CN113495252B - Forest canopy active combustible material load estimation method based on polarization decomposition - Google Patents

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

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CN113495252B
CN113495252B CN202110689110.3A CN202110689110A CN113495252B CN 113495252 B CN113495252 B CN 113495252B CN 202110689110 A CN202110689110 A CN 202110689110A CN 113495252 B CN113495252 B CN 113495252B
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CN113495252A (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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Abstract

The invention discloses a forest canopy active combustible material load estimation method based on polarization decomposition, and relates to the technical field of remote sensing inversion. The invention adopts full polarization SAR data and combines polarization decomposition technology to extract vegetation volume scattering part in SAR observation information; and combining with an expanded water cloud model (EWC) to directly model a vegetation canopy, so that the vegetation canopy is prevented from being influenced by surface scattering signals. The method can accurately estimate the CAFL and has good generalization performance, provides technical support for CAFL estimation on a large scale on a space-time scale, and further assists fire management staff in performing fire prevention and extinguishing work.

Description

Forest canopy active combustible material 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
Forest wildfires of large area and long duration produce large amounts of smoke and carbon dioxide in a short period of time, reduce air quality, and pose a serious threat to human lives and properties. The extra-large fire is usually a canopy fire and has the characteristics of high strength, high spreading speed, serious and more continuous influence on an ecological system and the like. Canopy fuels are an essential component of crown fire events and management, especially in conifer forests. Better understanding of forest crown combustibles and their characteristics is critical to controlling forest fires and successfully predicting fire behavior. As a basic material for the ignition, burning and spread of fires, forest canopy combustible load is the only component that humans can alter through management in connection with fires. Spatially accurate forest canopy combustible load information is critical for assessing fire risk and designing fuel cut-off measures, thereby reducing casualties. The active combustible material load of the forest canopy is the fuel which is most easy to ignite and is consumed at the earliest stage in the early 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 loads (including CAFL) is based primarily on extensive field sampling or application of heterospeedy growth equations derived from destructive sampling and stand parameters. Although this is probably the most accurate method in a particular type and region of combustibles. However, this method consumes a lot of manpower, financial resources and material resources, and it is difficult to implement periodic description of the dynamic status of the CAFL.
The development of remote sensing technology provides a new opportunity for the estimation of CAFL. Remote sensing is capable of providing large-area, periodic medium and high resolution earth-to-earth observations, has significant advantages over conventional methods in CAFL estimation, and has been used for decades. But current research is almost universally dependent on various optical data. In the areas of cloud and fog frequent in southwest, etc., the validity of optical data is greatly limited. Synthetic Aperture Radar (SAR) can operate weather-proof throughout the day and is very sensitive to stand structure and biomass of conifer forests. At present, the SAR data has less application in forest combustible load estimation, and an empirical statistical method is adopted in few researches. Empirical models are derived from specific study data in specific study areas and are therefore less generalizable and more difficult to apply to other areas.
Disclosure of Invention
Aiming at the problem that an empirical model is weak in current forest canopy active combustible load estimation and the problem that the research of SAR data is insufficient in the existing research, the invention provides a forest canopy active combustible load estimation method based on polarization decomposition.
The technical scheme provided by the invention is as follows: a method for estimating forest canopy active combustible load based on polarization decomposition, the method comprising:
step 1: acquiring full polarization SAR data, including full polarization Synthetic Aperture Radar (SAR) data, vegetation coverage data and ground cover type data; the full-polarization SAR data comprise any one of an X-band, a C-band, an S-band, an L-band and a P-band; and finishing the pretreatment of the data, wherein the pretreatment comprises the following steps: radiation correction, filtering and topography correction, and generating a polarization coherence matrix T for polarization decomposition 3 ；
Step 2: extracting polarization parameters, and performing polarization decomposition on the polarization coherence matrix T in the step 1 3 Performing polarization decomposition; SAR observation information is decomposed into several scattering mechanisms with specific physical significance, and the polarization decomposition method is a polarization decomposition method specific to forest areas.
Specifically comprising 5 parts:
step 2.1: angle of direction removal
The direction angles of different pixels may vary greatly, especially in areas of rough terrain, which may lead to uncertainty in polarization decomposition, and hence the direction angle removal process is performed before polarization decomposition; the direction angle is removed, namely the polarization coherence matrix T obtained by extraction after pretreatment in the step 1 3 And rotated by theta degrees along the radar observation line. T is used herein 3_θ Representing the polarization coherence matrix after the depirectional angle treatment:
Figure GDA0003314150210000021
step 2.2: selecting a volume scattering model
The choice of the volume scattering model depends on the orientation P of the dipoles of the vegetation region r ：
Figure GDA0003314150210000022
wherein ,Svv Representing complex form of VV polarization observation data, S hh Representing complex forms of HH polarization observations, according to P r Values to determine the volume scattering model T v ：
Figure GDA0003314150210000023
Figure GDA0003314150210000024
Figure GDA0003314150210000025
wherein ,fv Representing the intensity coefficient of the bulk scattering portion;
step 2.3: volumetric and surface scattering and secondary scattering mechanism decomposition
Polarization decomposition is a process of decomposing observed data into bulk scattering, surface scattering and secondary scattering, wherein the polarization decomposition is based on T after the past direction angle treatment in step 2.1 3_θ Matrix-wise and volume scattering model T employed in this step v The selection is performed by the step 2.2:
Figure GDA0003314150210000031
wherein ,Tij Representing T 3_θ The ith row and jth column matrix elements in this 3*3 matrix, superscript ". X" means conjugate transpose, T s Is a surface scattering model:
Figure GDA0003314150210000032
T d is a secondary scattering model:
Figure GDA0003314150210000033
wherein ,fs Representing the intensity coefficient of surface scattering, f d Representing the intensity coefficient of the secondary scattering, β representing the normalized difference of the H-polarized and V-polarized bragg scattering, α representing the normalized difference of the H-polarized and V-polarized earth-vegetation stalk fresnel scattering;
step 2.4: negative energy constraint
F of each SAR observation pixel can be obtained according to the calculation in the step 2.3 s 、f d 、f v Alpha and beta, based on this
Calculating the energy of each partial scattering mechanism:
P s ＝f s (1+|β| 2 ) (9)
P d ＝f d (1+|α| 2 ) (10)
P v ＝f v (11)
P s representing the surface scattering energy, P d Represents the energy of secondary scattering, P v Representing volume scattering energy; when calculated surface powder
When the energy of the emission or the secondary scattering is negative, the pixel with negative energy is defined as follows:
if P s <0：
P s ＝0 (12)
P v ＝P sum -P d (13)
If P d <0：
P d ＝0 (14)
P v ＝P sum -P s (15)
P sum ＝T 11 +T 22 +T 33 (16)
wherein ,Psum Represent STotal scattered energy observed by AR;
step 2.5: calculating the backscatter coefficient of volume scattering
Based on the polarization decomposition operation, the volume scattering energy P obtained in the step 2.4 is obtained v Bringing again the volume scattering model T selected in step 2.2 according to equation (11) v According to the volume scattering model T v Calculating to obtain final volume scattering HV backscattering coefficient
Figure GDA0003314150210000041
HH backscattering coefficient->
Figure GDA0003314150210000042
And VV backscattering coefficient->
Figure GDA0003314150210000043
Figure GDA0003314150210000044
Figure GDA0003314150210000045
Figure GDA0003314150210000046
T vij Representing a volume scattering model T v The ith row and jth column elements in this 3*3 matrix;
step 3: for Senlin canopy scattering mechanism, calculating volume scattering backscattering coefficient after polarization decomposition
Figure GDA0003314150210000047
Comprising HV, HH and VV polarizations, i.e. the polarization decomposition in step 2 +.>
Figure GDA0003314150210000048
and />
Figure GDA0003314150210000049
Figure GDA00033141502100000410
Figure GDA00033141502100000411
Is->
Figure GDA00033141502100000412
Or->
Figure GDA00033141502100000413
(20)
wherein ,
Figure GDA00033141502100000414
backscattering coefficient representing pure soil pixels, is->
Figure GDA00033141502100000415
Backscattering coefficient, CAFL, representing the pixel of a thick forest covered area df Representing the combustible leaf load of the pixels of the coverage area of the thick forest, wherein beta represents the attenuation coefficient of the forest canopy to the microwave data, and CAFL represents the active combustible load of the forest canopy; />
Step 4: calculating the active combustible load CAFL of the forest canopy.
The invention adopts full polarization SAR data and combines polarization decomposition technology to extract vegetation volume scattering part in SAR observation information; and combining with an expanded water cloud model (EWC) to directly model a vegetation canopy, so that the vegetation canopy is prevented from being influenced by surface scattering signals. The method can accurately estimate the CAFL and has good generalization performance, provides technical support for CAFL estimation on a large scale on a space-time scale, and further assists fire management staff in performing fire prevention and extinguishing work.
Drawings
FIG. 1 is a schematic flow chart of the whole method of the invention.
FIG. 2 is a schematic diagram of the geographic location of a research plot in an embodiment of the invention.
Fig. 3 is a graph showing the results of inversion of forest canopy active combustible load in an embodiment of the present invention.
Detailed Description
The method for estimating the forest canopy active combustible load based on polarization decomposition provided by the invention is further described below with reference to specific embodiments and the accompanying drawings in the specification:
a forest canopy active combustible load estimation method based on polarization decomposition, as shown in figure 1, comprises the following steps:
(1) Data preparation
The actual measurement of forest canopy active combustible load (CAFL) was obtained by field investigation in southwest region of Sichuan province of China during the period of 16 to 20 days of 2021, 3 months. As shown in fig. 2, the geographical location distribution of the actual measurement data used in the present embodiment is shown. Wherein each sample point represents a 10 x 10 meter effective range. The fully polarized SAR data used in this embodiment is the C band Radarsat-2 data. The data is subjected to preprocessing operations including radiation correction, polarization filtering, geocoding and generation of a polarization coherence matrix (T 3 )。
(2) Polarization parameter extraction
Based on the post-preprocessing extracted polarization coherence matrix (T 3 ) And performing polarization decomposition operation to extract volume scattering part information of inversion vegetation canopy scattering characteristics.
Specifically, it includes 5 parts:
a. angle of direction removal
The direction angles of the different picture elements may vary considerably, especially in areas of rough terrain, which may lead to uncertainty in the polarization resolution. The direction-removal angle is the direction-removal angle obtained by combining the observed polarization coherence matrix (T 3 ) Rotation θ degrees along radar observation line:
Figure GDA0003314150210000051
b. selecting a volume scattering model
The choice of the volume scattering model depends on the orientation of the dipoles of the vegetation region (P r )：
Figure GDA0003314150210000052
When P r >At 2dB, represents the horizontal direction; when P r <-2dB, representing the vertical direction; when-2 dB<P r <At 2dB, a random direction is represented. P according to practical situation r Values to determine the volume scattering model (T v )：
Figure GDA0003314150210000053
Figure GDA0003314150210000054
Figure GDA0003314150210000055
c. Volumetric and surface scattering and secondary scattering mechanism decomposition
Polarization decomposition is the process of decomposing observed data into bulk, surface and secondary scattering:
Figure GDA0003314150210000061
wherein ,Ts Refers to a surface scattering model:
Figure GDA0003314150210000062
T d refers to the secondary scattering model:
Figure GDA0003314150210000063
d. negative energy constraint
The energy of each partial scattering mechanism can be calculated after polarization decomposition:
P s ＝f s (1+|β| 2 ) (9)
P d ＝f d (1+|α| 2 ) (10)
P v ＝f v (11)
P s representing the surface scattering energy, P d Represents the energy of secondary scattering, P v Representing the volume scattering energy. In actual operation, it may occur that the surface scattering energy or the secondary scattering energy is negative. However, this case is clearly not of physical significance, and therefore the following is defined for the picture elements where negative energy occurs:
if P s <0：
P s ＝0 (12)
P v ＝P sum -P d (13)
If P d <0：
P d ＝0 (14)
P v ＝P sum -P s (15)
e. Calculating the backscatter coefficient of volume scattering
Based on the polarization decomposition operation, the volume scattering HV backscattering coefficient can be calculated
Figure GDA0003314150210000064
HH backscattering coefficient->
Figure GDA0003314150210000065
And VV backscattering coefficient->
Figure GDA0003314150210000066
Figure GDA0003314150210000067
/>
Figure GDA0003314150210000068
Figure GDA0003314150210000069
(3) Modeling
And establishing a model for inverting the active combustible load of the forest canopy aiming at a forest canopy scattering mechanism. The concrete form is as follows:
Figure GDA0003314150210000071
wherein ,
Figure GDA0003314150210000072
representing the volume scattering backscattering coefficient after polarization decomposition (+)>
Figure GDA0003314150210000073
Or->
Figure GDA0003314150210000074
)；/>
Figure GDA0003314150210000075
A backscattering coefficient representing a pure soil pixel; />
Figure GDA0003314150210000076
Representing the backscattering coefficient of the pixel in the thick forest coverage area; CAFL (control over the air traffic flow) df The combustible leaf load of the pixels of the thick forest covered area is represented; 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
According to a model (formula (19)) established by the volume scattering backscattering coefficient extracted after polarization decomposition and a scattering mechanism of a forest canopy, solving an analytical solution of the forest canopy active combustible load:
Figure GDA0003314150210000077
Figure GDA0003314150210000078
and according to the analytic solution, solving the active combustible load of the forest canopy. The inversion result of the active combustible material load of the forest canopy in the present embodiment is shown in fig. 3.

Claims (1)

1. A method for estimating forest canopy active combustible load based on polarization decomposition, the method comprising:
step 1: acquiring full polarization SAR data, including full polarization Synthetic Aperture Radar (SAR) data, vegetation coverage data and ground cover type data; the full-polarization SAR data comprise any one of an X-band, a C-band, an S-band, an L-band and a P-band; and finishing the pretreatment of the data, wherein the pretreatment comprises the following steps: radiation correction, filtering and topography correction, and generating a polarization coherence matrix T for polarization decomposition 3 ；
Step 2: extracting polarization parameters, and performing polarization decomposition on the polarization coherence matrix T in the step 1 3 Performing polarization decomposition; decomposing SAR observation information into a plurality of scattering mechanisms with specific physical significance, wherein the polarization decomposition method is a specific polarization decomposition method aiming at a forest region;
specifically comprising 5 parts:
step 2.1: angle of direction removal
Carrying out direction angle removal treatment before polarization decomposition; the direction angle is removed, namely the polarization coherence matrix T obtained by extraction after pretreatment in the step 1 3 Rotating θ degrees along the radar observation line; t is used herein 3_θ Representing the polarization coherence matrix after the depirectional angle treatment:
Figure FDA0004119168930000011
step 2.2: selecting a volume scattering model
The choice of the volume scattering model depends on the orientation P of the dipoles of the vegetation region r ：
Figure FDA0004119168930000012
wherein ,Svv Representing complex form of VV polarization observation data, S hh Representing complex forms of HH polarization observations, according to P r Values to determine the volume scattering model T v ：
Figure FDA0004119168930000013
Figure FDA0004119168930000014
Figure FDA0004119168930000015
wherein ,fv Representing the intensity coefficient of the bulk scattering portion;
step 2.3: volumetric and surface scattering and secondary scattering mechanism decomposition
Polarization decomposition is a process of decomposing observed data into bulk scattering, surface scattering and secondary scattering, wherein the polarization decomposition is based on T after the past direction angle treatment in step 2.1 3_θ Matrix-wise and volume scattering model T employed in this step v The selection is performed by the step 2.2:
Figure FDA0004119168930000021
wherein ,Tij Representing T 3_θ The ith row and jth column matrix elements in this 3*3 matrix, superscript ". X" means conjugate transpose, T s Is a surface scattering model:
Figure FDA0004119168930000022
T d is a secondary scattering model:
Figure FDA0004119168930000023
wherein ,fs Representing the intensity coefficient of surface scattering, f d Representing the intensity coefficient of the secondary scattering, β representing the normalized difference of the H-polarized and V-polarized bragg scattering, α representing the normalized difference of the H-polarized and V-polarized earth-vegetation stalk fresnel scattering;
step 2.4: negative energy constraint
F of each SAR observation pixel can be obtained according to the calculation in the step 2.3 s 、f d 、f v α and β, based on which the energy of the respective partial scattering mechanism is calculated:
P s ＝f s (1+|β| 2 ) (9)
P d ＝f d (1+|α| 2 ) (10)
P v ＝f v (11)
P s representing the surface scattering energy, P d Represents the energy of secondary scattering, P v Representing volume scattering energy; when the calculated surface scattering energy or the secondary scattering energy is negative, the pixel with negative energy is defined as follows:
if P s <0：
P s ＝0 (12)
P v ＝P sum -P d (13)
If P d <0：
P d ＝0 (14)
P v ＝P sum -P s (15)
P sum ＝T 11 +T 22 +T 33 (16)
wherein ,Psum Representing the total scattered energy observed by the SAR;
step 2.5: calculating the backscatter coefficient of volume scattering
Based on the polarization decomposition operation, the volume scattering energy P obtained in the step 2.4 is obtained v Bringing again the volume scattering model T selected in step 2.2 according to equation (11) v According to the volume scattering model T v Calculating to obtain final volume scattering HV backscattering coefficient
Figure FDA0004119168930000031
HH backscattering coefficient->
Figure FDA0004119168930000032
And VV backscattering coefficient->
Figure FDA0004119168930000033
Figure FDA0004119168930000034
Figure FDA0004119168930000035
Figure FDA0004119168930000036
T vij Representing a volume scattering model T v The ith row and jth column elements in this 3*3 matrix;
step 3: for Senlin canopy scattering mechanism, calculating volume scattering backscattering coefficient after polarization decomposition
Figure FDA0004119168930000037
Comprising HV, HH and VV polarizations, i.e. the polarization decomposition in step 2 +.>
Figure FDA0004119168930000038
and />
Figure FDA0004119168930000039
Figure FDA00041191689300000310
/>
wherein ,
Figure FDA00041191689300000311
backscattering coefficient representing pure soil pixels, is->
Figure FDA00041191689300000312
Backscattering coefficient, CAFL, representing the pixel of a thick forest covered area df Representing the combustible leaf load of the pixels of the coverage area of the thick forest, wherein beta represents the attenuation coefficient of the forest canopy to the microwave data, and CAFL represents the active combustible load of the forest canopy;
step 4: calculating the active combustible load CAFL of the forest canopy.
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