Method and system for rapidly measuring and calculating earth surface biomass of sloping field and small watershed

Abstract

The invention discloses a slope and small watershed surface biomass rapid measuring and calculating method and system. According to the method, a three-dimensional physical model covering rainfall, soil and vegetation characteristics is constructed based on water-sediment dynamics and a similarity theory, model biomass is obtained through a dynamic simulation test, a biomass scale lambda BH is deduced in combination with a static geometric scale and a vegetation volume-weight scale, and actual biomass of a prototype area is obtained through conversion according to a conversion formula MY = lambda BHMM. The corresponding system comprises a modeling module, a dynamic simulation device, a data processing unit and a verification module. The method has the characteristics of high measurement and calculation precision, strong non-destructive property and wide applicability, and is suitable for biomass monitoring, ecological evaluation and vegetation recovery process analysis in ecologically fragile areas such as loess plateau and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and water conservation, ecological restoration and watershed management, and in particular to a method for quickly calculating surface biomass for slope land and small watershed areas and a supporting system thereof. Background Art

[0002] With the in-depth implementation of ecological projects such as "returning farmland to forests and grasslands," the ecological environment of the Loess Plateau has significantly improved. For example, in the Yellow River Basin, vegetation cover had increased from 31.6% before 1999 to 65% by 2017. Ecosystem functions have been significantly enhanced, soil erosion has been significantly reduced, and the amount of water and sediment in the Yellow River and its tributaries has also decreased. While this achievement demonstrates the effectiveness of regional ecological restoration, it has also given rise to several new environmental problems.

[0003] In actual research and governance, surface vegetation biomass, as an important indicator reflecting the structure and functional status of watershed ecosystems, is widely used in ecological restoration assessments, carbon storage accounting, hydrological process simulations, and future governance planning. However, existing biomass measurement technologies mainly rely on two methods: one is large-scale on-site logging sampling. Although the sampling data is reliable, it is time-consuming, labor-intensive, and highly destructive. In particular, the difficulty of sampling underground parts and the overall representativeness of the sampling points limit the accuracy; the other is inversion estimation based on remote sensing images. Although it has the advantage of non-destructive measurement, its accuracy is easily affected by factors such as terrain, climate, and sensor resolution. Especially in small watersheds with complex terrain and diverse vegetation structures, its measurement results often cannot accurately reflect the actual biomass.

[0004] Furthermore, traditional methods often operate out of context with hydrodynamic processes such as water and sediment transport, making it impossible to couple biomass measurement with dynamic processes such as rainfall, erosion, and runoff. This results in a mismatch between biomass assessment results and the actual hydrological situation. Therefore, a new method is urgently needed that can rapidly and nearly non-destructively complete biomass assessment while balancing measurement efficiency, result accuracy, and ecological integrity. This method should be able to integrate watershed geometry, topographical conditions, vegetation distribution, and hydrodynamic processes to provide a scalable, simulatable, and predictable biomass measurement method.

[0005] In response to the above-mentioned needs and the shortcomings of the existing technology, the present invention proposes a method for deriving and measuring the surface vegetation biomass of a watershed based on water-sediment dynamics and similar principles, using a physical scale model. By constructing a watershed model that covers the three-dimensional physical structure of rainfall, soil, and vegetation, and combining parameters such as the static geometric scale and the vegetation bulk density scale, a biomass similarity conversion relationship between the prototype and the model is established, thereby achieving rapid estimation and simulation of biomass at the watershed scale. This method has a simple calculation process, clear physical meaning, and has been verified to have high accuracy through field measurements. It is particularly suitable for ecological restoration monitoring and management assessment work in ecologically fragile areas such as the Loess Hilly Gully Area. Summary of the Invention

[0006] To address the problems of existing biomass measurement methods, such as limited representativeness of sampling points, difficulty in sampling underground biomass, high destructiveness of felling measurements, low precision, and difficulty in coupling with water and sediment dynamic processes, the present invention provides a method and system for rapidly measuring surface biomass on slopes and in small watersheds. This method, based on water and sediment dynamics and similarity theory, constructs a physical model and obtains model biomass through simulation experiments. It then derives a biomass scale based on the introduction of a static geometric scale and a vegetation bulk density scale, thereby achieving rapid, non-destructive, and high-precision conversion of surface vegetation biomass in the prototype watershed. This technology can significantly improve the efficiency of vegetation evolution monitoring in ecological restoration areas, especially in typical soil erosion areas such as the Loess Plateau, filling the gap in existing technologies in "multi-factor coupling and rapid quantitative estimation," and has important theoretical value and practical engineering significance.

[0007] In one possible implementation, a method for rapidly measuring surface biomass on slopes and small watersheds is provided. This method, based on the principles of water-sediment dynamics and similarity theory, constructs a three-dimensional solid model that can reflect the actual natural characteristics of the watershed, such as landforms, rainfall, soil structure, and vegetation distribution. The simulated output data of the model is then used to achieve high-precision prediction and quantitative evaluation of the biomass of the prototype area.

[0008] The method comprises the following steps:

[0009] Construct a 3D solid model: Based on the topography, landforms, rainfall, soil, and vegetation characteristics of the prototype watershed, construct a solid model with geometric motion dynamics similar to the prototype. The model must at least meet the similarity requirements of key factors such as water and sediment transport and vegetation distribution;

[0010] Dynamic simulation to obtain model biomass M M : Under artificially controlled rainfall and water flow conditions, dynamic simulation tests are carried out on the physical model to record the vegetation growth status and related biomass parameters at different time nodes and watershed sections;

[0011] Calculation of biomass scale λ BH: Combined static geometric scale λ between the prototype basin and the model l Ratio of vegetation bulk density to λ ρ , based on the derivation relation λ BH =λ ρ λ l , establish biomass conversion relationship;

[0012] Converted prototype biomass M Y :Use conversion formula M Y =λ BH M M , converting the biomass results measured in the model into vegetation biomass values ​​in the actual prototype area.

[0013] In a possible implementation, the construction of the entity model includes the following technical means:

[0014] Collect basic data such as the length, width, slope, gradient and geomorphic features of the watershed;

[0015] Use these data to calculate the static geometric scale λ l , as the basis for model scaling;

[0016] A physical model was constructed based on the water-sand similarity theory, using natural sand, gravel and other materials to simulate the topography and soil of the actual basin;

[0017] Rainfall simulation tests were conducted under controlled conditions to verify the consistency of the physical model with the prototype basin in terms of hydrodynamic processes.

[0018] In a possible implementation, in order to improve the accuracy of vegetation feature conversion, the vegetation bulk density ratio λ needs to be measured by the following method: ρ :

[0019] Select a typical area in the prototype watershed and determine the dry mass of aboveground plants by sampling or digging method;

[0020] Correspondingly, artificial vegetation materials of equivalent height and density are laid out in the physical model;

[0021] The scale λ is calculated based on the ratio of vegetation bulk density between the prototype and the model. ρ , and use the mean of multiple regional samples as the final scale input value.

[0022] In one possible embodiment, the biomass ratio λ BH The derivation of follows a similar theoretical process:

[0023] Establish an expression for biomass per unit area

[0024] Combined with the first law of similarity, it is deduced from the perspective of geometry, mass and volume scale:

[0025]

[0026] consider (where λ V is the volume scale; λ A is the area scale; λ l is a static geometric scale), the final form is obtained by simplification.

[0027] In one possible implementation, the similarity conditions of biomass per unit area are further clarified to improve the consistency and adaptability of the results. The conditions include:

[0028] The geometrical morphology of leaves, stems, and poles of the above-ground parts of vegetation is similar between the model and the prototype;

[0029] The vegetation coverage of the simulated area reached a similar coverage as that of the prototype area, with an error within 10%;

[0030] The layout of the simulated root structure conforms to the root distribution pattern or density of the prototype area.

[0031] In one possible implementation, in order to verify the accuracy and reliability of the measurement results, the following verification steps are required:

[0032] Select no less than three typical areas with different vegetation types or slope aspects in the prototype watershed;

[0033] Conduct on-site biomass measurements respectively;

[0034] The converted model prediction value is compared with the measured value, and the relative error between the two is required to be controlled within 5% to ensure the credibility of the model output.

[0035] In a possible embodiment, the present invention further provides a system for rapidly calculating surface biomass that supports the above-mentioned method, the system comprising:

[0036] 3D modeling module: used to collect prototype watershed data and build physical models;

[0037] Dynamic simulation device: including artificial rainfall equipment, water delivery system, etc., used to reproduce actual meteorological and hydrological conditions;

[0038] Data processing unit: configure data acquisition and processing software to perform scale estimation and biomass calculation;

[0039] Verification module: used to upload measured data, compare and analyze with simulation output results and generate error analysis report.

[0040] In one possible implementation, the method has good applicability in ecological governance projects, and is particularly suitable for:

[0041] Monitoring biomass recovery during the process of returning farmland to forest (grass) in the Loess Plateau;

[0042] Simulate biomass evolution trends under different rainfall scenarios and management options;

[0043] Provide scientific biomass data support for regional soil and water conservation planning and ecological engineering design.

[0044] Based on the above technical solution, the method and system for rapid measurement of surface biomass on slopes and small watersheds provided by the present invention, by introducing physical model similarity theory and combining rainfall simulation, water and sand transport and vegetation layout characteristics, can achieve rapid, non-destructive and high-precision measurement of surface vegetation biomass in a highly controllable manner.

[0045] Compared with the prior art, this application has the following beneficial effects:

[0046] 1. Non-destructive measurement: no need for destructive sampling of the prototype basin, thus protecting the ecological environment;

[0047] 2. High precision: Through scale derivation and error verification control, the measurement accuracy can be controlled within 5%;

[0048] 3. Wide applicability: It can be applied to ecosystem simulations of different scales and types of watersheds;

[0049] 4. Strong dynamic simulation capability: Through adjustable simulation tests, it is suitable for extreme weather or future scenario prediction;

[0050] 5. Systematized implementation: The supporting system is complete and can be easily extended to laboratory teaching, scientific research and engineering practice.

[0051] In summary, the present invention not only solves the problems of insufficient accuracy and highly destructive operation of existing biomass measurement technologies, but also provides a new scientific, fast, efficient and visual tool and method for regional ecosystem monitoring and governance assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a technical flow chart of the method for rapid measurement of surface biomass in small watersheds described in the present invention, which shows the overall technical route from prototype watershed parameter acquisition, physical model construction, biomass scale derivation, dynamic simulation test to prototype biomass conversion and verification.

[0053] Figure 2The schematic diagram of the biomass scale derivation process in the present invention illustrates the biomass per unit area expression, the similarity analysis between the model and the prototype, the scale relationship, the derivation path of formula (3) to formula (10), and the geometric scale λ l and bulk density ratio λ ρ The logic of its role in biomass scale calculation. DETAILED DESCRIPTION

[0054] This invention provides a rapid surface biomass measurement method and supporting system suitable for slopes and small watersheds. Relying on water-sediment dynamics and similarity theory, this method achieves rapid, non-destructive, and high-precision measurement of vegetation biomass in prototype areas through the construction of a physical model, simulation testing, and scale conversion. The following describes a specific implementation of this invention, using a practical application scenario in a typical small watershed on the Loess Plateau.

[0055] During the implementation process (such as Figure 1 As shown in the figure, we first select a prototype watershed with representative and typical water and sand erosion characteristics as the basis for modeling. This example selects the Kangjia Gulao small watershed in the Yanhe River Basin in the middle reaches of the Yellow River as the research area. The small watershed is located between 109°20′ and 109°35′ east longitude and 36°21′ and 36°32′ north latitude. The landform type is an erosive loess hilly gully area with a gully density of 3 to 4 km / km. 2 The overall terrain is fragmented, erosion and cutting are strong, and it has typical water and sand transport characteristics. The basin area is about 0.35km 2 The main ditch is 0.903 km long, with a maximum width of 0.723 km, an average width of 0.52 km, a shape coefficient of 0.52, and a height difference of 189.7 m. A check dam is located at the outlet of the basin, facilitating the control and monitoring of runoff and sediment yield. The basin's vegetation is primarily composed of trees, shrubs, and herbaceous plants, with a coverage exceeding 80%. Typical vegetation includes Robinia pseudoacacia, Hippophae rhamnoides, and Artemisia annua. The vegetation structure is distinct, and the ecological restoration is high.

[0056] Based on the above prototype parameters, the static geometric scale λ is determined according to the geometric characteristics of the watershed. l In this example, a 1:100 scale was selected for physical modeling. The physical model was constructed using a steel frame structure as the support platform. A compacted layer of coarse sand and prototype soil was laid on the base to replicate the underlying surface conditions and soil erosivity of the prototype area. The terrain was recreated using laser template cutting and manual sculpting to ensure that the main trench, secondary trench, and slope structure maintained consistent aspect and gradient with the prototype.

[0057] The model was filled with simulated vegetation materials to simulate ground cover. Fiber bundles and plastic plants at varying heights corresponded to herbs, shrubs, and trees, respectively. These plants were arranged at a density per unit area to ensure consistent coverage and structural hierarchy at the model scale, consistent with the prototype area. Furthermore, an adjustable artificial rainfall device was installed above the model, simulating natural rainfall processes through high-density sprinklers. Rainfall intensity was controlled between 0.01 and 6 mm / min, with raindrop kinetic energy and particle size close to natural levels. The spraying height was approximately 10 meters to achieve dynamic similarity.

[0058] During the simulation test, the runoff process, erosion, water and sediment transport path and simulated vegetation status were recorded after applying the set rainfall intensity. After the test, the dry mass of the vegetation simulation material was collected and measured in the standard sample plot preset in the model to obtain the model biomass per unit area M. M The average biomass measured by the model in this embodiment is 0.038g / m 2 Subsequently, the vegetation bulk density ratio λ was calculated by combining the model material density obtained in the previous field survey with the prototype measured vegetation bulk density data. ρ is about 4.1. According to the geometric scale λ l =100, apply formula (2)λ BH =λ ρ λ l , and the biomass conversion scale λ is obtained BH =410. Finally, according to M Y =λ BH M M Complete the calculation of the biomass per unit area of ​​the prototype and obtain M Y About 15.58g / m 2 .

[0059] In order to verify the accuracy of the measurement, three representative small sample areas were selected on the south slope, north slope and ditch bottom in the Kangjiagulao small watershed, and three 1m 2 The sample was collected, dried and weighed on site, and the biomass was 15.2-16.3 g / m 2 The relative error between the conversion result and the measured data is controlled within 5%, which fully verifies the applicability of the method and the accuracy of the model calculation.

[0060] In addition, to further strengthen the theoretical support for verification, the present invention cites the results of a 2002 study on the biomass of vegetation in the Yangou Basin of the Loess Plateau by Xu Xuexuan et al. (as shown in Table 1). The study found that the biomass per unit area of ​​trees, shrubs, and herbs was 522, 504, and 491 g / m, respectively. 2 , the corresponding bulk density range is 0.40~0.80, 0.2~0.45, 0.02~0.10t / m 3In the model construction, the present invention uses grass materials of different heights to replace the prototype trees, shrubs, and grasses, and adjusts the density of the simulation materials so that the bulk density ratio is 4. Under the 10×10cm sample plot of the model, the simulated biomass of the trees, shrubs, and grasses are 672, 560, and 576g / m 2 The relative error is controlled at 1% to 2%, which is much lower than the error range of traditional methods (5% to 10%), further demonstrating that this method is superior in unit area precision control and vegetation structure restoration.

[0061] Table 1 Vegetation biomass similarity verification characteristic parameters

[0062]

[0063] The above results show that the physical model and similarity scale system established in the present invention can effectively reproduce the distribution and density characteristics of aboveground biomass under various vegetation types in the prototype small watershed, verifying the accuracy and versatility of the method in actual scenarios, and further confirming the feasibility and engineering adaptability of the scale derivation formula and model simulation system.

[0064] In order to further clarify the theoretical basis of the scale conversion relationship of the present invention, the derivation process of the biomass scale is now publicly explained (eg Figure 2 shown).

[0065] This paper assumes that the prototype watershed vegetation includes forests, shrubs, grasses, and leaf litter. Based on similarity theory, the model and prototype are assumed to share morphological and parameter consistency in terms of geometry, cover density, and spatial distribution. To uniformly describe vegetation quality characteristics across different regions, an expression for plant biomass per unit area is introduced.

[0066] According to formula (3):

[0067]

[0068] Among them, pH is the plant biomass per unit area (kg / m 2 ), M is the dry mass per unit area, V is the volume, A is the area of ​​a region, and ρ is the bulk density. This formula uniformly combines the three-dimensional structure and density characteristics into a single areal density parameter.

[0069] This formula is applied to the prototype and model areas respectively, and we can get:

[0070] Formula (4):

[0071] Formula (5):

[0072] Among them, PH y With PH mare the biomass per unit area of ​​the prototype and model, M y 、A y 、V y , ρ y and M m 、A m 、V m , ρ m are the biomass parameters of the prototype and model, respectively.

[0073] Convert the above ratio into scale form, and we get:

[0074] Formula (6): λ BH =PH y / PH m =(M y / M m )·(A m / A y )=(λ V ·λ ρ ) / λ A

[0075] This formula reveals the biomass ratio per unit area (λ BH ) is measured by the volume scale (λ V ), bulk density ratio (λ ρ ) and area ratio (λ A ) are jointly determined and are the core physical relationship of biomass conversion.

[0076] In order to verify the consistency between the model and the prototype from another perspective, according to the first law of similarity, the model expression can also be written as:

[0077] Formula (7):

[0078] Comparing formula (5) and (7), in order to make the equation valid, we have:

[0079] Formula (8):

[0080] Further deformation leads to the general expression of biomass scale:

[0081] Formula (9):

[0082] Among them, λ m =λ V ×λ ρ

[0083] Due to the volume ratio Area scale Substituting it into formula (9), we get the final simplified formula:

[0084] Formula (10)

[0085] That is: the biomass scale is equal to the product of the static geometric scale and the vegetation bulk density scale.

[0086] Therefore, the biomass per unit area M of the prototype region in the present invention is Y It can be calculated by the following formula:

[0087] Formula (1) (final conversion relationship):

[0088] M Y =λ BH M M

[0089] Among them, M M The biomass per unit area measured by the model. This entire set of derivational pathways constitutes the theoretical core of the present invention's "model-to-prototype conversion." While maintaining physically measurable parameter inputs, it establishes a measurement framework with a clear mathematical structure and reliable traceability.

[0090] In order to realize the systematic application of the above method, the present embodiment also constructs a supporting rapid measurement system, including a three-dimensional modeling module, a dynamic simulation test platform, a data processing unit and a result verification module. The modeling module can import DEM data to automatically generate model terrain parameters. The test platform includes an adjustable rainfall system and a runoff collection system. The data unit is embedded with a conversion algorithm. The verification module supports measured data comparison and error report output. The system has strong adaptability in ecological governance projects and can be used for vegetation biomass monitoring and dynamic simulation in different ecological types such as the Loess Plateau, karst areas, and arid areas. It is particularly suitable for comparative analysis of the effects of different governance intensities and strategies on the evolution of surface vegetation in scenarios such as returning farmland to forest (grass), closed-off governance, and comprehensive soil and water conservation. By adjusting the simulated rainfall parameters, it can also be applied to ecological response prediction studies under extreme weather conditions.

[0091] In summary, this method, by constructing a physical model that shares geometric, kinematic, and dynamic similarities with the prototype watershed, and combining scale derivation with simulation calculation methods, enables efficient simulation and accurate measurement of surface biomass in small watersheds under non-destructive conditions. This method boasts a rigorous structure, reliable conversion, and a combination of theoretical depth and engineering practicality, making it suitable for applications in a variety of fields, including ecological restoration, vegetation monitoring, and soil and water conservation.

Claims

1. A method for rapidly calculating surface biomass on sloping land and small watersheds, characterized in that: include: S10: Construct a physical model with geometric and dynamic similarity to the prototype watershed, wherein the model at least includes similar three-dimensional water and sediment transport characteristics of rainfall, soil, and vegetation; S20: Obtaining model biomass M through dynamic simulation experiments M ; S30: Based on static geometric scale λ l and vegetation density ratio λ ρ , according to the formula λ BH =λ ρ λ l Calculate biomass ratio; S40: According to the conversion formula M Y =λ BH M M Calculation of prototype biomass M Y .

2. The method according to claim 1, characterized in that The construction of the entity model includes: Determine the static geometric scale λ based on the length, width, gradient and topographic indicators of the watershed l ; Use the principles of water and sediment dynamics to create physical models at the slope, cross-section or watershed scale; The dynamic similarity between the model and the prototype was verified through rainfall water and sediment transport tests.

3. The method according to claim 1 or 2, characterized in that The vegetation density scale λ ρ The determination includes: Plant dry bulk density was measured in a representative area of ​​the prototype watershed; Calculate the vegetation density ratio between the model and the prototype; Determine λ by averaging multiple region measurements ρ .

4. The method according to any one of claims 1 to 3, characterized in that The biomass scale λ BH The derivation includes: Establish an expression for biomass per unit area Derived from the first law of similarity Substitute geometric relations (where λ V is the volume scale; λ A is the area scale; λ l is the static geometric scale), and finally we get 5. The method according to claim 4, characterized in that The similarity requirements for biomass per unit area include: (1) The leaves, stems, and trunks of the aboveground parts of the vegetation have similar geometric shapes; (2) vegetation cover similarity ≥ 90%; (3) The underground root distribution pattern is consistent.

6. The method according to claim 1, characterized in that Also includes a verification step: (1) Select at least three validation areas in the prototype watershed for field biomass measurement; (2) The relative error between the calculated results and the measured data is required to be ≤5%.

7. A system for implementing the method according to any one of claims 1 to 6, characterized in that: include: (1) 3D modeling module, used to construct geometric and dynamic similarity models; (2) Dynamic simulation device, equipped with test equipment with adjustable rainfall intensity; (3) a data processing unit for performing scale calculations and biomass conversions; (4) Verification module, used to compare the calculated values ​​with the measured data.

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