An improved remote sensing method for estimating canopy shade and sun leaves and its application

Abstract

The present invention relates to the technical field of total primary productivity estimation, and solves the technical problem that the traditional remote sensing method for estimating the LAI of "yin-yang leaves" does not fully consider the directionality of the aggregation index, and in particular relates to an improved remote sensing method for estimating canopy shade leaves and sun leaves and its application, including establishing a "yin-yang leaves" LAI estimation model, calculating the directional aggregation index, and estimating the leaf area index of the canopy shade leaves and sun leaves. The method is applied to respectively calculate the photosynthetically active radiation absorbed by the canopy shade leaves and sun leaves, and the total primary productivity generated thereby. The present invention more comprehensively depicts the vegetation structure characteristics by introducing the directional aggregation index, thereby achieving an accurate estimation of the "yin-yang leaves" LAI, and the algorithm has been verified by simulation of a small-area computer ray tracing algorithm. This improvement provides important support for improving the accuracy of global total primary productivity estimation, and helps to more comprehensively and accurately evaluate global total primary productivity.

Description

Technical Field

[0001] The invention relates to the technical field of total primary productivity estimation, and in particular to an improved remote sensing estimation method for canopy shade leaves and sun leaves and an application thereof. Background Art

[0002] Vegetation is an important component of terrestrial ecosystems, the largest carbon storage reservoir and key carbon sink on Earth, and plays an indispensable role in the global carbon cycle. Gross Primary Productivity (GPP), as a core indicator for measuring plant growth and function, directly affects the intensity of carbon exchange between the atmosphere and terrestrial ecosystems. Accurately estimating regional and global gross primary productivity (GPP) and analyzing its spatiotemporal distribution characteristics are of great significance for studying how the carbon cycle of terrestrial ecosystems responds to climate change and human activities, and has become one of the hot issues in global change research.

[0003] Leaves that are exposed to direct sunlight (sun leaves) and leaves that are only exposed to scattered light (shade leaves) have different photosynthesis efficiencies. Many studies have shown that there is a significant difference in photosynthesis efficiency between sun leaves and shade leaves (referred to as "yin-yang leaves"). If we do not distinguish between "yin-yang leaves", we will misjudge the carbon sequestration capacity of vegetation.

[0004] However, the traditional remote sensing method for estimating the LAI of "yin-yang leaves" does not fully consider the directionality of the aggregation index (CI), while the leaf distribution in the actual canopy usually has significant directional heterogeneity, that is, different angles often have different aggregation indexes (CI). Therefore, the current "yin-yang leaves" LAI estimation method has serious errors. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an improved remote sensing method for estimating canopy shade leaves and sun leaves and its application, which solves the technical problem that the traditional remote sensing method for estimating "shade leaves and sun leaves" LAI does not fully consider the directionality of the aggregation index (CI).

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an improved remote sensing method for estimating canopy shade leaves and sun leaves, the method comprising the following process:

[0007] Based on the difference in solar incidence angles under different time series, a leaf area index for estimating canopy shade and sun leaves was established. and The “Yin Yang Leaf” LAI estimation model;

[0008] Based on the directional heterogeneity of leaf distribution in the canopy, the directional aggregation index of leaves in the canopy under random distribution and hemispherical canopy overlap was calculated. ;

[0009] Directional Aggregation Index Substitute into the "yin-yang leaf" LAI estimation model to estimate the leaf area index of the canopy yin and yang leaves and .

[0010] Furthermore, the expression of the “yin-yang leaf” LAI estimation model is:

[0011] ;

[0012] In the formula, It represents the projection of unit leaf area on the vertical plane of the observation direction; LAI is the leaf area index; for Yangye LAI; for shade leaf LAI; is the solar zenith angle; is the directional aggregation index.

[0013] Further, the directional aggregation index is calculated The process includes:

[0014] Definition of the canopy gap ratio to describe the random distribution of leaves in the canopy , the expression is:

[0015] ;

[0016] In the formula, is the solar zenith angle The probability of a beam of light passing through the canopy at , i.e., the canopy gap rate of randomly distributed leaves in the canopy;

[0017] Definition used to describe the canopy gap ratio of leaves in the canopy under hemispherical canopy overlap ;

[0018] According to the canopy gap ratio and Calculation of directional aggregation index .

[0019] Furthermore, the canopy gap ratio , the expression is:

[0020] ;

[0021] In the formula, is the directional crown closure, which is defined as the sum of the crown projection areas per unit area on the plane perpendicular to the observation direction; is the characteristic parameter.

[0022] Further, the canopy gap ratio and Calculation of directional aggregation index The specific process includes:

[0023] The canopy structure data of the sample plot is obtained by ground measurement or remote sensing technology. The canopy structure data of the sample plot includes the canopy gap ratio of individual trees in the representative canopy of the sample plot. , the relative variance of the number of trees contained in the projection area of ​​a single tree on the plane perpendicular to the observation direction , Directional crown closure ;

[0024] according to and Calculate feature parameters , the calculation formula is:

[0025] ;

[0026] Based on the directional aggregation index estimation model combined with characteristic parameters Directional Aggregation Index The calculation formula is:

[0027] ;

[0028] In the formula, is the characteristic parameter; The canopy gap ratio of a single tree representing the canopy of the sample plot.

[0029] Furthermore, the ground measurement methods include transparent grid method, projection method, laser radar scanning and Nilson observation method, which obtain canopy structure data by directly measuring the coverage, void ratio or directional closure of the tree canopy;

[0030] The remote sensing technology method estimates canopy structure data by combining satellite or UAV images with vegetation index and lidar data.

[0031] Furthermore, the remote sensing technology method estimates the canopy structure data using the following estimation formula:

[0032] Directional crown closure The estimation formula is:

[0033] ;

[0034] In the formula, is the number of tree crowns in the sample plot; is the sample plot area; It is the projection area of ​​the representative tree crown of the sample plot on the vertical plane of the observation direction;

[0035] Canopy gap ratio The estimation formula is:

[0036] ;

[0037] In the formula, It is the leaf area of ​​a single tree in the representative tree crown of the sample plot;

[0038] Obtaining relative variance through field survey method .

[0039] The technical solution also provides an application of a remote sensing method for estimating canopy shade leaves and sun leaves, by which the photosynthetically active radiation absorbed by the canopy shade leaves and sun leaves and the resulting gross primary productivity (GPP) are calculated respectively, so as to accurately reflect the functional contributions of different types of leaves in the canopy.

[0040] By means of the above technical solution, the present invention provides an improved remote sensing method for estimating canopy shade leaves and sun leaves and its application, which has at least the following beneficial effects:

[0041] 1. The present invention introduces a directional aggregation index , which more comprehensively depicts the structural characteristics of vegetation, thereby achieving an accurate estimation of the LAI of the "yin-yang leaf". The algorithm has been verified by the simulation of a small-area computer ray tracing algorithm. This improvement provides important support for improving the accuracy of global gross primary productivity (GPP) estimates and helps to more comprehensively and accurately assess global gross primary productivity (GPP).

[0042] 2. The proposed method for estimating the “yin-yang leaf” LAI provides a scientific basis for vegetation management and ecological restoration, and by regularly monitoring its changes, assesses ecosystem health and supports environmental protection and sustainable development. Therefore, accurate estimation of the “yin-yang leaf” LAI is of great significance in the fields of vegetation management, agricultural production and ecological restoration.

[0043] 3. The remote sensing method for estimating the LAI of "yin-yang leaves" proposed in this invention is expected to provide an effective theoretical basis and reliable practical tools for scientific research such as the accurate estimation of the true LAI and the ratio of "yin-yang leaves". The research results will promote the application of quantitative remote sensing in global vegetation carbon sink research and other aspects, and have important scientific significance and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0045] Figure 1 is a schematic diagram of a natural forest scene in the present invention;

[0046] Figure 2 is a schematic diagram of a plantation scene in the present invention;

[0047] Figure 3 This is a verification result diagram of the distribution of six tree crowns on flat ground in the present invention;

[0048] Figure 4 It is the relative error diagram of the two remote sensing estimation methods of "yin-yang leaf" LAI and the true value results in the present invention;

[0049] Figure 5 This is a result diagram of the verification of the artificial forest on a slope with a slope of 30 degrees in the present invention;

[0050] Figure 6 This is a relative error result diagram of the remote sensing estimation of the "Yin Yang Leaf" LAI method in the present invention and the LESS true value result in the slope scene;

[0051] Figure 7 This is a schematic diagram of the forest stand distribution in the HET-16 scenario of the present invention;

[0052] Figure 8 It is a schematic diagram of the three-dimensional presentation of the HET-16 scene in LESS in the present invention;

[0053] Fig. 9 This is the result diagram of the HET-16 scene at a 90-degree observation azimuth in the present invention;

[0054] Fig.10 The present invention is a flowchart of the remote sensing method for estimating canopy shade leaves and sun leaves. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0056] Remote sensing technology, as an efficient ground monitoring method, has become a core tool for studying carbon cycle and vegetation growth by acquiring large-scale, high-time surface ecosystem data through satellites, drones and other platforms. Remote sensing can not only provide comprehensive spatiotemporal distribution information, but also provide important support for the improved remote sensing estimation of the "yin-yang leaf" LAI method in this embodiment, promoting the monitoring and research of vegetation carbon absorption and storage at regional and global scales.

[0057] Leaf Area Index (LAI) is usually defined as the total area of ​​plant leaves per unit ground area. It is an important variable in describing the biophysical changes and canopy structure of vegetation in terrestrial ecosystems. It directly affects the transpiration efficiency, photosynthesis and energy balance of vegetation, and is a key parameter in carbon cycle and energy flux models.

[0058] The Clumping Index (CI) is defined as the ratio between the effective LAI and the true LAI. It quantitatively describes the degree to which the spatial distribution of leaves in the canopy deviates from the Poisson random distribution, affects the amount of light intercepted by leaves in the canopy, and is a key parameter for calculating the true LAI and distinguishing the LAI area ratio of "yin-yang leaves". It is often used as an important parameter to characterize the degree of discreteness of canopy leaf distribution and is applied to the forward and inversion of canopy reflectance of various vegetations and regional and even global carbon sink model research.

[0059] Leaves that are illuminated by direct light (sun leaves) and leaves that are only illuminated by scattered light (shade leaves) have different photosynthetic efficiencies. Many studies have shown that there is a significant difference in photosynthetic efficiency between sun leaves and shade leaves (referred to as "yin-yang leaves"). If the "yin-yang leaves" are not distinguished, the carbon sink capacity of vegetation will be misjudged. Dividing the canopy into shade leaves and sun leaves, and calculating the photosynthetically active radiation absorbed by them and the resulting gross primary productivity (GPP) can more accurately reflect the functional contribution of different types of leaves in the canopy, and improve the reliability of carbon flux and energy flux calculation results.

[0060] Please refer to Figure 1-Figure 10 This embodiment proposes an improved remote sensing method for estimating canopy shade leaves and sun leaves by introducing a directional aggregation index. , which more comprehensively depicts the structural characteristics of vegetation, thereby achieving accurate estimation of the LAI of the "yin-yang leaves", and the algorithm has been verified by a computer ray tracing algorithm. This improvement provides important support for improving the accuracy of global gross primary productivity (GPP) estimates and helps to more comprehensively and accurately assess global gross primary productivity (GPP). Fig.10 As shown, the method comprises the following steps:

[0061] S1. Based on the difference in solar incidence angles under different time series, the hemispherical concentration index Ω is improved into a directional concentration index. , to establish a method for estimating the leaf area index of canopy shade leaves and sun leaves and In this embodiment, the current mainstream remote sensing method for estimating the LAI of "yin-yang leaves" is based on the "yin-yang leaves" distinction algorithm of Norman (1982). By dividing the canopy into two parts, yin leaves and yang leaves, a "two-leaf model" is constructed. The calculation formula is as follows:

[0062] ;

[0063] Where, LAI is leaf area index; is the LAI of the sun leaf; is the LAI of the shade leaf; is the solar zenith angle; The hemispherical aggregation index is a correction parameter used by Nilson to describe the non-random distribution of leaves in the canopy. It is defined as the ratio of the effective LAI to the true LAI. The formula is as follows:

[0064] ;

[0065] In the formula, is the effective LAI.

[0066] Nilson did not consider the directional heterogeneity when calculating the clustering index (CI), and assumed that the distribution of trees was mostly clustered, which is often not the case in reality. This results in the accuracy of the estimated LAI of the "yin-yang leaf" being reduced at different observation angles, especially when the distribution of trees is uneven.

[0067] Directional Aggregation Index The calculation of directional overlap between tree crowns has been expanded and different types of tree distribution patterns have been considered, which has higher accuracy and applicability. Improved Directional Aggregation Index , that is, an improved vegetation canopy “yin-yang leaf” LAI estimation method is obtained, so the “yin-yang leaf” LAI estimation model is:

[0068] ;

[0069] In the formula, It represents the projection of the unit leaf area on the vertical plane of the observation direction, and has a spherical distribution for the leaf inclination angle. is 0.5.

[0070] S2. Based on the directional heterogeneity of leaf distribution in the canopy, the directional aggregation index of leaves in the canopy under random distribution and hemispherical space is calculated. In this embodiment, the directional concentration index , can be calculated by the following process, namely:

[0071] For a canopy with randomly distributed leaves, the relationship between the canopy gap fraction (GF) and the leaf area index (LAI) can be described by the famous Beer-Lambert theory, as follows:

[0072] ;

[0073] In the formula, is the solar zenith angle The probability of a beam of light passing through the canopy at is the canopy gap rate when leaves are randomly distributed in the canopy. This formula is applicable to vegetation scenes with randomly distributed leaves, and the aggregation index (CI) under this distribution is 1.

[0074] Since leaves usually have a non-random distribution in reality, that is, they are often clustered in the crown of vegetation, Nilson first introduced the aggregation index (CI) in the modified Beer-Lambert theory to calculate the canopy gap fraction (GF) of the canopy, the formula is as follows:

[0075] ;

[0076] From the mathematical relationship between the two, we can deduce:

[0077] ;

[0078] In order to overcome the shortcomings of the model in terms of directionality, applicability and accuracy, a new canopy gap ratio (GF) model considering the canopy overlap in hemispherical space is proposed. The specific calculation formula is as follows:

[0079] ;

[0080] In the formula, It is the directional crown closure, which is defined as the sum of the crown projection areas of the unit area on the plane perpendicular to the observation direction, while the crown projection overlapping area is multiply counted, taking into account the number of overlapping crowns, and is calculated by the sum of the crown projections of the units in each direction on the plane perpendicular to the observation direction. is the characteristic parameter; The canopy gap ratio of a single tree representing the canopy of the plot; It is the relative variance of the number of trees contained in the projection area of ​​a single tree on the plane perpendicular to the observation direction. It is a parameter used to describe the distribution pattern of trees in vegetation.

[0081] Referring to the calculation of the aggregation index (CI) above, the same form of the estimation model of the directional aggregation index of the general vegetation canopy is given here, that is, the expression of the directional aggregation index estimation model is:

[0082] ;

[0083] In the formula, is the canopy gap ratio of randomly distributed leaves in the canopy; is the canopy gap ratio of leaves in the canopy under the canopy overlap in hemispherical space.

[0084] As a further derivation, the directional aggregation index is obtained The calculation formula is as follows:

[0085] When distinguishing the leaf area index of “yin-yang leaves” through the “yin-yang leaves” LAI estimation model, it is necessary to obtain three types of canopy structure data of the sample plot, namely, the representative canopy of the sample plot. , and In practice, it can be obtained through ground measurement and remote sensing technology. , and Common ground measurement methods include transparent grid method, projection method, LiDAR scanning and Nilson observation method. These methods obtain canopy structure data by directly measuring the coverage, porosity or directional closure of the tree canopy. Remote sensing technology can estimate canopy structure data through satellite or drone images combined with vegetation index, LiDAR data, etc.

[0086] in, It can be calculated by the following approximate calculation formula:

[0087] ;

[0088] In the formula, is the number of tree crowns in the sample plot; is the sample plot area; It is the projection area of ​​the representative tree crown of the sample plot on the vertical plane of the observation direction.

[0089] is the canopy gap ratio of a single tree in the representative canopy of the plot. The approximate calculation formula is given here:

[0090] ;

[0091] In the formula, It is the leaf area of ​​a single tree in the representative tree crown of the sample plot.

[0092] in, The field survey method can be used to select representative sample areas in the study area. In each sample area, standard field survey methods (such as fixed sample methods) are used to record the number and related characteristics of trees, estimate the projected area, and obtain the relative variance of the number of trees contained in the projected area of ​​a single tree. It can also be obtained by combining new methods such as image classification, 3D LiDAR scanning, and ecological simulation.

[0093] according to and Calculate feature parameters , the calculation formula is:

[0094] ;

[0095] Combining the above formula with the directional aggregation index estimation model, the directional aggregation index is further derived: The calculation formula is:

[0096] ;

[0097] In the formula, is the characteristic parameter; The canopy gap ratio of a single tree representing the canopy of the sample plot.

[0098] S3, Directional Aggregation Index Substitute into the "yin-yang leaf" LAI estimation model to estimate the leaf area index of the canopy yin and yang leaves and This step is accomplished by and Substitute into the formula to calculate the directional aggregation index , and then the calculated directional aggregation index Substitute it into the "Yin Yang Leaf" LAI estimation model, and the leaf area index of the "Yin Yang Leaf" can be calculated. and .

[0099] In this embodiment, LAI is an important parameter to describe the characteristics of the vegetation canopy, which directly affects the efficiency of vegetation in intercepting solar energy, thereby determining the photosynthesis intensity and carbon absorption capacity of plants. By accurately distinguishing the LAI of "yin-yang leaves" and considering the difference in light energy utilization between yin leaves and sun leaves, the estimation accuracy of gross primary productivity (GPP) can be significantly improved, which is of great value for studying the carbon cycle process, evaluating the response of vegetation to climate change, and optimizing carbon sink management strategies. In addition, accurate estimation of the LAI of "yin-yang leaves" provides a scientific basis for vegetation management and ecological restoration, and by regularly monitoring its changes, it assesses ecosystem health and supports environmental protection and sustainable development. Therefore, accurate estimation of the LAI of "yin-yang leaves" is of great significance in the fields of vegetation management, agricultural production, and ecological restoration.

[0100] The aggregation index (CI) plays an important role in the theory and application of the algorithm for distinguishing the "yin-yang leaf" LAI. By more accurately describing the leaf distribution characteristics of the canopy, the aggregation index (CI) provides key parameter support for improving ecosystem models. , however, the hemispheric aggregation index It cannot accurately represent the aggregation of vegetation canopies. Since the calculation of the LAI ratio of "yin-yang leaves" in the canopy depends on the direction of solar incidence, the directional aggregation index should be used. Rather than the currently used hemispheric concentration index .

[0101] Based on remote sensing technology, this embodiment deeply studies the quantitative relationship between vegetation concentration index (CI) and sun angle, and scientifically constructs an improved remote sensing method for estimating the LAI of "yin-yang leaves", in order to provide an effective theoretical basis and reliable practical tools for scientific research such as accurate estimation of the true leaf LAI and the ratio of "yin-yang leaves". The research results will promote the application of quantitative remote sensing in global vegetation carbon sink research and other aspects, and have important scientific significance and practical application value.

[0102] In this embodiment, a computer simulation method is used to verify the model calculation results. Computer simulation provides a highly controllable environment to distinguish the "yin-yang leaves" LAI, and can avoid various interference factors affecting the measurement results. It is usually based on the real scene structure and can simulate the radiation transmission process from the leaf scale to the canopy scale. At present, a series of three-dimensional radiation transmission models for simulation have been developed in the field of quantitative remote sensing. This embodiment uses a high-precision ray tracing (three-dimensional radiation transmission) LESS model, considering six different verification scenarios of tree crown distribution on flat land. Considering the influence of terrain, three regular tree crown distributions on slopes are also verified, and the improved remote sensing estimation method of "yin-yang leaves" LAI in this embodiment ( Figure 3 The results of the triangle in the figure are compared with the traditional remote sensing method for estimating the “Yin Yang Leaf” LAI ( Figure 3 The circle in the figure) and the LESS simulation “Yin Yang Leaf” LAI true value result ( Figure 3 The straight line in the figure is used for comparison to verify the effectiveness and accuracy of the improved remote sensing method for estimating the “Yin Yang Leaf” LAI.

[0103] Generally speaking, tree distribution patterns can be divided into three main types in reality: clustered, random, and regular. Based on the theories of Neyman, Poisson, and hypergeometric models, three natural forest scenarios were generated as verification scenarios, and their crown distributions showed a certain degree of clustering, randomness, and regularity, respectively. Figure 1 As shown, from left to right there are three natural forest scenes: clustered, random, and regular (hypergeometric).

[0104] Most estimation models perform well for natural forest scenarios, but generally perform poorly for plantation forest scenarios. In addition, most existing estimation models are based on the assumption that azimuth is isotropy in natural forests, but this assumption is not applicable to plantation forest scenarios. In this example, the distribution of trees in plantation forests is considered, and three plantation forest scenarios with obvious azimuth and observation azimuth effects are generated as verification scenarios, such as Figure 2 As shown, from left to right are three artificial forest scenes: artificial forest one, artificial forest two, and artificial forest three.

[0105] As mentioned above, the verification results of the six tree crown distributions on flat land are as follows: Figure 3 As shown, Figure 3 The a in is randomly distributed. Figure 3 The b in is the hypergeometric distribution, Figure 3 The c in is the aggregate distribution, Figure 3 The df in is the observation result of plantation 1, 2 and 3 at 0 degree observation azimuth. Figure 3 The gi in the figure is the observation result of plantations one, two and three at an observation azimuth of 90 degrees.

[0106] The relative errors of the two remote sensing estimation methods of “Yin Yang Leaf” LAI and the true value results are shown in Figure 4 As shown, Figure 4 The ac in the table corresponds to the relative errors under random distribution, hypergeometric distribution, and clustered distribution, respectively. Figure 4 The df in the table are the relative errors of the observations of plantations 1, 2, and 3 at 0 degree observation azimuth. Figure 4 The gi in the figure are the relative errors of plantations 1, 2 and 3 at 90 degree observation azimuth.

[0107] Taking into account the directional heterogeneity of artificial forests, this embodiment selects two different observation azimuths of 0 degrees and 90 degrees for verification. The traditional remote sensing method for estimating the "Yin Yang Leaves" LAI has certain relative errors in both natural forest and artificial forest scenes, and cannot effectively reflect the significant observation azimuth effect in artificial forest scenes. The improved remote sensing method for estimating the "Yin Yang Leaves" LAI (that is, the method proposed in this embodiment) shows a high consistency with the true value results in six flat vegetation scenes with different crown distributions. In scenes where the crown distribution is regular or clustered, the improved "Yin Yang Leaves" model still maintains a high accuracy, indicating that the improved model has strong applicability. Compared with the traditional model, the improved "Yin Yang Leaves" LAI estimation model can provide more accurate "Yin Yang Leaves" LAI estimation results in hemispherical space based on different observation azimuths and zenith angle information.

[0108] Most of the estimation results perform well on flat land, but poorly on slopes. Traditional estimation models are mainly used to verify the results on flat land, without fully considering the impact of terrain. Therefore, they cannot be effectively applied to complex terrain conditions such as slopes. In this example, the impact of terrain on the estimation results of the "yin-yang leaf" LAI is considered, and three artificial forests are verified on a slope with a slope of 30 degrees. The results are as follows: Figure 5 As shown, Figure 5 Where ac are the observation results of upslope and downslope of plantations 1, 2 and 3 at 0 degree observation azimuth (positive solar zenith angle corresponds to upslope direction, negative solar zenith angle corresponds to downslope direction). Figure 5The df in the figure is the uphill and downhill observation results of plantations 1, 2 and 3 at an observation azimuth of 90 degrees.

[0109] The traditional "Yin Yang Leaf" LAI estimation model is not suitable for slope scenes, and cannot reflect the directional heterogeneity of uphill and downhill directions and different observation angles. The improved remote sensing estimation method of "Yin Yang Leaf" LAI shows better estimation performance. Figure 6 The relative error results in Figure 6 The ac in the equation is the relative error of observations of plantations 1, 2, and 3 at an observation azimuth of 0 degrees uphill and downhill (positive solar zenith angle corresponds to the uphill direction, and negative solar zenith angle corresponds to the downhill direction). Figure 6 df in is the relative error of uphill and downhill observations at 90-degree observation azimuths for plantations 1, 2, and 3. The relative error of the improved remote sensing estimation method for “yin-yang leaves” LAI in slope scenes compared with the LESS true value results is significantly reduced, and compared with the traditional method, the improved method can be applied to hemispherical space, and can provide accurate “yin-yang leaves” LAI estimation results in hemispherical space according to different observation azimuths and zenith angles.

[0110] In addition to the above simulation scenarios, this embodiment also uses simplified and standardized data (HET-16 scenario) based on actual measurements from the RAdiative transfer Model Intercomparison exercise (RAMI), and the data sources are:

[0111] https: / / rami-benchmark.jrc.ec.europa.eu / _www / phase / phase_exp.php?strTag=level3&strNext=filter_testcases&strPhase=RAMI5&strTagValue=ACT_HET16_SRF_UND, This scenario is a description of the architectural, spectral and lighting related properties of a short rotation poplar clonal woodland located in Ticino Park, northern Italy. The scenario is based on data provided by Roberto Colombo, Michele Meroni, Lorenzo Busetto and colleagues (DISAT, Laboratory for Dynamics of Remote Environments, University of Milan-Bicocca, Italy), obtained during a detailed measurement campaign carried out at this site in 2004 within the framework of the EC Joint Research Centre Kyoto Experiment. The specific parameters of the scenario are shown in Table 1, and the specific stand distribution is shown in Table 2. Figure 7 As shown, the three-dimensional presentation of the scene in LESS is as follows Figure 8 As shown in the figure, the red arrow points to the positive direction of the x-axis, the blue arrow points to the positive direction of the y-axis, the blue arrow points to the positive direction of the z-axis, and yellow indicates the direction of incident sunlight.

[0112] Table 1 Stand parameters in HET-16 scenario

[0113]

[0114] The tree model and scene parameters of the HET-16 scene provided by RAMI were input into the LESS platform, and the observation azimuth was set to 90 degrees, the solar zenith angle (0-70 degrees, step size 10 degrees), and the canopy GF was calculated by simulating the four-component (illuminated soil, illuminated leaves, shadowed soil, shadowed leaves) images. Using the tree outline to obtain the opaque tree canopy model, add it to the LESS platform, and repeat the above simulation operations to obtain and , calculate the leaf area index in combination with this embodiment and The method can be used to estimate the results of the "Yin Yang Leaf" LAI. At the same time, compared with the actual results provided by the scene and the results calculated by the traditional "Yin Yang Leaf" algorithm, the results are as follows Fig. 9 shown.

[0115] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0116] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An improved remote sensing method for estimating canopy shade leaves and sun leaves, characterized in that: The Method The process includes: Based on the difference in solar incidence angles under different time series, a leaf area index for estimating canopy shade and sun leaves was established. and The "Yin Yang Leaf" LAI estimation model is expressed as: ； In the formula, It represents the projection of unit leaf area on the vertical plane of the observation direction; LAI is the leaf area index; is the leaf area index of the sun leaf; is the leaf area index of shade leaves; is the solar zenith angle; is the directional aggregation index; Based on the directional heterogeneity of leaf distribution in the canopy, the directional aggregation index of leaves in the canopy in random distribution and hemispherical space is calculated. ,include: Definition of the canopy gap ratio to describe the random distribution of leaves in the canopy , the expression is: ； In the formula, is the solar zenith angle The probability of a beam of light passing through the canopy at , i.e., the canopy gap rate of randomly distributed leaves in the canopy; Definition used to describe the canopy gap ratio of leaves in the canopy under hemispherical canopy overlap , the expression is: ； In the formula, is the directional crown closure, which is defined as the sum of the crown projection areas per unit area on the plane perpendicular to the observation direction; is the characteristic parameter; According to the canopy gap ratio and Calculation of directional aggregation index The specific process includes: The canopy structure data of the sample plot is obtained by ground measurement methods or remote sensing technology methods. The canopy structure data of the sample plot includes the canopy gap ratio of individual trees in the representative canopy of the sample plot. , the relative variance of the number of trees contained in the projection area of ​​a single tree on the plane perpendicular to the observation direction , Directional crown closure ; according to and Calculate feature parameters , the calculation formula is: ； Based on the directional aggregation index estimation model combined with characteristic parameters Directional Aggregation Index The calculation formula is: ； Directional Aggregation Index Substitute into the "yin-yang leaf" LAI estimation model to estimate the leaf area index of the canopy yin and yang leaves and .

2. The remote sensing estimation method for canopy shade leaves and sun leaves according to claim 1, characterized in that: The ground measurement methods include transparent grid method, projection method, laser radar scanning and Nilson observation method, which obtain canopy structure data by directly measuring the coverage, void ratio or directional closure of the tree canopy; The remote sensing technology method estimates canopy structure data by combining satellite or UAV images with vegetation index and lidar data.

3. The remote sensing estimation method for canopy shade leaves and sun leaves according to claim 2 is characterized in that: The estimation formula for estimating canopy structure data using the remote sensing technology method is as follows: Directional crown closure The estimation formula is: ； In the formula, is the number of tree crowns in the sample plot; is the sample plot area; It is the projection area of ​​the representative tree crown of the sample plot on the vertical plane of the observation direction; Canopy gap ratio The estimation formula is: ； In the formula, It is the area of ​​single leaf of the representative tree crown of the sample plot; Obtaining relative variance through field survey method .

4. An application of the remote sensing estimation method for canopy shade leaves and sun leaves as described in any one of claims 1 to 3, characterized in that: The remote sensing method for estimating canopy shade leaves and sun leaves is used to calculate the photosynthetically active radiation absorbed by the canopy shade leaves and sun leaves, and the total primary productivity generated thereby, so as to accurately reflect the functional contributions of different types of leaves in the canopy.

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