CN115236667B - Potential landslide volume estimation method fusing multisource SAR data - Google Patents
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Abstract
The invention provides a potential landslide volume estimation method fusing multisource SAR data, which relates to the technical field of geological disaster prevention and control safety, and comprises the following steps: inSAR deformation monitoring, sliding surface determination and landslide volume estimation. The InSAR deformation monitoring comprises SAR data acquisition, deformation monitoring and landslide boundary determination; the sliding surface is determined by an Okada elastic dislocation model, and the determined content comprises the position and the form of the sliding surface; the landslide volume estimation is calculated from the integral. According to the invention, the position, the shape and the size of the sliding surface are obtained, the sliding depth is combined, the volume of the potential sliding is calculated by utilizing the plane, the square quantity of the potential sliding body is accurately judged, and the data support is provided for the subsequent sliding disaster prediction methods such as numerical simulation and the like, so that the method has a good application value.
Description
Technical Field
The invention relates to the technical field of geological disaster prevention and control safety, in particular to a potential landslide volume estimation method integrating multisource SAR data.
Background
In recent years, in the landslide monitoring field, the monitoring means of 'sky-earth' is continuously developed, and disaster monitoring and prediction are greatly improved. The synthetic aperture radar interference (SYNTHETIC APERTURE RADAR INTERFEROMETRY, inSAR) technology is used as an active remote sensing technology, has the advantages of wide monitoring range, no limitation of weather, and capability of acquiring images all day long and all weather, and is well applied to monitoring landslide disasters.
In the aspect of common disaster monitoring, more researches are still carried out based on ground surface deformation, the dangerous degree of landslide cannot be specifically revealed, and for the researches on the harm of landslide and landslide volume, the common single-point contact detection has the advantages of high cost, long time consumption and limitation of data volume, and the adoption of a small number of points for large-scale interpolation inevitably influences the estimation precision of the position and the shape of the landslide, so that the accurate estimation of the landslide volume and the deformation mode is not facilitated, and the dangerous evaluation of the landslide is influenced.
The InSAR technology is used for acquiring the planar deformation monitoring result of the earth surface, and the elastic dislocation model is combined for searching the optimal sliding surface, so that the landslide volume can be estimated, and data support is provided for disaster risk prediction. The existing research method for estimating the volume of the potential landslide is mainly focused on contact detection such as drilling and ground penetrating radar, has high cost and long time consumption, and is not beneficial to detection of the sliding surface and depth of a large-scale landslide and a deep landslide.
Disclosure of Invention
The invention provides a potential landslide volume estimation method for fusing multisource SAR data, and aims to solve the defects in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions: a potential landslide volume estimation method fusing multisource SAR data comprises the following steps:
acquiring SAR data of a potential landslide area;
performing sequential InSAR processing on SAR data by STACKING INSAR technology to obtain a lifting track visual line deformation rate diagram;
Determining the boundary of landslide surface deformation according to the lifting track visual line deformation rate diagram, the digital terrain elevation model DEM and the optical image;
determining geometrical parameters and kinematic parameters required by the Okada elastic dislocation model when inverting the sliding surface of the landslide;
Converting the ascending and descending track visual line into discrete points from the deformation rate diagram, and adding corresponding longitude and latitude coordinates to serve as inversion surface constraint conditions;
Inputting surface constraint conditions into the Okada elastic dislocation model to invert the sliding surface, selecting different smoothing factors, and performing inversion calculation for a plurality of times;
Obtaining a plurality of simulation results through inversion, comparing the simulation results with the observation results, and selecting the simulation result with the best fitting degree, thereby obtaining the depth of the sliding body from the ground surface and the deformation of the sliding surface, and determining the deformation range and the deformation direction of the sliding surface;
and calculating the landslide volume through integration of the sliding surface deformation area and the sliding body depth, and determining the deformation type of the landslide according to the sliding surface deformation direction on the sliding surface.
Preferably, the deformation monitoring is performed on the SAR data, and the deformation monitoring process includes:
And (3) data acquisition: downloading lifting and lowering track data of the Sentinel-1, SRTM data of 30 meters and precise track data of the Sentinel-1, and preprocessing the data to obtain single-view complex images SLC;
deformation monitoring: obtaining a deformation rate diagram of a landslide area by adopting STACKING INSAR technology;
Landslide deformation area determination: and comparing the obtained deformation monitoring results with the lifting and falling track image monitoring results, determining a landslide deformation boundary by combining the DEM topographic map and the optical image, and calculating the area of the boundary range.
Preferably, the specific process of obtaining the deformation rate map includes:
Through setting a certain space and time base line threshold, interference processing is carried out among SAR images meeting the conditions, and a plurality of interference atlas are generated;
The phase of each interferogram is comprised primarily of the phase due to DEM error Phase of deformationPhase generated by atmospheric delay/>Phase due to noise/>
By rejecting phasesAcquiring phase information generated by deformation;
And obtaining an annual average deformation rate graph of the radar vision line by adopting STACKING INSAR technology.
Preferably, when estimating the deformation rate, the STACKING INSAR technology obtains a deformation rate diagram by performing phase superposition on a plurality of interference unwrapping diagrams;
The weights are calculated according to the time interval of the master image and the slave image, and the mathematical model of STACKING INSAR is as follows:
Where ph_rate is the phase deformation rate, Δt i is the time interval between two images of the ith unwrapped graph, The unwrapped phase value for the ith interferogram.
Preferably, the geometric parameters and the kinematic parameters required for inverting the sliding surface include width, depth, trend angle, inclination angle, maximum sliding amount and sliding angle, wherein the specific method for determining the parameters is as follows:
Initial depth setting: the thickness of the landslide body is subjected to iterative optimization according to the average thickness;
initial width setting: the width of the deformation region along the main sliding direction;
Setting of trend angle: firstly determining the main sliding direction of a landslide, and then making a perpendicular line to the main sliding direction, wherein an included angle between the perpendicular line and the north direction is an initial trend angle;
Initial tilt angle setting: setting a section close to the gradient of the side slope for searching, and determining the optimal inclination angle through continuous adjustment;
maximum slip: setting a maximum sliding limit value of the sliding surface;
Initial sliding angle: setting a certain search range, continuously adjusting the maximum and minimum sliding angle values of the intervals, and determining the sliding angle through multiple inversion calculation.
Preferably, the process of inverting the sliding surface by using the Okada elastic dislocation model comprises the following steps:
determining a deformation boundary and a region of the landslide by combining a deformation monitoring result obtained by the InSAR technology;
Downsampling the data using a quadtree sampling method;
Setting the scale and direction of a sliding surface area to be inverted, constructing a dislocation inversion model, dividing the sliding surface into a plurality of sub-dislocation sources along the trend and the trend, and giving the length, width and depth of the sliding surface and the blocking condition of the sub-dislocation.
Preferably, the deformation of each sub-block dislocation is calculated by an Okada elastic dislocation model, and the specific calculation formula is as follows:
S＝[GTC-1G]-1GC-1X
Wherein S is the sliding quantity of each sub dislocation source, G is a green function established by the connection between the ground deformation and the sliding surface, C is the covariance of the observed value data, and X is the deformation of the ground surface.
Preferably, the specific calculation formula of the landslide volume is as follows:
The method comprises the following steps that a, b are respectively represented by a1, a2, b1, b2 and h, a1 and b1 are respectively long and short half shafts of external ellipses of sliding surfaces, a2 and b2 are respectively long and short half shafts of external ellipses of sliding surfaces, h represents the depth of a sliding body, and a specific deformation formula of the formula is as follows:
a＝a1+z(a2-a1)/h
b＝b2+z(b2-b1)/h
preferably, the parameters of the landslide surface deformation comprise deformation range and deformation magnitude.
Compared with the prior art, the invention has the following beneficial effects:
1. The method comprises the steps of carrying out multi-source data deformation detection by utilizing a time sequence InSAR technology, obtaining deformation monitoring results of different data, determining the area and the perimeter of a potential landslide on the ground surface by comparison, searching a dislocation plane which is most likely to slide on a landslide substrate by combining an elastic dislocation model, obtaining the position, the shape and the size of the sliding plane, and calculating the volume of the potential landslide by utilizing the plane by combining the landslide depth, so that the method accurately judges the square quantity of the potential landslide body, provides data support for a subsequent landslide disaster prediction method such as numerical simulation, and has good application value.
2. The method is different from a mass conservation inversion landslide body thickness method, external parameters such as rheological parameters are not needed, the potential landslide body thickness is obtained, deformation information of a sliding surface is inverted, and the volume of the landslide body is calculated by combining InSAR deformation information on the basis.
Drawings
FIG. 1 is a specific flowchart of a potential landslide volume estimation method for fusing multisource SAR data provided by the invention;
FIG. 2 is a schematic diagram of a landslide dislocation model and parameters provided by the invention;
FIG. 3 is a schematic diagram of landslide mass volume estimation according to the present invention;
FIG. 4 is a graph of the rate of change of the lifting rail provided by the invention;
FIG. 5 is a graph of roughness and fit residuals provided by the present invention;
FIG. 6 is a graph of the inversion results of lifting and lowering the track provided by the invention;
FIG. 7 is an optimal slip profile provided by the present invention;
FIG. 8 is a deformation direction diagram of each landslide unit of the landslide body 1;
fig. 9 is a slope view of the landslide body 1 provided by the invention;
FIG. 10 is a deformation direction diagram of each landslide unit of the landslide body 2;
Fig. 11 is a slope view of the landslide body 2 provided by the invention.
Detailed Description
The following describes the embodiments of the present invention further with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
A potential landslide volume estimation method for multi-source SAR data fusion combines ground surface deformation obtained by InSAR technology and a sliding surface determined by an elastic dislocation model to estimate landslide volume. As shown in fig. 1, the technical scheme of a potential landslide volume estimation method for multi-source SAR data fusion is as follows:
Step 1: SAR data processing is carried out by adopting Sentinel-1A data and using STACKING INSAR technology, and a lifting track visual line deformation rate diagram is obtained.
Step 2: and determining parameters of landslide surface deformation, including deformation range, deformation magnitude and the like, according to the deformation rate graph result, the digital terrain elevation model DEM and the optical image.
Step 3: determining geometrical parameters and kinematic parameters required when the Okada elastic dislocation model inverts the sliding surface of the landslide: parameters such as width, depth, trend angle, dip angle, maximum sliding amount, sliding angle and the like; the specific method for determining the parameters is as follows:
(1) Initial depth setting: and (5) iteratively optimizing the thickness of the landslide body according to the average thickness.
(2) Initial width setting: width of the deformation region in the main sliding direction.
(3) Setting of trend angle: firstly determining the main sliding direction of the landslide, and then making a perpendicular line to the main sliding direction, wherein the included angle between the perpendicular line and the north direction is the initial trend angle.
(4) Initial tilt angle setting: and setting a section close to the gradient of the side slope for searching, and determining the optimal inclination angle through continuous adjustment.
(5) Maximum slip: the maximum sliding limit value of the sliding surface is set.
(6) Initial sliding angle: setting a certain search range, continuously adjusting the maximum and minimum sliding angle values of the intervals, and determining the sliding angle through multiple inversion calculation.
Step 4: converting the deformation rate diagram of the lifting rail into discrete points, and adding corresponding longitude and latitude coordinates to serve as inversion surface constraint conditions.
Step 5: downsampling the up-and-down track line-of-sight map of the rate of change. The four-way tree and the uniform downsampling method are generally adopted, so that the purposes of reducing the calculated amount and improving the calculation speed can be achieved.
Step 6: and inputting the surface constraint condition into the Okada elastic dislocation model to invert the sliding surface. And selecting different smoothing factors, performing inversion calculation for multiple times, selecting the optimal smoothing factor according to the L curve of the fitting result, reducing uncertainty, and improving the stability of the inversion result. And comparing a plurality of simulation results and observation results obtained by inversion, and selecting a simulation result with the best fitting degree, thereby obtaining the depth of the sliding body and the deformation of the sliding surface, and determining the deformation range and the deformation direction of the sliding surface.
Step 7: and calculating the landslide volume through integration, and determining the deformation type of the landslide according to the deformation direction of the sliding surface on the sliding surface.
The invention mainly comprises three key steps: SAR deformation monitoring, sliding surface determination and landslide volume estimation.
1. SAR deformation monitoring includes:
1. SAR data acquisition: and downloading lifting rail data of the Sentinel-1, and obtaining single-view complex images SLC through preprocessing.
2. Deformation monitoring: and obtaining a deformation rate graph of the landslide area by adopting STACKING INSAR technology. First, by setting a certain spatial and temporal threshold, interference is performed between SAR images satisfying a condition, and a plurality of interference atlas are generated. For each interferogram, the phaseMainly comprises phase/>, caused by DEM errorsPhase of deformationPhase generated by atmospheric delay/>Phase due to noise/>
Then, by rejecting phasesAnd acquiring phase information generated by deformation. In the data processing process, the terrain phase under the SAR coordinate system can be obtained through external DEM data simulation, and the differential interference pattern is obtained through the terrain phase obtained through differential elimination simulation. For the terrain error, removing by constructing a relation between the elevation error and the track base line; and in the atmospheric phase error, simulating and removing by constructing a quadratic polynomial between the DEM and the atmospheric phase to obtain a deformation phase diagram. Finally, a STACKING INSAR technology is adopted to obtain an annual average deformation rate graph of the radar vision line, when the STACKING INSAR technology estimates the deformation rate, the weight is calculated according to the time interval of the master image and the slave image, and the mathematical model of STACKING INSAR is as follows:
Where ph_rate is the phase deformation rate, Δt i is the time interval between two images of the ith unwrapped graph, The unwrapped phase value for the ith interferogram. In STACKING INSAR processing, in order to reduce errors generated by atmospheric disturbance, an unwrapped interferogram from which atmospheric errors are removed is used, and an interferogram with a larger error is removed, and under the condition that coherence is met, an interferogram with a longer time base line is selected for processing, so that a more accurate deformation rate is obtained.
3. Landslide deformation area determination: and comparing the obtained deformation monitoring results with the lifting and falling track image monitoring results, determining a landslide deformation boundary by combining the DEM topographic map and the optical image, and calculating the area of the boundary range.
2. As shown in fig. 2, the determination of the sliding surface position and morphology based on the Okada elastic dislocation model specifically includes:
Only information on the earth's surface can be obtained by remote sensing means, GNSS and other monitoring means, and it is difficult to determine changes in the earth by these techniques. However, for landslide, the sliding surface plays an important role in landslide movement. According to previous studies, the sliding surfaces of some large landslides can be assumed to be structural faults. Therefore, in order to obtain the volume of the landslide, the position, the shape and the size of the sliding surface can be determined by adopting an Okada dislocation model, assuming that the landslide is an isotropic medium, the displacement field observed by InSAR is simulated by plane dislocation in an isotropic elastic half space, and the position, the shape and the size of the sliding surface are further determined by searching the result that the observed value corresponds to the best fitting degree of the simulation value, so that data is provided for estimating the volume of the landslide.
Firstly, the deformation boundary and the area of the landslide are determined by combining the deformation monitoring result obtained by the InSAR technology, and the data are downsampled by using a quadtree sampling method, so that the calculation efficiency is improved. Then, setting the scale and the direction of the sliding surface, constructing a dislocation inversion model, dividing the sliding surface into a plurality of sub-dislocation sources along the trend and the trend, and giving the length, the width and the depth of the sliding surface and the blocking condition of the sub-dislocation, wherein specific parameters are shown in fig. 2. Finally, the deformation of each sub-block dislocation was calculated by means of an Okada elastic dislocation model. The specific calculation formula is as follows:
S＝[GTC-1G]-1GC-1X (3)
Wherein S is the slip of each sub dislocation source, G is the green' S function established by the connection between the ground deformation and the sliding surface, C is the covariance of the observed value data, and X is the deformation of the ground surface. In the inversion process, because the slippage of the sub dislocation source sometimes has a jump phenomenon, a certain degree of smoothing is needed to ensure the stability of the inversion result.
3. As shown in fig. 3, the specific process for landslide volume estimation is:
By combining the surface deformation monitoring results obtained by InSAR, the upper surface area of the landslide body can be determined, the area of the lower surface of the landslide body can be determined according to the sliding surface inverted by the elastic dislocation model, the average depth h of the landslide body is obtained, and in general, the upper surface of the landslide body is assumed to be approximately parallel to the sliding surface, at the moment, the upper surface and the lower surface of the landslide body are selected to be the nearest ellipse, which can be shown as figure 3.
The external elliptic long and short half shafts provided with the sliding surfaces are respectively as follows: a1, b1; the external elliptic minor semi-axis on the landslide surface is respectively: a2, b2, the landslide body depth is: h. the integration unit dz is taken. The distance from the top surface is z high. The volume of the landslide body can be calculated through integration, and the specific calculation formula is as follows:
Wherein a and b are represented by a1, a2, b1, b2, h, respectively:
a＝a1+z(a2-a1)/h (5)
b＝b2+z(b2-b1)/h (6)
substituting a and b into a formula to obtain the volume V of the landslide body:
Example 1:
The invention takes a tile heap village potential landslide as an example to carry out experiments, calculates the volume of the potential landslide body, and analyzes the landslide sliding type according to the sliding surface inversion result.
1. Deformation rate map obtained by InSAR: and determining the geometric size and the position of the inverted sliding surface according to the deformation rate result. When sliding bodies with different deformation directions are included, a plurality of sliding surfaces are selected for inversion. The deformation rate diagram is shown in fig. 4, wherein fig. 4a is a diagram of the tracking monitoring result, fig. 4b is a tracking monitoring result, and the white frame is an inversion region of the sliding surface.
2. Roughness and fit residual curve of Okada model as shown in fig. 5, in the sliding distribution inversion process, the sliding distribution result is evaluated by weighing the curve between the roughness of the model and the fit residual, and the inversion result of the model is optimal when the smoothness factor is 0.03 through multiple simulation inversion of experiments.
3. The sliding surface inversion results of the Okada model are shown in table 1:
TABLE 1 elastic dislocation model inversion results
| Sliding surface | Length of | Width of (L) | Depth (km) | Trend angle | Inclination angle | Average slip |
| 1 | 1.6 | 0.7 | 0.069 | 20 | 35 | 0.24 |
| 2 | 0.9 | 1 | 0.060 | 25 | 23 | 0.21 |
The comparison of the landslide deformation observation result and the slip distribution is shown in fig. 6. In fig. 6, a, b, and c are respectively observed values, analog values, and residual values of the ascending track, and e, f, and g are respectively observed values, analog values, and residual values of the descending track.
The optimum sliding distribution result of the sliding surface is shown in fig. 7.
4. Landslide volume estimation
According to the deformation rate diagram, the maximum external ellipse of the landslide surface is defined, the external ellipse of the sliding surface is determined by combining the inverted sliding surface, so that the lengths of a long half shaft and a short half shaft of the upper surface and the lower surface of the landslide body are obtained, and the depth values obtained by combining inversion are combined, so that the volumes of the two landslide bodies existing in the area are calculated as follows:
(1) Landslide body 1
Fig. 8 and 9 are a deformation direction diagram of each landslide unit of the landslide body 1 and a slope direction diagram of the landslide body 1, respectively. From the calculations in fig. 4, 8 and 9, a1=0.35 km, b1=0.19 km, a2=0.6 km, b2=0.26 km, h=0.069 km. According to the volume calculation formula, the volume v=1.69×10 7m3 of the landslide body 1 is obtained. The deformation direction of the landslide can be roughly judged by combining the trend and the inclined deformation, and the landslide body 1 at the toe part mainly moves along the slope direction by combining slope direction joint analysis, so that the trend and the inclined deformation exist. The movement of a landslide can generally be categorized as translational, rotational, or complex movement. The rotary sliding slope generally moves downwards and outwards, the moving direction of the translational sliding body is parallel to the hillside, and the sliding surface is relatively flat in structure. According to the result of inverting the sliding surface, the landslide body 1 is a translational landslide along the slope direction.
(2) Landslide body 2
Fig. 10 and 11 are a deformation direction diagram of each landslide unit of the landslide body 2 and a slope direction diagram of the landslide body 2, respectively. According to the calculation of fig. 4, 10 and 11, a1=0.40 km, b1=0.24 km, a2=0.5 km, b2=0.31 km, h=0.06 km, and the volume v=2.03×10 6m3 of the sliding body 2 can be obtained by taking into a volume calculation formula.
The above embodiments are merely preferred embodiments of the present invention, the protection scope of the present invention is not limited thereto, and any simple changes or equivalent substitutions of technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention disclosed in the present invention belong to the protection scope of the present invention.
Claims (9)
1. The potential landslide volume estimation method fusing the multisource SAR data is characterized by comprising the following steps of:
acquiring SAR data of a potential landslide area;
performing sequential InSAR processing on SAR data by STACKING INSAR technology to obtain a lifting track visual line deformation rate diagram;
Determining the boundary of landslide surface deformation according to the lifting track visual line deformation rate diagram, the digital terrain elevation model DEM and the optical image;
determining geometrical parameters and kinematic parameters required by the Okada elastic dislocation model when inverting the sliding surface of the landslide;
Converting the ascending and descending track visual line into discrete points from the deformation rate diagram, and adding corresponding longitude and latitude coordinates to serve as inversion surface constraint conditions;
Inputting surface constraint conditions into the Okada elastic dislocation model to invert the sliding surface, selecting different smoothing factors, and performing inversion calculation for a plurality of times;
Obtaining a plurality of simulation results through inversion, comparing the simulation results with the observation results, and selecting the simulation result with the best fitting degree, thereby obtaining the depth of the sliding body and the deformation of the sliding surface, and determining the deformation range and the deformation direction of the sliding surface;
and calculating the landslide volume through integration of the sliding surface deformation area and the sliding body depth, and determining the deformation type of the landslide according to the sliding surface deformation direction on the sliding surface.
2. The method for estimating potential landslide volume of fusion of multi-source SAR data of claim 1, wherein deformation monitoring is performed on the SAR data, and wherein the deformation monitoring comprises:
And (3) data acquisition: downloading lifting and lowering track data of the Sentinel-1, SRTM data of 30 meters and precise track data of the Sentinel-1, and preprocessing the data to obtain single-view complex images SLC;
deformation monitoring: obtaining a deformation rate diagram of a landslide area by adopting STACKING INSAR technology;
Landslide deformation area determination: and comparing the obtained deformation rate results with the monitoring results of the ascending and descending rail images, determining a landslide deformation boundary by combining the DEM topographic map and the optical image, and calculating the area of the boundary range.
3. The method for estimating potential landslide volume of fusion of multisource SAR data of claim 2, wherein the specific process of obtaining the deformation rate map comprises:
Through setting a certain space and time base line threshold, interference processing is carried out among SAR images meeting the conditions, and a plurality of interference atlas are generated;
The phase of each interferogram is comprised primarily of the phase due to DEM error Phase of deformationPhase generated by atmospheric delay/>Phase due to noise/>
By rejecting phasesAcquiring phase information generated by deformation;
And obtaining an annual average deformation rate graph of the radar vision line by adopting STACKING INSAR technology.
4. A potential landslide volume estimation method fusing multisource SAR data according to claim 3, wherein the STACKING INSAR technique obtains a deformation rate map by phase overlapping a plurality of interference unwrapping maps when estimating the deformation rate;
The weights are calculated according to the time interval of the master image and the slave image, and the mathematical model of STACKING INSAR is as follows:
Where ph_rate is the phase deformation rate, Δt i is the time interval between two images of the ith unwrapped graph, The unwrapped phase value for the ith interferogram.
5. The method of claim 1, wherein the geometric parameters and kinematic parameters required for inverting the sliding surface include width, depth, strike angle, dip angle, maximum slip, slip angle, and the specific method for determining the parameters is as follows:
Initial depth setting: the thickness of the landslide body is subjected to iterative optimization according to the average thickness;
initial width setting: the width of the deformation region along the main sliding direction;
Setting of trend angle: firstly determining the main sliding direction of a landslide, and then making a perpendicular line to the main sliding direction, wherein an included angle between the perpendicular line and the north direction is an initial trend angle;
Initial tilt angle setting: setting a section close to the gradient of the side slope for searching, and determining the optimal inclination angle through continuous adjustment;
maximum slip: setting a maximum sliding limit value of the sliding surface;
Initial sliding angle: setting a certain search range, continuously adjusting the maximum and minimum sliding angle values of the intervals, and determining the sliding angle through multiple inversion calculation.
6. A potential landslide volume estimation method of fusing multisource SAR data of claim 1 wherein inverting the sliding surface with the Okada elastic dislocation model comprises the steps of:
determining a deformation boundary and a region of the landslide by combining a deformation monitoring result obtained by the InSAR technology;
Downsampling the data using a quadtree sampling method;
Setting the scale and direction of a sliding surface area to be inverted, constructing a dislocation inversion model, dividing the sliding surface into a plurality of sub-dislocation sources along the trend and the trend, and giving the length, width and depth of the sliding surface and the blocking condition of the sub-dislocation.
7. The method for estimating a potential landslide volume fusing multisource SAR data of claim 6, wherein the deformation of each sub-block dislocation is calculated by means of an Okada elastic dislocation model, and the specific calculation formula is:
S＝[GTC-1G]-1GC-1X
Wherein S is the sliding quantity of each sub dislocation source, G is a green function established by the connection between the ground deformation and the sliding surface, C is the covariance of the observed value data, and X is the deformation of the ground surface.
8. The method for estimating potential landslide volume of fusion of multisource SAR data of claim 1, wherein the landslide volume is calculated as follows:
The method comprises the following steps that a, b are respectively represented by a1, a2, b1, b2 and h, a1 and b1 are respectively long and short half shafts of external ellipses of sliding surfaces, a2 and b2 are respectively long and short half shafts of external ellipses of sliding surfaces, h represents the depth of a sliding body, and a specific deformation formula of the formula is as follows:
a＝a1+z(a2-a1)/h
b＝b2+z(b2-b1)/h
9. A method of estimating a potential landslide volume fusing multisource SAR data according to claim 1, wherein the parameters of landslide surface deformation include deformation range, deformation magnitude.
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