A forest fire monitoring method coupling remote sensing data with geometric optical model
By combining remote sensing data with geometric optical model, the reflectivity curves of different canopy types in forests are simulated and inverted, the pathological and universal problems of FMC inversion in the physical model are solved, and accurate monitoring and risk assessment of forest fires are achieved. .
Patent Information
- Application Number
- CN202210490855.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-05-07
- Publication Date
- 2024-12-10
- Estimated Expiration
- 2042-05-07
AI Technical Summary
In the monitoring of forest fires, the inversion of vegetation canopy FMC based on physical model has pathological problems, weak sensitivity of intermediate parameters DMC spectral and interference with complex vegetation types on the inversion results, resulting in low accuracy and insufficient universality. .
Coupled remote sensing data and geometric optical model are used to simulate the canopy reflectivity curves under different canopy types using the 4-Scale model, and the canopy reflectivity spectrum curves are obtained by combining satellite remote sensing data. By comparing simulation and actual measurement data, the blades are inverted. Moisture content and dry matter content improve the accuracy and universality of FMC inversion.
Accurate assessment of forest fire monitoring has been achieved, the accuracy and universality of FMC inversion of vegetation canopy has been improved, and the reliability and space-time continuity of forest fire risk prediction have been enhanced.
Smart Images
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Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of forest fire monitoring, and relates to a forest fire monitoring method coupling remote sensing data with a geometric optical model. Background Art
[0002] Forest fires seriously damage forest ecosystems and ecological environments, release large amounts of CO2, pollute the atmosphere, aggravate the impact of the greenhouse effect, and seriously threaten people's lives and property. The occurrence of forest fires is random and is the accidental result of multiple factors, but it also has its own inherent regularity. Fuel moisture content (FMC) is the ratio of the moisture content of the vegetation canopy to its dry matter mass. It determines the ease with which a forest can be ignited and is a key indicator factor in forest fire risk assessment and fire spread rate calculation.
[0003] The traditional forest FMC measurement method is to collect samples on the spot in the study area, weigh the samples (wet weight), dry them and weigh them again (dry weight), calculate the ratio of their moisture content (wet weight - dry weight) to dry weight, and obtain FMC data for discrete plots. Because the field sampling method is time-consuming and labor-intensive, and it is impossible to obtain continuous surface source data in time and space, but it has high accuracy, it is only used as verification data to verify the FMC data products generated by other research methods. In addition, the meteorological element regression method is the most widely used because of its simple modeling. This method first collects the FMC and related meteorological factor data of the plots in the sample areas of interest at the same time, and then performs multivariate statistical regression to establish a meteorological element regression model. However, because the FMC of the vegetation canopy is not only affected by meteorological factors, but the life activities of the vegetation itself will also affect the changes in its moisture content, the meteorological element regression method is not accurate and has no universality. Compared with traditional FMC measurements and FMC estimation methods based on meteorological data, remote sensing technology can provide large-scale spatial and temporal continuous image data, and the near-infrared and short-wave infrared bands are sensitive to the moisture content of vegetation canopies, making remote sensing technology the only means to monitor FMC over large areas in near real time and spatial and temporal continuity.
[0004] The large-scale spatiotemporally continuous thematic dataset of vegetation canopy FMC obtained based on remote sensing technology can effectively predict the forest fire risk level and provide strong support for forest fire prevention and protection of forest resources in forest areas.
[0005] At present, the methods of estimating FMC using remote sensing technology can be divided into two categories: (i) Estimation methods based on empirical statistical models: directly using empirical statistical models such as multivariate linear regression to perform remote sensing estimation of vegetation canopy FMC, the purpose is to find the linear (or nonlinear) relationship between vegetation canopy FMC and multispectral reflectance data or related typical vegetation indices (such as leaf area index LAI, normalized difference vegetation index NDVI, normalized infrared index NDII and enhanced vegetation index EVI, etc.). (ii) Inversion methods based on physical models (canopy reflectance models); based on physical models, simulate vegetation canopy reflectance and invert key biochemical parameters, including leaf water content (Equivalent Water Thickness, EWT) and leaf dry matter content (DMC), and then invert the FMC based on the ratio of the two.
[0006] The empirical statistical model based on ground-measured data is simple to apply but not universal. Although it can estimate FMC more accurately in a specific study area, it is easily affected by factors such as surface vegetation type, underlying soil background, and sensor type, and has poor universality. Because the vegetation canopy reflectance model describes the clear physical relationship between various biochemical parameters of vegetation and soil background and vegetation canopy reflectance, it is more universal than the empirical statistical model. However, this method also has three obvious disadvantages: 1) Since the number of unknown parameters of the physical model is generally greater than the number of multispectral reflectance bands, and the combination of different input parameters may correspond to the same or similar multispectral reflectance curves, the inversion of key vegetation parameters based on the physical model is essentially pathological; the introduction of prior knowledge can effectively alleviate the pathological problem, but the availability and universality of prior knowledge is another issue that needs attention. At present, prior knowledge is generally introduced by limiting the range of free parameters, but it is impossible to avoid the occurrence of incorrect combinations, which affects the reliability and stability of the inversion results.
[0007] 2) In the FMC inversion based on the physical model, the spectral weak sensitivity of the intermediate parameter DMC makes the FMC inversion accuracy of the vegetation canopy low. Although FMC is a quantity independent of the leaf area index LAI, under natural conditions, when EWT and DMC change, other parameters (such as LAI, chlorophyll, etc.) will also change accordingly. Therefore, in the FMC inversion based on the physical model, the uncertainty of LAI and leaf chlorophyll content has a strong interference effect on the correct inversion of FMC. 3) In the FMC estimation of forest canopy based on the physical model, the correct simulation of the spectral reflectance of the vegetation canopy is the prerequisite for the correct inversion of canopy FMC. The types and structures of vegetation in nature are complex and changeable, and the physical relationship between vegetation canopy parameters and canopy reflectance is more complex. Finding a universal method to describe the relationship between the two is the key to improving the universality of the canopy FMC inversion method. Although the vegetation canopy FMC inversion based on the physical model has developed rapidly, the reliability and universality of its inversion still need to be improved.
[0008] Therefore, a forest fire monitoring method that couples remote sensing data with a geometric optical model is proposed to improve the accuracy and universality of FMC inversion of vegetation canopy. Summary of the invention
[0009] The purpose of the present invention is to provide a forest fire monitoring method that couples remote sensing data with a geometric optical model.
[0010] The specific steps include:
[0011] Step 1: Use the 4-Scale model of geometric optics to simulate the canopy reflectance curves under different canopy types:
[0012] Based on the observation data of different seasonal phases of non-uniform canopy structures in different ecosystems, the most representative leaf biochemical parameters of each type of vegetation in each growing season were selected to determine the input combination under different leaf biochemical parameter values; using the 4-Scale model, the corresponding numerical combination between leaf biochemical parameters and canopy reflectance was established to obtain the canopy reflectance curves under different canopy types, and the canopy reflectance inversion model under different canopy types was obtained; leaf biochemical parameters include leaf dry matter content, leaf structural parameters, leaf area index LAI, chlorophyll content, carotene content, lutein content and leaf water content;
[0013] Step 2: Classify the monitored forests according to different canopy types based on satellite remote sensing data, and use nadir reflectance data to obtain canopy reflectance spectral curves of different canopy types of the monitored forests under satellite remote sensing;
[0014] Step 3, converting the canopy reflectance of different canopy types simulated in step 1 into leaf reflectance curves;
[0015] Step 4: compare the leaf reflectance curve simulated in step 3 with the canopy reflectance spectral curves of different canopy types of the monitored forest obtained by satellite remote sensing in step 2, and output an inverted single leaf reflectance curve with the highest relative overlap under each canopy type. The leaf biochemical parameter simulation corresponding to this curve is the leaf biochemical parameter under the canopy type of the monitored forest, thereby obtaining the leaf moisture content and leaf dry matter content;
[0016] Step 5. When the leaf moisture content and leaf dry matter content change, other leaf biochemical parameters will also change accordingly. Then, the leaf moisture content and leaf dry matter content are monitored through steps 1 to 4, and the forest fire risk of the monitored forest is assessed in real time to achieve accurate monitoring of forest fires.
[0017] The FMC inversion of vegetation canopy requires obtaining two parameters, namely, leaf water content (Equivalent Water Thickness, EWT) and leaf dry matter content (DMC), and usually, when FMC changes, other leaf biochemical parameters (such as leaf area index LAI, chlorophyll, etc.) will also change accordingly. The present invention obtains the canopy reflectance spectral curves under different canopy types of monitored forests through satellite remote sensing, combines the geometric optical model, simulates the canopy reflectance curves under different canopy types for complex and changeable vegetation types and structures, converts the simulated canopy reflectance under different canopy types into leaf reflectance curves, inverts the leaf water content EWT and leaf dry matter content DMC, and monitors the leaf water content EWT and leaf dry matter content DMC under various types of forest canopies in real time, which can improve the accuracy and universality of vegetation canopy FMC inversion and make forest fire monitoring more accurate. DETAILED DESCRIPTION
[0018] A forest fire monitoring method coupling remote sensing data with a geometric optical model comprises the following steps:
[0019] Step 1: Use the geometric optics model 4-Scale model to simulate the canopy reflectance curves under different canopy types.
[0020] Based on the observation data of different seasonal phases of non-uniform canopy structures in different ecosystems, the most representative leaf biochemical parameters of each type of vegetation in each growing season are selected to determine the input combination under different leaf biochemical parameter values. Using the 4-Scale model, the corresponding numerical combination between leaf biochemical parameters and canopy reflectance is established, so as to obtain the canopy reflectance curves under different canopy types and the canopy reflectance inversion models under different canopy types;
[0021] In this embodiment, according to the 4Scale model, three types of forest canopy spectra, namely shrub, coniferous forest, and broad-leaved forest, are simulated. The following table is an example of input parameters for simulating the forest canopy spectrum of the shrub type:
[0022] Table 1. Forest canopy input parameters
[0023]
[0024] Step 2: Classify typical objects based on satellite remote sensing data:
[0025] The remote sensing data used for the classification of land objects is the MODIS land cover dataset (MCD12Q1), which contains 17 major land cover types, including 11 natural vegetation types. Based on the downloaded and preprocessed land cover dataset (MCD12Q1), combined with the forest canopy classification in step 1, the monitored forest is divided into three canopy types: shrubs, coniferous forests, and broad-leaved forests in this embodiment; and the nadir reflectance data (MCD43A4) is used to obtain the canopy reflectance spectral curves of different canopy types of the monitored forest under satellite remote sensing.
[0026] Step three: convert the canopy reflectance under different canopy types simulated in step one into a leaf reflectance curve. This method has been disclosed in the invention patent with application number 201710789027.7 and invention name "A method for satellite remote sensing extraction of leaf reflectance that eliminates the influence of vegetation canopy structure and surface background".
[0027] Step 4: Compare the leaf reflectance curve simulated in step 3 with the canopy reflectance spectral curves of different canopy types of the monitored forest by satellite remote sensing in step 2, and output an inverted single-leaf reflectance curve with the highest relative overlap under each canopy type. The leaf biochemical parameter simulation corresponding to this curve is the leaf biochemical parameter under this canopy type. The leaf biochemical parameters include leaf water content EWT and leaf dry matter content DMC.
[0028] Table 3 Main biochemical parameters of leaves
[0029]
[0030] Step 5. When the leaf moisture content EWT and the leaf dry matter content DMC change, other leaf biochemical parameters (such as leaf area index LAI, chlorophyll, etc.) will also change accordingly. Then, the leaf moisture content and leaf dry matter content are monitored through steps 1 to 4, and the forest fire risk of the monitored forest is evaluated in real time to achieve accurate monitoring of forest fires.
Claims
1. A forest fire monitoring method coupling remote sensing data with a geometric optical model, characterized in that: The specific steps include: Step 1: Use the 4-Scale model of geometric optics to simulate the canopy reflectance curves under different canopy types: Based on the observation data of different seasonal phases of non-uniform canopy structures in different ecosystems, the most representative leaf biochemical parameters of each type of vegetation in each growing season were selected to determine the input combination under different leaf biochemical parameter values; using the 4-Scale model, the corresponding numerical combination between leaf biochemical parameters and canopy reflectance was established to obtain the canopy reflectance curves under different canopy types, and the canopy reflectance inversion model under different canopy types was obtained; leaf biochemical parameters include leaf dry matter content, leaf structural parameters, leaf area index LAI, chlorophyll content, carotene content, lutein content and leaf water content; Step 2: Classify the monitored forests according to different canopy types based on satellite remote sensing data, and use nadir reflectance data to obtain canopy reflectance spectral curves of different canopy types of the monitored forests under satellite remote sensing; Step 3, converting the canopy reflectance under different canopy types simulated in step 1 into leaf reflectance curves; Step 4: compare the leaf reflectance curve simulated in step 3 with the canopy reflectance spectral curves of different canopy types of the monitored forest obtained by satellite remote sensing in step 2, and output an inverted single leaf reflectance curve with the highest relative overlap under each canopy type. The leaf biochemical parameter simulation corresponding to this curve is the leaf biochemical parameter under the canopy type of the monitored forest, thereby obtaining the leaf moisture content and leaf dry matter content; Step 5. When the leaf moisture content and leaf dry matter content change, other leaf biochemical parameters will also change accordingly. Then, the leaf moisture content and leaf dry matter content are monitored through steps 1 to 4, and the forest fire risk of the monitored forest is assessed in real time to achieve accurate monitoring of forest fires.