CN112084712A - Flood submerging range dynamic simulation method fusing active and passive microwave remote sensing information - Google Patents
Flood submerging range dynamic simulation method fusing active and passive microwave remote sensing information Download PDFInfo
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Abstract
A flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information comprises the following steps: extracting a time sequence of an M/C signal by adopting satellite-borne passive microwave brightness temperature data, and preliminarily determining the occurrence time of a flood event; based on the occurrence time of the flood event, searching a satellite-borne active microwave remote sensing SAR image corresponding to the flood event, and classifying water bodies/lands based on a machine learning method; extracting the surface water area ratio FWS of the M pixels based on the SAR image water body/land classification result, carrying out correlation analysis on the surface water area ratio FWS and corresponding M/C signals, and establishing a relation model of M/C and FWS; and the M/C signals are utilized to calculate the space-time distribution of the FWS at the permanent water body pixel position, so that the dynamic simulation of the flood submerging range at the permanent water body position is realized. The invention can obtain the dynamic flood submergence range based on real-time or quasi-real-time satellite microwave remote sensing observation information, practically guide flood simulation and provide important data support and decision basis for flood emergency rescue and prevention.
Description
Technical Field
The invention relates to the technical field of flood dynamic simulation, in particular to a flood submerging range dynamic simulation method fusing active and passive microwave remote sensing information.
Background
Flood disaster prevention and control are important fields of national scientific research and development. The correct control of flood disasters is a great demand of national development and is an important guarantee for sustainable development of social economy and ecological environment. The dynamic simulation of the flood submerging range can provide direct guidance for flood disaster prevention and control, and has great scientific research significance and practical application value.
Dynamic simulation of flood submergence range is always an important field of multidisciplinary cross research such as hydrology, remote sensing, disasters and the like. Generally, in order to effectively guide flood risk assessment and flood prevention, dynamic simulation of a flood inundation range at least needs to provide day-by-day inundation information, and the size of a visual area is determined in terms of spatial resolution. At present, a hydrodynamics model is a main method for flood inundation simulation, a calculation area is generally divided into fine grids, and calculation resources required for simulating a river flood evolution process are large. The simulation effect of the hydrodynamics model on the flood submerging range is limited by the acquirability and precision of the basic data such as the DEM (digital Elevation model) and the river cross section form, so that the high-precision flood dynamic simulation can be usually only carried out in a small watershed (usually, the area is only a few square kilometers), and the large-range flood submerging information cannot be effectively provided.
The remote sensing is wide in earth observation coverage, simple in information acquisition approach and relatively low in data processing requirement, and a new approach and means are provided for dynamic simulation of the flood submerging range. However, optical/infrared remote sensing is usually long in revisit period, and is seriously affected by problems such as cloud layer shielding, and the time precision requirement of flood monitoring and simulation cannot be met. And the common observation wave band of microwave remote sensing is 8-300 mm, and the penetration capability to cloud layers, surface vegetation, ice and snow and the like is superior to that of optical/infrared remote sensing. At present, the star-lap microwave radiometers are various in types and show the development trend of integration, multiple channels and high frequency; active microwave remote sensing data is rapidly popularized, and global large-breadth, multi-mode and high-resolution Synthetic Aperture Radar (SAR) image information can be freely obtained. However, the space-time precision of satellite remote sensing data is generally restricted, the passive microwave observation frequency is high (generally, the frequency can reach twice a day), the passive microwave observation frequency is influenced by objective factors such as the performance of a sensor and the use frequency band, and the spatial resolution is generally 10-70 km; the active microwave imaging precision is high (generally 1-10 m), the observation period is determined by objective factors such as the number of satellites and observation orbits, and the observation period is usually 2-4 weeks. The inherent property of remote sensing observation determines that any one of the properties is used independently, the requirement of high space-time precision of flood simulation is difficult to meet, and the method is a bottleneck problem in the existing scientific research and technical application. The inventor of the application finds out through research in China in the process of realizing the invention that: the dynamic simulation of the flood range by fusing the active and passive microwave remote sensing information can be used as a breakthrough point for solving the bottleneck problem, and the relevant technologies are not reported in documents at present.
Disclosure of Invention
The invention aims to solve the technical problem of breaking through the bottleneck in the prior art and provides a flood submerging range dynamic simulation method fusing active and passive microwave remote sensing information.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a flood submerging range dynamic simulation method fusing active and passive microwave remote sensing information comprises the following steps:
step 1, extracting a historical time sequence of M/C signal values by adopting satellite-borne passive microwave brightness temperature data, determining a threshold value for judging whether flood occurs or not, and preliminarily determining the occurrence time of a flood event;
step 2, searching a satellite-borne active microwave remote sensing SAR image before/during/after a flood corresponding to the flood event based on the occurrence time of the flood event determined in the step 1, classifying water bodies on the land based on a machine learning method, and calculating the water body/land distribution condition of a research area before/during/after the flood;
step 3, extracting the surface Water area ratio FWS (frame of Water surface) of the M pixels based on the Water/land distribution condition in the step 2, carrying out correlation analysis on the surface Water area ratio FWS and corresponding M/C signals, and establishing a relation model of M/C and FWS;
and 4, based on the M/C and FWS relation model constructed in the step 3, utilizing the M/C signals obtained in the step 1 to calculate the space-time dynamic distribution of the FWS at the permanent water body pixel, and realizing high space-time precision continuous dynamic simulation of the flood submergence range at the permanent water body.
Further, the specific method of step 1 is as follows:
step 1.1, selecting proper high-space-time precision passive microwave brightness temperature data according to the river width of a research area, and selecting a river channel observation pixel and a calibration pixel of an observation pixel value of the research area, wherein the river channel observation pixel is abbreviated as an M pixel, and the calibration pixel is abbreviated as a C pixel;
step 1.2, extracting day-by-day brightness temperature value sequences of the M pixel and the C pixel, calculating an M/C signal value, and performing moving average processing;
and step 1.3, extracting a threshold value for judging whether flood occurs according to the historical time sequence of the M/C signal values acquired in the step 1.2, and preliminarily determining the occurrence time of the flood event in a certain time period of the research area.
Further, the step 1.2 of extracting day-by-day brightness temperature value sequences of the M pixel and the C pixel, and the specific implementation steps of calculating the M/C signal value are as follows:
in the formula, TB,measurementIs the brightness temperature value of the observation pixel; t ismeasurementIs the actual temperature of the observation pixel;waterthe emissivity of the water body in the area;landthe average terrestrial emissivity of the area; t isB,calibrationIs the brightness temperature value of the calibration pixel; t iscalibrationIs the actual temperature of the calibration pixel; the FWS is the area ratio occupied by the surface water in the pixel.
Further, the specific method of step 2 is as follows:
step 2.1, based on the occurrence time of the flood event preliminarily determined in the step 1, defining the corresponding time period before/during/after the flood event, selecting a proper satellite-borne active microwave remote sensing SAR image, and obtaining normalized SAR image radar backscattering coefficient distribution through a pretreatment process;
2.2, based on the comparison and selection results of various supervised classification and unsupervised classification methods, constructing a water body/land classification algorithm based on the SAR image, and acquiring a water body/land classification result of a research area by utilizing a normalized SAR image radar backscattering coefficient;
and 2.3, correcting the water body/land classification result obtained in the step 2.2 by utilizing a post-processing algorithm considering the landform and the hydrological factor distribution characteristics to obtain the optimized water body/land distribution condition before/during/after the disaster in the research area.
Further, the step 2.1, through the pretreatment process, obtains the normalized SAR image radar backscattering coefficient distribution specifically as follows:
in the formula (I), the compound is shown in the specification,
is the normalized radar backscattering coefficient; theta is a radar incident angle; thetarefIs a reference angle, and is generally averaged according to the range distribution of θ.
Further, the pretreatment process involved in step 2.1 specifically comprises: reading in data, adding track files, thermal noise removal, radiation correction, filtering, terrain correction, and exporting data.
Further, the supervised classification and unsupervised classification method related in step 2.2 specifically comprises:
and (3) supervision and classification: three typical classifiers of a random forest method, a maximum likelihood method and a minimum distance method are adopted;
unsupervised classification: the Otsu method is a self-adaptive global threshold determining method, determines a global threshold aiming at histogram distribution of an image, divides the global threshold into L gray levels, distinguishes land and water by using inter-class variance, and has an objective function as follows:
in the formula (I), the compound is shown in the specification,
is a function of the class mean corresponding to the threshold k;
as k changes, it represents its corresponding total variance, global threshold k*Making the objective function, i.e. the between-class variance η, maximum or, equivalently, making
To the maximum, it can be achieved by iterative search:
further, the topographic and topographic factors and hydrological factors adopted in step 2.3 are hand (height Above Nearest drainage) indexes, and the indexes represent the difference between the elevation of each pixel and the elevation of the Nearest river network pixel.
Further, the specific method of step 3 is as follows:
3.1, extracting specific distribution of water/land classification in the M pixels according to the water/land distribution condition obtained in the step 2, and calculating the surface water area ratio FWS of the M pixels;
and 3.2, constructing a relation model of M/C and FWS based on the FWS of the M pixels obtained in the step 3.1 and the M/C signal values of the corresponding time periods in the step 1.
Further, the M/C and FWS relation model established in the step 3.2 is specifically established by respectively adopting three methods of quadratic polynomial curve fitting, power exponent fitting and linear fittingIn relation to each other, and with R2The maximum is a criterion to select an optimal method.
The invention has the following beneficial effects:
1. scientific and reasonable breaks through the bottleneck problem: the advantages of satellite microwave remote sensing observation are fully exerted, active and passive microwave remote sensing data are fused based on underlying surface factors such as landform, hydrological factors and the like through the combination of satellite remote sensing information and geospatial auxiliary information, the limitation that the space-time precision of the microwave remote sensing data is difficult to be completed is broken through, and therefore the simulation precision and reliability of the flood dynamic submergence range are improved;
2. practical and effective guidance of engineering practice: the method has the advantages that the operability of finding the dynamic flood submerging range is high based on real-time or quasi-real-time satellite microwave remote sensing observation information, the flood three-dimensional simulation can be practically guided, the actual engineering requirements in the field are fully met, and important data support and decision basis is provided for flood emergency rescue and prevention.
Drawings
FIG. 1 is a flow chart of one embodiment of a flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information according to the present invention;
FIG. 2 is a schematic diagram of a time sequence of M/C signals extracted from a region under study with a Sentinel-1SAR image coverage area according to an embodiment of the present invention;
FIG. 3 is a normalized radar backscattering coefficient distribution histogram of a Sentinel-1SAR image at a certain observation date in the study area;
FIG. 4 is a plot of the hand (height Above neost Drainage) index profile of the study area;
FIG. 5 is a schematic diagram of a water body/land classification result of a Sentinel-1SAR image based on a threshold method in the research area;
fig. 6 is a schematic diagram of a relationship between FWS and M/C signal models of the research region based on the SAR image water body land classification result.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The embodiment of the invention provides a dynamic simulation method of a flood inundation range fusing active and passive microwave remote sensing information, which comprises the steps of firstly utilizing passive microwave brightness temperature data to obtain a long-time sequence of M/C signals of a research area, extracting a threshold value for judging whether flood happens or not, and preliminarily estimating the occurrence time of a flood event; screening out satellite-borne active microwave remote sensing SAR images before/during/after a flood corresponding to the flood event based on the occurrence time of the flood event, processing the SAR images by adopting a machine learning technology, and acquiring the water body/land distribution condition of a research area before/during/after the flood; extracting M pixel surface water area ratio FWS of corresponding time based on water body/land distribution conditions, and establishing a relation model with corresponding M/C signals; and finally, acquiring the FWS at the pixel position of the permanent water body of the research area by utilizing the long-time sequence of the M/C signals based on a relation model of the M/C and the FWS, and realizing the high-space-time-precision continuous dynamic simulation of the flood submergence range of the permanent water bodies such as riverways, lakes and the like, wherein the specific flow is shown in figure 1 in detail.
The technical scheme of the invention is further explained in detail by the following embodiments and the accompanying drawings:
step 1, solving a long-time sequence of M/C signals of a research area by using satellite-borne passive microwave brightness temperature data, determining a threshold value for judging whether flood occurs, and preliminarily estimating the occurrence time of a flood event;
the step 1 specifically comprises the following substeps:
step 1.1, selecting proper passive microwave brightness temperature data according to the river width of a research area, and determining a river channel observation pixel (M pixel) and a Calibration pixel (C pixel) of the observation pixel value of the research area.
Furthermore, satellite-borne passive Microwave brightness temperature data can be selected according to the principle that the river width is not larger than the minimum spatial resolution of the brightness temperature data, satellite brightness temperature data and dimension reduction products thereof commonly used in the field can be generally used, for example, Microwave radiometers commonly used for brightness temperature observation comprise TMI Microwave imagers (TRMM Microwave imagers) carried by AMSR-E, TRMM satellites carried by NASA EOS Aqua satellites, SSM/I carried by F series polar orbit satellites of the U.S. department of defense DMSP, and the like, the spatial resolution of the brightness temperature data listed above is generally 10-70 km, and the method is suitable for a river basin with a large river width. In addition, the downscale bright temperature data set commonly used in the field also comprises a bright temperature data set (ESDR) based on multi-platform calibration under the measrues plan published by the national ice and snow data center nsics, the data set uniformly resamples the observation results of microwave radiometers/imagers such as SMMR, SSM/I-SSMIS, AMSR-E and the like to the highest precision of 3.125km, the downscale bright temperature data set is a bright temperature data product set which is internationally leading and widely applied in the field at present, and the downscale bright temperature data set is suitable for a river basin with a small river width.
As shown in fig. 2, in the embodiment in which a coverage area of a Sentinel-1SAR image is taken as a research area, because the river width of a river basin is small, the MEaSUREs ESDR bright temperature data with the spatial resolution of 3.125km × 3.125km is adopted, and the bright temperature data horizontally polarized in the 37GHz band of the SSMIS sensor is concentrated.
Furthermore, in order to select a representative M pixel and a corresponding C pixel, the following principle needs to be followed:
m pixel: the river channel image element is partially or completely covered with the river channel, the water level of the river channel in the image element rises along with the increase of the flow and generates a flood, land areas on two sides of a river bank have periodic or seasonal dry-wet changes, and the brightness temperature value can also generate obvious changes. As shown in fig. 2 (a), a station 1 and a station 2 are two selected M pixels;
c picture element: an automatic screening method is adopted, and pixels with brightness temperature values of 95 quantiles in a pixel matrix of 9 multiplied by 9 with M pixels as the center are determined as C pixels. Theoretically, compared with a water body, the brightness temperature value of the land is higher, so that the brightness temperature value of the C pixel is the largest. However, in order to exclude an abnormal value which may occur in the observation of the light temperature, a screening rule of 95 quantile is set.
Step 1.2, extracting day-by-day brightness temperature value sequences of the M pixel and the C pixel, calculating an M/C signal value, and performing moving average processing. According to planck's law of radiation, there are:
TB＝T (1)
in the formula, the brightness temperature T of the objectBIs the product of the emissivity of the object and the actual physical temperature T, and if 1, the object is an absolute blackbody. The brightness temperature value of a pixel can be expressed as:
TB＝(1-FWS)TB,land+FWS×TB,water (2)
in the formula, TB,waterIs the TB value of the water body; t isB,landIs the TB value of land. The brightness temperature value of the M pixel is marked as M, the brightness temperature value of the C pixel is marked as C, and then M and C can be expressed as:
C＝TB,calibration＝TB,land＝Tcalibration land (4)
in the formula, TB,measurementIs the brightness temperature value of the observation pixel, namely M; t ismeasurementIs the actual temperature of the observation pixel; marking the water emissivity of the area aswater(ii) a And marking the average land emissivity of the area as the average land emissivity of different ground objectsland；TB,calibrationIs the brightness temperature value of the calibration pixel, i.e. C; t iscalibrationIs the actual temperature of the calibration pixel. Since the observation pixel and the calibration pixel are located in substantially the same area, it is assumed that the actual temperatures of the two pixels are the same, i.e.:
Tmeasurement＝Tcalibration (5)
also, although the terrain type of each pixel may not be exactly the same, the emissivity of the land in the observation and calibration pixels may be considered to be equal to each other due to being in the same arealandThen, there are:
land,measurement≈ land,calibration≈land (6)
the ratio M/C can be expressed by a function with respect to FWS:
and carrying out moving average processing on the calculated M/C signal value to obtain the time sequence of the M/C signal. In fig. 2 (C), the corresponding daily M/C signal values after 4-day moving average processing at two M pixels (i.e., site 1 and site 2) are shown (time: 2016 (6 months) to 2016 (12 months)).
And step 1.3, extracting a threshold value for judging whether flood occurs according to the long-time sequence of the M/C signal values acquired in the step 1.2, and preliminarily determining the occurrence time of flood events in a certain time period of the research area.
In fig. 2 (b), through distribution of M/C signal box diagrams (sample statistical time: 2008-2016-4-1), the two M pixels (i.e., site 1 and site 2) select decile numerical values (site 1: 0.95; site 2: 0.975) of respective long-time sequences as thresholds, and perform judgment in combination with air temperature, if the M/C signal values are lower than the thresholds and the corresponding average daily air temperatures are greater than the average daily air temperatures of multiple years in the snow season of the region, flood is considered to occur, so that 5 flood events can be judged to occur in a period from 2016 6 months to 2016 months 12 months, and the occurrence time is preliminarily determined to be respectively 6 months 17 days, 7 months 3 days-7 months 4 days, 7 months 7 days-7 months 9 days, 8 months 1 day, 9 months 22 days-9 months 24 days.
Step 2, searching satellite-borne active microwave remote sensing SAR images before disaster, in disaster and after disaster corresponding to the flood event based on the time range determined in the step 1, carrying out water body land classification based on a machine learning method, and respectively calculating the water body/land distribution conditions of the research area before disaster, in disaster and after disaster;
step 2 further comprises the following substeps:
step 2.1, determining the corresponding time period before/during/after the flood event based on the time range of the flood event obtained in the step 1, selecting a proper satellite-borne active microwave remote sensing SAR image, and obtaining normalized SAR image radar backscattering coefficient distribution through pretreatment:
in the formula (I), the compound is shown in the specification,
is the normalized radar backscattering coefficient; theta is a radar incident angle; thetarefIs a reference angle, and is generally averaged according to the range distribution of θ.
FIG. 3 shows the histogram distribution of the normalized radar backscattering coefficient of the Sentiniel-1 SAR image in the study area of 2016, 8, 10.
Further, the pretreatment process involved in step 2.1 of the present invention specifically comprises:
reading in data, adding track files, thermal noise removal, radiation correction, filtering, terrain correction, and exporting data. The processing can be generally carried out by adopting remote sensing image software platforms such as ENVI, SNAP and the like which are commonly used in the field.
Further, for selecting the satellite-borne active microwave remote sensing SAR image, a Sentinel satellite SAR image which is advanced in technology and universal internationally can be generally adopted. Taking the flood event of 7 months in the embodiment as an example, according to the flood event occurrence time preliminarily determined in the step 2, selecting the SAR images of the flood event at the corresponding time period before disaster, during disaster and after disaster, specifically adopting image data of a Sentinel-1 satellite primary product GRD IW mode, and the space precision is 10m multiplied by 10 m. Since the revisit cycle of the research area in the example of the Sentinel-1 satellite product in 2016 is about 12 days, the selected SAR images correspond to the period of 5 days in 2016 years 7 months in 2016, 6 days in 2016 years 5 months in before disaster, 18 days in 5 months, 30 days in 5 months, 11 days in 6 months in 2016, and 17 days in 2016 years 7 months in after disaster.
And 2.2, based on the comparison and selection results of various supervised classification and unsupervised classification methods, constructing a water body/land classification algorithm based on the SAR image, and acquiring a water body/land classification result of the research area by utilizing the normalized SAR image radar backscattering coefficient.
Further, the supervised classification and unsupervised classification method involved in step 2.2 is specifically as follows:
and (3) supervision and classification: three typical classifiers such as a random forest method, a maximum likelihood method and a minimum distance method which are commonly used in the field are adopted. The random forest method is a Bagging type machine learning integration algorithm, the basic unit of the algorithm is a decision tree, the random forest is composed of a large number of mutually independent decision trees, the training samples are sampled in a centralized mode in a return mode, and the accuracy of the overall result and the higher generalization capability of a model are ensured by summarizing the decision tree results; the maximum likelihood method is established on the Bayes criterion basis, characteristic values of various types are extracted through a sample training set, classification discrimination functions are established, and characteristic vectors of pixels to be judged are substituted into the discrimination functions, so that the pixels to be judged are classified into a class which enables the discrimination function values to be maximum; the minimum distance method is to calculate the feature set of each class of samples and calculate the feature center, so as to classify the pixel to be judged into the class with the minimum distance from the feature center in the feature space.
Unsupervised classification: also called as a threshold method, the present embodiment employs Otsu method (the ohio method) which is commonly used in the art. The Ostu method is a self-adaptive global threshold determining method, the Otsu method determines a global threshold aiming at the histogram distribution of an image, the global threshold is divided into L gray levels, a background value (land) and a target value (water body) are distinguished by using an inter-class variance, and the target function is as follows:
in the formula (I), the compound is shown in the specification,
is a function of the class mean corresponding to the threshold k;
changes with k value represent their corresponding total variance. Global threshold k*So thatThe objective function, i.e. the between-class variance η, reaching a maximum or, equivalently, causing
To the maximum, it can be achieved by iterative search:
further, a water body/land classification algorithm based on the SAR image is constructed in the step 2.2:
firstly, a Water body/land extraction is carried out based on a high-precision optical remote sensing image covering a certain local area of a research area by adopting a Water body index method such as a normalized Water body index (NDWI) (normalized Difference Water index) and the like which are conventional in the art, and the Water body/land extraction is used as reference data for evaluating the classification effect of a supervision classification algorithm and a threshold method.
Specifically, the water body/land classification effect of a supervised classification algorithm and a threshold method is judged by using the general classification precision OA and Kappa coefficients commonly used in the field as evaluation indexes, and if the evaluation index of the threshold method is not lower than that of the supervised classification algorithm, the threshold method is preferably selected; and if the evaluation index of the threshold value method is too low, selecting the optimal index in the supervised classification algorithm.
And 2.3, correcting the water body/land classification result obtained in the step 2.2 by utilizing a post-processing algorithm considering the landform and the hydrological factor distribution characteristics to obtain the optimized water body/land distribution condition before/during/after the disaster in the research area.
Further, the topographic and geomorphic factors and hydrological factors adopted in step 2.3 of the present invention are:
hand (height Above Nearest drainage) index. The index represents the difference between the elevation of each pixel and the elevation of the river network pixel closest to the pixel.
FIG. 4 shows the distribution of the HAND index in the study area of the examples.
Further, the post-processing algorithm used in step 2.3 of the present invention to correct the water/land classification result obtained in step 2.2 is:
and taking the pixels with the HAND index larger than a certain threshold value as areas which cannot be classified into surface water bodies, and removing false positive and false positive classification from the primary classification result. The threshold may be taken to be 15 m. FIG. 5 shows the water body/land classification result of the Sentinel-1SAR image based on the threshold method after the research area optimization in the embodiment.
Step 3, extracting the surface water area ratio FWS of the M pixels based on the water body/land classification result in the step 2, carrying out correlation analysis on the surface water area ratio FWS and corresponding M/C signals, and establishing a relation model of M/C and FWS;
step 3 further comprises the following substeps:
step 3.1, extracting the classified distribution of the water bodies/lands inside the M pixels according to the water body/land distribution condition obtained in the step 2, and calculating the surface water area ratio FWS of the M pixels:
in the formula, AwaterIs the area of the water body in the M pixel; a is the total area of M pixels, which in the example is 3.125km × 3.125 km.
And 3.2, constructing a relation model of M/C and FWS based on the M pixel FWS obtained in the step 3.1 and the M/C value of the corresponding time period in the step 1.
Further, the method involved in constructing the M/C and FWS relationship model in step 3.2 of the present invention is as follows:
respectively constructing correlation relations by using three methods of quadratic polynomial curve fitting, power exponent fitting, linear fitting and the like, and using R2The maximum is a criterion to select an optimal method.
FIG. 6 shows the core relationship curve of the M/C and FWS relationship model constructed in the example.
And 4, based on the M/C and FWS relation model constructed in the step 3, utilizing the M/C signal sequence value obtained in the step 1 to calculate the space-time distribution of the FWS at the permanent water body pixels in the research area, so that the continuous dynamic simulation of the flood submerging range at the permanent water body pixels of riverways, lakes and the like is realized.
The existing research shows that the damage of the flood caused by the rising and overflowing of water levels such as flood beaches, ponds and lakes and the like to the surrounding environment is large due to the persistence of the existing permanent water body, and the areas affected by the flood disastrous effect are often distributed around the permanent water body in the flood event. Because the passive microwave brightness and temperature data adopted in the invention generally requires higher space-time accuracy, the finally obtained flood submerging range dynamic simulation result at the permanent water body pixel also correspondingly has higher accuracy, for example, the finally achieved flood submerging dynamic simulation accuracy is 3.125 km/day in the embodiment.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A flood submerging range dynamic simulation method fusing active and passive microwave remote sensing information is characterized by comprising the following steps: the method comprises the following steps:
step 1, extracting a historical time sequence of M/C signal values by adopting satellite-borne passive microwave brightness temperature data, determining a threshold value for judging whether flood occurs or not, and preliminarily determining the occurrence time of a flood event;
step 2, searching a satellite-borne active microwave remote sensing SAR image before/during/after a flood corresponding to the flood event based on the occurrence time of the flood event determined in the step 1, classifying water bodies on the land based on a machine learning method, and calculating the water body/land distribution condition of a research area before/during/after the flood;
step 3, extracting the surface water area ratio FWS of the M pixels based on the water body/land distribution condition in the step 2, carrying out correlation analysis on the surface water area ratio FWS and corresponding M/C signals, and establishing a relation model of M/C and FWS;
and 4, based on the M/C and FWS relation model constructed in the step 3, utilizing the M/C signals obtained in the step 1 to calculate the space-time dynamic distribution of the FWS at the permanent water body pixel, and realizing high space-time precision continuous dynamic simulation of the flood submergence range at the permanent water body.
2. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 1, wherein: the specific method of the step 1 comprises the following steps:
step 1.1, selecting proper high-space-time precision passive microwave brightness temperature data according to the river width of a research area, and selecting a river channel observation pixel and a calibration pixel of an observation pixel value of the research area, wherein the river channel observation pixel is abbreviated as an M pixel, and the calibration pixel is abbreviated as a C pixel;
step 1.2, extracting day-by-day brightness temperature value sequences of the M pixel and the C pixel, calculating an M/C signal value, and performing moving average processing;
and step 1.3, extracting a threshold value for judging whether flood occurs according to the historical time sequence of the M/C signal values acquired in the step 1.2, and preliminarily determining the occurrence time of flood events in a certain time period of the research area.
3. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 2, wherein: the specific implementation steps of extracting day-by-day brightness temperature value sequences of the M pixel and the C pixel in the step 1.2 and calculating an M/C signal value are as follows:
in the formula, TB,measurementIs the brightness temperature value of the observation pixel; t ismeasurementIs the actual temperature of the observation pixel;waterthe emissivity of the water body in the area;landthe average terrestrial emissivity of the area; t isB,calibrationIs the brightness temperature value of the calibration pixel; t iscalibrationIs the actual temperature of the calibration pixel; the FWS is the area ratio occupied by the surface water in the pixel.
4. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 1, wherein: the specific method of the step 2 comprises the following steps:
step 2.1, based on the occurrence time of the flood event preliminarily determined in the step 1, defining the corresponding time period before/during/after the flood event, selecting a proper satellite-borne active microwave remote sensing SAR image, and obtaining normalized SAR image radar backscattering coefficient distribution through a pretreatment process;
2.2, based on the comparison and selection results of various supervised classification and unsupervised classification methods, constructing a water body/land classification algorithm based on the SAR image, and acquiring a water body/land classification result of a research area by utilizing a normalized SAR image radar backscattering coefficient;
and 2.3, correcting the water body/land classification result obtained in the step 2.2 by utilizing a post-processing algorithm considering the landform and the hydrological factor distribution characteristics to obtain the optimized water body/land distribution condition before/during/after the disaster in the research area.
5. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 4, wherein: in step 2.1, the normalized SAR image radar backscattering coefficient distribution obtained through the pretreatment process is specifically as follows:
in the formula (I), the compound is shown in the specification,
is the normalized radar backscattering coefficient; theta is a radar incident angle; thetarefIs a reference angle, and is generally averaged according to the range distribution of θ.
6. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 4, wherein: the pretreatment process involved in step 2.1 is specifically as follows: reading in data, adding track files, thermal noise removal, radiation correction, filtering, terrain correction, and exporting data.
7. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 4, wherein: the method for supervised classification and unsupervised classification in the step 2.2 specifically comprises the following steps:
and (3) supervision and classification: three typical classifiers of a random forest method, a maximum likelihood method and a minimum distance method are adopted;
unsupervised classification: the Otsu method is a self-adaptive global threshold determining method, determines a global threshold aiming at histogram distribution of an image, divides the global threshold into L gray levels, distinguishes land and water by using inter-class variance, and has an objective function as follows:
in the formula (I), the compound is shown in the specification,
is a function of the class mean corresponding to the threshold k;
as k changes, it represents its corresponding total variance, global threshold k*Making the objective function, i.e. the between-class variance η, maximum or, equivalently, making
To the maximum, it can be achieved by iterative search:
8. the flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 4, wherein: the topographic and geomorphic factors and hydrological factors adopted in step 2.3 are HAND indexes, and the indexes represent the difference of the elevation of each pixel between the pixel and the nearest river network pixel.
9. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 1, wherein: the specific method of the step 3 comprises the following steps:
3.1, extracting specific distribution of water/land classification in the M pixels according to the water/land distribution condition obtained in the step 2, and calculating the surface water area ratio FWS of the M pixels;
and 3.2, constructing a relation model of M/C and FWS based on the FWS of the M pixels obtained in the step 3.1 and the M/C signal values of the corresponding time periods in the step 1.
10. The flood inundation range dynamic simulation method fusing active and passive microwave remote sensing information as claimed in claim 9, wherein: 3.2, constructing an M/C and FWS relation model, specifically, respectively constructing correlation relations by adopting a quadratic polynomial curve fitting method, a power exponent fitting method and a linear fitting method, and using R to2The maximum is a criterion to select an optimal method.
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