CN114612793A - Multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of water sideline - Google Patents
Multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of water sideline Download PDFInfo
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Abstract
A multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of a water sideline comprises seven steps: reading and preprocessing multi-source and multi-temporal data; step two, calculating the multi-temporal water body spectral index; step three, water body distribution and extraction; step four, removing terrain shadow interference; step five: calculating the occurrence probability of the water body; step six: detecting a high-tide-level low-tide-level water line; step seven: the position of the coastline is determined and the range of the tidal flat is determined. Can solve a large amount of interferences that the shoreline that leads to because of the morning and evening tides sideline changes, cloud and topography shadow cause to coastline, tidal flat remote sensing detection to showing degree of accuracy, the degree of automation that improves coastline and tidal flat remote sensing and detect, belonging to the environment remote sensing field, being suitable for coastline, tidal flat change monitoring.
Description
Technical Field
The invention relates to a multi-temporal remote sensing coastline detection and tidal flat detection method based on high-frequency observation of a water sideline, which can solve the problem that a great deal of interference is caused to the coastline and tidal flat remote sensing detection by the water sideline change and cloud and terrain shadow caused by tides, thereby obviously improving the accuracy and the automation degree of the coastline and tidal flat remote sensing detection, belonging to the field of environmental remote sensing and being suitable for monitoring the coastline and tidal flat change.
Background
According to the "dictionary of Earth science", the coastline generally refers to the borderline that high tide reaches near the sea during the climax of many years, and the tidal flat refers to the sand beach exposed between the tides. The sea-land water sideline is influenced by tide action, storm tide and the like, is constantly in change, only can the instantaneous water sideline position be observed in single and low-frequency satellite remote sensing transit, the coastline position cannot be obtained, and the tidal flat position cannot be obtained. Moreover, when the low-frequency remote sensing data is used for extracting a water line by using the water body spectral index, the automation degree of data processing can be reduced due to the interference of cloud shadow. In conclusion, the low-frequency secondary satellite remote sensing is difficult to effectively detect the coastline and the tidal flat, and the requirement of coastal zone environment monitoring cannot be met.
Under the background of the current large increase in the number of remote sensing satellites and the rapid development of a remote sensing big data technology, the multisource and multi-temporal remote sensing observation technology is fully utilized, the advantage of high-frequency dynamic observation of a water line is exerted, the water line when high and low tide levels are observed can be used for achieving the purpose of shoreline and tidal flat detection, the interference of cloud shadow can be overcome, the automation degree of information extraction is remarkably improved, and the application requirement of large-range rapid detection of the shoreline and the tidal flat is met.
Disclosure of Invention
1. The purpose is as follows: the invention aims to provide a multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of a water line, which plays the advantages of high-frequency remote sensing dynamic observation of the water line, solves the problem that the coastline and the tidal flat are difficult to effectively detect by low-frequency satellite remote sensing, overcomes the interference of cloud shadow, overcomes the influence of terrain shadow by adding digital elevation Data (DEM), and obviously improves the automation degree of information extraction.
2. Technical problem to be solved
The first problem is how to accurately detect the high tide level and low tide level water line by utilizing multi-temporal data; secondly, the cloud and the terrain shadow are easily confused with the water body in the water body identification process, so that the effect of the water line identification is influenced, and the other problem is how to overcome the interference of the cloud and the terrain shadow in the water line detection process.
3. Technical scheme
The present invention provides a corresponding solution to the above technical problems to be solved. The whole solution is shown in figure 1. The invention relates to a multi-temporal remote sensing coastline detection and tidal flat method based on high-frequency dynamic observation of a water sideline, which comprises the following steps:
the method comprises the following steps: reading and preprocessing of multi-source and multi-temporal data
And selecting and reading the remote sensing data source meeting the requirement. Remote sensing data with green light wave band (about 550 nm) and short wave infrared wave band (about 1600 nm) are selected. And processing all data into remote sensing data with unified geographic coordinate system, projection and spatial resolution by applying conventional projection conversion and spatial resampling method in remote sensing data processing according to the spatial resolution, coordinate system and projection condition of the read data.
Step two: multi-temporal water spectral index calculation
And (4) processing the data output in the first step by using the normalized water body spectral index. The formula of the normalized water body spectral index is as follows,
NDWI＝[R(Green)-R(SWIR)]/[R(Green)+R(Green)]
wherein NDWI is normalized water body spectral index, R(Green)Is a remote sensing reflectivity image of green light wave band (about 550 nm), R(SWIR)The image is a remote sensing reflectivity image of a short wave infrared band (about 1600 nm).
Step three: water distribution extraction
And (4) with 0 as a threshold, segmenting the area which is larger than the threshold on the NDWI image, namely the water body distribution.
Step four: removal of terrain shadowing disturbances
And calculating the terrain gradient based on the DEM data. And (4) taking-20 meters as an elevation threshold value and-10 degrees of gradient threshold value, dividing the area smaller than the threshold value (without a terrain shadow area), and removing the area larger than the threshold value (with the terrain shadow area). And C, masking the water body distribution data in the step three by using the divided areas, reserving the water body distribution data in the terrain-free shadow area, and removing the water body distribution in the terrain-containing shadow area.
Step five: calculation of water occurrence probability
And calculating the ratio of the times of dividing each pixel into the water body in multi-temporal observation to the total observation times pixel by pixel, namely the occurrence probability of the water body.
Step six: detection of high-tide and low-tide water-side lines
On the water body occurrence probability image, the occurrence probability of the water body between the low-tide level water line and the sea is highest, the occurrence probability of the water body in the tidal flat is lower, and the closer the position is to the high-tide level water line, the lower the occurrence probability of the water body is. Analyzing the water body occurrence probability image characteristics, setting a proper high-tide level low-tide level water boundary water body occurrence probability threshold value, carrying out image binarization segmentation, and converting the raster image into a vector image, so as to obtain the vector distribution data of the high-tide level low-tide level water boundary. The probability of cloud shadow appearing in each pixel in the area can be considered to be the same or very close, and the cloud shadow appearing in a certain pixel part time phase does not influence the space distribution change characteristic of the probability value appearing in the water body, so that the position detection of the high-tide-level and low-tide-level water line is not influenced.
Step seven: determination of coastline position and delineation of tidal flat area
Editing and modifying the water level line of the high tide level, and removing the water side line of the artificial water bodies such as the culture pond and the like, namely the position of the coastline. According to the positions of the high tide level water line and the coastline, the direct area of the high tide level water line and the coastline is defined, namely the tidal range.
4. Excellence and efficacy
The invention relates to a multi-temporal remote sensing coastline detection and tidal flat method based on high-frequency dynamic observation of a water sideline, which has the advantages that: (1) the advantages of high-frequency remote sensing dynamic observation of the water line are exerted, and the accuracy of detection of the position of the coastline and the tidal flat range is obviously improved; (2) the interference of cloud and topography shadow in the water line detection is effectively overcome, and the degree of automation is improved.
Drawings
Fig. 1 is a flow chart of an implementation of the multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of a water line.
Fig. 2-4 are diagrams illustrating processes and effects according to embodiments.
Detailed Description
In order to better illustrate the multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of the water line, an implementation example of coastline and tidal flat detection in 2020 is made in the yellow river delta. The method comprises the following specific steps:
the method comprises the following steps: reading and preprocessing of multi-source and multi-temporal data
Obtaining Landsat-8 and Sentinel-2 remote sensing data of 2020 yellow river delta for 652 scenes in total. The data download websites of Landsat-8 and Sentinel-2 are respectively
https:// earth xplor. usgs. gov, https:// scihub. copper. eu. The two wave bands are B3(Green) and B6(SWIR), B3(Green) and B11(SWIR) respectively. Green is a Green band (around 550 nm) and SWIR is a short-wave infrared band (around 1600 nm).
The spatial resolution of Landsat-8 data is 30m, the spatial resolution of Sentinel-2 data B3 and B11 wave bands is 10m and 20m respectively, and the Sentinel-2 data needs to be spatially resampled to be consistent with Landsat-8. The spatial resampling is finished by adopting a 'Resize Data' tool of a trial version of ENVI remote sensing image processing software, wherein the resolution of a spatial resampling target is set to be 30 m. The two data are projected in a consistent manner, and projection transformation processing is avoided. The processed data is remote sensing reflectivity data containing a green light wave band and a short wave infrared wave band. The projection system is UTM-WGS84, the spatial resolution is 30m, and the number of wave bands is 2.
Step two: multi-temporal water spectral index calculation
And (4) processing the data output in the first step by using the normalized water body spectral index. The formula of the normalized water body spectral index is as follows,
NDWI＝[R(Green)-R(SWIR)]/[R(Green)+R(Green)]
wherein NDWI is normalized water body spectral index, R(Green)Is a remote sensing reflectivity image of green light wave band (about 550 nm), R(SWIR)The image is a remote sensing reflectivity image of a short wave infrared band (about 1600 nm).
The method comprises the steps of realizing multi-temporal water spectral index calculation by applying a normalized water spectral index formula, inputting the normalized water spectral index calculation formula into a Band math tool by using a Band math tool of a trial version of ENVI remote sensing image processing software, and setting R(Green)、R(SWIR)And the corresponding wave band numbers are used for completing the calculation of the normalized water body spectral index.
Step three: water distribution extraction
The numerical value of water body distribution areas such as the sea surface on the NDWI image is larger than that of the land, the index of the general water body is a positive value, and the index of the land surface is a negative value. Therefore, the area larger than the threshold value on the NDWI image is segmented by taking 0 as the threshold value, and the segmented area is water body distribution.
Step four: removal of terrain shadowing disturbances
And calculating the terrain gradient based on the DEM data. And (4) taking-20 meters as an elevation threshold value and-10 degrees of gradient threshold value, dividing the area smaller than the threshold value (without a terrain shadow area), and removing the area larger than the threshold value (with the terrain shadow area). And C, masking the water body distribution data in the step three by using the divided areas, reserving the water body distribution data in the terrain-free shadow area, and removing the water body distribution in the terrain-containing shadow area. The Mask adopts a Mask-Apply Mask tool of a trial version of ENVI remote sensing image processing software, after an elevation threshold range and a gradient threshold range are set, the NDWI pixel values which do not meet the threshold range are reset to zero, and only the NDWI pixel values which meet the threshold range are reserved.
Step five: calculation of water occurrence probability
Calculating the ratio of the number of times that each pixel is divided into the water body in the multi-temporal observation to the total observation number pixel by pixel, namely obtaining the water body occurrence probability (as shown in fig. 2). Calculating by adopting an ENVI remote sensing image processing software trial version 'Statistics-Sum Data Bands' tool, obtaining a 'water times' image for the 'water distribution Data' obtained in the step four, and processing the 652 scene Data to obtain a 'total observation times' image. And finally, setting a 'water frequency' image by dividing the 'total observation frequency' image by using a trial version 'Band math' tool of ENVI remote sensing image processing software to obtain a water occurrence probability image.
Step six: detection of high-tide and low-tide water-side lines
Setting 0.1 and 0.85 as high-tide-level low-tide-level water boundary water body occurrence probability threshold values, carrying out image binarization segmentation to obtain high-tide-level and low-tide-level water body distribution grid data (as shown in figure 3), and converting the grid data into vector data representing the positions of the high-tide-level and low-tide-level water boundaries to obtain vector distribution data of the high-tide-level and low-tide-level water boundaries.
And (3) carrying out image binarization segmentation by adopting a trial version 'Masking-structured Mask' tool of ENVI remote sensing image processing software, setting '0.1-1.0' as a binarization threshold range, segmenting the 'water body occurrence probability image' obtained in the fifth step to obtain high-tide-level water body distribution raster data, setting '0.85-1.0' as the binarization threshold range, and segmenting the 'water body occurrence probability image' obtained in the fifth step to obtain low-tide-level water body distribution raster data.
And respectively carrying out vectorization on the high tide water body distribution grid data and the low tide water body distribution grid data by using a grid-to-vector tool of QGIS software to obtain vector data representing the high tide water position and the low tide water position edge line position.
Step seven: determination of coastline position and delineation of tidal flat area
And editing and modifying the low-tide-level water line, and removing the water lines of artificial water bodies such as a culture pond and the like, namely the position of the coastline. According to the high-tide water level line and the coastline position, a 'digital tool' of the QGIS software is adopted to define the area between the high-tide water level line and the coastline position, and vector data representing the area between the high-tide water level line and the coastline position is formed, namely the tidal range (as shown in figure 4). The low-tide water level line is edited and modified by adopting a vector editing tool of QGIS software, and the aquaculture pond judges and identifies the regular shape (such as a rectangle) of the artificial water body according to the water body occurrence probability image.
Claims (10)
1. A multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of a waterside line is characterized in that: the method comprises the following specific steps:
the method comprises the following steps: reading and preprocessing of multi-source and multi-temporal data
Selecting and reading a remote sensing data source meeting the requirement; processing all data into remote sensing data with unified geographic coordinate system, projection and spatial resolution by applying conventional projection conversion and spatial resampling method in remote sensing data processing according to spatial resolution, coordinate system and projection condition of read data;
step two: multi-temporal water spectral index calculation
Processing the data output in the first step by using the normalized water body spectral index; the formula of the normalized water body spectral index is as follows,
NDWI＝[R(Green)-R(SWIR)]/[R(Green)+R(Green)]
wherein NDWI is normalized water body spectral index, R(Green)Is a green band remote sensing reflectivity image, R(SWIR)The image is a short wave infrared band remote sensing reflectivity image;
step three: water body distribution and extraction
Dividing the area larger than the threshold value on the NDWI image by taking 0 as the threshold value, namely obtaining water body distribution;
step four: removal of terrain shadowing disturbances
Calculating the terrain gradient based on DEM data; taking-20 meters as an elevation threshold value and-10 degrees of slope threshold value, dividing the area smaller than the threshold value into terrain-free shadow areas, and removing the area larger than the threshold value, namely the terrain-containing shadow areas; masking the water body distribution data in the third step by using the divided areas, reserving the water body distribution data in the terrain-free shadow area, and removing the water body distribution containing the terrain shadow area;
step five: calculation of water occurrence probability
Calculating the ratio of the times of dividing each pixel into water bodies in multi-temporal observation to the total observation times pixel by pixel, namely the occurrence probability of the water bodies;
step six: detection of high-tide and low-tide water-side lines
On the water body occurrence probability image, the occurrence probability of the water body between the low-tide level water line and the sea is highest, the occurrence probability of the water body in the tidal flat is lower, and the closer the position is to the high-tide level water line, the lower the occurrence probability of the water body is; analyzing the water body occurrence probability image characteristics, setting a proper high-tide level low-tide level water boundary water body occurrence probability threshold value, carrying out image binarization segmentation, and converting a raster image into a vector image to obtain vector distribution data of the high-tide level low-tide level water boundary;
step seven: determination of coastline position and delineation of tidal flat area
Editing and modifying the high-tide-level water line, and removing the artificial water body water line of the culture pond to obtain the position of the coastline; according to the positions of the high tide level water line and the coastline, the direct area of the high tide level water line and the coastline is defined, namely the tidal range.
2. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1, wherein: in the first step, obtaining 652 scenes in total of Landsat-8 and Sentiniel-2 remote sensing data in 2020 of yellow river delta; landsat-8, Sentinel-2 data download sites are https:// earth xplor
https:// scihub. copernius. eu; selecting the two bands as B3(Green) and B6(SWIR), B3(Green) and B11(SWIR) respectively; green is a 550nm Green light band, and SWIR is a 1600nm short wave infrared band.
3. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1 or 2, wherein: in the first step, the spatial resolution of Landsat-8 data is 30m, the spatial resolutions of Sentinel-2 data B3 and B11 wave bands are 10m and 20m respectively, and the Sentinel-2 needs to be subjected to spatial resampling so as to be consistent with Landsat-8; the spatial resampling is finished by adopting a trial Data tool of ENVI remote sensing image processing software, wherein the resolution of a spatial resampling target is set to be 30 m; the two data are projected in a consistent manner, and projection transformation processing is avoided; the processed data is remote sensing reflectivity data containing a green light wave band and a short wave infrared wave band; the projection system is UTM-WGS84, the spatial resolution is 30m, and the number of wave bands is 2.
4. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1, wherein: in the second step, the normalized water body spectral index formula is applied to realize multi-temporal water body spectral index calculation, a Band math tool of a trial version of ENVI remote sensing image processing software is adopted, the normalized water body spectral index calculation formula is input into the Band math tool, and R is set(Green)、R(SWIR)And the corresponding wave band numbers are used for completing the calculation of the normalized water body spectral index.
5. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1, wherein: in step three, the numerical value of the water body distribution area on the sea surface of the NDWI image is larger than that of the land, the index of the water body is a positive value, and the index of the land surface is a negative value.
6. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of watershed lines as claimed in claim 1, wherein: in the fourth step, the Mask adopts a Masking-Apply Mask tool of a trial version of ENVI remote sensing image processing software, after an elevation threshold range and a gradient threshold range are set, the NDWI pixel values which do not meet the threshold range are reset to zero, and only the NDWI pixel values which meet the threshold range are reserved.
7. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1, wherein: in the fifth step, calculating and adopting a trial version 'Statistics-Sum Data Bands' tool of ENVI remote sensing image processing software, obtaining a water frequency image for the water distribution Data obtained in the fourth step, and processing 652 scene Data to obtain a total observation frequency image; and finally, setting the water body frequency image to be divided by the total observation frequency image by using a trial version 'Band math' tool of ENVI remote sensing image processing software to obtain a water body occurrence probability image.
8. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1, wherein: in the sixth step, because the probability of the cloud shadow appearing in each pixel in the area is considered to be the same or close to the probability, the cloud shadow appearing in a certain pixel part time phase does not influence the space distribution change characteristic of the probability value appearing in the water body, and therefore the position detection of the high-tide level and low-tide level water boundary line is not influenced.
9. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1 or 8, wherein: in the sixth step, setting 0.1 and 0.85 as high-tide level and low-tide level water boundary water body occurrence probability threshold values, carrying out image binarization segmentation to obtain high-tide level and low-tide level water body distribution grid data, converting the grid data into vector data representing the positions of the high-tide level and low-tide level water boundary lines, and obtaining vector distribution data of the high-tide level and low-tide level water boundary lines;
the image binarization segmentation adopts a trial version Mask-structured Mask tool of ENVI remote sensing image processing software, sets '0.1-1.0' as a binarization threshold range, segments the water body occurrence probability image obtained in the fifth step to obtain high tide level water body distribution grid data, sets '0.85-1.0' as the binarization threshold range, and segments the water body occurrence probability image obtained in the fifth step to obtain low tide level water body distribution grid data;
and respectively carrying out vectorization on the high-tide water body distribution grid data and the low-tide water body distribution grid data by using a grid vector conversion tool of QGIS software to obtain vector data representing the positions of the high-tide water edge lines and the low-tide water edge lines.
10. The multi-temporal remote sensing coastline and tidal flat detection method based on high-frequency observation of waterside lines as claimed in claim 1, wherein: in the seventh step, a digitalized tool of QGIS software is adopted to define the area between the two, and vector data which represents the area between the high tide level water line and the coastline position is formed, namely the tidal flat range; the low-tide water level line is edited and modified by adopting a vector editing tool of QGIS software, and the aquaculture pond judges and identifies the regular shape of the artificial water body on the water body occurrence probability image.
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| CN116341932B (en) * | 2023-05-31 | 2023-08-22 | 自然资源部第二海洋研究所 | Tidal flat change monitoring method and system based on double remote sensing time sequence indexes |
| CN117423002A (en) * | 2023-12-18 | 2024-01-19 | 中国水产科学研究院南海水产研究所 | Small-scale tidal flat image processing method based on DEM |
| CN117423002B (en) * | 2023-12-18 | 2024-03-15 | 中国水产科学研究院南海水产研究所 | Small-scale tidal flat image processing method based on DEM |
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