CN109919070B - Coastline remote sensing calculation method with profile shape self-adaptive fitting function - Google Patents
Coastline remote sensing calculation method with profile shape self-adaptive fitting function Download PDFInfo
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Abstract
The invention provides a coastline remote sensing calculation method with a profile form self-adaptive fitting function, which comprises the following steps of: s1, extracting an instantaneous water boundary line and an artificial shoreline from the multi-temporal satellite remote sensing image of the coastal zone; s2, dividing and discretizing the water line to obtain a water line discrete point sequence of each section; s3, completing tide level assignment of each section water line discrete point sequence; s4, judging the morphological characteristics of the section and determining the type of the section; s5, carrying out section form fitting to obtain a form fitting equation of the section; s6, calculating the average climax point position of the section by using a section form fitting equation, and sequentially connecting the average climax points along the direction of the coast to form an average climax line; and S7, processing the average climax line and the artificial shoreline by using a space topological analysis method to obtain a coastline calculated by remote sensing. The method breaks through the hypothesis of single slope of the beach, and has better applicability and higher accuracy compared with the traditional method.
Description
Technical Field
The invention relates to a coastline remote sensing calculation method, and belongs to the technical field of ocean remote sensing information technology and application.
Background
The coastline is a boundary between sea and land, and accurate determination of the coastline is of great significance to the use and management of space resources in the coastline. The traditional coastline measuring method has a field measuring method and a photogrammetry method, and although the coastline generated by the method has high precision, the method is time-consuming, labor-consuming, and has poor timeliness, and is not easy to monitor and popularize on a large scale in a coastline area. Since the 21 st century, the appearance and the large-scale use of multi-source, multi-resolution and multi-temporal remote sensing images greatly improve the accuracy, the accuracy and the timeliness of information extraction of the coastal zone, the rapid development of the remote sensing technology provides a new technical means for dynamic change monitoring of a coastline.
The existing coastline remote sensing identification or calculation methods mainly comprise two methods: one is the general high tide method, Li, W.Y., & Gong, P.published article "continuos monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat image", which selects the remote sensing image at the high tide moment and extracts the instantaneous water line as the coastline; the other is an average slope method, Liu Yan Xia and the like are respectively endowed with a tide level value at the image imaging moment by extracting two water line lines at different plane positions in the shoreline monitoring research based on tidal flat terrain correction between images, taking yellow river delta as an example, and then the average slope of the shoreline is obtained according to the tidal difference and the average distance, and the position of the shoreline corresponding to the average high tide and high tide moment is calculated; in addition, Chen volume et al in the article "remote sensing-based space-time evolution of continental coastline beach in Jiangsu province and mainland of Jiangsu province" assumes that the gradient of intertidal zone is approximately uniform, firstly, the measured tide level data of the tide level control station is used for tide blending calculation and tide level prediction, the segmented tide level interpolation correction is carried out on the instantaneous water side lines extracted by remote sensing, then the average gradient of the corresponding tidal flat is calculated according to the tide level difference of the two water side lines, and the average high tide line is calculated.
Aiming at the existing coastline remote sensing calculation method, a general climax method is influenced by the time resolution and the imaging quality of an image, and the coastline remote sensing image imaged at the average climax moment is often difficult to obtain; the average slope method is theoretically only suitable for areas with smooth terrain and single slope. According to the existing investigation, the section form of the actual muddy tidal flat is changed greatly, taking the muddy coast in the middle of Jiangsu as an example, the muddy coast mainly has four section forms of a concave type, a slope type, a combination of the slope and a convex type, the washing, stabilizing, erosion-silt conversion and siltation states of the beach are respectively indicated, the coastline is calculated by only depending on the average slope of two water lines, the difference of different section forms cannot be expressed, and a large error also exists between the obtained coastline and the actual coastline. Therefore, in the area with large section form change, the coastline estimated by the average slope method does not have universality.
Disclosure of Invention
Aiming at the problems that in the conventional coastline remote sensing calculation method, the contingency of a general high tide line method is high, and an average slope method is not suitable for an area with large section form change, the invention provides a coastline remote sensing calculation method for section form adaptive fitting, which improves the fitness with the section form of a beach during the simulation of the position of a coastline and improves the position precision of the coastline calculated by remote sensing by simulating the characteristics of the scouring and silting forms of the beach.
In order to solve the technical problems, the invention adopts the following technical means:
a shoreline remote sensing calculation method of profile form adaptive fitting specifically comprises the following steps:
s1, extracting an instantaneous water line and an artificial shoreline from the multi-temporal satellite remote sensing image in the coastal zone;
s2, segmenting and discretizing the multi-temporal borderline by utilizing the segmentation line cluster to obtain a borderline discrete point sequence of each section of the coast;
s3, completing tide level assignment of the discrete point sequence of each section water line by using a tide harmony analysis method;
s4, selecting 3 discrete points on the section to judge the shape and the characteristics of the section, and determining the type of the section;
s5, selecting 5 discrete points on the section to perform section form fitting to obtain a form fitting equation of the section;
s6, calculating the average climax point position of the section by using a section form fitting equation, and sequentially connecting the average climax points along the direction of the coast to form an average climax line;
and S7, processing the average climax and climax line and the artificial shoreline by using a space topology analysis method to obtain a coastline calculated by remote sensing.
Further, the specific operation of step S1 is as follows:
s11, performing water body information enhancement and threshold segmentation on the coastal zone multi-temporal satellite remote sensing image;
s12, carrying out edge detection and grid-vector conversion processing on the image processed in the S11 to obtain an instantaneous water line in a vector format;
and S13, correcting the instantaneous water line by using a method of combining remote sensing supervision and classification with visual interpretation, and extracting an artificial shoreline.
Further, the specific operation of step S2 is as follows:
s21, drawing a dividing base line approximately parallel to the direction of a coast on the sea side of the multi-temporal water line, drawing a plurality of dividing lines perpendicular to the dividing base line, and forming a dividing line cluster;
and S22, segmenting and discretizing the multi-temporal borderline by utilizing the segmentation line cluster to obtain a borderline discrete point sequence of each section of the coast.
Further, the specific operation of step S3 is as follows:
s31, determining tide control sites, and calculating the tide level of each tide control site at the imaging moment of the remote sensing image by using a tide harmony analysis method;
s32, calculating the average climax and climax tide level of each control station;
s33, counting the average error delta of the tide level calculation of the control station;
and S34, linearly interpolating the tide level value of the control station obtained in the step S31 to the corresponding water line discrete point through distance weighting, and finishing tide level assignment of each section water line discrete point sequence.
Further, the specific operation of step S4 is as follows:
s41, selecting a water line discrete point sequence on a section, selecting two water line discrete points closest to the land side and the sea side in the sequence as two side discrete points of the section, halving a connecting line of the two side discrete points, and selecting the water line discrete point closest to the halving point as a middle discrete point of the section;
s42, taking the land side end point of the division line as an origin, taking the horizontal distance L from the discrete point to the origin as an abscissa, taking the tide level H of the discrete point as an ordinate, and establishing a rectangular coordinate system, wherein the coordinates of the land side discrete point, the sea side discrete point and the middle discrete point are respectively (L) 1 ，H 1 )、(L 2 ，H 2 ) And (L) 0 ，H 0 )；
S43, establishing a linear equation:
H＝aL+b (1)
wherein, a represents the average gradient of the section, and b represents the tide level value corresponding to the origin position;
s44, horizontal distance L from the middle discrete point to the origin 0 Substituting the linear equation H to aL + b to obtain the reference tide level H of the middle discrete point * ；
S45, comparing the reference tide level H of the middle discrete point by using the average error delta of tide level estimation of the control station * And the actual tidal level H 0 Judging the type of the section:
when | H * -H 0 When | < delta/2, the section is smooth, and the section form is slope type;
when H is present * -H 0 When the ratio is more than delta/2, the section is washed, and the section is concave;
when H is present * -H 0 When < -delta/2, the section is long and the section is of an upward convex shape.
Further, the specific operation of step S5 is as follows:
s51, selecting a water line discrete point sequence on a section, selecting two water line discrete points closest to the land side and the sea side in the sequence as two side discrete points of the section, equally dividing a connecting line 4 of the two side discrete points, sequentially selecting 3 water line discrete points closest to 3 equally divided points, and adopting the 5 discrete points to perform section form fitting;
s52, performing curve fitting on the concave-down scouring profile, wherein the fitting function equation is as follows:
H＝h 0 +Ae -L/t (2)
wherein h is 0 A, t are the coefficients of the fitting function, respectively, and the 5 discrete point coordinates selected in S51 are substituted in the above formula to calculate the coefficient values;
s53, performing curve fitting on the slope type gentle section, wherein the fitting function equation is as follows:
H＝aL+b (3)
wherein, a and b are the coefficients of the fitting function respectively, and the values of the coefficients are calculated by substituting the coordinates of 5 discrete points selected from S51 in the above formula;
s54, performing curve fitting on the upper convex silt length section, wherein a fitting function equation is as follows:
H＝cL 2 +dL+e (4)
wherein c, d and e are coefficients of a fitting function respectively, and 5 discrete point coordinates selected in S51 are substituted in the above formula to calculate coefficient values.
Further, the specific operation of step S6 is as follows:
s61, selecting a section, and using the origin plane coordinate (x) on the section 0 ，y 0 ) And sea side discrete point plane coordinates (x) 1 ，y 1 ) Calculate the azimuth α of the profile:
α＝arctan(y 0 -y 1 )/(x 0 -x 1 ) (5)
s62, substituting the average climax value H of the position of the section into the section fitting equation, and calculating the horizontal distance L between the average climax point and the origin H ；
S63, using the horizontal distance L between the average climax point and the origin H And a section azimuth angle alpha, and calculating a plane coordinate (x) corresponding to the average climax of the section 2 ，y 2 )：
x 2 ＝x 0 +L H cosα (6)
y 2 ＝y 0 +L H sinα (7)
And S64, repeating the steps S61, S62 and S63, calculating the plane coordinates of the average climax points on all the sections, and sequentially connecting the average climax points of all the sections along the direction of the coast to form an average climax line.
Further, the specific operation of step S7 is as follows:
s71, when the average climax line and the artificial shore line obtained by calculation exist at the same position, the average climax line and the artificial shore line are spatially superposed;
when the average climax line of the same position is positioned on the sea side of the artificial shoreline, taking the average climax line as the shoreline of the position;
when the average high tide line at the same position is on the land side of the artificial shoreline, taking the artificial shoreline as the shoreline of the position;
and S72, processing the whole coasts, and combining the selected average climax line and the artificial shoreline to obtain the coastline calculated by remote sensing.
The following advantages can be obtained by adopting the technical means:
the invention provides a coastline remote sensing calculation method with profile form self-adaptive fitting, which is characterized by segmenting a plurality of profiles along the trend of a coastline, judging the profile form according to three water line discrete points approximately uniformly distributed on the same profile, fitting by utilizing five water line discrete points approximately uniformly distributed on the same profile on the basis, determining a profile fitting equation, further obtaining an average climax line, and finally performing spatial topology analysis processing on the average climax line and an artificial shoreline to obtain the coastline calculated by remote sensing. Compared with the prior coastline remote sensing calculation technology of an average slope method, the method provided by the invention breaks through the assumption of single slope of the beach in the prior coastline remote sensing calculation method, the calculated coastline is closer to the actual situation, and the applicability of the calculation method is greatly improved. The remote sensing coastline of the method automatically comprises a natural coastline and an artificial coastline, and has natural advantages for calculating the retention rate of the natural coastline.
Drawings
FIG. 1 is a flow chart of steps of a shoreline remote sensing estimation method with adaptive fitting of a profile shape according to the invention.
FIG. 2 is a diagram of the instantaneous water line extraction result of one embodiment of the method of the present invention; the method comprises the following steps of (a) obtaining an original image of a satellite remote sensing image in a coastal zone, (b) obtaining an NDWI water body index enhancement result image, (c) obtaining a binary image after water and land separation, (d) obtaining a water boundary line raster image extracted by a Sobel operator, and (e) obtaining an effect image of vector water boundary lines and the original image of the remote sensing image in a superposition mode.
Fig. 3 is a schematic diagram of a result of extracting a water line discrete point sequence according to an embodiment of the method of the present invention.
FIG. 4 is a schematic diagram of the calculation of tidal level interpolation in the method of the present invention.
FIG. 5 is a schematic diagram illustrating the sectional shape determination in the method of the present invention.
FIG. 6 is a schematic diagram of a curve fit for three cross-sectional shapes in the method of the present invention; wherein, (a) is a concave scouring section fitting curve diagram, (b) is a slope type gentle section fitting curve diagram, and (c) is an convex silt length section fitting curve diagram.
FIG. 7 is a schematic diagram of the remote sensing coastline derived by the method of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the accompanying drawings as follows:
in the specific embodiment, a multi-temporal Landsat8 OLI satellite image covering the middle coast of Jiangsu province in 2018 is selected as a remote sensing data source, tide level data come from three tide level control sites of Xinyang harbor, Dafeng harbor and Wangchong, Envi5.1 is adopted as a remote sensing image processing tool, ArcGISI 10.2 is adopted as a vector data processing tool, and Origin9.1 is adopted as a section curve batch fitting tool.
A shoreline remote sensing calculation method with a profile shape self-adaptive fitting function is shown in figure 1 and specifically comprises the following steps:
s1, cutting coastal zone areas on two sides of the east-chuan port in the landrace 8 OLI satellite original image of 23/2/2018 as original images of coastline remote sensing images, as shown in fig. 2 (a), extracting instantaneous water lines and artificial coastlines from the coastal zone multi-temporal satellite remote sensing images, specifically operating as follows:
and S11, performing water body information enhancement and threshold segmentation on the multi-temporal satellite remote sensing image in the coastal zone. The invention can adopt NDWI water body index, MNDWI water body index or TGDWI water body index to enhance the water body information. The NDWI water body index is a common method, and the calculation formula is as follows:
wherein R is g Representing the reflectivity, R, of the green band in the remote-sensed image nir Representing the reflectivity of the near infrared band in the remote sensing image.
By analyzing the difference of the wave spectrum characteristics of soil/buildings and water bodies in the mid-infrared wave band, an improved normalized difference water body index MNDWI is proposed, and the calculation formula is as follows:
wherein R is mir Representing the reflectivity of the mid-infrared band in the remote sensing image.
To the coastal zone high resolution satellite remote sensing image when the low tide level be difficult to distinguish the mud flat of ponding and the decomposition between the high water of suspension silt concentration at the red wave band, the near-infrared wave band is difficult to distinguish the circumstances of mud flat and sea water that the water content is high, can adopt TGDWI water index to carry out the water information reinforcing, specific computational formula is as follows:
wherein R is ir Representing the reflectance, R, of the near-infrared band in remote-sensing images r Reflectance, λ, representing the red band in remote-sensing images ir Denotes the wavelength, λ, of the near infrared band r Indicating the wavelength, λ, of the red wavelength band g Indicating the wavelength of the green band.
Taking the NDWI water body index as an example, pixel histogram statistics is performed on the generated NDWI water body index map layer, a numerical value between a land pixel peak value and a water body pixel peak value is selected for linear stretching, a water-land boundary in an image is highlighted, and the image obtained after water body enhancement is performed through the NDWI water body index is shown in (b) in fig. 2.
After the water body information of the original image is enhanced, the original image is further processed by a threshold segmentation method, which is also called a density segmentation method and aims to enlarge the difference between the gray value of a target ground object to be extracted from the image and the gray value of a background. Setting a water body threshold value, extracting a seawater part in the enhanced NDWI water body index layer, setting the pixel value of the seawater part as 1, and setting the pixel values of the rest parts as 0, and forming a water-land separation binary image, wherein the result is shown as (c) in FIG. 2.
And S12, carrying out edge detection and grid-vector conversion processing on the image processed in the step S11 to obtain an instantaneous water line in a vector format. In this embodiment, the Sobel operator is used to extract the image edge, and the specific operations are as follows: firstly, the gray values of upper, lower, left and right four adjacent domains of each pixel in a binary image are weighted and averaged, and noise is smoothed; then, the gradient is calculated through differentiation, so that the gradient amplitude of the pixel close to the center of the template is maximum; and finally, setting a threshold value TH, and if the pixel gradient value is greater than the preset threshold value TH, considering the pixel point as an edge point. Performing convolution operation on the whole remote sensing image to obtain an edge image detected by a Sobel operator, setting the pixel value of the grid where the water edge is located as 1, and setting the pixel values of the rest parts as 0 to form a water edge binary image, wherein (d) in FIG. 2 is a water edge grid image extracted by the Sobel operator.
And carrying out grid vectorization processing on the water line binary image by using an ArcScan digitization tool in ArcGIS. Regarding the raster water sideline as a line with actual width, then extracting a central line to generate a vector water sideline, then editing a water sideline vector object in an ArcGIS by using an ArcEdit editing tool, deleting mistakenly-extracted and unnecessary water sideline line segments, and manually finely adjusting a locally discontinuous water sideline to obtain a final vector water sideline, wherein (e) in FIG. 2 shows the overlapping effect of the extracted vector water sideline and the original remote sensing image.
And S13, extracting a linear boundary which is formed by artificial ground objects and has artificial construction traces on the sea side closest to the beach of the coastal zone by utilizing remote sensing supervision and classification combined with visual interpretation correction processing, and taking the linear boundary as an artificial shoreline.
And (4) finishing the remote sensing extraction of the instantaneous waterside line of the multi-temporal remote sensing image by utilizing the steps to generate a multi-temporal waterside line shp file of the middle coast of Jiangsu. The multi-temporal instantaneous water line is uniformly converted into UTM projection (taking the northern hemisphere 51 band), CGCS2000 coordinate system.
S2, segmenting and discretizing the multi-temporal borderline by utilizing the segmentation line cluster to obtain a borderline discrete point sequence of each section of the coast, wherein the operations are as follows:
and S21, drawing a dividing base line approximately parallel to the coast direction on the sea side of the multi-temporal waterplane line according to the method of extrapolating the envelope line, and drawing a plurality of dividing lines vertical to the dividing base line to form a dividing line cluster.
In consideration of the size of the research area and the accuracy of the experiment, the embodiment takes 500m intervals as the equant distance of the water line segmentation base line, selects the segmentation base line object in the editing state, and performs the equant segmentation of the segmentation base line by using the Divide tool of ArcGIS. Through the merging processing of line segments, the line segment endpoints formed after equal division become internal nodes (Vertex) of the same broken line, and redundant nodes formed because the base line segments of the water line cannot be equally divided are deleted; in an editing state, selecting a Midpoint (Midpoint) of a base line segment formed by equally dividing a division base line, making a vertical line segment passing through the Midpoint, and generating a division line cluster shp file.
And S22, segmenting and discretizing the multi-temporal borderline by utilizing the segmentation line cluster to obtain a borderline discrete point sequence of each section of the coast. The embodiment combines the split line cluster shp file and the multi-temporal horizontal line shp file by using the layer combining function. Selecting a line breaking function in an editing state, and breaking lines in the merged file; merging the broken dividing lines, and outputting a Vertex node of the dividing line, namely the required water line dividing point; and sequentially outputting the x and y coordinates of the segmentation points to obtain discrete water line points and form a water line discrete point sequence of each section, wherein fig. 3 shows the extraction result of the water line discrete point sequence of the experimental area.
S3, utilizing a tide harmony analysis method to complete tide level assignment of the discrete point sequence of each section water line, and specifically operating as follows:
s31, in this embodiment, 3 tide level control sites are selected, the distribution of which is shown in fig. 3, tidal observation data of each site for more than one month is collected, and the tide level of each tidal control site at the time of imaging of the remote sensing image is calculated by using a tidal harmony analysis method.
Carrying out harmonic analysis on tide level observation data of a tide measuring station by using a least square method to obtain an average sea level, a tide harmonic constant, the number of related tide divisions, related parameters and a residual error, and calculating the tide height of a tide control station according to the tide harmonic constant, wherein the formula is as follows:
where H (t) is the tidal height of the tidal control site at time t, A 0 To average sea level, H i Tidal height, R, of partial tide i i Amplitude, ω, of the partial tide i i Frequency of partial tide i, phi i The phase of the partial tide i is 1.
And S32, calculating the average climax and tide level of each control station.
And S33, counting the average error delta of the tide level estimation of the control station.
And S34, linearly interpolating the tide level value of the control station obtained in the step S31 to the corresponding water line discrete point through distance weighting, and finishing tide level assignment of each section water line discrete point sequence.
The tidal level change caused by the change of the earth curvature, the propagation deformation of the tidal wave and the like is mainly shown in the latitudinal direction, so the tidal level interpolation calculation is mainly carried out in the latitudinal direction. As shown in fig. 4, during interpolation calculation, two adjacent tidal level interpolation stations closest to an interpolation point need to be selected, the interpolation point needs to be located between the two stations, and then the tidal level value calculation of the interpolation point is completed by using a linear interpolation method with the latitude difference between the interpolation point and the tidal level interpolation station as a weight, and the specific formula is as follows:
wherein H j Indicates the tidal height, H, of the interpolation point j 1 Tidal height of the tidal level station 1, H 2 Tidal height, Δ Y, of the tidal level station 2 1 Is the difference in latitude, Δ Y, of the interpolation point j from the tide station 1 2 The difference in the latitude between the interpolation point j and the tide level station 2 is j ═ 1.
And forming a water line discrete point sequence by the discrete points on the same dividing line according to the sequence of the dividing line cluster, and carrying out tide level assignment on the discrete points to obtain the water line discrete point sequence of all the calculated sections subjected to tide level assignment.
S4, selecting 3 discrete points on the section to judge the section shape feature, and determining the type of the section, the concrete operation is as follows:
s41, selecting a water line discrete point sequence on a section, selecting two water line discrete points closest to the land side and the sea side in the sequence as two side discrete points of the section, halving a connecting line of the two side discrete points, and selecting the water line discrete point closest to the halving point as a middle discrete point of the section.
S42, taking the land side end point of the dividing line as an origin, taking the horizontal distance L from the discrete point to the origin as an abscissa, taking the tide level H of the discrete point as an ordinate, establishing a rectangular coordinate system, and defining the coordinates of the land side discrete point, the sea side discrete point and the middle discrete point as (L) 1 ，H 1 )、(L 2 ，H 2 ) And (L) 0 ，H 0 )。
S43, establishing a linear equation:
H＝aL+b (13)
wherein a represents the average gradient of the section, b represents the tide level value corresponding to the origin position, and L is 1 ，H 1 ) And (L) 2 ，H 2 ) Substituting equation (13) solves the equation coefficients.
S44, horizontal distance L from the middle discrete point to the origin 0 Substituting the linear equation H to aL + b to obtain the reference tide level H of the middle discrete point * 。
S45, as shown in FIG. 5, comparing the average error Delta of the tide level reckoning of the control station with the reference tide level H of the middle discrete point * In relation to the actual tide level H0, the type of profile is judged:
when | H * -H 0 When | < delta/2, the section is considered to be smooth, and the section form is a slope type;
when H is present * -H 0 When the ratio is more than delta/2, the section is considered to be washed, and the section is concave;
when H is present * -H 0 When <. DELTA/2, the cross-sectional sludge length is considered to be long, and the cross-sectional shape is convex.
And S5, after judging the type of the section, performing section fitting, and performing a form fitting experiment on a plurality of sections of different types, wherein the 5 discrete points are the most economical discrete points for performing the section form fitting. Selecting 5 discrete points on the section to carry out section form fitting to obtain a form fitting equation of the section, wherein the specific operation is as follows:
s51, selecting a water line discrete point sequence on a section, selecting two water line discrete points closest to the land side and the sea side in the sequence as two side discrete points of the section, equally dividing a connecting line 4 of the two side discrete points, sequentially selecting 3 water line discrete points closest to 3 equally divided points, and adopting the 5 discrete points to perform section form fitting.
S52, performing curve fitting on the concave-down type scouring profile, and selecting a fitting curve as an exponential decay function, as shown in (a) of fig. 6, wherein the fitting function equation is as follows:
H＝h 0 +Ae -L/t (14)
wherein h is 0 And A, t are coefficients of the fitting function, and the values of the coefficients are calculated by substituting the 5 discrete point coordinates selected in S51 in the above formula.
S53, performing curve fitting on the slope-type gentle section, and selecting a fitting curve as a linear function, as shown in (b) of fig. 6, where the fitting function equation is as follows:
H＝aL+b (15)
wherein, a and b are the coefficients of the fitting function respectively, and 5 discrete point coordinates selected by substituting S51 in the above formula are used for calculating the coefficient values.
S54, performing curve fitting on the convex silt length section, where the selected fitting curve is a second-order polynomial function, as shown in (c) of fig. 6, and the fitting function equation is as follows:
H＝cL 2 +dL+e (16)
wherein c, d and e are coefficients of a fitting function respectively, and 5 discrete point coordinates selected in S51 are substituted in the above formula to calculate coefficient values.
S6, calculating the average climax point position of the section by using a section form fitting equation, and sequentially connecting the average climax points along the direction of the coast to form an average climax line, wherein the method comprises the following specific operations
S61, selecting a section, and using the origin plane coordinate (x) on the section 0 ，y 0 ) And sea side discrete point plane coordinates (x) 1 ，y 1 ) Calculate the azimuth α of the profile:
α＝arctan(y 0 -y 1 )/(x 0 -x 1 ) (17)
s62, substituting the average climax value H of the position of the section into the section fitting equation to calculate the horizontal distance L between the average climax point and the origin H 。
S63, using the horizontal distance L between the average climax point and the origin H And a section azimuth angle alpha, and calculating a plane coordinate (x) corresponding to the average climax of the section 2 ，y 2 )：
x 2 ＝x 0 +L H cosα (18)
y 2 ＝y 0 +L H sinα (19)
And S64, repeating the steps S61, S62 and S63, calculating the plane coordinates of the average climax points on all the sections, and sequentially connecting the average climax points of all the sections along the direction of the coast to form an average climax line.
S7, processing the average climax line and the artificial shoreline by using a space topological analysis method to obtain a shoreline calculated by remote sensing, wherein the specific operation is as follows:
s71, performing spatial topology analysis processing on the average climax line and the artificial shore line obtained by calculation, and performing spatial superposition on the average climax line and the artificial shore line when the average climax line and the artificial shore line exist at the same position;
when the average climax line of the same position is positioned on the sea side of the artificial shoreline, taking the average climax line as the shoreline (natural shoreline) of the position;
and when the average high tide line at the same position is located on the land side of the artificial shoreline, taking the artificial shoreline as the shoreline of the position.
And S72, processing the whole coasts, and combining the selected average climax line and the artificial shoreline to obtain the shoreline calculated by remote sensing, wherein the calculation result of the remote sensing shoreline of the specific embodiment is shown in FIG. 7.
The method of the invention is compared with an average slope method, and errors are calculated by respectively comparing two methods by utilizing the on-site measured coastline. The coastline is measured by RTK based on a JSCORS network in an on-site measurement mode, UTM projection and a CGCS2000 coordinate system are carried out, and the error of the actual measurement point of the coastline relative to the point position of the adjacent control point is not more than +/-0.1 meter. Tables 1, 2 and 3 show the errors of the concave profile, the slope profile and the convex profile:
TABLE 1
TABLE 2
TABLE 3
According to the table, the absolute errors of the distances between the coastlines calculated by the method of the invention and the measured coastlines in the erosion bank section, the slope bank section and the silt bank section are 49.49m, 100.13m and 86.23m respectively, the accuracy of the method of the invention is obviously better than that of the average slope method, and compared with the average slope method, the error of the coastlines calculated by the method of the invention is reduced by about 56.5%.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (7)
1. A shoreline remote sensing calculation method with a profile shape self-adaptive fitting is characterized by comprising the following steps:
s1, extracting an instantaneous water line and an artificial shoreline from the multi-temporal satellite remote sensing image in the coastal zone;
s2, segmenting and discretizing the multi-temporal waterways by utilizing the segmentation line clusters to obtain a waterways discrete point sequence of each section of the coast;
s3, completing tide level assignment of the discrete point sequence of each section water line by using a tide harmony analysis method;
s4, selecting 3 discrete points on the section to judge the shape and the characteristics of the section, and determining the type of the section;
s5, selecting 5 discrete points on the section to perform section form fitting to obtain a form fitting equation of the section;
s6, calculating the average high tide point position of the section by using a section form fitting equation, and sequentially connecting the average high tide points along the direction of the coast to form an average high tide line;
s7, processing the average climax line and the artificial shoreline by using a space topological analysis method to obtain a coastline calculated by remote sensing;
the specific operation of step S4 is as follows:
s41, selecting a water line discrete point sequence on a section, selecting two water line discrete points closest to the land side and the sea side in the sequence as two side discrete points of the section, halving a connecting line of the two side discrete points, and selecting the water line discrete point closest to the halving point as a middle discrete point of the section;
s42, taking the land side end point of the dividing line as an origin, taking the horizontal distance L from the discrete point to the origin as an abscissa, taking the tide level H of the discrete point as an ordinate, establishing a rectangular coordinate system, and taking the land side end point as an origin, and establishing a rectangular coordinate systemThe coordinates of the discrete point, the sea-side discrete point and the middle discrete point are respectively (L) 1 ，H 1 )、(L 2 ，H 2 ) And (L) 0 ，H 0 )；
S43, establishing a linear equation:
H＝aL+b
wherein, a represents the average gradient of the section, and b represents the tide level value corresponding to the origin position;
s44, horizontal distance L from the middle discrete point to the origin 0 Substituting the linear equation H to aL + b to obtain the reference tide level H of the middle discrete point * ；
S45, comparing the reference tide level H of the middle discrete point by using the average error delta of tide level calculation of the control station * And the actual tidal level H 0 Judging the type of the section:
when | H * -H 0 When | < delta/2, the section is smooth, and the section form is slope type;
when H is present * -H 0 When the ratio is more than delta/2, the section is washed, and the section is concave;
when H is present * -H 0 When < -delta/2, the section is long and the section is of an upward convex shape.
2. The shoreline remote sensing estimation method of adaptive fitting of profile morphology according to claim 1, wherein the specific operation of step S1 is as follows:
s11, performing water body information enhancement and threshold segmentation on the coastal zone multi-temporal satellite remote sensing image;
s12, carrying out edge detection and grid-vector conversion processing on the image processed in the S11 to obtain an instantaneous water line in a vector format;
and S13, correcting the instantaneous water line by using a method of combining remote sensing supervision and classification with visual interpretation, and extracting an artificial shoreline.
3. The shoreline remote sensing estimation method of adaptive fitting of profile morphology according to claim 1, wherein the specific operation of step S2 is as follows:
s21, drawing a dividing base line approximately parallel to the direction of a coast on the sea side of the multi-temporal water line, drawing a plurality of dividing lines perpendicular to the dividing base line, and forming a dividing line cluster;
and S22, segmenting and discretizing the multi-temporal borderline by utilizing the segmentation line cluster to obtain a borderline discrete point sequence of each section of the coast.
4. The shoreline remote sensing estimation method of adaptive fitting of profile morphology according to claim 1, wherein the specific operation of step S3 is as follows:
s31, determining tide control sites, and calculating the tide level of each tide control site at the imaging moment of the remote sensing image by using a tide harmony analysis method;
s32, calculating the average high tide level of each control station;
s33, counting the average error delta of the tide level calculation of the control station;
and S34, linearly interpolating the tide level value of the control station obtained in the step S31 to the corresponding water line discrete point through distance weighting, and finishing tide level assignment of each section water line discrete point sequence.
5. The shoreline remote sensing estimation method of adaptive fitting of profile morphology according to claim 1, wherein the specific operation of step S5 is as follows:
s51, selecting a water line discrete point sequence on a section, selecting two water line discrete points closest to the land side and the sea side in the sequence as two side discrete points of the section, equally dividing a connecting line 4 of the two side discrete points, sequentially selecting 3 water line discrete points closest to 3 equally divided points, and adopting the 5 discrete points to perform section form fitting;
s52, performing curve fitting on the concave scouring profile, wherein the fitting function equation is as follows:
H＝h 0 +Ae -L/t
wherein h is 0 A, t are the coefficients of the fitting function, respectively, and the 5 discrete point coordinates selected in S51 are substituted in the above formula to calculate the coefficient values;
s53, performing curve fitting on the slope type gentle section, wherein the fitting function equation is as follows:
H＝aL+b
wherein, a and b are the coefficients of the fitting function respectively, and 5 discrete point coordinates selected by S51 are substituted in the above formula to calculate the coefficient value;
s54, performing curve fitting on the upper convex silt length section, wherein a fitting function equation is as follows:
H＝cL 2 +dL+e
wherein c, d and e are coefficients of a fitting function respectively, and 5 discrete point coordinates selected in S51 are substituted in the above formula to calculate coefficient values.
6. The shoreline remote sensing estimation method of adaptive fitting of profile morphology according to claim 5, wherein the specific operation of step S6 is as follows:
s61, selecting a section, and using the origin plane coordinate (x) on the section 0 ，y 0 ) And sea side discrete point plane coordinates (x) 1 ，y 1 ) Calculate the azimuth α of the profile:
α＝arctan(y 0 -y 1 )/(x 0 -x 1 )
s62, substituting the average climax value H of the position of the section into the section fitting equation to calculate the horizontal distance L between the average climax point and the origin H ；
S63, using the horizontal distance L between the average climax point and the origin H And a section azimuth angle alpha, and calculating a plane coordinate (x) corresponding to the average climax of the section 2 ，y 2 )：
x 2 ＝x 0 +L H cosα
y 2 ＝y 0 +L H sinα
And S64, repeating the steps S61, S62 and S63, calculating the plane coordinates of the average climax points on all the sections, and sequentially connecting the average climax points of all the sections along the direction of the coast to form an average climax line.
7. The shoreline remote sensing estimation method of adaptive fitting of profile morphology according to claim 6, wherein the specific operation of step S7 is as follows:
s71, when the average climax line and the artificial shore line obtained by calculation exist at the same position, the average climax line and the artificial shore line are spatially superposed;
when the average climax line of the same position is positioned on the sea side of the artificial shoreline, taking the average climax line as the shoreline of the position;
when the average high tide line at the same position is on the land side of the artificial shoreline, taking the artificial shoreline as the shoreline of the position; and S72, processing the whole coasts, and combining the selected average climax line and the artificial shoreline to obtain the coastline calculated by remote sensing.
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