CN110058262B - Method for inverting forest fire combustion intensity by vegetation radiation transmission model - Google Patents
Method for inverting forest fire combustion intensity by vegetation radiation transmission model Download PDFInfo
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Abstract
A method for inverting forest fire burning intensity based on a vegetation radiation transmission model layered by tree coverage parameters belongs to the technical field of remote sensing inversion. The method comprises the steps of introducing tree coverage parameters into vegetation radiation transmission model forward simulation, and carrying out layered parameterization according to the variation range of the tree coverage parameters to construct a lookup table suitable for different tree coverage ranges; in the backward inversion process, the tree coverage area is determined by inputting the tree coverage remote sensing product, so that the combustion intensity inversion is carried out in the lookup table corresponding to the tree coverage area, the matching degree of the combustion intensity and the remote sensing spectral response curve is improved, the influence of spectral confusion on the combustion intensity inversion is reduced, and the combustion intensity inversion precision is improved. The method can simulate and invert the actual situation of the ground after the fire happens more truly and accurately, the inverted burning intensity is higher in accuracy, and the indication effect on the evaluation of the fire situation after the fire happens and the forest management and protection after the fire happens is more definite.
Description
Technical Field
The invention belongs to the technical field of remote sensing inversion, and particularly relates to a method for inverting forest fire combustion intensity by using a vegetation radiation transmission model
Background
Forest fire burning intensity is generally defined as the impact of a fire on vegetation and soil in a particular area. Accurate estimation of forest fire combustion intensity has important guiding significance for quantifying the influence of fire on key ecological processes (such as tree death situation, post-disaster vegetation recovery and intra-species/inter-species competition) and forest protection and management after fire.
Fire can have a direct impact on the physicochemical properties of surface vegetation and soil. Vegetation, soil and post-disaster ground ashes in different states generate different echo signals for the remote sensing sensor, so that the forest fire burning intensity estimation based on the satellite remote sensing data becomes possible. Unlike the biochemical parameters of general vegetation (chlorophyll, water content of canopy and dry matter content), the burning intensity is not only a single vegetation parameter, but also relates to the comprehensive evaluation of multiple factors such as vegetation, soil and post-disaster surface ash. At present, the most representative ground quantitatively evaluates the burning intensity and verifies that the actually measured intensity index of the remote sensing inversion precision is a Comprehensive Burning Index (CBI), the variation range is 0-3, 0 represents unburned, and 3 represents complete burning. CBI divides the forest vegetation structure into 5 layers (ground soil layer, lower vegetation, middle and upper vegetation) from top to bottom, and evaluates the burning degree of each layer of vegetation respectively to obtainSpecific class of combustion severity values CBI i Wherein i is the number of the hierarchy, and finally calculating the average combustion intensity values of 5 hierarchies to obtain the CBI of all the hierarchies. In practice, these 5 levels are often reduced to 4 levels, namely, the ground soil level, the lower vegetation, the middle vegetation (middle-lower vegetation, middle-upper vegetation) and the upper vegetation.
The existing method for estimating the combustion intensity based on an optical remote sensing means mainly comprises the following four types: an experience statistical method, a spectrum mixed decomposition method, a machine learning method and a vegetation radiation transmission model method based on the vegetation index and the pre-disaster and post-disaster difference form thereof. The experience statistical method based on the vegetation index and the pre-disaster and post-disaster difference form is convenient and efficient, but the experience relationship established according to statistics is different from time to place, has no universality, and depends on measured data to be used for fitting an experience model. The method of decomposing mixed pixels to estimate the burning intensity after a fire has the advantages that all spectral band information of a single image after the fire can be used, but the application of the method is limited because of the requirement of a wide end-member spectrum library in a specific region. The machine learning method represented by the random forest method has the advantages that the multivariate environment variables can be fully utilized, but the estimation accuracy of the multivariate environment variables is seriously dependent on the selection of training data, and the trained model does not have universality. The vegetation radiation transmission model is a physical model, the basis of construction is the physical mechanism of radiation transmission of solar photons in vegetation, and compared with the relationship between the reflectivity and the burning intensity, the established reflectivity and burning intensity are more universal. The method mainly comprises the following steps of estimating the combustion intensity based on a vegetation radiation transmission model, firstly carrying out parameterization on the model and carrying out forward simulation, and constructing a lookup table for inverting CBI; secondly, in the backward inversion process, the earth surface reflectivity extracted based on the remote sensing image is matched with the earth surface reflectivity simulated in the model lookup table, and then the CBI value corresponding to the simulated reflectivity with the highest matching degree is searched. The method does not depend on ground actual measurement data, can fully utilize all reflectivity waveband information of the remote sensing image, and is an inversion method which has strong universality and is easy to implement.
Since the CBI estimation takes into account the combustion information of the multi-layered vegetation levels, it is necessary to select a physical model (e.g., ACRM, MCRM, FRT model, etc.) that can simulate the radiation transmission characteristics of the multi-layered vegetation. However, in the imaging process of the optical remote sensing image, the contribution of the underlying soil and ash to the signal of the remote sensing sensor is different due to the change of the coverage degree of the trees. Therefore, when the vegetation radiation transmission model is used for inverting the combustion intensity, even the same combustion intensity can correspond to different spectral response curves; the same spectral response curves may also correspond to different combustion intensities. Researchers have improved (GeoCBI) the influence of tree coverage on the estimation of burning intensity by using the vegetation coverage of each vegetation level as the weight for calculating CBI in the field actual measurement process, as shown in formula 1.
Wherein GeoCBI represents the burning intensity index after modification; m represents each vegetation level; n represents the total number of layers; FCOV m Representing the vegetation coverage of a particular level; CBI m Representing the CBI value for a particular level. The result of this improvement is an improved one-to-one correspondence of the reflectance of the remote-sensed image to the burn intensity index. However, the operation mode has the defect of artificially improving or reducing the combustion intensity estimation grade, namely, in the field actual measurement process, the combustion intensity of the low tree coverage area estimated according to the index calculation method is overestimated; there is an underestimate of combustion intensity in areas of high tree coverage. When the GeoCBI is calculated in an area with low tree coverage, the weight value of trees on the upper layer is small, the burning degree is low, the proportion of grassland and shrub burnt on the lower layer is large, and burning is serious, so the calculated GeoCBI is higher than the original CBI; in the area with high tree coverage, when GeoCBI is calculated, the weight value of trees on the upper layer is larger, the burning degree is lower, the proportion of grassland and shrub burnt on the lower layer is smaller, and burning is serious, so the calculated GeoCBI is lower than the original CBI, which is not consistent with the actual burning intensity conditionAnd (4) synthesizing. Therefore, the problem of how to accurately establish the corresponding relation between the fire burning intensity and the remote sensing image reflectivity is ubiquitous in the research of inverting the burning intensity based on the vegetation radiation transmission model, and cannot be effectively solved so far.
Disclosure of Invention
Aiming at the problem of spectrum confusion caused by tree coverage change when the forest fire combustion intensity is inverted based on the vegetation radiation transmission model in the existing forest fire combustion intensity inversion process, a novel method for inverting the forest fire combustion intensity by a planted radiation transmission model is provided.
The technical scheme provided by the invention is as follows:
a method for inverting forest fire burning intensity by a vegetation radiation transmission model is characterized by comprising the following steps:
step A: acquiring an optical remote sensing image after a forest fire in a research area and tree coverage remote sensing data before the fire;
and B: selecting a vegetation radiation transmission model to simulate the surface reflectivity of multilayer vegetation after a fire occurs in a research area in a forward direction, carrying out layered parameterization operation on the vegetation radiation transmission model, dividing tree coverage parameters into a plurality of areas, and then respectively carrying out simulation and lookup table construction according to tree coverage values in the areas;
and C: and in the backward inversion process, inputting the tree coverage extracted according to the remote sensing image before the fire disaster into the model, determining the interval where the tree coverage value is located, and then performing combustion intensity inversion in a lookup table corresponding to the specific tree coverage interval.
Further, the earth surface reflectivity data after the fire occurs should be preprocessed before being used for inverting the burning intensity, and the preprocessing comprises radiometric calibration, atmospheric correction and cloud shadow removal.
Further, the tree coverage remote sensing data before the fire disaster occurs can be obtained through calculation of the earth surface reflectivity of the image before the fire disaster or through related remote sensing products.
Further, since the ground-based actual measurement CBI considers the respective combustion states of the soil level and the multi-layer vegetation level, the vegetation radiation transmission model should be a multi-layer model capable of simulating the radiation transmission characteristics of the multi-layer vegetation structure.
Further, the model should be subjected to parameter sensitivity analysis prior to the model parameterization operation. Because the vegetation radiation transmission model is complex and has numerous related parameters, but the change of all the parameters is not sensitive to the target reflectivity, the sensitivity analysis operation is carried out on the model in order to improve the execution efficiency of the model and reduce the excessive uncertainty introduced by the ill-conditioned inversion of the model.
Furthermore, the layering parameterization according to the tree coverage parameter variation range (namely, the interval division of the tree coverage parameter) mainly depends on the precision of the tree coverage auxiliary product, the number of the divided intervals is not limited, and the lengths of the divided intervals can be the same or different.
As a specific implementation mode, the tree coverage (0-100%) parameter is divided into 5 intervals at intervals of 20% to respectively carry out simulation and lookup table construction.
As a specific implementation mode, the tree coverage (0-100%) parameter is divided into 10 intervals at intervals of 10% to respectively carry out simulation and lookup table construction.
As a specific implementation mode, the tree coverage (0-100%) parameter is divided into 20 intervals at intervals of 5% to respectively carry out simulation and lookup table construction.
As a specific implementation mode, the tree coverage (0-100%) parameter is divided into 25 intervals at intervals of 4% to respectively carry out simulation and lookup table construction.
The design concept of the invention is that a lookup table suitable for different tree coverage ranges is constructed by introducing tree coverage parameters in vegetation radiation transmission model forward simulation and carrying out layered parameterization on the tree coverage parameters according to the variation range of the tree coverage parameters; the tree coverage is limited by inputting a tree coverage remote sensing product in the backward inversion process, so that the combustion intensity inversion is operated in a lookup table corresponding to a specific tree coverage range, the matching degree of the combustion intensity and a remote sensing spectral response curve is improved, the influence of spectral confusion on the combustion intensity inversion is reduced, and the combustion intensity inversion precision is improved.
Compared with the prior art, the invention has the beneficial effects that:
the method is suitable for solving the spectrum confusion problem caused by the tree coverage change in the process of inverting the combustion intensity of the forest fire based on the vegetation radiation transmission model. According to the method, firstly, a lookup table is simulated and constructed in different regions according to tree coverage parameters in a model, secondly, an inversion region is determined according to the tree coverage before a fire disaster occurs in backward inversion, the combustion intensity is inverted in the lookup table corresponding to a specific tree coverage region instead of global optimal retrieval, and the corresponding relation of the combustion intensity and the canopy reflectivity obtained by remote sensing images can be effectively improved by the processing method, so that the inversion accuracy of the combustion intensity is improved. Compared with the traditional method for inverting the forest fire combustion intensity based on the vegetation radiation transmission model, the method provided by the invention considers tree coverage in forward simulation and backward inversion instead of in a field actual measurement link, so that the actual situation of the ground after the fire occurs can be simulated and inverted more truly and accurately, the inverted combustion intensity is higher in precision, and the indication effect on the evaluation of the fire situation after the fire occurs and the management and protection of the forest after the fire occurs is more definite.
Drawings
FIG. 1 is a scheme for evaluating the ground measured burning intensity (CBI).
FIG. 2 is a schematic diagram of a ground reflection signal after a fire with the same burning intensity is obtained by remote sensing images. Wherein fig. 2a represents a case where the coverage of the tree is high, and fig. 2b represents a case where the coverage of the tree is low.
Fig. 3 is a spectral response curve of a vegetation radiation transmission model after being improved by a traditional method according to the invention under different tree coverage when the CBI value is 2.0, wherein TC represents the tree coverage.
FIG. 4 is a graph of the accuracy of the inversion of the method for estimating combustion intensity based on the proposed method and a conventional radiation transfer model, wherein RTM + TCC in the graph (a) represents the inversion result of the combustion intensity of the present invention, and RTM + GOS in the graph (b) represents the inversion result of the conventional method.
Detailed Description
The method for inverting the burning intensity of the forest fire by the improved vegetation radiation transmission model provided by the invention is further explained by combining the specific embodiment and the attached drawings of the specification:
the method comprises the following steps: data preparation
Firstly, obtaining an optical remote sensing image after a forest fire in a research area and tree coverage remote sensing data before the fire. The remote sensing data after fire disaster is subjected to radiometric calibration, atmospheric correction and cloud shadow removal operation to obtain surface reflectivity data. The tree coverage data can be obtained by the existing remote sensing products or by a tree coverage extraction algorithm according to the surface reflectivity of the remote sensing image before disaster. If the existing tree coverage remote sensing product is adopted, resampling operation and spatial resolution of post-disaster remote sensing data are kept unified.
Step two: model selection
The evaluation of the ground actual measurement index CBI of the fire burning intensity is the result of comprehensively considering the burning degrees of the multilayer vegetation and the ground soil layer. In the actual evaluation process, as shown in fig. 1, CBI divides the forest vegetation structure into 5 layers (ground soil layer, lower vegetation, middle and upper vegetation, and upper vegetation) from top to bottom, evaluates each layer, and calculates the final average CBI. Therefore, the vegetation transmission model should select a multilayer model capable of simulating the radiation transmission characteristics of the multilayer vegetation structure, such as ACRM, MCRM, FRT model, and the like.
Step three: model sensitivity analysis and model forward simulation
The vegetation radiation transmission model has many parameters, but not all the parameters are sensitive to a target waveband used for fire burning intensity inversion, so that the parameters sensitive to the target waveband reflectivity in the model need to be screened out through sensitivity analysis of the model, and forward simulation and lookup table construction are carried out.
Due to the change of the tree coverage, the contribution degree of ground ash and soil to the remote sensing sensor signal changes, and different spectral responses can be generated on the remote sensing image even if the same combustion intensity is achieved. For the case of fig. 2a, the green crown will block the reflected signal from the ground due to dense tree coverage, while fig. 2b, due to sparse tree coverage, will have much more signal from the burning vegetation and soil on the underlying surface than fig. 2a, so fig. 2a and 2b will correspond to two very different spectral reflectance curves, but in reality the burning intensity of both will be the same, since the burning level is the same for each vegetation level. According to the method for calculating GeoCBI, since the tree coverage in fig. 2a is dense, the weight of the upper tree is higher when calculating GeoCBI, and the burning degree of the tree layer is lower, so the calculated GeoCBI is lower than the GeoCBI value in fig. 2b, which is not consistent with the actual situation.
The invention introduces the influence of Tree Coverage (TC) based on the traditional method, performs interval simulation according to the variation range of the TC, and respectively constructs a lookup table suitable for the specific tree coverage range. The result of the spectral response curve of the same combustion intensity value (CBI ═ 2.0) in different tree coverage intervals of the model simulation is shown in fig. 3, in the process of the model simulation, other parameters are kept fixed, and only the Tree Coverage (TC) is changed, as can be seen from fig. 3, the TC is changed from less than 10% to more than 50%, the shape of the spectral curve is changed greatly, and the influence of the TC on the combustion intensity is very large.
Therefore, for the combustion intensity inversion under different tree coverage conditions, in the vegetation radiation transmission model forward simulation process, the regional simulation is carried out according to the variation range of the tree coverage, the lookup tables suitable for the specific tree coverage range are respectively constructed, the actual conditions of the ground after the fire disaster occurs can be simulated and inverted more truly and accurately, and the accuracy of the combustion intensity obtained by inversion is higher.
Step four: backward inversion
And in the backward inversion process, simultaneously using the remote sensing data prepared in the step one as input data, determining an inversion interval according to tree coverage data before a fire disaster occurs, and inverting the combustion intensity in a lookup table corresponding to a specific tree coverage interval. Through the operation, the corresponding relation between the burning intensity and the canopy reflectivity obtained by the remote sensing image can be effectively improved.
As can be seen from FIG. 4, the method provided by the invention obviously improves the inversion accuracy of the combustion intensity CBI, and compared with the traditional inversion method based on the vegetation radiation transmission model, the new method provided by the invention actually measures the coefficient of determination R between the CBI and the inverted CBI 2 (closer to 1, better) from 0.33 to 0.54; increase the slope of the fit line (closer to 1, better) from 0.56 to 0.8; the root mean square error RMSE (smaller is better) is reduced from 0.53 to 0.43. Experimental results show that the method provided by the invention effectively solves the problem of influence of tree coverage on combustion intensity estimation. Therefore, the new method provided by the invention can effectively solve the problem that the tree coverage influences the combustion intensity estimation, and improves the accuracy of the inversion result.
Claims (5)
1. A method for inverting forest fire burning intensity by a vegetation radiation transmission model is characterized by comprising the following steps:
step A: acquiring an optical remote sensing image after a forest fire in a research area occurs and tree coverage remote sensing data before the fire occurs; preprocessing the earth surface reflectivity data after the fire disaster occurs before the earth surface reflectivity data is used for inverting the combustion intensity, wherein the preprocessing comprises radiometric calibration, atmospheric correction and cloud shadow removal;
and B: selecting a vegetation radiation transmission model to simulate the surface reflectivity of multilayer vegetation after a fire occurs in a research area in a forward direction, carrying out layered parameterization operation on the vegetation radiation transmission model, dividing tree coverage parameters into a plurality of areas, and then respectively simulating tree coverage values in the areas and constructing a lookup table;
and C: and in the backward inversion process, inputting the tree coverage extracted according to the remote sensing image before the fire disaster into the model, determining the interval where the tree coverage value is located, and then performing combustion intensity inversion in a lookup table corresponding to the specific tree coverage interval.
2. The method of claim 1, wherein the remote sensing data of tree coverage before the fire occurs is obtained by calculating the reflectivity of the earth surface of the image before the fire or related remote sensing products.
3. The method of claim 1, wherein the vegetation radiation transmission model is a multi-layer model capable of simulating radiation transmission characteristics of a multi-layer vegetation structure.
4. The method of claim 1, wherein the model is subjected to a parameter sensitivity analysis prior to a model hierarchical parameterization operation.
5. The method according to claim 1, wherein the parameters are parameterized in layers according to the variation range of the tree coverage parameters, and the division results in the same or different lengths of the sections.
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