Large-scale forest surface litter load calculation method

Abstract

The invention relates to the technical field of forest fire prevention, in particular to a large-scale forest ground surface litter load calculation method. The method comprises the steps of obtaining a forest resource partition to which a target area belongs; determining the forest type of each forest class in the target area based on the national forest resource checking data; according to the forest resource subarea to which the target area belongs and the forest type of each forest class in the target area, establishing an above-ground biomass and stock regression model of each forest class in the target area; acquiring the aboveground biomass of each forest class in the target area according to the aboveground biomass and accumulation regression model; obtaining the land surface litter carrying capacity of each forest class in the target area by inquiring the proportion of the land surface litter carrying capacity in the aboveground biomass in the forest class of each forest type; and obtaining spatial distribution of the surface litter load of the target area according to the surface litter load of all forest classes in the target area. According to the method, the efficiency and accuracy of large-scale forest surface litter load calculation can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of forest fire prevention, in particular to a large-scale forest ground litter load calculation method. BACKGROUND

[0002] Forest fires are extremely harmful, not only directly destroying the forest ecosystem, but also having a profound impact on human health, economy and social stability. Among them, the load and spatial distribution of forest ground litter (such as dry branches, fallen leaves, and fallen tree bark) are one of the key factors affecting the occurrence and spread of forest fires. The load of litter directly determines the total amount of flammable combustibles. The higher the load, the greater the potential energy released by the fire, and the greater the harm caused.

[0003] The determination of forest ground litter load is an important part of forest combustible investigation and fire risk assessment. Currently, there are mainly two methods for obtaining it: direct measurement method and indirect estimation method. The direct measurement method is suitable for accurate measurement at plot scale. It involves randomly or systematically laying out sample plots in the study area to collect ground litter, then drying and weighing to calculate the load per unit area. Although this method can obtain relatively accurate data, it is time-consuming and labor-intensive, and is a destructive sampling method, which is not suitable for large-scale promotion. The indirect estimation method is used for large-scale dynamic monitoring, which uses optical remote sensing and vegetation index to retrieve litter coverage. This method has a wide coverage and can be updated periodically, but it is limited by weather and resolution, and the data accuracy is low. SUMMARY

[0004] To solve the problems in the prior art, the present application provides a large-scale forest ground litter load calculation method. The method obtains the forest resource partition to which the target area belongs; determines the forest type of each forest block in the target area based on the national forest resource inventory data; establishes an aboveground biomass and volume regression model for each forest block in the target area according to the forest resource partition to which the target area belongs and the forest type of each forest block in the target area; obtains the aboveground biomass of each forest block in the target area according to the aboveground biomass and volume regression model; obtains the ground litter load of each forest block in the target area by querying the proportion of ground litter load in aboveground biomass in each type of forest block; and obtains the spatial distribution of ground litter load in the target area according to the ground litter load of all forest blocks in the target area. The present application can effectively improve the efficiency and accuracy of large-scale forest ground litter load calculation.

[0005] The present application adopts the following technical solution: a large-scale forest ground litter load calculation method, comprising: obtaining the forest resource partition to which the target area belongs; determining the forest type of each forest block in the target area based on the national forest resource inventory data; According to the forest resource partition to which the target region belongs and the forest types of each forest plot in the target region, a ground biomass and volume regression model of each forest plot in the target region is established; According to the ground biomass and volume regression model, the ground biomass of each forest plot in the target region is obtained; By querying the proportion of the ground litter load in the ground biomass in each forest type forest plot, the ground litter load of each forest plot in the target region is obtained; According to the ground litter load of all forest plots in the target region, the spatial distribution of the ground litter load of the target region is obtained.

[0006] Further, according to the forest resource partition to which the target region belongs and the forest types of each forest plot in the target region, a ground biomass and volume regression model of each forest plot in the target region is established, comprising: According to the forest resource partition to which the target region belongs and the forest types of each forest plot in the target region, a first model parameter and a second model parameter are obtained; According to the first model parameter and the second model parameter, a ground biomass and volume regression model of each forest plot in the target region is established, represented as: wherein, is the ground biomass, is the volume, is the first model parameter, is the second model parameter.

[0007] Further, the forest resource partition includes: Northeast China, North China, Northwest China, Central and Southern China, Southeast Coastal China, Southwest China and Tibet.

[0008] Further, the forest type includes: coniferous forest, broad-leaved forest and coniferous and broad-leaved mixed forest.

[0009] The beneficial effects of the present application are: the present application has constructed a ground biomass-volume regression model and a ground litter proportion database covering seven partitions, three forest types and different dominant tree species in the country based on the forest resource inventory system, which realizes that the ground litter load of the forest plot can be quickly and accurately calculated by querying the forest resource partition, forest type and dominant tree species of the forest plot, and the fine spatial distribution of the ground litter load at a large scale is obtained, effectively solving the technical problem that the traditional method cannot balance large scale and high precision, providing reliable data support and operational ability for business for dynamic assessment of forest fire risk, fine simulation of forest fire behavior, accurate management of combustible materials and optimization of fire extinguishing resources. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description only represent some of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on these drawings.

[0011] Figure 1 A large-scale forest ground litter load calculation method flowchart of an embodiment of the present application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort fall within the protection scope of the present application.

[0013] A large-scale forest ground litter load calculation method flowchart of an embodiment of the present application is shown in FIG. 1, which comprises the following steps. Figure 1 Obtaining the forest resource partition to which the target region belongs; In the embodiment of the present application, according to the different geographical location distribution, the existing forest resource partition divides the forest resources into the northeast region, the north China region, the northwest region, the central south region, the southeast coastal region, the southwest region and the Tibet region, and the specific division is shown in Table 1. Table 1: Forest resource partition table Because the climate is different in different regions, the hydrothermal conditions have great differences, which not only leads to different forest types, but also leads to different ground litter decomposition rates, and finally leads to great differences in forest ground litter load. Therefore, according to the geographical location of the target region, the corresponding partition to which the target region belongs can be found in Table 1, that is, the forest resource partition of the target region can be obtained.

[0014] Determining the forest type of each forest plot in the target region based on the national forest resource inventory data; ​In this embodiment of the invention, the forest resource situation in the target area can be obtained by obtaining the national forest resource inventory and fixed sample plot survey data. Then, the forest compartment is used as the basic spatial unit in forest resource management. By obtaining the forest resource inventory fixed sample plot survey data representing the forest resource situation of each forest compartment, including data such as tree species composition, dominant tree species, diameter at breast height (DBH) and tree height, the forest type of the target area is mainly divided by the tree species composition in each forest compartment. That is, by statistically analyzing the tree species composition in all forest compartments, the tree species with the largest proportion can be used to classify the forest type of the target area. For example, if the proportion of coniferous trees in all forest compartments is greater than or equal to 80%, the forest type in the current target area can be considered as coniferous forest. Similarly, if the proportion of broad-leaved trees in all forest compartments is greater than or equal to 80%, the forest type in the target area can also be broad-leaved forest. If the proportions of both coniferous and broad-leaved trees in all forest compartments are less than 80%, the forest type in the current target area is mixed coniferous and broad-leaved forest.

[0015] Based on the forest resource zoning of the target area and the forest type of each forest compartment in the target area, establish a regression model for aboveground biomass and stock volume of each forest compartment in the target area. In this embodiment of the invention, firstly, based on the forest resource zoning to which the target area belongs and the forest type of each forest compartment in the target area, firstly, secondly, model parameters and secondly, model parameters are obtained; based on the firstly, model parameters and secondly, regression models of aboveground biomass and stock volume for each forest compartment in the target area are established, expressed as follows: in, For aboveground biomass, As the accumulation amount, For the first model parameters, These are the parameters for the second model.

[0016] In this embodiment of the invention, the first model parameter and the second model parameter are used to reflect the differences in the decomposition rate of surface litter caused by different climates in different regions. This can be reflected in the calculation of aboveground biomass and volume. In a specific embodiment of the invention, multiple standard trees that can represent the average diameter at breast height (DBH) and tree height of the fixed sample plot are selected in the forest resource inventory. After the standard trees are felled, the debarked volume of the standard trees is calculated using the differential volume method. The trunk, bark, branches, and leaves are all separated, dried at 105°C to constant weight, weighed separately, and summed to obtain the aboveground biomass of a single standard tree. The volume and aboveground biomass of all standard timbers are weighted and averaged according to tree species to obtain the average timber volume and average aboveground biomass of the fixed sample plots. Then, the total volume and aboveground biomass of the fixed sample plots are multiplied by the total number of trees in the fixed sample plots and summed to obtain the total volume and total aboveground biomass of the fixed sample plots. For fixed sample plots of forest resource inventory in different forest resource zones, the corresponding model parameters can be determined by using a power function regression fitting based on their corresponding total volume and total aboveground biomass. The embodiments of this invention thus provide a schematic table of aboveground biomass and volume regression models established for different forest types in various forest resource zones, as shown in Table 2: In Table 2, A, B, and C are the codes for forest types. In this embodiment of the invention, A can represent a coniferous forest, B can represent a broad-leaved forest, and C can represent a mixed coniferous and broad-leaved forest.

[0017] The aboveground biomass of each forest compartment in the target area was obtained based on the regression model of aboveground biomass and volume. In this embodiment of the invention, based on obtaining the forest resource zoning corresponding to the target area, the corresponding aboveground biomass and stock volume regression model is selected from Table 2. At the same time, by querying the forest resource inventory data, the stock volume of the fixed sample plots representing each forest compartment in the target area can be obtained, and the aboveground biomass of each forest compartment in the target area can be calculated.

[0018] In this embodiment of the invention, through a nationwide forest resource inventory, surface litter survey plots are set up at the four corners and the center of each fixed sample plot. All surface litter within the plots is collected, and the average value is taken as the surface litter load for each fixed sample plot. Then, based on the forest resource inventory data, the proportion of surface litter load in the aboveground biomass for each fixed sample plot can be calculated. This embodiment of the invention provides a schematic table illustrating the proportion of surface litter load in aboveground biomass for various forest types under different forest resource zones, as shown in Table 3: Table 3. Schematic diagram of the proportion of surface litter carrying capacity in aboveground biomass. By querying the proportion of surface litter load in the aboveground biomass of each forest compartment, the surface litter load of each forest compartment in the target area can be obtained. The refined spatial distribution of surface litter load in the target area is obtained based on the surface litter load of all forest compartments in the target area.

[0019] Based on the proportion of surface litter load in the aboveground biomass of each forest compartment as recorded in Table 3, and on the basis of the aboveground biomass of each forest compartment calculated by the model, the surface litter load of each forest compartment can be obtained. This allows for the acquisition of a refined spatial distribution of surface litter load in the target area, which can be used for forest fire management practices such as dynamic assessment of forest fire risk, refined simulation of forest fire behavior, precise management of combustibles, and optimized allocation of fire extinguishing resources.

[0020] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating the litter load of large-scale forest surfaces, characterized in that, include: Obtain the forest resource zoning of the target area; Based on the national forest resource inventory data, the forest types of each forest compartment in the target area were determined; Based on the forest resource zoning of the target area and the forest type of each forest compartment in the target area, establish a regression model for aboveground biomass and stock volume of each forest compartment in the target area. The aboveground biomass of each forest compartment in the target area is obtained based on the aboveground biomass and volume regression model. By querying the proportion of surface litter load in the aboveground biomass of each forest type compartment, the surface litter load of each forest compartment in the target area can be obtained. The spatial distribution of surface litter load in the target area is obtained based on the surface litter load of all forest compartments in the target area.

2. The method for calculating large-scale forest litter load according to claim 1, characterized in that: Based on the forest resource zoning of the target area and the forest type of each forest compartment in the target area, a regression model for aboveground biomass and stock volume of each forest compartment in the target area is established, including: Based on the forest resource zoning of the target area and the forest type of each forest compartment in the target area, obtain the first model parameters and the second model parameters; Based on the first model parameters and the second model parameters, a regression model for aboveground biomass and stock volume of each forest compartment in the target area is established, expressed as follows: in, For aboveground biomass, For accumulation, These are the parameters of the first model. These are the parameters for the second model.

3. The method for calculating large-scale forest litter load according to claim 1, characterized in that: The forest resource zones include: Northeast Region, North China Region, Northwest Region, Central and Southern Region, Southeast Coastal Region, Southwest Region, and Tibet Region.

4. The method for calculating large-scale forest litter load according to claim 1, characterized in that: The forest types include: coniferous forests, broad-leaved forests, and mixed coniferous and broad-leaved forests.

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