CN113495252A - Forest canopy active combustible load estimation method based on polarization decomposition - Google Patents

Forest canopy active combustible load estimation method based on polarization decomposition Download PDF

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CN113495252A
CN113495252A CN202110689110.3A CN202110689110A CN113495252A CN 113495252 A CN113495252 A CN 113495252A CN 202110689110 A CN202110689110 A CN 202110689110A CN 113495252 A CN113495252 A CN 113495252A
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polarization
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CN113495252B (en
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何彬彬
李彦樨
全兴文
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University of Electronic Science and Technology of China
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9021—SAR image post-processing techniques
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Abstract

The invention discloses a forest canopy active combustible load estimation method based on polarization decomposition, and relates to the technical field of remote sensing inversion. The vegetation volume scattering part in SAR observation information is extracted by adopting full polarization SAR data and combining a polarization decomposition technology; and the vegetation canopy is directly modeled by combining an Expanded Water Cloud Model (EWCM), so that the influence of a ground surface scattering signal is avoided. The method can accurately estimate the CAFL, has good generalization performance, provides technical support for the CAFL estimation in a large range on a space-time scale, and further assists fire managers to carry out fire prevention and extinguishing work.

Description

Forest canopy active combustible load estimation method based on polarization decomposition
Technical Field
The invention relates to the technical field of remote sensing inversion, in particular to a forest canopy active combustible load estimation method based on polarization decomposition.
Background
The large-area and long-lasting forest wildfire can generate a large amount of smoke and carbon dioxide in a short time, reduce the air quality and seriously threaten human life and property. Such a fire is often a canopy fire, and has the characteristics of high intensity, rapid propagation, severe and more persistent impact on ecosystem, and the like. Canopy fuel is an essential component of crown fire events and management, particularly in conifers. Better understanding of crown combustibles and their characteristics is crucial to controlling forest fires and to successfully predicting fire behavior. As a basic material for the initiation, burning and spreading of fires, forest canopy combustible load is the only fire-related component that humans can change through management. The accurate information of the combustible load of the forest canopy in space and time is the key for evaluating the fire risk and designing fuel reduction measures, thereby reducing casualties. The active combustible load of the forest canopy is the fuel which is easy to ignite and is consumed at the earliest stage of fire.
Forest canopy active combustible load (CAFL) refers specifically to the sum of the dry weights of all leaves and twigs (less than 0.6 cm in diameter) per unit area. Traditional acquisition of forest combustible load (including CAFL) is based primarily on extensive field sampling or application of an equation of heterodynic growth derived from destructive sampling and forest stand parameters. Although this may be the most accurate method within a particular combustible type and region. But the method consumes a lot of manpower, financial resources and material resources, and the periodic description of the CAFL dynamic condition is difficult to realize.
The development of remote sensing technology provides a new opportunity for CAFL estimation. Remote sensing can provide large-area, periodic medium and high resolution earth observation data, has obvious advantages in CAFL estimation compared with the traditional method, and has been used for decades. But existing research is almost universally dependent on a variety of optical data. In the cloud and fog frequent region of southwest of Sichuan, etc., the validity of optical data is greatly limited. Synthetic Aperture Radars (SAR) can work all day long without being affected by weather and are very sensitive to forest stand structure and biomass of conifers. At present, SAR data are less applied to forest combustible load estimation, and an empirical statistical method is adopted in few researches. Empirical models are derived from specific research data in a specific research area, and thus are less generalized and more difficult to apply to other areas.
Disclosure of Invention
The invention provides a forest canopy active combustible load estimation method based on polarization decomposition, and aims to solve the problems that the generalization performance of an experimental model in the existing forest canopy active combustible load estimation is weak and the SAR data is not fully researched by the existing research.
The technical scheme provided by the invention is as follows: a forest canopy active combustible load estimation method based on polarization decomposition comprises the following steps:
step 1: acquiring full-polarization Synthetic Aperture Radar (SAR) data, vegetation coverage data and ground object coverage type data; the fully polarized SAR data comprises any one of an X wave band, a C wave band, an S wave band, an L wave band and a P wave band; and completing the preprocessing of the data, wherein the preprocessing comprises the following steps: radiation correction, filtering and terrain correction, and generating a polarization coherence matrix T for polarization decomposition3；
Step 2: extracting polarization parameters, and performing polarization decomposition on the polarization coherent matrix T in the step 13Carrying out polarization decomposition; SAR observation information is decomposed into a plurality of scattering mechanisms with specific physical significance, and the polarization decomposition method is a polarization decomposition method specific to forest regions.
Specifically, it comprises 5 parts:
step 2.1: angle of departure from direction
The direction angles of different image elements can be greatly different, particularly in rugged regions, which can cause uncertainty of polarization decomposition, so that the direction angle removing treatment is carried out before the polarization decomposition; removing the direction angle, namely extracting the polarization coherent matrix T obtained after the pretreatment in the step 13Rotated by theta degrees along the radar line of sight. Here by T3_θRepresents the polar coherence matrix after de-azimuth processing:
Figure BDA0003125843290000021
step 2.2: selecting a volume scattering model
The choice of the volume scattering model depends on the implantOrientation P of the local dipoler：
Figure BDA0003125843290000022
wherein ,SvvRepresenting the complex form, S, of VV polarization observationshhRepresenting complex forms of HH polarization observations, according to PrValue to determine a volume scattering model Tv：
Figure BDA0003125843290000023
Figure BDA0003125843290000024
Figure BDA0003125843290000025
wherein ,fvRepresenting the intensity coefficient of the scattered portion of the volume;
step 2.3: decomposition of bulk, surface and secondary scattering mechanisms
Polarization decomposition is the process of decomposing the observed data into volume scattering, surface scattering and secondary scattering, where polarization decomposition is based on T after the previous direction angle processing in step 2.13_θMatrix processing and the volume scattering model T used in this stepvSelected from step 2.2:
Figure BDA0003125843290000031
wherein ,TijRepresents T3_θThe i-th row and j-th column matrix elements in this 3 x 3 matrix are labeled "+" to denote the conjugate transpose, TsFor the surface scattering model:
Figure BDA0003125843290000032
Tdfor the secondary scattering model:
Figure BDA0003125843290000033
wherein ,fsIntensity coefficient representing surface scattering, fdThe intensity coefficient of the secondary scattering is represented, beta represents the normalized difference of H polarization and V polarization Bragg scattering, and alpha represents the normalized difference of H polarization and V polarization earth surface-vegetation stem Fresnel scattering;
step 2.4: negative energy confinement
F of each SAR observation pixel can be obtained according to the calculation in the step 2.3s、fd、fvα and β, based on which the energy of the respective partial scattering mechanism is calculated:
Ps＝fs(1+|β|2) (9)
Pd＝fd(1+|α|2) (10)
Pv＝fv (11)
Psrepresenting the surface scattered energy, PdRepresents the secondary scattered energy, PvRepresents the volume scattering energy; when the calculated surface scattering energy or secondary scattering energy is a negative value, the pixel with the negative energy is defined as follows:
if Ps<0：
Ps＝0 (12)
Pv＝Psum-Pd (13)
If Pd<0：
Pd＝0 (14)
Pv＝Psum-Ps (15)
Psum＝T11+T22+T33 (16)
wherein ,PsumRepresents the total scattered energy observed by the SAR;
step 2.5: calculating volume scattering backscattering coefficient
Based on the polarization decomposition operation, the volume scattering energy P obtained in step 2.4vThe volume scattering model T selected in step 2.2 is substituted again according to equation (11)vAccording to the volume scattering model TvCalculating to obtain the final volume scattering HV backscattering coefficient
Figure BDA0003125843290000041
HH backscattering coefficient
Figure BDA0003125843290000042
And VV backscattering coefficient
Figure BDA0003125843290000043
Figure BDA0003125843290000044
Figure BDA0003125843290000045
Figure BDA0003125843290000046
TvijRepresenting a volume scattering model TvRow i and column j in this 3 x 3 matrix;
and step 3: calculating the volume scattering backscattering coefficient after polarization decomposition aiming at the forest canopy scattering mechanism
Figure BDA0003125843290000047
Involving HV, HH and VV polarization, i.e. obtained by decomposition of the polarization in step 2
Figure BDA0003125843290000048
And
Figure BDA0003125843290000049
Figure BDA00031258432900000410
is composed of
Figure BDA00031258432900000411
Or
Figure BDA00031258432900000412
wherein ,
Figure BDA00031258432900000413
representing the backscattering coefficient of the pure soil pixel,
Figure BDA00031258432900000414
backscattering coefficient, CAFL, representing pixels of dense forest coverage areasdfThe combustible load of leaves representing the pixels of the dense forest coverage area, beta represents the attenuation coefficient of the forest canopy to microwave data, and CAFL represents the active combustible load of the forest canopy;
and 4, step 4: and calculating the active combustible load CAFL of the forest canopy.
The vegetation volume scattering part in SAR observation information is extracted by adopting full polarization SAR data and combining a polarization decomposition technology; and the vegetation canopy is directly modeled by combining an Expanded Water Cloud Model (EWCM), so that the influence of a ground surface scattering signal is avoided. The method can accurately estimate the CAFL, has good generalization performance, provides technical support for the CAFL estimation in a large range on a space-time scale, and further assists fire managers to carry out fire prevention and extinguishing work.
Drawings
FIG. 1 is a schematic flow diagram of the overall process of the present invention.
Fig. 2 is a schematic diagram of the geographical location of a research plot in an embodiment of the present invention.
FIG. 3 is a result of inverting forest canopy active combustible load in an embodiment of the invention.
Detailed Description
The forest canopy active combustible load estimation method based on polarization decomposition provided by the invention is further explained by combining the specific embodiment and the attached drawings in the specification:
a forest canopy active combustible load estimation method based on polarization decomposition is shown in figure 1 and comprises the following steps:
(1) data preparation
The actual measurement of forest canopy active combustible load (CAFL) was obtained by field investigation in the southwest region of Sichuan province in China during 3, 16 to 20 months in 2021. As shown in fig. 2, the measured data is the geographical location distribution used in this embodiment. Where each sample point represents a valid range of 10 x 10 meters. The fully polarized SAR data used in this embodiment is Radarsat-2 data of the C-band. Preprocessing the data, including radiation correction, polarization filtering, geocoding, and generation of a polarized coherence matrix (T)3)。
(2) Polarization parameter extraction
Polarized coherent matrix (T) based on extraction after preprocessing3) And carrying out polarization decomposition operation to extract the volume scattering part information of the inversion vegetation canopy scattering characteristics.
Specifically, it includes 5 parts:
a. angle of departure from direction
The direction angles of different picture elements may vary greatly, especially in rugged areas, which may lead to uncertainty in the polarization decomposition. Removing the angle of orientation to obtain the observed polarization coherence matrix (T)3) Rotation by θ degrees along the radar line of sight:
Figure BDA0003125843290000051
b. selecting a volume scattering model
The selection of the volume scattering model depends on the orientation (P) of the dipoles in the vegetation arear)：
Figure BDA0003125843290000052
When P is presentr>2dB, representing the horizontal direction; when P is presentr<-2dB, representing the vertical direction; when-2 dB<Pr<At 2dB, it represents a random direction. According to actual situation PrValue to determine a volume scattering model (T)v)：
Figure BDA0003125843290000053
Figure BDA0003125843290000054
Figure BDA0003125843290000055
c. Decomposition of bulk, surface and secondary scattering mechanisms
Polarization decomposition is the process of decomposing the observed data into volume scattering, surface scattering and secondary scattering:
Figure BDA0003125843290000061
wherein ,TsThe method refers to a surface scattering model:
Figure BDA0003125843290000062
Tdthe second scattering model:
Figure BDA0003125843290000063
d. negative energy confinement
After polarization decomposition, the energy of each partial scattering mechanism can be calculated:
Ps＝fs(1+|β|2) (9)
Pd＝fd(1+|α|2) (10)
Pv＝fv (11)
Psrepresenting the surface scattered energy, PdRepresents the secondary scattered energy, PvRepresenting the volume scatter energy. In actual operation, the earth surface scattered energy or the secondary scattered energy may be negative. However, this case obviously has no physical significance, and therefore, the following limitation is made for the pixel in which negative energy occurs:
if Ps<0：
Ps＝0 (12)
Pv＝Psum-Pd (13)
If Pd<0：
Pd＝0 (14)
Pv＝Psum-Ps (15)
e. Calculating volume scattering backscattering coefficient
Based on the polarization decomposition operation, the volume scattering HV backscattering coefficient can be calculated
Figure BDA0003125843290000064
HH backscattering coefficient
Figure BDA0003125843290000065
And VV backscattering coefficient
Figure BDA0003125843290000066
Figure BDA0003125843290000067
Figure BDA0003125843290000068
Figure BDA0003125843290000069
(3) Modeling
And aiming at a forest canopy scattering mechanism, establishing a model for inverting the active combustible load of the forest canopy. The concrete form is as follows:
Figure BDA0003125843290000071
wherein ,
Figure BDA0003125843290000072
representing the volume scattering backscattering coefficient after polarization decomposition (
Figure BDA0003125843290000073
Or
Figure BDA0003125843290000074
)；
Figure BDA0003125843290000075
Representing the backscattering coefficient of the pure soil pixel;
Figure BDA0003125843290000076
representing the backscattering coefficient of the pixel of the dense forest coverage area; CAFLdfThe combustible load of the leaves representing the pixels of the dense forest coverage area; beta represents the attenuation coefficient of the forest canopy to the microwave data; CAFL stands for forest canopy active combustible load.
(4) Forest canopy active combustible load inversion
And (3) solving an analytic solution of the forest canopy active combustible load according to a model (formula (19)) established by the volume scattering backscattering coefficient extracted after the polarization decomposition and the scattering mechanism of the forest canopy:
Figure BDA0003125843290000077
Figure BDA0003125843290000078
and solving the load capacity of the forest canopy active combustible according to the analytic solution. Fig. 3 shows the inversion result of the forest canopy active combustible load amount in this embodiment of the present invention.

Claims (1)

1. A forest canopy active combustible load estimation method based on polarization decomposition comprises the following steps:
step 1: acquiring full-polarization Synthetic Aperture Radar (SAR) data, vegetation coverage data and ground object coverage type data; the fully polarized SAR data comprises any one of an X wave band, a C wave band, an S wave band, an L wave band and a P wave band; and completing the preprocessing of the data, wherein the preprocessing comprises the following steps: radiation correction, filtering and terrain correction, and generating a polarization coherence matrix T for polarization decomposition3；
Step 2: extracting polarization parameters, and performing polarization decomposition on the polarization coherent matrix T in the step 13Carrying out polarization decomposition; SAR observation information is decomposed into a plurality of scattering mechanisms with specific physical significance, and the polarization decomposition method is a polarization decomposition method specific to forest regions.
Specifically, it comprises 5 parts:
step 2.1: angle of departure from direction
The direction angles of different pixels may vary greatly, especially in rugged terrain, which may lead to uncertainty in polarization decomposition, and hence in polarization decompositionRemoving the direction angle before the solution; removing the direction angle, namely extracting the polarization coherent matrix T obtained after the pretreatment in the step 13Rotated by theta degrees along the radar line of sight. Here by T3_θRepresents the polar coherence matrix after de-azimuth processing:
Figure FDA0003125843280000011
step 2.2: selecting a volume scattering model
The selection of the volume scattering model depends on the orientation P of the dipoles in the vegetation arear：
Figure FDA0003125843280000012
wherein ,SvvRepresenting the complex form, S, of VV polarization observationshhRepresenting complex forms of HH polarization observations, according to PrValue to determine a volume scattering model Tv：
Figure FDA0003125843280000013
Figure FDA0003125843280000014
Figure FDA0003125843280000015
wherein ,fvRepresenting the intensity coefficient of the scattered portion of the volume;
step 2.3: decomposition of bulk, surface and secondary scattering mechanisms
Polarization decomposition is the process of decomposing the observed data into volume scattering, surface scattering and secondary scattering, where polarization decomposition is based on the past in step 2.1Angle of orientation treated T3_θMatrix processing and the volume scattering model T used in this stepvSelected from step 2.2:
Figure FDA0003125843280000021
wherein ,TijRepresents T3_θThe i-th row and j-th column matrix elements in this 3 x 3 matrix are labeled "+" to denote the conjugate transpose, TsFor the surface scattering model:
Figure FDA0003125843280000022
Tdfor the secondary scattering model:
Figure FDA0003125843280000023
wherein ,fsIntensity coefficient representing surface scattering, fdThe intensity coefficient of the secondary scattering is represented, beta represents the normalized difference of H polarization and V polarization Bragg scattering, and alpha represents the normalized difference of H polarization and V polarization earth surface-vegetation stem Fresnel scattering;
step 2.4: negative energy confinement
F of each SAR observation pixel can be obtained according to the calculation in the step 2.3s、fd、fvα and β, based on which the energy of the respective partial scattering mechanism is calculated:
Ps＝fs(1+|β|2) (9)
Pd＝fd(1+|α|2) (10)
Pv＝fv (11)
Psrepresenting the surface scattered energy, PdRepresents the secondary scattered energy, PvRepresents the volume scattering energy; when the calculated surface scattering energy or second scatteringWhen the emission energy is a negative value, the pixel with the negative energy is limited as follows:
if Ps<0：
Ps＝0 (12)
Pv＝Psum-Pd (13)
If Pd<0：
Pd＝0 (14)
Pv＝Psum-Ps (15)
Psum＝T11+T22+T33 (16)
wherein ,PsumRepresents the total scattered energy observed by the SAR;
step 2.5: calculating volume scattering backscattering coefficient
Based on the polarization decomposition operation, the volume scattering energy P obtained in step 2.4vThe volume scattering model T selected in step 2.2 is substituted again according to equation (11)vAccording to the volume scattering model TvCalculating to obtain the final volume scattering HV backscattering coefficient
Figure FDA0003125843280000031
HH backscattering coefficient
Figure FDA0003125843280000032
And VV backscattering coefficient
Figure FDA0003125843280000033
Figure FDA0003125843280000034
Figure FDA0003125843280000035
Figure FDA0003125843280000036
TvijRepresenting a volume scattering model TvRow i and column j in this 3 x 3 matrix;
and step 3: calculating the volume scattering backscattering coefficient after polarization decomposition aiming at the forest canopy scattering mechanism
Figure FDA0003125843280000037
Involving HV, HH and VV polarization, i.e. obtained by decomposition of the polarization in step 2
Figure FDA0003125843280000038
And
Figure FDA0003125843280000039
Figure FDA00031258432800000310
Figure FDA00031258432800000311
is composed of
Figure FDA00031258432800000312
Or
Figure FDA00031258432800000313
wherein ,
Figure FDA00031258432800000314
representing the backscattering coefficient of the pure soil pixel,
Figure FDA00031258432800000315
backscattering coefficient, CAFL, representing pixels of dense forest coverage areasdfThe combustible load of leaves of the pixels representing the dense forest coverage area, beta represents the forest canopyAttenuation coefficient of the layer to microwave data, CAFL represents forest canopy active combustible load;
and 4, step 4: and calculating the active combustible load CAFL of the forest canopy.
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