A method for inversion of understory biomass

Abstract

The present invention discloses a method for inverting understory biomass, relating to the field of remote sensing collaborative inversion technology. An observation formula for total forest surface biomass is determined based on radar remote sensing data, optical remote sensing data, and total forest surface biomass for a historical region. Radar remote sensing data and optical remote sensing data for a target region are substituted into the observation formula to determine the total forest surface biomass for the target region. The average leaf area index for the target region is inverted based on the optical remote sensing data for the target region. Leaf biomass is calculated based on the average leaf area index and the dry matter weight of leaves per unit area. The understory vegetation and litter biomass for the target region are determined based on the total forest surface biomass, leaf biomass, and aboveground woody biomass for the target region. This method can accurately calculate the understory biomass for the target region.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing collaborative inversion, and in particular to an inversion method for forest understory biomass. Background Art

[0002] The anti-erosion effect of vegetation is the result of the combined effects of various vertical levels of vegetation communities. Compared with the forest canopy layer, the surface cover layers such as near-surface shrubs and fallen leaves under the forest intercept raindrops from the canopy, store runoff, significantly weaken the energy of rainfall splashing and runoff erosion, increase surface roughness and enhance infiltration, play a key role in the anti-erosion function of vegetation, are one of the most sensitive parameters in regional soil erosion assessment, and are also an important indicator of ecological environmental quality.

[0003] The Normalized Difference Vegetation Index (NDVI) and its modified counterpart, combined with land use classification, have long been directly applied to regional, national, and even global soil erosion assessment and dynamic monitoring. Canopy cover can be directly estimated using photosynthetic vegetation (PV) cover derived from NDVI. However, forest parameters such as biomass of non-photosynthetic vegetation (NPV), such as tree trunks and litter, cannot be directly measured through optical remote sensing due to their stratification and obstructive effects. However, determining understory biomass has long been a challenge and bottleneck in ecological and environmental assessment and soil erosion modeling. Summary of the Invention

[0004] Based on this, it is necessary to provide an inversion method for understory biomass to address the above technical problems, which can accurately calculate the understory biomass of the target area.

[0005] The present invention adopts the following technical solutions:

[0006] The present invention provides an inversion method for understory biomass, comprising:

[0007] Determine the observation formula for total forest surface biomass based on radar remote sensing data, optical remote sensing data and total forest surface biomass in the historical area;

[0008] Substitute the radar remote sensing data and optical remote sensing data of the target area into the observation formula to determine the total surface biomass of the forest land in the target area;

[0009] The average leaf area index of the target area is inverted based on the optical remote sensing data of the target area; and the leaf biomass is calculated based on the average leaf area index of the target area and the dry matter weight of leaves per unit area;

[0010] The biomass of understory vegetation and litter in the target area is determined based on the total surface biomass, leaf biomass and above-ground woody part biomass of the forest land in the target area.

[0011] Optionally, based on the radar remote sensing data, optical remote sensing data and the total surface biomass of forest land in the historical area, an observation formula for the total surface biomass of forest land is determined, including:

[0012] Based on optical remote sensing parameters and water cloud model, the initial observation formula of total forest surface biomass was constructed;

[0013] Based on the radar remote sensing data, optical remote sensing data and total forest surface biomass of the historical area, the parameters in the initial observation formula are solved to obtain the optimal parameter combination of the parameters in the initial observation formula;

[0014] Substitute the optimal parameter combination into the initial observation formula to obtain the observation formula.

[0015] Optionally, the process of constructing the initial observation formula includes:

[0016] According to optical remote sensing parameters, a vegetation coverage information compensation formula is constructed;

[0017] The water cloud model and the vegetation coverage information compensation formula are linked to obtain the initial observation formula;

[0018] Optical remote sensing parameters include vegetation coverage with photosynthesis and vegetation coverage without photosynthesis. The vegetation coverage information compensation formula is:

[0019] ;

[0020] in, represents the coverage of photosynthetic vegetation. Indicates the coverage of vegetation that does not photosynthesize. represents the extinction ratio parameter of the forest relative to the optical signal, It represents the total surface biomass of forest land;

[0021] The water cloud model is:

[0022] ;

[0023] in, represents the total backscatter from the forest floor, represents the backscatter directly from the ground surface in the forest gap, represents the backscatter directly from the canopy surface, Parameters representing the forest transmittance situation;

[0024] The initial observation formula is:

[0025] ;

[0026] Among them, the symbol " ", represents any of the four arithmetic operations.

[0027] Optionally, the radar remote sensing data includes total backscatter from forest land, backscatter directly returned from the surface of forest gaps, and backscatter directly returned from the forest canopy surface; the optical remote sensing data includes the coverage of vegetation with photosynthesis and the coverage of vegetation without photosynthesis; based on the radar remote sensing data, optical remote sensing data, and total forest surface biomass of the historical area, the parameters in the initial observation formula are solved to obtain the optimal parameter combination of the parameters in the initial observation formula, including:

[0028] Based on the total backscatter of forest land in the historical area, the backscatter directly returned from the surface of forest gaps, the backscatter directly returned from the canopy surface, as well as the vegetation cover with photosynthesis, the vegetation cover without photosynthesis and the total biomass of forest land, the nonlinear least squares algorithm was used to optimize the parameters in the initial observation formula to obtain and The optimal parameter combination.

[0029] Optionally, the optical remote sensing data includes canopy reflectance; and the average leaf area index of the target area is obtained by inverting the optical remote sensing data of the target area, including:

[0030] From the pre-established correspondence between the canopy reflectance and the average leaf area index, the average leaf area index corresponding to the canopy reflectance of the target area is obtained, that is, the average leaf area index of the target area.

[0031] Optionally, the process of constructing the correspondence between the canopy reflectance and the average leaf area index includes:

[0032] Obtain multiple sets of historical observation data for the reference area; each set of historical observation data includes leaf biochemical parameters, canopy structure parameters, sensor and geographic parameters, and soil background parameters; canopy structure parameters include average leaf area index;

[0033] Each set of historical observation data was substituted into the PROGeoSAIL model to obtain the canopy reflectance of the reference area;

[0034] Based on multiple sets of historical observation data and the corresponding canopy reflectance, the corresponding relationship between canopy reflectance and average leaf area index is constructed.

[0035] Optionally, the leaf biomass is calculated as:

[0036] ;

[0037] in, represents leaf biomass, Indicates the dry matter weight of leaves per unit area, It represents the average leaf area index, which is half of the sum of the surface areas of leaves per unit area.

[0038] Optionally, the calculation formula for the biomass of understory vegetation and litter in the target area is:

[0039] ;

[0040] in, represents the biomass of understory vegetation and litter, represents the total surface biomass of forest land, represents leaf biomass, Represents the above-ground woody biomass of forest land.

[0041] The present invention provides an inversion device for forest understory biomass, comprising:

[0042] A solution module is used to determine the observation formula of the total surface biomass of forest land based on the radar remote sensing data, optical remote sensing data and the total surface biomass of forest land in the historical area;

[0043] The first determination module is used to substitute the radar remote sensing data and the optical remote sensing data of the target area into the observation formula to determine the total surface biomass of the forest land in the target area;

[0044] The second determination module is used to invert the average leaf area index of the target area based on the optical remote sensing data of the target area, and calculate the leaf biomass based on the average leaf area index of the target area and the dry matter weight of leaves per unit area;

[0045] The third determination module is used to determine the understory vegetation and litter biomass in the target area based on the total surface biomass, leaf biomass and above-ground woody biomass of the forest land in the target area.

[0046] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the inversion method of the forest understory biomass is implemented.

[0047] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned inversion method for understory biomass is implemented.

[0048] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects:

[0049] In this method, radar and optical remote sensing data are combined to determine the total surface biomass of the forest floor. Secondly, the total leaf biomass is inverted from the optical remote sensing data. Then, based on the total surface biomass and leaf biomass, the understory biomass of the target area is determined. This method overcomes the limitations of regional-scale research on understory biomass by combining radar and optical remote sensing data to calculate understory biomass, achieving synergistic and complementary active and passive remote sensing capabilities, resulting in more accurate understory biomass determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0051] Figure 1 A schematic flow chart of a method for inverting understory biomass provided by the present invention;

[0052] Figure 2 A schematic diagram of a computer device for implementing an inversion method for understory biomass provided by the present invention. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] Ground-based sample plot surveys, as the traditional method for surveying understory biomass and cover, are the most accurate, but they are labor-intensive, time-consuming, and destructive, making them inadequate for large-scale mapping. Advances in aerospace remote sensing technology have made regional, national, and global scales possible. Currently, optical remote sensing technologies and products remain the most widely used and mature. The most commonly used green vegetation indices, such as NDVI and the Enhanced Vegetation Index (EVI), provide a direct reflection of vegetation photosynthetic activity and productivity, and serve as surrogates for leaf area index and vegetation cover. Due to their sensitivity to components such as lignin and cellulose, multispectral and hyperspectral indices such as the Cellulose Absorption Index (CAI), the Dead Fuel Index (DFI), and the Short-wave Infrared Ratio Index (SWIR32) are being used. Using a pixel triangulation model, sub-pixel-level features such as PV, NPV, and bare soil (BS) can be distinguished. NPV is a land feature classification method based on optical remote sensing. While it provides some information on the three-dimensional structure of tree branches, trunks, and fallen leaves, it is essentially an identification of land feature types in two-dimensional projection space. The layered and obstructive nature of forest floor structure makes accurately inverting understory biomass and effectively representing the erosion protection function of actual vegetation a challenge at the regional scale.

[0055] Optical and near-infrared remote sensing signals only interact with the foliage of the forest canopy through radiation, are easily saturated, and are also affected by weather conditions such as clouds, rain, and fog. Compared to optical signals, microwaves have longer wavelengths (1mm-1m) and greater transmissivity. They are sensitive to forest vertical structure and unaffected by clouds or sunlight, making them an indispensable tool for current forest resource surveys. Microwave remote sensing, which can "see through" the forest understory and complements optical remote sensing's ability to "classify" ground features, is considered a promising integrated approach for three-dimensional forest monitoring. However, further research on how to coordinate microwave and optical remote sensing remains limited.

[0056] Compared with optical remote sensing, the main reasons hindering the widespread application of microwave remote sensing technology are factors such as the small number of satellites, lack of data, high cost (such as LiDAR), discontinuous time series, coarse spatial resolution (such as radiometer spatial resolution >25km), and severe radio frequency interference (especially in the L low-frequency band). However, the development of Synthetic Aperture Radar (SAR) technology still provides great advantages and broad prospects for regional forest resource surveys.

[0057] The execution subject of the inversion method of forest understory biomass provided in the present invention can be a server set up on a business platform, or a device such as a desktop computer, a laptop computer, etc. that can execute the solution of the present invention.

[0058] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0059] Figure 1 The figure is a flow chart of a method for inverting understory biomass in the present invention, which specifically includes the following steps:

[0060] S101, determining an observation formula for the total surface biomass of forest land based on radar remote sensing data, optical remote sensing data and the total surface biomass of forest land in the historical area.

[0061] In one embodiment, an observation formula for the total surface biomass of forest land is determined based on radar remote sensing data, optical remote sensing data and the total surface biomass of forest land in a historical area, including: constructing an initial observation formula for the total surface biomass of forest land based on optical remote sensing parameters and a water-cloud model; solving the parameters in the initial observation formula based on radar remote sensing data, optical remote sensing data and the total surface biomass of forest land in the historical area to obtain an optimal parameter combination of the parameters in the initial observation formula; substituting the optimal parameter combination into the initial observation formula to obtain the observation formula.

[0062] The process of constructing the initial observation formula includes: constructing a vegetation coverage information compensation formula based on optical remote sensing parameters; and associating the water cloud model with the vegetation coverage information compensation formula to obtain the initial observation formula.

[0063] Based on the traditional water cloud model WCM, the high-order scattering mechanism is ignored and the water cloud model is adopted as shown in the following equation (1).

[0064] (1);

[0065] in, represents the total backscatter from the forest floor, represents the backscatter directly from the ground surface in the forest gap, represents the backscatter directly from the canopy surface, Parameter indicating forest transmittance, unit is ha / t; B tot It represents the total surface biomass of forest land, with the unit of t / ha.

[0066] Introducing optical remote sensing information such as surface reflectivity in the red, near-infrared, and green bands to compensate for vegetation coverage information: Introducing optical remote sensing data to compensate for the shortcomings of radar backscatter coefficients in different polarization modes that cannot well represent vegetation coverage information, thereby improving the simulation ability of forest scattered signals. Generally speaking, the higher the biomass, the lower the forest transmittance, and it exhibits an exponential decay form. Based on this, the optical remote sensing forest transmittance is used as the T for , characterized by a relationship with the surface coverage f vc It is related to the surface biomass and has an exponential function relationship with it, such as formula (2), and formula (3) is also obtained from it.

[0067] (2);

[0068] (3);

[0069] in, is the optical remote sensing forest transmittance, represents the surface coverage, represents the extinction ratio parameter of the forest relative to the optical signal.

[0070] Optical remote sensing parameters include vegetation cover with photosynthesis and vegetation cover without photosynthesis. A vegetation coverage information compensation formula is constructed to further compensate for the reflection of NPV on forest structure. The vegetation coverage information compensation formula is formula (4).

[0071] (4);

[0072] in, represents the coverage of photosynthetic vegetation. Indicates the coverage of vegetation that does not photosynthesize.

[0073] The optical remote sensing forest cover information expressed by formula (4) is associated with the microwave remote sensing forest structure information expressed by formula (1) to obtain the initial observation formula, as shown in formula (5).

[0074] (5);

[0075] Among them, the symbol " ", represents any of the four arithmetic operations to ensure model equivalence.

[0076] Radar remote sensing data include total backscatter of forest land, backscatter directly returned from the surface of forest gaps, and backscatter directly returned from the surface of forest canopy; optical remote sensing data include vegetation cover with photosynthesis and vegetation cover without photosynthesis; according to radar remote sensing data, optical remote sensing data and total surface biomass of forest land in historical areas, the parameters in the initial observation formula are solved to obtain the optimal parameter combination of the parameters in the initial observation formula, including: total backscatter of forest land based on historical areas, backscatter directly returned from the surface of forest gaps, backscatter directly returned from the surface of forest canopy, as well as vegetation cover with photosynthesis, vegetation cover without photosynthesis and total surface biomass of forest land. The nonlinear least squares algorithm is used to optimize the parameters in the initial observation formula to obtain and The optimal parameter combination.

[0077] in, It can be directly obtained from satellite observations. and It can be inverted from optical remote sensing data. and It can be simulated by the water cloud model. is a natural constant, and the total surface biomass of forest land in the historical area is actually measured. 、 The optimal value can be obtained by nonlinear least squares method based on satellite observation results and ground measurement results.

[0078] Specifically, 、 、 , which represent the total backscatter of SAR data, pure surface backscatter, and backscatter observed in dense forests. In the study area, according to the classification results of land use type and vegetation cover, bare soil or land cover <25% was selected as pure surface, and its backscatter median value was taken as ; Select the land type with coverage greater than 75% as dense forest land, and take its backscatter median as . δ 、 τ It is an empirical parameter related to forest structure and biochemical characteristics. It is optimized based on the nonlinear least squares algorithm. Specifically, by setting a large number of evenly distributed sample points (multiple points in the historical area), combining the actual observed backscatter directly returned from the surface of forest gaps, backscatter directly returned from the canopy surface, as well as the vegetation cover with photosynthesis, the vegetation cover without photosynthesis and the total surface biomass of the forest, based on the parameter combination ( δ and τ The root mean square error between the simulated total backscatter of the forest and the actual observed total backscatter of the forest is minimized to determine the optimal 、 combination.

[0079] S102, substituting the radar remote sensing data and the optical remote sensing data of the target area into the initial observation formula to determine the total surface biomass of the forest land in the target area.

[0080] Among them, radar remote sensing data include total backscatter from forest land, backscatter directly returned from the surface of forest gaps, and backscatter directly returned from the surface of forest canopy; optical remote sensing data include vegetation cover with photosynthesis and vegetation cover without photosynthesis.

[0081] Substitute the optimal parameter combination, radar remote sensing data and optical remote sensing data of the target area into formula (5) to obtain the total surface biomass of the forest land .

[0082] S103 , inverting the average leaf area index of the target area based on the optical remote sensing data of the target area; and calculating the leaf biomass based on the average leaf area index of the target area and the weight of leaf dry matter per unit area.

[0083] The mean leaf area index was obtained by inversion using the PROGeoSAIL optical model based on radiative transfer theory.

[0084] Optionally, the optical remote sensing data includes canopy reflectance; and based on the optical remote sensing data of the target area, the average leaf area index of the target area is inverted, including: obtaining the average leaf area index corresponding to the canopy reflectance of the target area from a pre-constructed correspondence between the canopy reflectance and the average leaf area index, that is, the average leaf area index of the target area.

[0085] The process of constructing the correspondence between canopy reflectance and average leaf area index includes: obtaining multiple sets of historical observation data of the reference area; each set of historical observation data includes leaf biochemical parameters, canopy structure parameters, sensor and geographical parameters, and soil background parameters; canopy structure parameters include average leaf area index; substituting each set of historical observation data into the PROGeoSAIL model to obtain the canopy reflectance of the reference area; and constructing the correspondence between canopy reflectance and average leaf area index based on multiple sets of historical observation data and the corresponding canopy reflectance.

[0086] Specifically, the PROGeoSAIL model is derived by coupling the PROSPECT model with the GeoSAIL model. Leaf biochemical parameters (see Table 1) are substituted into the leaf reflectance and transmittance results obtained from the PROSPECT simulation. The leaf reflectance and transmittance results, along with canopy structural parameters, sensor and geographic parameters, and soil background parameters, are then used as inputs into the GeoSAIL model to simulate the radiation transfer process of the tree canopy and determine the canopy reflectance. The GeoSAIL model is capable of effectively simulating the canopy reflectance of heterogeneous and discontinuous vegetation types. Coupling the PROSPECT and GeoSAIL models simulates the canopy reflectance corresponding to different leaf area indices, thereby obtaining a corresponding relationship between canopy reflectance and mean leaf area index. The mean leaf area index can then be derived from canopy reflectance obtained from satellite observations.

[0087] The PROGeoSAIL radiative transfer model is represented as follows.

[0088] ρ veg = f (θ s , φ s , θ v , φ v , f d , LAI, LAD, N, C a+b , C w , C m , ρ s , f PV , S cp , R cp ) (6);

[0089] The meaning and observation methods of each parameter in the formula are shown in Table 1. Table 1 shows the meaning and acquisition methods of the parameters required by the PROGeoSAIL model.

[0090] Table 1 Meaning and acquisition method of parameters required by PROGeoSAIL model

[0091]

[0092] Leaf biomass, as measured by the average leaf area index ( LAI ) and leaf dry matter content ( DMC ) is obtained, and the leaf biomass inversion formula is as shown in formula (7).

[0093] (7);

[0094] in, represents leaf biomass, DMC Indicates the dry matter weight of leaves per unit area, g / cm 2 , obtained through field measurements. LAI It represents the average leaf area index, which is half of the sum of the surface areas of leaves per unit area and is dimensionless.

[0095] S104, determining the understory vegetation and litter biomass in the target area based on the total surface biomass, leaf biomass, and aboveground woody biomass of the forest land in the target area.

[0096] The total aboveground biomass of a forest is composed of the following components:

[0097] (8);

[0098] in, represents the biomass of understory vegetation and litter, represents the total surface biomass of forest land, represents leaf biomass, Represents the above-ground woody biomass of forest land.

[0099] Therefore, the calculation formula for the biomass of understory vegetation and litter in the target area is:

[0100] (9);

[0101] in, It can be estimated by the coordinated active and passive remote sensing model (Formula (5)); Radiative transfer model estimation based on optical remote sensing (Equations (6) and (7)); It is the biomass of the above-ground woody parts of forest land, such as trunks and branches. Based on this, the biomass of understory vegetation and litter can be calculated using formula (9): .

[0102] The National Standard of the People's Republic of China, "Standing Biomass Model and Carbon Accounting Parameters for Major Tree Species" (GB / T43648-2024), clearly provides a model for estimating standing biomass of major tree species in China. By measuring parameters such as the diameter at breast height and tree height of different tree species in the target area and substituting them into the model, accurate standing biomass can be obtained, which can be used to estimate .

[0103] Understory shrubs and grasses, as well as litter layers, and other vegetation close to the ground play a key role in preventing and controlling soil erosion and improving ecological service functions. The amount of understory vegetation will cause the vegetation cover and the biological measure factor value (C / B) to differ by 1-2 orders of magnitude. Due to deficiencies in monitoring methods and technologies, questions such as what the understory biomass of the Loess Plateau is like, what are the distribution characteristics, and what are the influencing factors are still difficult and hot issues in regional-scale ecological environmental evaluation. Starting from the analysis of the limitations of understory biomass surveys and regional-scale studies, the complementary characteristics of active and passive remote sensing functions, and the ability of SAR data to estimate the total surface biomass of forest land, the present invention combines remote sensing estimation models for biomass at different levels of the surface and proposes a new algorithm for inverting understory biomass. This algorithm has important theoretical significance and urgent practical needs for promoting in-depth cross-disciplinary integration, improving the application accuracy of regional soil erosion models, and scientifically guiding regional ecological construction and high-quality development.

[0104] This framework comprehensively applies the advantages of different remote sensing data to monitor forest understory biomass and cover. It is theoretically feasible and has great potential.

[0105] When applying the inversion method of forest understory biomass provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0106] The above is a method for inverting understory biomass provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding understory biomass inversion device, which includes:

[0107] A solution module is used to determine the observation formula of the total surface biomass of forest land based on the radar remote sensing data, optical remote sensing data and the total surface biomass of forest land in the historical area;

[0108] The first determination module is used to substitute the radar remote sensing data and the optical remote sensing data of the target area into the observation formula to determine the total surface biomass of the forest land in the target area;

[0109] The second determination module is used to invert the average leaf area index of the target area based on the optical remote sensing data of the target area, and calculate the leaf biomass based on the average leaf area index of the target area and the dry matter weight of leaves per unit area;

[0110] The third determination module is used to determine the understory vegetation and litter biomass in the target area based on the total surface biomass, leaf biomass and above-ground woody biomass of the forest land in the target area.

[0111] The specific limitations of the understory biomass inversion device can be found in the limitations of the understory biomass inversion method described above and will not be further elaborated here. Each module in the above-mentioned understory biomass inversion device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0112] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The inversion method of understory biomass is provided.

[0113] The present invention also provides Figure 2 The structural diagram of the computer equipment shown in FIG. Figure 2 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The inversion method of understory biomass is provided.

[0114] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes in the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0115] The technical features of the above embodiments can be combined arbitrarily. In order 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 the present invention.

Claims

1. A method for inverting understory biomass, characterized in that: include: Determine the observation formula for total forest surface biomass based on radar remote sensing data, optical remote sensing data and total forest surface biomass in the historical area; Substitute the radar remote sensing data and optical remote sensing data of the target area into the observation formula to determine the total surface biomass of the forest land in the target area; The average leaf area index of the target area is inverted based on the optical remote sensing data of the target area, and the leaf biomass is calculated based on the average leaf area index of the target area and the dry matter weight of leaves per unit area. Determine the understory vegetation and litter biomass in the target area based on the total surface biomass, leaf biomass, and aboveground woody biomass of the forest land in the target area; The method of determining an observation formula for the total surface biomass of forest land based on radar remote sensing data, optical remote sensing data, and the total surface biomass of forest land in the historical region comprises: constructing an initial observation formula for the total surface biomass of forest land based on optical remote sensing parameters and a water-cloud model; solving parameters in the initial observation formula based on the radar remote sensing data, optical remote sensing data, and the total surface biomass of forest land in the historical region to obtain an optimal parameter combination of the parameters in the initial observation formula; and substituting the optimal parameter combination into the initial observation formula to obtain the observation formula; The process of constructing the initial observation formula includes: constructing a vegetation coverage information compensation formula based on optical remote sensing parameters; associating the water cloud model with the vegetation coverage information compensation formula to obtain the initial observation formula; Optical remote sensing parameters include vegetation coverage with photosynthesis and vegetation coverage without photosynthesis. The vegetation coverage information compensation formula is: ； in, represents the coverage of photosynthetic vegetation. Indicates the coverage of vegetation that does not photosynthesize. represents the extinction ratio parameter of the forest relative to the optical signal, It represents the total surface biomass of forest land; The water cloud model is: ； in, represents the total backscatter from the forest floor, represents the backscatter directly from the ground surface in the forest gap, represents the backscatter directly from the canopy surface, Parameters representing the forest transmittance situation; The initial observation formula is: ； Among them, the symbol " ", represents any of the four arithmetic operations.

2. The method according to claim 1, characterized in that Radar remote sensing data includes total backscatter from forest land, backscatter directly from the surface of forest gaps, and backscatter directly from the forest canopy surface. Optical remote sensing data includes the cover of photosynthetic vegetation and the cover of non-photosynthetic vegetation. Based on the radar remote sensing data, optical remote sensing data, and total forest surface biomass of the historical area, the parameters in the initial observation formula were solved to obtain the optimal parameter combination of the initial observation formula, including: Based on the total backscatter of forest land in the historical area, the backscatter directly returned from the surface of forest gaps, the backscatter directly returned from the canopy surface, as well as the vegetation cover with photosynthesis, the vegetation cover without photosynthesis and the total biomass of forest land, the nonlinear least squares algorithm was used to optimize the parameters in the initial observation formula to obtain and δ The optimal parameter combination.

3. The method according to claim 1, characterized in that Optical remote sensing data includes canopy reflectance. Based on the optical remote sensing data of the target area, the average leaf area index of the target area is inverted, including: From the pre-established correspondence between the canopy reflectance and the average leaf area index, the average leaf area index corresponding to the canopy reflectance of the target area is obtained, that is, the average leaf area index of the target area.

4. The method according to claim 3, characterized in that The process of constructing the correspondence between canopy reflectance and mean leaf area index includes: Obtain multiple sets of historical observation data for the reference area; each set of historical observation data includes leaf biochemical parameters, canopy structure parameters, sensor and geographic parameters, and soil background parameters; canopy structure parameters include average leaf area index; Each set of historical observation data was substituted into the PROGeoSAIL model to obtain the canopy reflectance of the reference area; Based on multiple sets of historical observation data and the corresponding canopy reflectance, the corresponding relationship between canopy reflectance and average leaf area index is constructed.

5. The method according to claim 1, wherein The calculation formula for leaf biomass is: ； in, represents leaf biomass, Indicates the dry matter weight of leaves per unit area, It represents the average leaf area index, which is half of the sum of the surface areas of leaves per unit area.

6. The method according to claim 1, characterized in that The calculation formula for the biomass of understory vegetation and litter in the target area is: ； in, represents the biomass of understory vegetation and litter, represents the total surface biomass of forest land, represents leaf biomass, Represents the above-ground woody biomass of forest land.

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