addition, due to the diversity of plateau areas, there are large differences in environmental conditions such as climate and soil in various places. The screening results of existing methods are difficult to generalize in different regions, resulting in inefficient and costly implementation of greening projects between different regions. Summary of the invention

[0005] The present application provides a method for screening plateau greening seedling species based on leaf lifespan, which realizes efficient screening of greening seedlings in plateau areas through rapid calculation based on a model. The present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for screening plateau greening seedling species based on leaf lifespan, the method comprising:

[0007] Collect climate data and leaf life data in the plateau area, and establish a relationship model between leaf life and climate conditions based on the climate data and leaf life data;

[0008] The model of the relationship between leaf lifespan and climatic conditions was validated based on specific regional data from the plateau region;

[0009] The validated model is used to calculate the leaf lifespan range of suitable seedling species in the target area, and the seedling species in the target area are screened and determined based on the leaf lifespan range of suitable seedling species.

[0010] In a specific implementation scheme, the collecting of climate data and leaf life data in the plateau region and establishing a relationship model between leaf life and climate conditions based on the climate data and leaf life data include:

[0011] Collect data on average annual temperature and annual precipitation in the plateau region;

[0012] Collect leaf lifespan data in highland areas;

[0013] The relationship model between leaf lifespan and annual average temperature and annual precipitation was established based on statistical methods as follows:

[0014] LL=8 / (1+e(-0.0056T 2 +0.2452T-1.0784))

[0015] LL=8 / (1+e(-0.00001P 2 -0.0095P+2.5802))

[0016] Among them, LL is the canopy leaf lifespan weighted by leaf biomass or cross-sectional area at breast height of tree species, T is the average annual temperature, P is the annual precipitation, e is the natural logarithm, and 8 represents the maximum observed leaf lifespan value.

[0017] In a specific embodiment, the leaf lifespan data in plateau areas are collected using a marking observation method.

[0018] In a specific implementation method, the collecting leaf lifespan data in the plateau region includes:

[0019] The average leaf lifespan is equal to the cumulative leaf length or area produced and died during the monitoring period divided by the leaf length or area, calculated as follows:

[0020] LL=(ABCD) / (S j -A i )

[0021]

[0022] Where: i represents the first monitoring time; j represents the last monitoring time; A n It represents the cumulative leaf length or area from the beginning of monitoring to n days; S n represents the cumulative dead leaf length or area from the beginning of monitoring to n days; K n Indicates the interval between monitoring time n and n+1; n sIndicates the time when the cumulative dead leaf length or area equals the cumulative leaf length or area at the first monitoring; n i Indicates the time when the cumulative length or area of ​​leaves produced equals the cumulative length or area of ​​leaves that died at the last monitoring.

[0023] In a specific implementation scheme, the model for verifying the relationship between leaf life and climate conditions based on specific regional data of the plateau region includes:

[0024] Select representative plateau areas with complete climate and vegetation data as validation areas;

[0025] Collecting the annual average temperature and annual precipitation data of the verification area, and obtaining the actual leaf lifespan value of the local vegetation canopy in the verification area through field investigation;

[0026] Substituting the annual average temperature and annual precipitation of the verification area into the established relationship model, calculating and predicting the leaf life value;

[0027] The actual measured leaf life value is compared with the predicted value. If the error is within the established range, the model verification is passed and no parameter adjustment is required. If the error exceeds the range, the source of the error is analyzed and the model parameters are adjusted, and the adjusted model is re-verified until the error meets the requirements.

[0028] In a specific embodiment, the use of a validated model to calculate the leaf lifespan range of suitable seedling species in the target area, and screening and determining the seedling species in the target area based on the leaf lifespan range of suitable seedling species include:

[0029] Obtain the annual average temperature and annual precipitation data of the target area, and substitute them into the verified leaf lifespan and climate condition relationship model to calculate the leaf lifespan value based on temperature and precipitation respectively;

[0030] Based on the leaf life calculation results based on temperature and precipitation, a comprehensive analysis is conducted to determine the leaf life range of suitable seedling species in the target area;

[0031] According to the leaf life range, the seedling species whose leaf life values ​​fall within the range are screened out.

[0032] In a specific implementation scheme, the leaf life range of suitable seedling species in the target area is determined by comprehensive analysis based on the leaf life calculation results based on temperature and precipitation, including:

[0033] The maximum value of the leaf lifespan calculated by the temperature model and the precipitation model is selected as the upper limit of the leaf lifespan of suitable seedlings in the target area;

[0034] The minimum value of leaf lifespan calculated by the temperature model and precipitation model was selected as the lower limit of leaf lifespan of suitable seedlings in the target area;

[0035] The maximum and minimum values ​​obtained from the above calculations determine the leaf life span range of suitable seedling species in the target area.

[0036] In the second aspect, the present application provides a plateau greening seedling species screening system based on leaf lifespan, which adopts the following technical solutions:

[0037] A plateau greening seedling species screening system based on leaf lifespan, comprising:

[0038] A model building module is used to collect climate data and leaf life data in plateau areas, and to establish a relationship model between leaf life and climate conditions based on the climate data and leaf life data;

[0039] Model validation module, used to validate the relationship model between leaf life and climate conditions based on specific regional data of the plateau region;

[0040] The model calculation module is used to calculate the leaf life span range of suitable seedling species in the target area using a verified model, and screen and determine the seedling species in the target area based on the leaf life span range of suitable seedling species.

[0041] In a third aspect, the present application provides an electronic device comprising a processor and a memory; the memory stores a program, and the program is loaded and executed by the processor to implement a method for screening plateau greening seedling species based on leaf life as described in the first aspect.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium, in which a program is stored. When the program is executed by a processor, it is used to implement a method for screening plateau greening seedling species based on leaf life as described in the first aspect.

[0043] In summary, the beneficial effects of this application include at least:

[0044] 1) This method ensures that the screening process is more scientific and accurate by establishing a theoretical model and verifying it with climate data and leaf life data in the actual area. Through model verification and adjustment, errors can be eliminated and more accurate recommendations for suitable seedling species can be provided, effectively avoiding reliance on subjective experience and excessive trial and error costs.

[0045] 2) By screening out seedling species with an appropriate leaf lifespan range, we can give priority to evergreen coniferous tree species or drought-tolerant shrubs that can adapt to the plateau's unique climate (such as low temperature, drought, etc.) according to the climate characteristics and ecological needs of the target area. This method helps to improve the survival and preservation rates of seedlings and ensure the long-term ecological benefits and sustainability of greening projects.

[0046] 3) The traditional greening seedling screening process requires a lot of tests and trial plantings, and the test results are not widely applicable, which may lead to the selection of inappropriate tree species and waste a lot of resources. The present invention can more accurately select suitable tree species through a model-based screening method, reduce resource waste, and reduce the cost of greening projects.

[0047] By collecting climate data and leaf life data in plateau areas, a relationship model between leaf life and climate conditions (temperature and precipitation) is constructed. After verifying and optimizing the model, the verified model is used to calculate the leaf life range of suitable seedling species in the target area, and then the seedling species suitable for the climate conditions in the target area are screened out. This application overcomes the problems of long screening process and poor scalability in the prior art. Through rapid calculation based on the model, efficient screening of greening seedlings in plateau areas is achieved, the scientificity and scalability of the screening results are improved, and reliable technical support is provided for the implementation of plateau greening projects.

[0048] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of the method for screening plateau greening seedling species based on leaf lifespan in an embodiment of the present application.

[0050] Figure 2 It is a schematic diagram of a use case for calculating leaf life in an embodiment of the present application.

[0051] Figure 3 It is a structural block diagram of a plateau greening seedling species screening system based on leaf lifespan in an embodiment of the present application.

[0052] Figure 4 It is a block diagram of an electronic device for screening plateau greening seedling species based on leaf life in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0054] Optionally, the present application uses the plateau greening seedling species screening method based on leaf life provided in each embodiment as an example of being used in an electronic device, where the electronic device is a terminal or a server, and the terminal can be a mobile phone, a computer, a tablet computer, etc. This embodiment does not limit the type of electronic device.

[0055] Reference Figure 1 , is a flow chart of a method for screening plateau greening seedling species based on leaf lifespan provided by an embodiment of the present application, the method comprising at least the following steps:

[0056] Step S101, collecting climate data and leaf life data in plateau areas, and establishing a relationship model between leaf life and climate conditions based on the climate data and leaf life data.

[0057] In step S101, a relationship model between leaf lifespan and temperature and precipitation is established by considering the annual average temperature and annual precipitation in the plateau area.

[0058] Specifically, first collect the data of the average annual temperature and annual precipitation in the plateau area. Temperature and precipitation are two key factors affecting the life span of plant leaves. This data can be obtained through meteorological station data, climate model prediction or satellite remote sensing data. Then collect the leaf life span data in the plateau area. In this application, the marked observation method is used to collect the leaf life span data in the plateau area.

[0059] Alternatively, the following method can be used to collect leaf lifespan data in plateau areas. Similar to the marking observation method, when a leaf is considered to be fully expanded, it is defined as its "birth time", and when at least 2 / 3 of the leaf turns yellow or brown, it is considered dead. Select a part of the plant's fully expanded leaves for marking, and monitor the leaf area or length of the marked leaves for a long time. This method assumes that some plant leaves already existed at the time of the first monitoring, and although they were born at different times, they would all die during the monitoring period. After the monitoring begins, some leaves that were born earlier will die one after another, while some leaves that were born later have not died after the test. Subtracting these two parts is the cumulative leaf area that was born and died during the monitoring period, and subtracting the cumulative leaf area that died is the cumulative leaf area during the lifespan, which is divided by the leaf area to obtain the leaf lifespan. This method measures the average lifespan of leaves of a certain species.

[0060] In practice, the average leaf lifespan (LL, day) is equal to the cumulative leaf length (m·day) or area (m 2 ·day) divided by the leaf length (m) or area (m 2 ), the calculation formula is as follows:

[0061] LL= ( ABCD ) / ( S j -A i )

[0062]

[0063]

[0064] Where: i represents the first monitoring time; j represents the last monitoring time; A n It represents the cumulative leaf length or area from the beginning of monitoring to n days; S n represents the cumulative dead leaf length or area from the beginning of monitoring to n days; K n Indicates the interval between monitoring time n and n+1; n s It means that when the cumulative dead leaf length or area is equal to the cumulative leaf length or area at the first monitoring (i.e. S = A i ) time; n i It means that when the accumulated leaf length or area is equal to the accumulated leaf length or area of ​​the last monitored dead leaf (i.e. A = S j ) time.

[0065] like Figure 2 As shown, curve A (square) represents the cumulative length or area of ​​leaves produced or died during the entire monitoring period (the above formula A); curve B (triangle) represents the cumulative length or area of ​​leaves that died during the entire monitoring period (the above formula B); the area under the curve is the total number of days for which leaf length or area is produced or died (the area under A) and the total number of days for which leaf length or area is produced (the area under B), and the difference between the two curves represents the number of days for which leaf length or area has not died. Area C represents the length or area of ​​leaves that already existed at the beginning of monitoring (date of birth is unknown) (i.e., equation C); area D represents the length or area of ​​leaves that have not died (date of death is unknown) at the end of monitoring (i.e., equation D). Subtracting C and D (ABCD) from the area between curves A and B gives the cumulative length or area of ​​leaves produced and died during the monitoring period (m·day or m 2 day, i.e., area E). Divide area E by the total leaf length or area (m or m 2 , the height of area E), we get the life of the blade, which is equivalent to the horizontal length of area E.

[0066] Finally, a relationship model between leaf lifespan and annual average temperature and annual precipitation was established based on statistical methods. The model is as follows:

[0067] LL=8 / (1+e(-0.0056T 2 +0.2452T-1.0784))

[0068] LL=8 / (1+e(-0.00001P 2 -0.0095P+2.5802))

[0069] Among them, LL is the canopy leaf lifespan weighted by leaf biomass or cross-sectional area at breast height of tree species, T is the annual mean temperature (℃), P is the annual precipitation (cm), e is the natural logarithm (2.71828), and 8 represents the maximum observed leaf lifespan.

[0070] Step S102: verifying the relationship model between leaf life and climate conditions based on specific regional data of the plateau area.

[0071] In step S102, first select a representative plateau area (such as a prefecture-level city or ecological sample area) with complete climate data and vegetation data as the verification area. The verification area should have the following characteristics: the climate characteristics can cover the main applicable scope of the model (such as the extreme range of temperature and precipitation). The vegetation composition is relatively stable, and reliable leaf life survey data are available. Subsequently, relevant data of the verification area are collected, mainly including the annual average temperature and annual precipitation. In the verification area, the actual leaf life value (LL) of the local vegetation canopy is obtained through field investigation, and the canopy leaf life is calculated by weighted average of the leaf biomass of the tree species or the cross-sectional area at breast height. The annual average temperature and annual precipitation data of the verification area are respectively substituted into the relationship model established in S101, and the results of the two models are combined to take the weighted average or other methods to merge the predicted leaf life value. The actual measured leaf life value is compared with the predicted value. If the error is within the established range, the model verification is passed, and there is no need to adjust the parameters. If the error exceeds the range, it is necessary to analyze the error source and adjust the model parameters, and re-verify the adjusted model until the error meets the requirements. This verification step confirms the applicability of the relationship model, providing a reliable basis for the subsequent calculation of the range of suitable seedling leaf lifespan in the target area.

[0072] Step S103: Use the verified model to calculate the leaf lifespan range of suitable seedling species in the target area, and screen and determine the seedling species in the target area based on the leaf lifespan range of suitable seedling species.

[0073] In step S103, the long-term statistical annual average temperature and annual precipitation are obtained from the target area to ensure the representativeness of the climate data. The temperature and precipitation data of the target area are substituted into the verified relationship model between leaf life and climate conditions, and the leaf life values ​​based on temperature and precipitation are calculated respectively. The results of the two models of temperature and precipitation are comprehensively analyzed, and a conservative value or a weighted average value is selected to determine the leaf life range of the target area suitable for the seedling species. For example, the maximum value of the leaf life value calculated by the temperature model and the precipitation model is selected as the upper limit of the leaf life of the target area suitable for the seedling, and the minimum value of the leaf life value obtained by the two models is selected as the lower limit of the leaf life of the target area suitable for the seedling. The leaf life range is the interval determined by the two values ​​calculated above, and this interval represents the leaf life range of the target area suitable for the seedling species. Then, the seedling species whose leaf life value falls within the leaf life range of the target area suitable for the seedling species are screened out. In addition, the ecological conditions of the target area (such as the low temperature and drought characteristics of the plateau area) are comprehensively considered during the screening, and evergreen coniferous tree species or drought-resistant shrubs that can adapt to the local climate are preferentially selected. Combined with the existing afforestation practice experience in the target area, priority is given to tree species with high survival rate and high preservation rate, such as coniferous tree species: Pinus tabulaeformis, Pinus sylvestris, Pinus koraiensis, etc., which usually have a leaf life of 2 to 3 years under natural conditions. Native shrubs: Hippophae rhamnoides in Gyantse, Rosa rugosa, Amorpha fruticosa, etc., which are adapted to drought and low temperature environments and have high ecological value. The screening results are sorted into a list of suitable seedling species to ensure that the tree species in the list can meet the needs of ecological restoration and seedling supply, and the recommended planting areas and corresponding leaf life ranges are marked. Through the above steps, the seedling species suitable for the target area can be quickly determined based on the relationship model to ensure the scientificity and operability of vegetation restoration work, while improving the survival rate and ecological benefits of seedlings.

[0074] For example, when this application was experimented in Lhasa, the annual average temperature and annual precipitation in Lhasa were investigated to verify the relationship model. From the perspective of temperature and precipitation change characteristics in Lhasa, the annual average temperature and annual precipitation in the past 63 years (1960-2022) were 8.3°C and 438.8mm, respectively, and the annual average temperature showed a significant increase trend, with a temperature increase of about 0.55°C / 10 years, but the overall precipitation change trend was not obvious. In the past 20 years, the annual average temperature in Lhasa was 9.6°C and the annual precipitation was 457.4mm, which belongs to the range of grass and shrub land. According to the annual average temperature in Lhasa, the relationship model was used to calculate the potential vegetation leaf lifespan at different altitudes. It was found that the potential vegetation leaf lifespan in Lhasa was 2.55 years. With the increase in altitude, the potential vegetation leaf lifespan at about 4000m can reach 3.07 years, indicating that under sufficient precipitation, the potential vegetation in Lhasa and surrounding areas should be evergreen vegetation. According to the survey data of forest canopy leaf life span in the plateau transect and the north-south transect in eastern China, from the perspective of temperature alone, the areas closer to the north and south mountains of Lhasa mainly include the alpine oak evergreen broad-leaved forest and evergreen coniferous broad-leaved mixed forest in Bayi District, Linzhi, and the evergreen broad-leaved forest and evergreen coniferous broad-leaved mixed forest in Gongga Mountain in western Sichuan. The average canopy leaf life span of these groups is between 1.35 and 2.5 years, which is slightly lower than the estimated value of the relationship model. However, considering that the relationship model only takes into account the influence of temperature when calculating, and the precipitation in these places is significantly higher, the high temperature and high humidity environmental conditions are relatively not conducive to the extension of organ life span, so the calculation conclusion of the relationship model can be considered accurate.

[0075] Judging from the leaf lifespan values, the potential suitable vegetation in Lhasa and its surrounding areas is species with relatively short leaf lifespans, such as evergreen coniferous trees such as Pinus tabulaeformis, Pinus sylvestris, and Pinus koraiensis, whose leaf lifespans are usually between 2 and 3 years under natural conditions. From the current woody plants in Lhasa, the naturally growing shrub species mainly include Hippophae rhamnoides, Buddaria alternifolia, Rosa sericea, and Sophora japonica, which are typical deciduous native shrubs with strong adaptability to the local drought and low temperature climate environment. The existing artificial afforestation tree species are mostly Pinus tabulaeformis, Pinus sylvestris, Populus alba, Pinus bungeana, Prunus armeniaca, and Ulmus pumila, which are in line with the calculation conclusions of the leaf lifespan relationship model, proving that the theoretical calculation conclusions of the relationship model are consistent with the actual experience conclusions verified by engineering. So far, based on the recommendation of theoretical calculation results, referring to the survival rate and preservation rate of previous afforestation tree species, and combining the supply of seedlings in Lhasa, the selected tree species are determined to be Prunus armeniaca, Pinus tabulaeformis, Juglans regia (Tibetan peach), Pinus sylvestris, Platycladus orientalis, Elaeagnus angustifolia, Populus simonii, Hippophae rhamnoides in Gyantse, Hippophae rhamnoides in China, Rosa rugosa, Amorpha fruticosa, Ulmus pumila, Rosa grandiflora, Rosa rubiginosa, Rosa yellowis, Juniperus macrocarpa, Coleus chinensis, and Tamarix multi-branched.

[0076] In summary, by collecting climate data and leaf life data in plateau areas, a relationship model between leaf life and climate conditions (temperature and precipitation) is constructed, and after verifying and optimizing the model, the verified model is used to calculate the leaf life range of suitable seedling species in the target area, and then the seedling species suitable for the climate conditions of the target area are screened out. This application overcomes the problems of time-consuming and poor scalability of the screening process in the prior art. Through rapid calculation based on the model, efficient screening of greening seedlings in plateau areas is achieved, the scientificity and scalability of the screening results are improved, and reliable technical support is provided for the implementation of plateau greening projects.

[0077] Figure 3 This is a structural block diagram of a plateau greening seedling species screening system based on leaf lifespan provided by an embodiment of the present application. The device includes at least the following modules:

[0078] A model building module is used to collect climate data and leaf life data in plateau areas, and to establish a relationship model between leaf life and climate conditions based on the climate data and leaf life data;

[0079] Model validation module, used to validate the relationship model between leaf life and climate conditions based on specific regional data of the plateau region;

[0080] The model calculation module is used to calculate the leaf life span range of suitable seedling species in the target area using a verified model, and screen and determine the seedling species in the target area based on the leaf life span range of suitable seedling species.

[0081] For relevant details, refer to the above method embodiment.

[0082] Figure 4 4 is a block diagram of an electronic device provided by an embodiment of the present application. The device at least includes a processor 401 and a memory 402.

[0083] The processor 401 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 401 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 401 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0084] The memory 402 may include one or more computer-readable storage media, which may be non-transitory. The memory 402 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 402 is used to store at least one instruction, which is used to be executed by the processor 401 to implement the plateau greening seedling species screening method based on leaf life provided in the method embodiment of the present application.

[0085] In some embodiments, the electronic device may further optionally include: a peripheral device interface and at least one peripheral device. The processor 401, the memory 402 and the peripheral device interface may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface via a bus, a signal line or a circuit board. Schematically, the peripheral devices include but are not limited to: a radio frequency circuit, a touch display screen, an audio circuit, and a power supply.

[0086] Of course, the electronic device may also include fewer or more components, which is not limited in this embodiment.

[0087] Optionally, the present application also provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the plateau greening seedling species screening method based on leaf life of the above method embodiment.

[0088] Optionally, the present application also provides a computer product, which includes a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the plateau greening seedling species screening method based on leaf life of the above-mentioned method embodiment.

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for screening plateau greening seedling species based on leaf lifespan, characterized in that: The method comprises: Collect climate data and leaf life data in the plateau area, and establish a relationship model between leaf life and climate conditions based on the climate data and leaf life data; The model of the relationship between leaf lifespan and climatic conditions was validated based on specific regional data from the plateau region; The validated model is used to calculate the leaf lifespan range of suitable seedling species in the target area, and the seedling species in the target area are screened and determined based on the leaf lifespan range of suitable seedling species.

2. The method for screening plateau greening seedling species based on leaf lifespan according to claim 1, characterized in that: The collecting of climate data and leaf life data in the plateau region and establishing a relationship model between leaf life and climate conditions based on the climate data and leaf life data include: Collect data on average annual temperature and annual precipitation in the plateau region; Collect leaf lifespan data in highland areas; The relationship model between leaf lifespan and annual average temperature and annual precipitation was established based on statistical methods as follows: LL=8 / (1+e(-0.0056T 2 +0.2452T-1.0784)) LL=8 / (1+e(-0.00001P 2 -0.0095P+2.5802)) Among them, LL is the canopy leaf lifespan weighted by leaf biomass or cross-sectional area at breast height of tree species, T is the average annual temperature, P is the annual precipitation, e is the natural logarithm, and 8 represents the maximum observed leaf lifespan value.

3. The method for screening plateau greening seedling species based on leaf lifespan according to claim 2, characterized in that: Leaf lifespan data were collected in the plateau region using the marking observation method.

4. The method for screening plateau greening seedling species based on leaf lifespan according to claim 2, characterized in that: The leaf lifespan data collected in the plateau area include: The average leaf lifespan is equal to the cumulative leaf length or area produced and died during the monitoring period divided by the leaf length or area, calculated as follows: LL=(A-B-C-D) / (S j -A i Where: i represents the first monitoring time; j represents the last monitoring time; A n It represents the cumulative leaf length or area from the beginning of monitoring to n days; S n K represents the cumulative dead leaf length or area from the beginning of monitoring to n days; n Indicates the interval between monitoring time n and n+1; n s Indicates the time when the cumulative dead leaf length or area equals the cumulative leaf length or area at the first monitoring; n i Indicates the time when the cumulative length or area of ​​leaves produced equals the cumulative length or area of ​​leaves that died at the last monitoring.

5. The method for screening plateau greening seedling species based on leaf lifespan according to claim 1, characterized in that: The relationship model between leaf life and climate conditions verified based on specific regional data of the plateau region includes: Select representative plateau areas with complete climate and vegetation data as validation areas; Collect the annual average temperature and annual precipitation data of the verification area, and obtain the actual leaf life value of the local vegetation canopy in the verification area through field investigation; Substituting the annual average temperature and annual precipitation of the verification area into the established relationship model, calculating and predicting the leaf life value; The actual measured leaf life value is compared with the predicted value. If the error is within the established range, the model verification is passed and no parameter adjustment is required. If the error exceeds the range, the source of the error is analyzed and the model parameters are adjusted, and the adjusted model is re-verified until the error meets the requirements.

6. The method for screening plateau greening seedling species based on leaf lifespan according to claim 1, characterized in that: The validated model is used to calculate the leaf life span range of suitable seedling species in the target area, and the seedling species in the target area are screened and determined based on the leaf life span range of suitable seedling species, including: Obtain the annual average temperature and annual precipitation data of the target area, and substitute them into the verified leaf lifespan and climate condition relationship model to calculate the leaf lifespan value based on temperature and precipitation respectively; Based on the leaf life calculation results based on temperature and precipitation, a comprehensive analysis is conducted to determine the leaf life range of suitable seedling species in the target area; According to the leaf life range, the seedling species whose leaf life values ​​fall within the range are screened out.

7. The method for screening plateau greening seedling species based on leaf lifespan according to claim 6, characterized in that: The leaf lifespan calculation results based on temperature and precipitation are comprehensively analyzed and determined to include the following leaf lifespan ranges for suitable seedling species in the target area: The maximum value of the leaf lifespan calculated by the temperature model and the precipitation model is selected as the upper limit of the leaf lifespan of suitable seedlings in the target area; The minimum value of leaf lifespan calculated by the temperature model and precipitation model was selected as the lower limit of leaf lifespan of suitable seedlings in the target area; The maximum and minimum values ​​obtained from the above calculations determine the leaf life span range of suitable seedling species in the target area.

8. A plateau greening seedling species screening system based on leaf lifespan, characterized in that: include: A model building module is used to collect climate data and leaf life data in plateau areas, and to establish a relationship model between leaf life and climate conditions based on the climate data and leaf life data; Model validation module, used to validate the relationship model between leaf life and climate conditions based on specific regional data of the plateau region; The model calculation module is used to calculate the leaf life span range of suitable seedling species in the target area using a verified model, and screen and determine the seedling species in the target area based on the leaf life span range of suitable seedling species.

9. An electronic device, characterized in that: The device includes a processor and a memory; a program is stored in the memory, and the program is loaded and executed by the processor to implement a method for screening plateau greening seedling species based on leaf life as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, which, when executed by the processor, is used to implement a method for screening plateau greening seedling species based on leaf life as described in any one of claims 1 to 7.

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