Method for measuring leaf area index of litter

Abstract

The application discloses a method for determining litter leaf area index, and the meaning of the proposed litter leaf area index is the same as that of vegetation leaf area index, the litter leaf area index can participate in correlation operation as the vegetation leaf area index, and the litter can be included in the vegetation investigation system; the index is the covering layer number of leaves on the ground, the measuring method is the total area of the spread litter leaves on the unit area of the ground, the power index relationship between the total area and the unit area dry weight of the litter is established, the litter leaf area index estimation model of coniferous forest, broad-leaved forest and coniferous and broad-leaved mixed forest is constructed, and the meaning conversion of the unit area dry weight index of the litter is achieved, so that the method has better adaptability in the forest vegetation investigation.

Description

Technical Field

[0001] This invention belongs to the field of ecological environment monitoring technology, specifically relating to a method for determining the leaf area index of litter. Background Technology

[0002] Litter layer plays a vital role in soil and water conservation; however, current vegetation surveys focus primarily on green vegetation cover rather than litter cover volume. Current litter survey techniques suffer from the following main problems:

[0003] Current technology cannot incorporate litter into vegetation surveys: The difficulty in including litter as a factor in current vegetation surveys has not yet been overcome. Field survey indicators, primarily based on the dry weight or thickness of litter per unit area, differ in units and meaning from vegetation cover, and can only be analyzed as an influencing factor. Existing technologies can obtain litter cover through photography and visual interpretation, using litter cover, aboveground and belowground cover as soil and water conservation factors, and setting soil and water conservation weighting coefficients to comprehensively obtain a structured vegetation factor index. However, this technology relies on soil and water loss monitoring data to incorporate the litter layer into vegetation surveys, and cannot directly establish a relationship with surface vegetation; furthermore, litter layer cover only reflects the vertical distribution characteristics of litter, and its ability to characterize the amount of litter cover is limited.

[0004] Litter survey techniques and existing problems: The dry weight per unit area of ​​litter is the weight of dry matter per unit area of ​​litter. It is widely used and has high reliability, and it can better reflect the total amount of litter cover than litter coverage. This indicator can be obtained by collecting samples, drying and weighing them, and this method is generally used in scientific research. However, the dry weight per unit area of ​​litter only reflects the total mass of litter and cannot reveal its actual spread area and accumulation pattern on the ground. Litter with the same mass may have significantly different coverage effects due to differences in leaf size, shape and accumulation method. Summary of the Invention

[0005] The purpose of this invention is to provide a method for determining the leaf area index of litter to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for determining the leaf area index of litter, the specific steps of which are as follows:

[0007] S1. Sample area selection: Select several fixed-area sampling areas in the natural forest according to the requirements, collect all the litter in the area, weigh it on site, and calculate the wet weight per unit area.

[0008] S2. Leaf Area Index (LAI) Calculation: A portion of the sampling area in S1 was selected as a typical quadrat. The collected litter was evenly spread out, and photographs of all leaves were taken vertically using a photographic method. The Leaf Area Index (LAI) was then calculated using the following formula:

[0009] LAI = T × A / Area;

[0010] In the formula, LAI is the leaf area index, T is the total number of pixels of the litter leaves, A is the size of a single pixel, and Area is the fixed area selected in S1.

[0011] S3. Calculate the dry weight and moisture content of the sample. Then, collect the litter after S2 and bring it to the laboratory. After drying at 60℃ to constant weight, weigh it and calculate the dry weight per unit area and the average moisture content of the litter.

[0012] S4. Estimation model establishment: For the unit area dry weight and leaf area index (LAI) of typical quadrats, linear relationship and three nonlinear relationships (exponential, logarithmic, and power exponential) are fitted to construct the corresponding leaf area index (LAI) estimation model.

[0013] S5. Determine the estimation model and the fitting accuracy R in the multiple leaf area index (LAI) estimation models established in S4. 2 The highest power-law equation nonlinear relationship curve equation is the leaf area index (LAI) estimation model for litter.

[0014] S6. Automatic estimation: For all litter samples from S1, input them into the S5 estimation model and calculate LAI.

[0015] Preferably, the sampling area of ​​the fixed region selected in S1 is surrounded by an iron frame to form the so-called final sampling selection boundary line.

[0016] Preferably, during the shooting in S2, a square reference object is placed on the ground and also included in the shot. The Leaf Area Index (LAI) is the number of layers of fallen leaves covering the ground, which means the leaf area of ​​fallen leaves per unit surface area.

[0017] Preferably, in the Leaf Area Index (LAI) estimation model in S4, multiple LAI estimation models are constructed according to the different leaf morphologies and combinations of litter in forest lands such as coniferous, broad-leaved, and mixed coniferous and broad-leaved forests.

[0018] Preferably, the automatic estimation of LAI (Local Area Index) of litter in S6 specifically involves substituting the litter data for the determined forest type into the corresponding LAI estimation model constructed in S4, including the wet weight per unit area of ​​litter in the sampling area of ​​that type of forest and the average moisture content α of litter in that type of forest in S3, wherein:

[0019] W dry=W wet (1-α);

[0020] In the formula W dry Let W be the dry weight per unit area of ​​the sampling area, α be the average moisture content of litter in the sampling area of ​​a specific forest type, and W be the dry weight per unit area of ​​the sampling area. wet The wet weight per unit area of ​​the sampling area.

[0021] The technical effects and advantages of this invention are as follows: The litter leaf area index proposed in this invention has the same meaning as the vegetation leaf area index and can participate in relevant calculations in the same way as the vegetation leaf area index, thus enabling litter to be incorporated into the vegetation survey system. This index is the number of leaf cover layers on the ground surface, and its calculation method is the total unfolded area of ​​litter leaves per unit area of ​​the ground surface. By establishing a power exponential relationship between it and the dry weight per unit area of ​​litter, a litter leaf area index estimation model for coniferous, broad-leaved, and mixed coniferous and broad-leaved forests is constructed, thereby achieving the meaning conversion of the litter dry weight per unit area index and having better adaptability in forest vegetation surveys. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the technical process of the present invention.

[0023] Figure 2 This is a comparison chart of the estimated and measured values ​​of dry weight per unit area of ​​broadleaf forest litter according to the present invention;

[0024] Figure 3 This is a graph showing the fitting relationship between leaf area index and dry weight per unit area of ​​broadleaf forest litter according to the present invention.

[0025] Figure 4 This is a comparison chart of the estimated and measured values ​​of broadleaf forest litter LAI according to the present invention;

[0026] Figure 5 This is a comparison chart of the estimated and measured values ​​of dry weight per unit area of ​​coniferous forest litter according to the present invention;

[0027] Figure 6 This is a graph showing the fitting relationship between leaf area index and dry weight per unit area of ​​coniferous forest litter according to the present invention.

[0028] Figure 7 This is a comparison chart of the estimated and measured values ​​of LAI (Liquidity Index) of coniferous forest litter according to the present invention;

[0029] Figure 8 This is a comparison chart of the estimated and measured values ​​of dry weight per unit area of ​​litter in mixed coniferous and broad-leaved forests according to the present invention;

[0030] Figure 9 This is a graph showing the fitting relationship between leaf area index and dry weight per unit area of ​​litter in mixed coniferous and broad-leaved forests according to the present invention.

[0031] Figure 10This is a comparison chart of the estimated and measured values ​​of LAI (Liquid Acidity) in litter from mixed coniferous and broad-leaved forests according to the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] This invention provides a method for determining the leaf area index of litter, as shown in the figure. The specific steps are as follows:

[0034] S1. Sample area selection: Select several fixed-area sampling areas in the natural forest according to the requirements, collect all the litter in the area, weigh it on site, and calculate the wet weight per unit area.

[0035] S2. Leaf Area Index (LAI) Calculation: A portion of the sampling area in S1 was selected as a typical quadrat. The collected litter was evenly spread out, and photographs of all leaves were taken vertically using a photographic method. The Leaf Area Index (LAI) was then calculated using the following formula:

[0036] LAI = T × A / Area;

[0037] In the formula, LAI is the leaf area index, T is the total number of pixels of the litter leaves, A is the size of a single pixel, and Area is the fixed area selected in S1.

[0038] S3. Calculate the dry weight and moisture content of the sample. Then, collect the litter after S2 and bring it to the laboratory. After drying at 60℃ to constant weight, weigh it and calculate the dry weight per unit area and the average moisture content of the litter.

[0039] S4. Estimation model establishment: For the unit area dry weight and leaf area index (LAI) of typical quadrats, linear relationship and three nonlinear relationships (exponential, logarithmic, and power exponential) are fitted to construct the corresponding leaf area index (LAI) estimation model.

[0040] S5. Determine the estimation model and the fitting accuracy R in the multiple leaf area index (LAI) estimation models established in S4. 2 The highest power-law equation nonlinear relationship curve equation is the leaf area index (LAI) estimation model for litter.

[0041] S6. Automatic estimation: For all litter samples from S1, input them into the S5 estimation model and calculate LAI.

[0042] Specifically, in S1, the sampling area of ​​the fixed region selected is surrounded by an iron frame to form the so-called final sampling selection boundary line.

[0043] Specifically, during the shooting in S2, a square reference object is placed on the ground and also included in the shot. The Leaf Area Index (LAI) is the number of layers of fallen leaves covering the ground, which means the leaf area of ​​fallen leaves per unit surface area.

[0044] Specifically, in the Leaf Area Index (LAI) estimation model in S4, multiple LAI estimation models are constructed according to the different leaf morphologies and combinations of litter in forest lands such as coniferous, broad-leaved, and mixed coniferous and broad-leaved forests.

[0045] Specifically, the automatic estimation of LAI (Local Area Index) of litter in S6 involves substituting litter data of the determined forest type into the corresponding LAI estimation model constructed in S4, including the wet weight per unit area of ​​litter in the sampling area of ​​that type of forest and the average moisture content α of litter in that type of forest in S3, wherein:

[0046] W dry =W wet (1-α);

[0047] In the formula W dry Let W be the dry weight per unit area of ​​the sampling area, α be the average moisture content of litter in the sampling area of ​​a specific forest type, and W be the dry weight per unit area of ​​the sampling area. wet The wet weight per unit area of ​​the sampling area.

[0048] Working principle: After collecting litter in the field, typical plots are selected to carry out litter LAI modeling, and then the LAI of litter in the plots is estimated based on the modeling results.

[0049] 1. Ground litter sampling. A 50cm x 50cm iron frame was selected in the natural woodland as the litter sampling area (Area = 0.25m). 2 Collect all fallen leaves. When the iron frame is set to a smaller size, the distribution of fallen leaves is greatly affected by the location of the fallen leaves; conversely, if the size is too small, the workload will be too large, affecting the sampling progress.

[0050] 2. Calculate the Leaf Area Index (LAI) from typical litter samples. Evenly spread the litter leaves, and use a sampling photographic method to vertically photograph all the leaves. During photography, a square reference object is placed on the ground and also included in the image. A self-developed program automatically identifies and calculates the LAI. This index represents the number of litter leaf layers, and its meaning is the leaf area per unit surface area (m²). 2 / m 2The core principle of the automatic calculation of this program is: (1) Identify and count the total number of pixels of fallen leaves (T); (2) Identify the number of pixels occupied by a square reference object of known size and calculate the size of a single pixel (A, m2); (3) Calculate the leaf area index, the formula is LAI=T×A / Area.

[0051] 3. After drying typical litter samples in the laboratory at 60℃ to constant weight, weigh them (m1, g) and calculate the dry weight per unit area (W). dry =m1 / Area, g / m 2 The average moisture content of litter (α, %) is used for calculation. The litter samples used for the average moisture content calculation should be representative and determined by type, such as before rainfall, after rainfall, and different forest land types.

[0052] 4. W for typical quadrats dry The LAI array is used to fit linear, exponential, logarithmic, and idempotent relationships, and the fitting accuracy R is selected. 2 The highest-performing relationship curve equation serves as the leaf area index (LAI) estimation model for litter. Due to the different leaf morphologies and arrangements of litter in coniferous, broadleaf, and mixed coniferous-broadleaf forests, LAI estimation models f(W) should be constructed separately for each type of forest. dry , LAI).

[0053] 5. Weigh the litter in the sample plot on-site (m2, g), and calculate the wet weight per unit area (W). wet =m2 / Area, g / m 2 )

[0054] 6. Based on the LAI estimation model f(W) established in step 4 dry The LAI (Layer Identification Area) of sample plots is automatically estimated. During estimation, the type of litter is determined, and α and W are used... wet Substitute the data into the corresponding litter type model f(W) dry , LAI), where W dry =W wet (1-α);

[0055] Verification 1: 32 sampling points were investigated in the broad-leaved forest according to the following steps.

[0056] 1. Ground litter sampling. A 50cm x 50cm iron frame was selected in the natural woodland as the litter sampling area (Area = 0.25m). 2 Collect all fallen leaves. When the iron frame is set to a smaller size, the distribution of fallen leaves is greatly affected by the location of the fallen leaves; conversely, if the size is too small, the workload will be too large, affecting the sampling progress.

[0057] 2. Calculate the Leaf Area Index (LAI) from typical litter samples. Evenly spread the litter leaves, and use a sampling photographic method to vertically photograph all the leaves. During photography, a square reference object is placed on the ground and also included in the image. A self-developed program automatically identifies and calculates the LAI. This index represents the number of litter leaf layers, and its meaning is the leaf area per unit surface area (m²). 2 / m 2 The core principle of the automatic calculation of this program is: (1) Identify and count the total number of pixels of fallen leaves (T); (2) Identify the number of pixels occupied by a square reference object of known size and calculate the size of a single pixel (A, m2); (3) Calculate the leaf area index, the formula is LAI=T×A / Area.

[0058] The program code is as follows:

[0059] def calc_LAI(image_file_path, side length of square reference object: L):

[0060] / / Read and verify the image

[0061] image = open(image_file_path)

[0062] / / Convert color space (BGR to RGB)

[0063] image_rgb = image2RGB(image)

[0064] / / Extract the green band and calculate the total number of reference pixels A using the thresholding method.

[0065] image_object = image_rgb['green'] > threshold

[0066] A = sum(image_object)

[0067] / / Remove reference objects from the image and fill them with NaN; these objects will not participate in clustering calculations.

[0068] image_rgb = where(bool(image_object), NaN, image_rgb)

[0069] / / Running the KMeans clustering algorithm, the number of clusters is 2

[0070] labels = KMeans(image_rgb, k=2)

[0071] / / Total number of pixels of fallen leaves (T)

[0072] T = sum(labels == fallen object labels)

[0073] / / Calculate leaf area index

[0074] Area = L*L

[0075] LAI = T*A / Area

[0076] return LAI

[0077] Typical litter samples were dried at 60°C in the laboratory until constant weight was achieved, and then weighed (m1, g). The dry weight per unit area (W) was calculated. dry =m1 / Area, g / m 2 The average moisture content (α, %) of litter is used in the calculation. The litter samples used for the average moisture content calculation should be representative and categorized by type, such as before rainfall, after rainfall, and different forest types. In this survey of broadleaf forest plots in Baiyun Mountain, the average moisture content of litter was 18.63%. The estimated dry weight per unit area of ​​litter obtained through moisture content estimation was compared with the measured value; the estimation accuracy R... 2 Since the value is 0.9957, the average moisture content can be used to estimate the dry weight per unit area of ​​litter, such as... Figure 2 As shown;

[0078] W for typical quadrats dry The LAI array is used to fit linear, exponential, logarithmic, and idempotent relationships, and the fitting accuracy R is selected. 2 The highest correlation curve equation serves as the LAI estimation model for litter. Since the leaf morphology and arrangement of litter in broad-leaved forests are similar, an LAI estimation model f(W) can be constructed. dry (LAI) such as Figure 3 As shown;

[0079] The optimal model for estimating leaf area index (LAI) of litter in broadleaf forests is the power-law equation. This equation can be applied to estimating LAI of litter in broadleaf forests in other regions.

[0080] (1)

[0081] 5. Weigh the litter in the sample plot on-site (m2, g), and calculate the wet weight per unit area (W). wet =m2 / Area, g / m 2 )

[0082] 6. Based on the LAI estimation model f(W) established in step 4 dryThe LAI (Layer Identification Area) of sample plots is automatically estimated. During estimation, the type of litter is determined, and α and W are used... wet Substitute the data into the corresponding litter type model f((1-α)W wet , LAI), where (1-α)W wet =W dry For example, the litter α and W in a broadleaf forest quadrat. wet They were 18.63% and 346 g / m³, respectively. 2 The estimated LAI value was 2.37. The following is a comparison between the estimated and measured LAI values ​​of litter from multiple quadrats, with the estimation accuracy R... 2 The value is 0.8898, therefore the broadleaf forest litter LAI estimation model can be used for field quadrat surveys, such as... Figure 4 As shown.

[0083] Verification 2: 27 sampling points were investigated in the coniferous forest of Changting according to the following steps.

[0084] 1. Ground litter sampling. A 50cm x 50cm iron frame was selected in the natural woodland as the litter sampling area (Area = 0.25m). 2 Collect all fallen leaves. When the iron frame is set to a smaller size, the distribution of fallen leaves is greatly affected by the location of the fallen leaves; conversely, if the size is too small, the workload will be too large, affecting the sampling progress.

[0085] 2. Select typical sample plots of litter to calculate the leaf area index (LAI). Spread the litter leaves evenly, take vertical photos of all the litter leaves using the sampling photography method. When taking photos, place a square reference object on the ground and include it in the photo. Use a self-developed code program to automatically identify and calculate the leaf area index (LAI). This index is the number of litter leaf layers, which means the area of ​​litter leaves per unit surface area (m2 / m2). The core principle of the automatic calculation by this program is: (1) Identify and count the total number of pixels of litter leaves (T); (2) Identify the number of pixels occupied by a square reference object of known size and calculate the size of a single pixel (A, m2); (3) Calculate the leaf area index, the formula is LAI=T×A / Area.

[0086] 3. After drying typical litter samples in the laboratory at 60℃ to constant weight, weigh them (m1, g) and calculate the dry weight per unit area (W). dry =m1 / Area, g / m 2 The average moisture content (α, %) of litter is used in the calculation. The litter samples used for the average moisture content calculation should be representative and categorized by type, such as before rainfall, after rainfall, and different forest types. In this survey in Changting, Fujian, the average moisture content of litter in coniferous forest plots was 58.98%. The estimated dry weight per unit area of ​​litter obtained through moisture content estimation was compared with the measured value; the estimation accuracy R...2 Since the value is 0.9828, the average moisture content can be used to estimate the dry weight per unit area of ​​litter, such as... Figure 5 As shown;

[0087] W for typical quadrats dry The LAI array is used to fit linear, exponential, logarithmic, and idempotent relationships, and the fitting accuracy R is selected. 2 The highest correlation curve equation serves as the LAI estimation model for litter. Since the leaf morphology and arrangement of litter in coniferous forests are similar, an LAI estimation model f(W) can be constructed. dry (LAI) such as Figure 6 As shown;

[0088] The optimal equation for estimating the leaf area index (LAI) of coniferous forest litter is the power-law equation. This equation can be applied to estimating the LAI of coniferous forest litter in other regions.

[0089] (2)

[0090] 5. Weigh the litter in the sample plot on-site (m2, g), and calculate the wet weight per unit area (W). wet =m2 / Area, g / m 2 )

[0091] 6. Based on the LAI estimation model f(W) established in step 4 dry The LAI (Layer Identification Area) of sample plots is automatically estimated. During estimation, the type of litter is determined, and α and W are used... wet Substitute the data into the corresponding litter type model f((1-α)W wet , LAI), where (1-α)W wet =W dry For example, the litter α and W in a certain coniferous forest quadrat. wet 58.98% and 644g / m³ respectively 2 The estimated LAI value was 2.75. The following is a comparison between the estimated and measured LAI values ​​of litter from multiple quadrats, with the estimation accuracy R... 2 The value is 0.918, therefore the LAI estimation model for coniferous forest litter can be used for field quadrat surveys, such as... Figure 7 As shown.

[0092] Verification 3: In the mixed coniferous and broad-leaved forest of Changting, 15 sampling points were investigated according to the above steps and methods.

[0093] 1. Ground litter sampling. A 50cm x 50cm iron frame was selected in the natural woodland as the litter sampling area (Area = 0.25m). 2Collect all fallen leaves. When the iron frame is set to a smaller size, the distribution of fallen leaves is greatly affected by the location of the fallen leaves; conversely, if the size is too small, the workload will be too large, affecting the sampling progress.

[0094] 2. Calculate the Leaf Area Index (LAI) from typical litter samples. Evenly spread the litter leaves, and use a sampling photographic method to vertically photograph all the leaves. During photography, a square reference object is placed on the ground and also included in the image. A self-developed program automatically identifies and calculates the LAI. This index represents the number of litter leaf layers, and its meaning is the leaf area per unit surface area (m²). 2 / m 2 The core principle of the automatic calculation of this program is: (1) Identify and count the total number of pixels of fallen leaves (T); (2) Identify the number of pixels occupied by a square reference object of known size and calculate the size of a single pixel (A, m2); (3) Calculate the leaf area index, the formula is LAI=T×A / Area.

[0095] 3. After drying typical litter samples in the laboratory at 60℃ to constant weight, weigh them (m1, g) and calculate the dry weight per unit area (W). dry =m1 / Area, g / m 2 The average moisture content (α, %) of litter is used in the calculation. The litter samples used for the average moisture content calculation should be representative and categorized by type, such as before rainfall, after rainfall, and different forest types. In this survey of mixed coniferous and broadleaf forest plots in Changting, Fujian, the average moisture content of litter was 64.08%. The estimated dry weight per unit area of ​​litter obtained through moisture content estimation was compared with the measured value; the estimation accuracy R... 2 Since the value is 0.9929, the average moisture content can be used to estimate the dry weight per unit area of ​​litter, such as... Figure 8 As shown;

[0096] For the Wdry and LAI arrays of typical quadrats, linear and three nonlinear relationships (exponential, logarithmic, and idempotent) were fitted. The relationship curve equation with the highest fitting accuracy (R²) was selected as the LAI estimation model. The leaf morphology and arrangement of litter in mixed coniferous and broadleaf forests are relatively similar, allowing for the construction of an LAI estimation model f(Wdry, logarithmic, idempotent). dry (LAI) such as Figure 9 As shown;

[0097] The optimal equation for estimating the leaf area index (LAI) of litter in mixed coniferous and broadleaf forests is the power-law equation. This equation can be applied to estimating the LAI of litter in coniferous forests in other regions.

[0098] (3)

[0099] 5. Weigh the litter in the sample plot on-site (m2, g), and calculate the wet weight per unit area (W). wet =m2 / Area, g / m 2 )

[0100] 6. Based on the LAI estimation model f(W) established in step 4 dry The LAI (Layer Identification Area) of sample plots is automatically estimated. During estimation, the type of litter is determined, and α and W are used... wet Substitute the data into the corresponding litter type model f((1-α)W wet , LAI), where (1-α)W wet =W dry For example, the litter α and W in a certain coniferous forest quadrat. wet They were 64.08% and 1407 g / m³, respectively. 2 The estimated LAI value was 5.16. The following is a comparison between the estimated and measured LAI values ​​of litter from multiple quadrats, with the estimation accuracy R... 2 The value is 0.9782, therefore the LAI estimation model for coniferous forest litter can be used for field quadrat surveys, such as... Figure 10 As shown.

[0101] In summary, by constructing leaf area index (LAI) estimation models for litter forests, including broadleaf forests, coniferous forests, and mixed coniferous and broadleaf forests, the nonlinear relationship curve equation based on the power exponent equation was determined to be the most accurate LAI estimation model. This model allows for rapid LAI estimation using only on-site wet weight measurement and average moisture content determination, innovatively incorporating the litter layer into the forest stratification vegetation survey system. Given the crucial function of the litter layer in directly protecting the soil and reducing runoff erosion, this invention has significant implications for vegetation surveys and soil and water conservation research.

[0102] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for determining the leaf area index of litter, characterized in that: The specific steps are as follows: S1. Sample area selection: Select several fixed-area sampling areas in the natural forest according to the requirements, collect all the litter in the area, weigh it on site, and calculate the wet weight per unit area. S2. Leaf Area Index (LAI) Calculation: A portion of the sampling area in S1 was selected as a typical quadrat. The collected litter was evenly spread out, and photographs of all leaves were taken vertically using a photographic method. The Leaf Area Index (LAI) was then calculated using the following formula: LAI = T × A / Area; In the formula, LAI is the leaf area index, T is the total number of pixels of the litter leaves, A is the size of a single pixel, and Area is the fixed area selected in S1. S3. Calculate the dry weight and moisture content of the sample. Then, collect the litter after S2 and bring it to the laboratory. After drying at 60℃ to constant weight, weigh it and calculate the dry weight per unit area and the average moisture content of the litter. S4. Estimation model establishment: For the unit area dry weight and leaf area index (LAI) of typical quadrats, linear relationship and three nonlinear relationships (exponential, logarithmic, and power exponential) are fitted to construct the corresponding leaf area index (LAI) estimation model. S5. Determine the estimation model and the fitting accuracy R in the multiple leaf area index (LAI) estimation models established in S4. 2 The highest power-law equation nonlinear relationship curve equation is the leaf area index (LAI) estimation model for litter. S6. Automatic estimation: For all litter samples from S1, input them into the S5 estimation model and calculate LAI.

2. The method for determining the leaf area index of litter according to claim 1, characterized in that: The sampling area of ​​the fixed region selected in S1 is surrounded by an iron frame to form the so-called final sampling selection boundary line.

3. The method for determining the leaf area index of litter according to claim 1, characterized in that: During the shooting in S2, a square reference object is placed on the ground and also included in the shot. The Leaf Area Index (LAI) is the number of layers of fallen leaves covering the ground, which means the leaf area of ​​fallen leaves per unit surface area.

4. The method for determining the leaf area index of litter according to claim 1, characterized in that: In the S4 leaf area index (LAI) estimation model, multiple LAI estimation models are constructed according to the different leaf morphologies and combinations of litter in coniferous, broad-leaved, and mixed coniferous and broad-leaved forests.

5. The method for determining the leaf area index of litter according to claim 1, characterized in that: The automatic estimation of LAI (Local Area Index) of litter in S6 specifically involves substituting litter data of the determined forest land type into the corresponding LAI estimation model constructed in S4, including the wet weight per unit area of ​​litter in the sampling area of ​​that type of forest land and the average moisture content α of litter in that type of forest land in S3, wherein: W dry =W wet (1-a); In the formula W dry Let W be the dry weight per unit area of ​​the sampling area, α be the average moisture content of litter in the sampling area of ​​a specific forest type, and W be the dry weight per unit area of ​​the sampling area. wet The wet weight per unit area of ​​the sampling area.

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