A forest ecosystem risk assessment method and device
By acquiring multi-scale data and performing preprocessing, parameter calibration, and scenario simulation, the problem of insufficient data fusion in forest ecosystem assessment was solved, enabling more accurate risk assessment and prevention and control plans.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING ACAD OF LANDSCAPING & LANDSCAPING SCI
- Filing Date
- 2026-01-30
- Publication Date
- 2026-06-16
Smart Images
Figure CN122222362A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of forest ecological risk assessment technology, and also to a method and apparatus for risk assessment of forest ecosystems. Background Technology
[0002] Forest ecosystems are a core component of the Earth's biosphere, undertaking key ecological service functions such as carbon sequestration regulation, biodiversity conservation, soil and water conservation, and climate stability. Particularly in soil and water conservation, clarifying the critical resilience threshold of forest ecosystems shifting from "water storage" to "runoff generation" and triggering flood peaks, and elucidating the regulatory mechanisms of forest community structure on this hydrological process, is crucial for accurately identifying high-risk areas for flooding and vulnerable ecosystems during extreme precipitation events. However, existing assessment methods for forest ecosystems focus on single spatial scales (such as regional remote sensing monitoring or plot-level field surveys), lacking the integration of data from different scales, resulting in inaccurate assessment results. Summary of the Invention
[0003] The technical problem to be solved by the present invention is to provide a risk assessment method and apparatus for forest ecosystems, so as to improve the accuracy of forest ecosystem assessment.
[0004] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A first aspect of the present invention provides a method for risk assessment of forest ecosystems, comprising: Obtain multi-scale data on the forest ecosystem to be assessed; The multi-scale data is preprocessed to obtain preprocessed data; Parameter calibration is performed based on the preprocessed data to obtain the target parameter data; Based on the acquired target precipitation events and the target parameter data, scenario simulations are performed to obtain scenario simulation data. A risk assessment of the forest ecosystem is conducted based on the scenario simulation data, and the risk assessment results are obtained.
[0005] Optionally, obtain multi-scale data on the forest ecosystem to be assessed, including: Acquire remote sensing data of the forest ecosystem to be assessed; the remote sensing data includes type distribution maps, vegetation cover, leaf area index, topography, and soil type; Obtain meteorological and hydrological data for the forest ecosystem to be assessed; the meteorological and hydrological data include precipitation, temperature, wind speed, sunshine duration, and river flow. Obtain soil geological data for the forest ecosystem to be assessed; the soil geological data includes soil type map, soil thickness, porosity, field water holding capacity, geological lithology and groundwater depth; Obtain plot data of the forest ecosystem to be evaluated; the plot data includes location coordinates, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, litter thickness, and biomass; Based on the remote sensing data, the meteorological and hydrological data, the soil geological data, and the sample plot data, multi-scale data of the forest ecosystem to be evaluated are obtained.
[0006] Optionally, the multi-scale data is preprocessed to obtain preprocessed data, including: Spatial registration is performed on the multi-scale data to obtain spatially registered data; The spatial registration data is converted to a new format to obtain converted data. The format-converted data is interpolated to obtain preprocessed data.
[0007] Optionally, parameter calibration is performed based on the preprocessed data to obtain target parameter data, including: Based on the preprocessed data, determine the input data; The input data is filtered to obtain the target parameters; Based on the target parameters, the preset network model is calibrated to obtain the first parameter; The first parameter is verified based on the obtained verification data to obtain the target parameter data.
[0008] Optionally, scenario simulation is performed based on the acquired target precipitation event and the target parameter data to obtain scenario simulation data, including: The target precipitation event is obtained based on historical precipitation data. Scenario simulation is performed based on the target precipitation event and the target parameter data to obtain scenario simulation data.
[0009] Optionally, acquire the target precipitation event, including: Obtain historical precipitation data; Based on the historical precipitation data, determine the annual maximum value sequence data; The precipitation threshold is calculated based on the annual maximum value sequence data. The target precipitation event is determined based on the precipitation threshold and the historical precipitation data.
[0010] Optionally, a risk assessment is conducted on the forest ecosystem based on the scenario simulation data to obtain risk assessment results, including: according to The soil saturation was calculated; among which, For soil saturation, This represents the actual water content of the soil profile in the scenario simulation data. This refers to the maximum water holding capacity of the soil. Determine the toughness threshold based on the soil saturation; Based on the scenario simulation data, determine the resilience data; The risk assessment results are obtained by performing a risk assessment on the forest ecosystem based on the resilience threshold, the resilience data, and the preset weights.
[0011] A second aspect of the present invention provides a risk assessment device for a forest ecosystem, comprising: The acquisition module is used to acquire multi-scale data of the forest ecosystem to be evaluated; The processing module is used to preprocess the multi-scale data to obtain preprocessed data; perform parameter calibration based on the preprocessed data to obtain target parameter data; perform scenario simulation based on the acquired target precipitation event and the target parameter data to obtain scenario simulation data; and perform risk assessment on the forest ecosystem based on the scenario simulation data to obtain risk assessment results.
[0012] A third aspect of the present invention provides a computing device, comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described in the first aspect.
[0013] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method as described in the first aspect.
[0014] The above-described solution of the present invention has at least the following beneficial effects: The above-mentioned solution of the present invention acquires multi-scale data of the forest ecosystem to be assessed, preprocesses the multi-scale data to obtain preprocessed data, calibrates parameters based on the preprocessed data to obtain target parameter data, performs scenario simulation based on the acquired target precipitation events and target parameter data to obtain scenario simulation data, and finally conducts risk assessment of the forest ecosystem based on the scenario simulation data to obtain risk assessment results. This can improve the accuracy of risk assessment of forest ecosystems and provide a scientific basis and spatial optimization scheme for precise prevention and control of regional flood risks and enhancement of forest resilience. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating the risk assessment method for forest ecosystems in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the risk assessment device for forest ecosystems in an embodiment of the present invention. Detailed Implementation
[0016] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
[0017] like Figure 1 As shown, embodiments of the present invention propose a risk assessment method for forest ecosystems, comprising the following steps: Step 101: Obtain multi-scale data of the forest ecosystem to be assessed; Step 102: Perform data preprocessing on the multi-scale data to obtain preprocessed data; Step 103: Perform parameter calibration based on the preprocessed data to obtain target parameter data; Step 104: Perform scenario simulation based on the acquired target precipitation event and the target parameter data to obtain scenario simulation data; Step 105: Conduct a risk assessment of the forest ecosystem based on the scenario simulation data to obtain the risk assessment results.
[0018] The risk assessment method for forest ecosystems proposed in this invention involves acquiring multi-scale data of the forest ecosystem to be assessed, preprocessing the multi-scale data to obtain preprocessed data, calibrating parameters based on the preprocessed data to obtain target parameter data, performing scenario simulation based on the acquired target precipitation events and target parameter data to obtain scenario simulation data, and finally conducting a risk assessment of the forest ecosystem based on the scenario simulation data to obtain the risk assessment results. This method can improve the accuracy of risk assessment of forest ecosystems and provide a scientific basis and spatial optimization scheme for precise prevention and control of regional flood risks and enhancement of forest resilience.
[0019] In an optional embodiment of the present invention, step 101, obtaining multi-scale data of the forest ecosystem to be evaluated, may include: Step 1011: Obtain remote sensing data of the forest ecosystem to be evaluated; the remote sensing data includes type distribution map, vegetation cover, leaf area index, topography and soil type; Specifically, optical remote sensing images of the forest ecosystem to be assessed, such as production land use / cover maps, can be downloaded from relevant websites; remote sensing data can be used to assist in water body identification and soil moisture inversion; airborne lidar can be used to acquire three-dimensional point cloud data of the forest ecosystem to be assessed, or high-precision DEMs (digital elevation models) can be generated using optical remote sensing data.
[0020] Step 1012: Obtain meteorological and hydrological data of the forest ecosystem to be assessed; the meteorological and hydrological data includes precipitation, temperature, wind speed, sunshine duration, and river flow. Specifically, data such as daily precipitation, temperature, wind speed, and sunshine hours of the meteorological stations of the forest ecosystem to be assessed can be obtained from relevant websites; as well as data on reservoir inflow and flow of major river hydrological stations located in the forest ecosystem to be assessed.
[0021] Step 1013: Obtain soil geological data of the forest ecosystem to be evaluated; the soil geological data includes soil type map, soil thickness, porosity, field water holding capacity, geological lithology and groundwater depth; Specifically, we will obtain 1:50,000 soil type maps and collect data on soil thickness, porosity, field water holding capacity, and other attributes; we will also collect data on geological lithology and groundwater depth.
[0022] Step 1014: Obtain plot data of the forest ecosystem to be evaluated; the plot data includes location coordinates, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, litter thickness, and biomass. Specifically, the sample plot data includes GPS location (location coordinates) and community survey data (tree species, diameter at breast height, tree height, crown width, canopy closure, litter thickness and biomass, etc.).
[0023] Step 1015: Based on the remote sensing data, the meteorological and hydrological data, the soil geological data, and the sample plot data, obtain multi-scale data of the forest ecosystem to be evaluated.
[0024] Specifically, this involves acquiring multi-scale data on the forest ecosystem to be assessed. This multi-scale data includes, but is not limited to: ecosystem baseline data: high-precision distribution maps of forest / grassland / wetland types, vegetation cover, leaf area index, topography, soil type, etc.; hydrological and meteorological data: data on extreme precipitation events and historical precipitation, runoff, evaporation, etc.; and field plot data: existing community structure plot information (tree species composition, diameter at breast height, tree height, canopy closure, litter, etc.). It should be noted that multi-scale data can be data from a historical time period or real-time data of the forest ecosystem.
[0025] In an optional embodiment of the present invention, step 102, which involves preprocessing the multi-scale data to obtain preprocessed data, may include: Step 1021: Spatial registration is performed on the multi-scale data to obtain spatially registered data; Specifically, the metadata / header files of each type of data in the multi-scale data are parsed to clarify the original coordinate attributes; through... The transformed Cartesian coordinates (X, Y) are obtained, which are the spatial registration data; where X is the ordinate (based on the equator) and Y is the abscissa (with an offset of 500 km to avoid negative values). R is the scale factor (which can take a value of 0.9996), and R is the mean radius of curvature of the ellipsoid. Longitude The longitude of the central meridian. Latitude The ellipsoidal flattening correction factor is obtained by... We obtain f as the Earth's eccentricity.
[0026] Step 1022: Convert the spatial registration data to obtain format-converted data; Specifically, spatial registration data in different formats are converted into a unified structured format (vector / raster) to achieve data read / write compatibility and improve processing efficiency. Here, remote sensing data in spatial registration data is converted into raster data, meteorological and hydrological data in spatial registration data is converted into vector point data or raster data, soil and geological data in spatial registration data is converted into vector polygon data, and sample plot data in spatial registration data is converted into vector point data. The format conversion data includes the converted raster data, vector point data, and vector polygon data.
[0027] Step 1023: Interpolate the format conversion data to obtain preprocessed data.
[0028] Specifically, interpolation converts discrete plot / meteorological station data (point data) into continuous raster surface data, which is then matched with remote sensing / soil raster data to support comprehensive risk assessment. Here, the appropriate interpolation method can be selected based on the different data formats being converted. For example, when the sample point data in the converted data exhibits a clustered distribution, the inverse distance weighting method is used; when it exhibits a continuous gradient, the Kriging interpolation method is used. The choice is made according to the specific circumstances.
[0029] In an optional embodiment of the present invention, step 103, which involves parameter calibration based on the preprocessed data to obtain target parameter data, may include: Step 1031: Determine the input data based on the preprocessed data; Specifically, based on the processing modules corresponding to each type of preprocessed data (such as remote sensing data, meteorological and hydrological data, soil and geological data, and sample plot data), the preprocessed data is formatted according to the format recognizable by the corresponding processing module to obtain the input data. In one specific embodiment, the processing module corresponding to the remote sensing data is a land use map and vegetation database. The coordinates are unified to the geographic coordinates located by the ellipsoid model. Land use is reclassified into standard types (such as forest and grassland). Leaf area index is interpolated monthly to generate time-series data. The processing module corresponding to the meteorological and hydrological data is a meteorological database and hydrological observation data. The meteorological data is organized into daily scales by station. The measured runoff data needs to be selected for continuous hydrological years without missing data (at least 3 years). The processing module corresponding to the soil and geological data is a soil database and digital elevation model. Soil data is associated with soil parameters (such as effective water content). The digital elevation model fills depressions and analyzes flow direction to generate sub-basins. The processing module corresponding to the sample plot data is vegetation cover parameters and hydrological response unit division. The sample plot data is interpolated into isometric data to optimize the vegetation cover coefficient of the hydrological response unit. Litter data is associated with soil infiltration parameters.
[0030] Step 1032: Filter the input data to obtain the target parameters; Specifically, the input data is filtered according to preset sensitivity parameters, and the input data that meets the preset sensitivity parameters is used as the target parameters. The preset sensitivity parameters include: number of runoff curves (affecting surface runoff), effective soil moisture content, soil evaporation compensation coefficient, groundwater evaporation coefficient, baseflow α factor (affecting groundwater recharge), maximum leaf area index, maximum canopy storage, and surface runoff lag time.
[0031] Step 1033: Perform parameter calibration on the preset network model according to the target parameters to obtain the first parameters; Specifically, the preset network model extracts water systems and divides sub-basins based on the digital elevation model. Combined with the forest ecosystem boundary trimming of the study area, hydrological response units are divided according to the combination of land use (remote sensing data), soil type (soil data), and slope (DEM derived). Forest areas need to set separate hydrological response units (distinguishing between trees, shrubs, and mixed forests). Initial values are set for the static parameters in the preset network model: soil parameters directly use the measured values of soil geological data; vegetation parameters use the maximum value of remote sensing time series data plus the measured calibration value of the sample plot; topographic parameters are derived from the DEM and corrected by combining the sample plot slope measurement data. The dynamic parameters in the preset network model were calibrated: Surface runoff parameters were adjusted iteratively using measured monthly / daily runoff data to gradually improve the Nash efficiency coefficient between simulated and measured runoff; evapotranspiration parameters were constrained by remote sensing evapotranspiration data, and the correlation coefficient between simulated evapotranspiration and remote sensing inversion values was adjusted to be no less than 0.6; groundwater parameters were adjusted by combining measured baseflow segmentation data (such as baseflow derived by digital filtering) to match the proportion of simulated baseflow to total runoff with measured values. Other parameters: Maximum canopy storage capacity was obtained using CANMX = 0.1 × C × H, where CANMX is the maximum canopy storage capacity, C is the plot canopy closure, and H is the tree height. The first parameter includes static parameters, dynamic parameters, and other parameters.
[0032] Step 1034: Verify the first parameter based on the obtained verification data to obtain the target parameter data.
[0033] Specifically, the first parameter is substituted into the original parameters of the preset network model to obtain the first network model. The obtained validation data is then input into the first network model to obtain the output result. The first parameter is validated based on the output result, the obtained validation data, and the preset index. If the obtained index value meets the expected range, the first parameter is used as the target parameter data; otherwise, the preset network model is recalibrated until the index value meets the expected range. Here, the obtained validation data is multi-scale data from different time periods than the multi-scale data of the forest ecosystem to be evaluated obtained in step 101.
[0034] In an optional embodiment of the present invention, step 104, performing scenario simulation based on the acquired target precipitation event and the target parameter data to obtain scenario simulation data, may include: Step 1041: Obtain the target precipitation event; the target precipitation event is obtained based on historical precipitation data; In an optional embodiment of the present invention, step 1041 includes: Step 10411: Obtain historical precipitation data; Specifically, historical precipitation data refers to the historical precipitation data of the forest ecosystem to be evaluated. Historical precipitation data from a past period (such as the past 30 years) can be selected. The longer the period, the more accurate the subsequent determination of the target precipitation event.
[0035] Step 10412: Determine the annual maximum value sequence data based on the historical precipitation data; Specifically, outliers can be removed and missing data can be added to historical precipitation data to ensure data continuity and reliability. One maximum daily precipitation (or maximum 3-day or 5-day cumulative precipitation) should be selected each year to form an annual maximum value sequence. .
[0036] Step 10413: Calculate the precipitation threshold based on the annual maximum value sequence data; Specifically, according to The precipitation threshold was calculated, where, The threshold value is the precipitation threshold, and T is the return period (which can be 30 or 50). These are estimated values for the location parameters. These are the estimated values for the scale parameters. Among them, the estimated values for the location parameters are... and scale parameter estimates It is obtained through the following steps: according to The log-likelihood function is obtained; where, Let n be the function value, and n be the total number of data points in the annual maximum value sequence. For the i-th data in the annual maximum value sequence, For position parameters, is the scale parameter, and e is the natural constant, approximately equal to 2.718281828459045.
[0037] The log-likelihood function is solved through numerical iteration until... and The change is less than a preset threshold (e.g.) ), to obtain convergent position parameter estimates. and scale parameter estimates .
[0038] Step 10414: Determine the target precipitation event based on the precipitation threshold and the historical precipitation data.
[0039] Specifically, the target precipitation event is selected from historical precipitation data based on the rainfall threshold closest to that event. For example, if the maximum daily rainfall in historical precipitation data is 180 mm, which matches the 50-year return period threshold of 178.24 mm, it is considered a typical event. If no matching event is found, the temporal distribution ratio of historical rainstorms is selected (e.g., rainfall distribution within 24 hours: 5% in the first hour, 10% in the second hour, etc.).
[0040] Step 1042: Perform scenario simulation based on the target precipitation event and the target parameter data to obtain scenario simulation data.
[0041] Specifically, by substituting the target parameter data into the relevant parameters of the preset network model, an updated network model is obtained. By inputting the target precipitation event into the updated network model, the runoff generation and confluence processes during the entire event (e.g., hourly) can be obtained. All data are used as scenario simulation data, such as those related to water conservation: soil moisture content, canopy interception, litter interception, and groundwater recharge; and those related to runoff generation: surface runoff, soil flow, and total runoff.
[0042] In an optional embodiment of the present invention, step 105, which involves conducting a risk assessment of the forest ecosystem based on the scenario simulation data to obtain the risk assessment result, may include: Step 1051, according to The soil saturation was calculated; among which, For soil saturation, This represents the actual water content of the soil profile in the scenario simulation data. This refers to the maximum water holding capacity of the soil. Step 1052: Determine the toughness threshold based on the soil saturation. Specifically, the Water Conservation Index (WSI) and Runoff Generation Index (RGI) are first calculated using the following formulas: ; ; in, As a water conservation function index, This is the normalized value of soil moisture content in the scenario simulation data. This is the normalized value of canopy interception in the scenario simulation data. This is the normalized value of litter interception in the scenario simulation data. This is the normalized value of groundwater recharge in the scenario simulation data. For the abortion function index, This represents the normalized value of surface runoff in the scenario simulation data. This is the normalized value of soil flow in the scenario simulation data. This is the normalized value of total runoff in the scenario simulation data.
[0043] Then, based on soil saturation, a relationship curve was plotted between the Water Conservation Index (WSI) and the Runoff Generation Index (RGI) (i.e., soil saturation as the horizontal axis and the Water Conservation Index and Runoff Generation Index as the vertical axes). The soil saturation corresponding to the difference between the water conservation function index and the runoff generation function index being equal to 0 is taken as the resilience threshold (i.e., the soil saturation corresponding to the water conservation function index being equal to the runoff generation function index is taken as the resilience threshold).
[0044] Here, when the water conservation function index is greater than the runoff generation function index, the water conservation function dominates; when the water conservation function index is equal to the runoff generation function index, it is the critical point of function transformation (resilience threshold); when the water conservation function index is less than the runoff generation function index, the runoff generation function dominates.
[0045] Step 1053: Determine the resilience data based on the scenario simulation data; Specifically, resilience is the speed and extent to which a forest ecosystem recovers from a runoff-dominated state to a water conservation-dominated state. Resilience data includes: pass The recovery time was calculated; where, For recovery time, The time when the Water Intake Index (WSI) first exceeds the Runoff Generation Index (RGI) is defined as the time when the WSI first exceeds the RGI. This refers to the end time of the extreme precipitation event; pass The recovery time was calculated; where, For recovery time, To restore the stable average WSI value (WSI>RGI for 3 consecutive days and fluctuation ≤5%). The average WSI value for the 15 days preceding the precipitation event; pass The resilience index is calculated; where RI is the resilience index. For recovery time, This represents the maximum recovery time in the scenario simulation data. This refers to the recovery time.
[0046] Step 1054: Perform a risk assessment on the forest ecosystem based on the resilience threshold, the resilience data, and the preset weights to obtain the risk assessment results.
[0047] Specifically, the threshold breakthrough probability is obtained by calculating the threshold breakthrough probability as the number of days when soil saturation exceeds the resilience threshold under the target precipitation event divided by the total number of days, the runoff intensity as the total runoff volume divided by the baseline period total runoff volume, and the resilience shortest board as 1 minus the resilience index. Flow intensity and resilience weakness (1-RI); through A comprehensive risk index is calculated; based on the comprehensive risk index and preset risk levels, a risk assessment is conducted on the forest ecosystem to obtain the risk assessment result. Here, the target level corresponding to the preset risk levels is selected from the comprehensive risk index as the risk assessment result. In specific implementation, an optimization scheme can also be determined based on the target level and forest structure parameters. The optimization scheme, together with the target level, serves as the risk assessment result. For example, if the forest structure parameters indicate a flood source area and the target level is high, the identified problems are rapid surface runoff and easy soil saturation. The corresponding optimization scheme includes transforming the forest into a multi-layered coniferous forest (such as a mixed forest of Masson pine and Chinese fir) to increase the canopy density to above 0.7; preserving the litter layer (thickness ≥ 5cm) and prohibiting its clearing; and planting deep-rooted tree species (such as pine) to enhance groundwater recharge.
[0048] A specific embodiment of the risk assessment method for forest ecosystems according to the present invention includes: Step 111: Obtain multi-scale data of the forest ecosystem to be assessed; Obtain heterogeneous data from multiple sources and at multiple scales on the forest ecosystem to be assessed, such as remote sensing data, meteorological and hydrological data, soil and geological data, and baseline data of sample plots.
[0049] Step 112: Perform data preprocessing on the multi-scale data to obtain preprocessed data; Multi-scale data are subjected to coordinate unification, format conversion, and spatial interpolation to construct a spatiotemporally consistent comprehensive watershed database, resulting in preprocessed data.
[0050] Step 113: Perform parameter calibration based on the preprocessed data to obtain target parameter data; Using preprocessed data to drive a pre-defined network model, hydrological response units were divided. Conventional hydrological and meteorological data prior to a major precipitation event in a given year (i.e., the acquired validation data) were used to calibrate and validate the model's parameters, ensuring the reliability of the model's baseflow simulation. By analyzing the runoff generation time series and contribution rate of each sub-basin, the key source areas for flood peak formation were accurately traced. Using the soil water and groundwater modules in the model, the transformation process between "green water flow" (evapotranspiration) and "blue water flow" (runoff) was analyzed, quantifying the forest's water storage capacity.
[0051] Step 114: Perform scenario simulation based on the acquired target precipitation event and the target parameter data to obtain scenario simulation data; Based on historical precipitation data, target precipitation events with different return periods (such as 50-year return periods and 100-year return periods) are designed. Model parameters are determined according to the target parameter data. Then, relevant data from the target precipitation events are input into the model with determined parameters. The model is run to analyze the critical point at which the ecosystem shifts from being dominated by the function of "water conservation" to being dominated by the function of "runoff generation" under different extreme scenarios (such as soil saturation exceeding 95%). This critical point is the resilience threshold.
[0052] Step 115: Conduct a risk assessment of the forest ecosystem based on the scenario simulation data to obtain the risk assessment results.
[0053] Resilience data is calculated based on scenario simulation data, and then the risk level and optimization plan are determined as the risk assessment results.
[0054] The risk assessment method for forest ecosystems proposed in this invention reveals the dynamic process (service flow) of water conservation services in forest ecosystems under extreme climate conditions based on multi-scale data of the forest ecosystems to be assessed. It clarifies the key resilience thresholds that enable the forest ecosystems to shift from "water storage" to "runoff generation" and trigger flood peaks, and conducts risk assessments on the forest ecosystems. Ultimately, it provides a scientific basis and spatial optimization scheme for precise prevention and control of regional flood risks and enhancement of forest resilience.
[0055] like Figure 2 As shown, an embodiment of the present invention provides a risk assessment device 200 for a forest ecosystem, comprising: The acquisition module 201 is used to acquire multi-scale data of the forest ecosystem to be evaluated; The processing module 202 is used to preprocess the multi-scale data to obtain preprocessed data; perform parameter calibration based on the preprocessed data to obtain target parameter data; perform scenario simulation based on the acquired target precipitation event and the target parameter data to obtain scenario simulation data; and perform risk assessment on the forest ecosystem based on the scenario simulation data to obtain risk assessment results.
[0056] Optionally, obtain multi-scale data on the forest ecosystem to be assessed, including: Acquire remote sensing data of the forest ecosystem to be assessed; the remote sensing data includes type distribution maps, vegetation cover, leaf area index, topography, and soil type; Obtain meteorological and hydrological data for the forest ecosystem to be assessed; the meteorological and hydrological data include precipitation, temperature, wind speed, sunshine duration, and river flow. Obtain soil geological data for the forest ecosystem to be assessed; the soil geological data includes soil type map, soil thickness, porosity, field water holding capacity, geological lithology and groundwater depth; Obtain plot data of the forest ecosystem to be evaluated; the plot data includes location coordinates, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, litter thickness, and biomass; Based on the remote sensing data, the meteorological and hydrological data, the soil geological data, and the sample plot data, multi-scale data of the forest ecosystem to be evaluated are obtained.
[0057] Optionally, the multi-scale data is preprocessed to obtain preprocessed data, including: Spatial registration is performed on the multi-scale data to obtain spatially registered data; The spatial registration data is converted to a new format to obtain converted data. The format-converted data is interpolated to obtain preprocessed data.
[0058] Optionally, parameter calibration is performed based on the preprocessed data to obtain target parameter data, including: Based on the preprocessed data, determine the input data; The input data is filtered to obtain the target parameters; Based on the target parameters, the preset network model is calibrated to obtain the first parameter; The first parameter is verified based on the obtained verification data to obtain the target parameter data.
[0059] Optionally, scenario simulation is performed based on the acquired target precipitation event and the target parameter data to obtain scenario simulation data, including: The target precipitation event is obtained based on historical precipitation data. Scenario simulation is performed based on the target precipitation event and the target parameter data to obtain scenario simulation data.
[0060] Optionally, acquire the target precipitation event, including: Obtain historical precipitation data; Based on the historical precipitation data, determine the annual maximum value sequence data; The precipitation threshold is calculated based on the annual maximum value sequence data. The target precipitation event is determined based on the precipitation threshold and the historical precipitation data.
[0061] Optionally, a risk assessment is conducted on the forest ecosystem based on the scenario simulation data to obtain risk assessment results, including: according to The soil saturation was calculated; among which, For soil saturation, This represents the actual water content of the soil profile in the scenario simulation data. This refers to the maximum water holding capacity of the soil. Determine the toughness threshold based on the soil saturation; Based on the scenario simulation data, determine the resilience data; The risk assessment results are obtained by performing a risk assessment on the forest ecosystem based on the resilience threshold, the resilience data, and the preset weights.
[0062] The forest ecosystem risk assessment device proposed in this invention acquires multi-scale data of the forest ecosystem to be assessed, preprocesses the multi-scale data to obtain preprocessed data, calibrates parameters based on the preprocessed data to obtain target parameter data, performs scenario simulation based on the acquired target precipitation events and target parameter data to obtain scenario simulation data, and finally conducts risk assessment of the forest ecosystem based on the scenario simulation data to obtain risk assessment results. This device can improve the accuracy of risk assessment of forest ecosystems and provide a scientific basis and spatial optimization scheme for precise prevention and control of regional flood risks and enhancement of forest resilience.
[0063] It should be noted that this device corresponds to the method described above, and all implementations in the method embodiments described above are applicable to the embodiments of this device and can achieve the same technical effect. Further details are omitted in this embodiment.
[0064] This invention also provides a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. Further details are omitted in this embodiment.
[0065] This invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method as described in any of the above embodiments. All implementations in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects. Further details are omitted in this embodiment.
[0066] It should be noted that in the apparatus and method of the present invention, the components or steps can obviously be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalent solutions of the present invention. Furthermore, the steps for performing the above series of processes can naturally be performed in the order described and in chronological order, but are not necessarily required to be performed in chronological order. Some steps can be performed in parallel, overlapping, or independently of each other.
[0067] It should be noted that in the above embodiments, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments described above is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0068] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A risk assessment method for forest ecosystems, characterized in that, include: Obtain multi-scale data on the forest ecosystem to be assessed; The multi-scale data is preprocessed to obtain preprocessed data; Parameter calibration is performed based on the preprocessed data to obtain the target parameter data; Based on the acquired target precipitation events and the target parameter data, scenario simulations are performed to obtain scenario simulation data. A risk assessment of the forest ecosystem is conducted based on the scenario simulation data, and the risk assessment results are obtained.
2. The risk assessment method for forest ecosystems according to claim 1, characterized in that, Obtain multi-scale data on the forest ecosystem to be assessed, including: Acquire remote sensing data of the forest ecosystem to be assessed; the remote sensing data includes type distribution maps, vegetation cover, leaf area index, topography, and soil type; Obtain meteorological and hydrological data for the forest ecosystem to be assessed; the meteorological and hydrological data include precipitation, temperature, wind speed, sunshine duration, and river flow. Obtain soil geological data for the forest ecosystem to be assessed; the soil geological data includes soil type map, soil thickness, porosity, field water holding capacity, geological lithology and groundwater depth; Obtain plot data of the forest ecosystem to be evaluated; the plot data includes location coordinates, tree species, diameter at breast height (DBH), tree height, crown width, canopy closure, litter thickness, and biomass; Based on the remote sensing data, the meteorological and hydrological data, the soil geological data, and the sample plot data, multi-scale data of the forest ecosystem to be evaluated are obtained.
3. The risk assessment method for forest ecosystems according to claim 1, characterized in that, The multi-scale data is preprocessed to obtain preprocessed data, including: Spatial registration is performed on the multi-scale data to obtain spatially registered data; The spatial registration data is converted to a new format to obtain converted data. The format-converted data is interpolated to obtain preprocessed data.
4. The risk assessment method for forest ecosystems according to claim 1, characterized in that, Based on the preprocessed data, parameter calibration is performed to obtain target parameter data, including: Based on the preprocessed data, determine the input data; The input data is filtered to obtain the target parameters; Based on the target parameters, the preset network model is calibrated to obtain the first parameter; The first parameter is verified based on the obtained verification data to obtain the target parameter data.
5. The risk assessment method for forest ecosystems according to claim 1, characterized in that, Based on the acquired target precipitation events and the target parameter data, scenario simulations are performed to obtain scenario simulation data, including: The target precipitation event is obtained based on historical precipitation data. Scenario simulation is performed based on the target precipitation event and the target parameter data to obtain scenario simulation data.
6. The risk assessment method for forest ecosystems according to claim 5, characterized in that, Acquire the target precipitation event, including: Obtain historical precipitation data; Based on the historical precipitation data, determine the annual maximum value sequence data; The precipitation threshold is calculated based on the annual maximum value sequence data. The target precipitation event is determined based on the precipitation threshold and the historical precipitation data.
7. The risk assessment method for forest ecosystems according to claim 1, characterized in that, A risk assessment of the forest ecosystem is conducted based on the scenario simulation data, yielding risk assessment results, including: according to The soil saturation was calculated; among which, For soil saturation, This represents the actual water content of the soil profile in the scenario simulation data. This refers to the maximum water holding capacity of the soil. Determine the toughness threshold based on the soil saturation; Based on the scenario simulation data, determine the resilience data; The risk assessment results are obtained by performing a risk assessment on the forest ecosystem based on the resilience threshold, the resilience data, and the preset weights.
8. A risk assessment device for a forest ecosystem, characterized in that, include: The acquisition module is used to acquire multi-scale data of the forest ecosystem to be evaluated; The processing module is used to preprocess the multi-scale data to obtain preprocessed data; Parameter calibration is performed based on the preprocessed data to obtain target parameter data; scenario simulation is performed based on the acquired target precipitation events and the target parameter data to obtain scenario simulation data; risk assessment is performed on the forest ecosystem based on the scenario simulation data to obtain risk assessment results.
9. A computing device, characterized in that, include: A processor, a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.