CN110147746B - Method and system for rapidly extracting maximum and minimum possible surface water body ranges based on Sentinel-2 image - Google Patents

Method and system for rapidly extracting maximum and minimum possible surface water body ranges based on Sentinel-2 image Download PDF

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CN110147746B
CN110147746B CN201910385392.0A CN201910385392A CN110147746B CN 110147746 B CN110147746 B CN 110147746B CN 201910385392 A CN201910385392 A CN 201910385392A CN 110147746 B CN110147746 B CN 110147746B
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王超
陈泽强
陈能成
王伟
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Wuhan University WHU
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Abstract

The invention discloses a method and a system for quickly extracting the maximum and minimum possible surface water body ranges based on a Sentinel-2 remote sensing image, which can solve the problem that the Sentinel-2MSI image is used for quickly extracting the maximum and minimum possible surface water body ranges in a designated area within a long time range. Through inspection, the method utilizes the specific narrow-wave near-infrared of Sentinel-2 to calculate the new normalized differential water body index (nNDWI) and the new normalized differential vegetation index (nNDVI) of each pixel of each image in the image set and the remote sensing image mosaic technology, overcomes the complex steps that each image needs to be independently extracted to surface water in the traditional method, and improves the extraction efficiency of the maximum and minimum coverage areas of the surface water body in a long-time interval. The water body extraction method established by the invention plays an important role in reflecting regional surface water resource storage and periodic change, researching and judging flood situations and researching regional ecological environment bearing capacity.

Description

Method and system for rapidly extracting maximum and minimum possible surface water body ranges based on Sentinel-2 image
Technical Field
The invention relates to the field of remote sensing data application, in particular to a method for quickly extracting the largest and smallest possible surface water body ranges based on a Sentinel-2 remote sensing image.
Background
Water resources are essential for promoting sustainable development, support human life, maintain ecosystem balance, and play an irreplaceable role in ensuring economic development. Surface water, as a type of surface covering, is an important index of water resources and plays an important role in many aspects such as climate control, biogeochemical circulation, surface energy balance and the like. In recent decades, many countries, especially developing countries, have undergone rapid urbanization, and changes in surface water caused by human activities have seriously affected surface temperature, soil humidity, biodiversity, ecosystem functions, and even human life. Therefore, monitoring the dynamic changes of surface water is critical to natural environmental health and economic sustainability.
The development of surface water research using satellite remote sensing began in 1970, and a number of related studies have subsequently emerged. Around 2000 years, with the rapid development of remote sensing satellites, several effective surface Water coverage indexes, such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), were proposed. Various satellite remote sensing sensors with visible light and microwave bands are used for estimating a submerged area and defining a water body boundary, including a medium-resolution Imaging spectrometer (MODIS), a Landsat special sensor (TM), a Synthetic Aperture Radar (SAR), an earth Observation test system (system basic object d' observer la Terre, SPOT), and the like. However, no method for rapidly extracting the maximum and minimum possible surface water body ranges in a long time interval by utilizing Sentinel-2 satellite multispectral remote sensing data (Sentinel-2MSI) with a revisitation period of 2-5 days and a spatial resolution of 10 meters is available at present. In addition, the maximum and minimum possible surface water ranges in a long time interval are extracted, and the method plays an important role in reflecting regional surface water resource storage and periodic change, researching and judging flood disasters and researching regional ecological environment bearing capacity.
Disclosure of Invention
The invention aims to solve the problem that the method for rapidly extracting the largest and smallest possible surface water body ranges in a designated area within a long time range by utilizing a Sentinel-2MSI image specifically comprises the following steps:
step one, obtaining an image set of all Sentinel-2MSI images in a target area within a specified time range;
removing the images with larger cloud coverage degree in all the images in the image set;
step three, calculating a New Normalized Difference Water Index (nNDWI) and a New Normalized Difference Vegetation Index (nNDVI) of each pixel of each image in the image set, and respectively taking the nNDWI value and the nNDVI value as New wave bands to be merged into the original image;
step four, inlaying the image sets obtained in the step three based on nNDWI, and taking the pixel with the maximum nNDWI value in all the image sets at the same spatial pixel position to obtain the most possible surface water coverage images;
step five, inlaying the image sets obtained in the step three based on nNDVI, and taking the pixel with the maximum nNDVI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image with the least possibility;
and step six, setting a nNDWI threshold value for the most possible surface water coverage image obtained in the step four and the least possible surface water coverage image obtained in the step five, and extracting all pixels higher than the set threshold value to obtain a most possible surface water coverage range and a least possible surface water coverage range.
Further, in step three, the calculation formulas of ndwi and ndvi are as follows;
nNDWI＝(PGreen-PnarrowNIR)/(PGreen+PnarrowNIR)
in the formula, PGreenAnd PnarrowNIRRespectively representing the reflectivity of a green wave band and a narrow-wave near-infrared wave band;
nNDWI＝(PnarrowNIR–PRed)/(PnarrowNIR+PRed)
in the formula, PnarrowNIRAnd PRedRespectively representing the reflectivity of the narrow-wave near-infrared band and the red band.
Further, in the second step, the images with the cloud coverage attribute value higher than 5% are filtered.
Further, in step six, the ndwi threshold is set to 0.
The invention also provides a system for rapidly extracting the maximum and minimum possible surface water body range based on the Sentinel-2 image, which comprises the following modules:
the system comprises a first module, a second module and a third module, wherein the first module is used for obtaining image sets of all Sentinel-2MSI in a target area within a specified time range;
the second module is used for removing the image with larger cloud coverage degree in all the images in the image set;
a third module, configured to calculate a New Normalized Difference Water Index (ndwi) and a New Normalized Difference Vegetation Index (ndvi) of each pixel of each image in the image set, and merge the ndwi value and the ndvi value into the original image as New bands, respectively;
the fourth module is used for inlaying the image set obtained by the third module based on nNDWI, and taking the pixel with the maximum nNDWI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image which is most possible;
the fifth module is used for inlaying the image set obtained by the third module based on the nNDVI, and taking the pixel with the maximum nNDVI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image with the least possibility;
and the sixth module is used for setting an nNDWI threshold value for the most possible surface water coverage image obtained by the fourth module and the least possible surface water coverage image obtained by the fifth module, extracting all pixels higher than the set threshold value and obtaining a most possible surface water coverage range and a least possible surface water coverage range.
Further, in the third module, the calculation formulas of ndwi and ndvi are as follows;
nNDWI＝(PGreen-PnarrowNIR)/(PGreen+PnarrowNIR)
in the formula, PGreenAnd PnarrowNIRRespectively representing the reflectivities of the green band and the narrow band near infrared band, P in the Sentinel-2MSI imageGreenCorresponding wave band is B3, PnarrowNIRThe corresponding wave band is B8A;
nNDWI＝(PnarrowNIR–PRed)/(PnarrowNIR+PRed)
in the formula, PnarrowNIRAnd PRedRespectively representing the reflectivities of the narrow-band near infrared band and the red band, P in the Sentinel-2MSI imagenarrowNIRThe corresponding wave band is B8A, PRedThe corresponding band is B4.
Further, the second module filters out images with cloud coverage attribute values higher than 5%.
Further, a ndwi threshold is set to 0 in a sixth module.
The invention realizes a method for rapidly extracting the largest and smallest possible surface water body ranges based on the Sentinel-2 remote sensing image, and can extract the largest and smallest possible surface water ranges in a long time interval aiming at a target area and a target time range. Through inspection, the invention utilizes new remote sensing index models (nNDWI and nNDVI) and remote sensing image mosaic technology, overcomes the complex steps that each image needs to be independently extracted to surface water in the traditional method, and improves the extraction efficiency of the maximum and minimum coverage areas of the surface water body in a long time interval. The water body extraction method established by the invention plays an important role in reflecting regional surface water resource storage and periodic change and researching regional ecological environment bearing capacity.
Drawings
FIG. 1 shows the NDVI and nNDVI comparative test areas and sample selections in an example of the invention.
FIG. 2 is a boxed plot of NDVI and nNDVI in vegetation, bodies of water, and urban areas, respectively, in an embodiment of the invention.
FIG. 3 is a flow chart of the present invention.
FIG. 4 shows the maximum possible surface water coverage in the Poyang lake area 2018 of Jiangxi province.
FIG. 5 shows the minimum possible surface water coverage in the Poyang lake area 2018 of Jiangxi province.
Detailed Description
In order to facilitate the understanding and implementation of the present invention by those of ordinary skill in the art, the present invention is further described in detail below by using a Google Earth Engine cloud platform, taking the example of extracting the maximum and minimum possible surface water ranges of the china Jiangxi province in 2018 by using the Sentinel-2MSI remote sensing image:
the method comprises the following steps: obtaining an image set of all Sentinel-2MSI images in the target area within a specified time range;
this procedure yielded a set of all available Sentinel-2MSI images, including 3767 scene images, in the west and Jiangxi provinces of 2018.
Step two: removing the image with larger cloud coverage degree in all the images in the image set;
setting a threshold value to be 5% through a CLOUD COVERAGE attribute CLOUD _ COVERAGE _ ASSESSMENT carried by a Sentinel-2 remote sensing image, filtering out images with a CLOUD COVERAGE attribute value higher than 5%, and obtaining a partial image list with a CLOUD COVERAGE attribute value lower than or equal to 5% after 238 images are left.
Step three: and calculating a new normalized differential water body index (nNDWI) and a new normalized differential vegetation index (nNDVI) of each pixel of each image in the image set, respectively taking the nNDWI value and the nNDVI value as new wave bands, and combining the new wave bands into the original image. Wherein, nNDWI and nNDVI have the following calculation formulas;
nNDWI＝(PGreen-PnarrowNIR)/(PGreen+PnarrowNIR)
in the formula, PGreenAnd PnarrowNIRRespectively representing the reflectivities of the green band and the narrow band near infrared band, P in the Sentinel-2MSI imageGreenCorresponding wave band is B3, PnarrowNIRThe corresponding band is B8A.
nNDWI＝(PnarrowNIR–PRed)/(PnarrowNIR+PRed)
In the formula, PnarrowNIRAnd PRedRespectively representing the reflectivities of the narrow-band near infrared band and the red band, P in the Sentinel-2MSI imagenarrowNIRThe corresponding wave band is B8A, PRedThe corresponding band is B4.
In the area shown in fig. 1, a vegetation area (upper rectangular area), a water body area (middle two areas) and an urban area (left two areas) are respectively selected, and NDVI of the three areas are respectively calculated, wherein the NDVI is calculated according to the following formula:
NDVI＝(PNIR–PRed)/(PNIR+PRed)
in the formula, PNIRAnd PRedRespectively representing the reflectivity of the near infrared band and the red band.
Then, the obtained box diagram is shown in fig. 2, wherein a in the abscissa represents the NDVI value range of the vegetation sample, B represents the NDVI value range of the water body sample, C represents the NDVI value range of the city sample, D represents the NDVI value range of the vegetation sample, E represents the NDVI value range of the water body sample, and F represents the NDVI value range of the city sample. Comparing A/B and D/E in FIG. 2, it can be seen that nNDVI has better discrimination between vegetation and water; comparing A and D, the result value range of the nNDVI calculated vegetation area is relatively more concentrated than NDVI, and the vegetation area is more beneficial to extraction.
Step four: inlaying the image sets obtained in the third step based on nNDWI, and taking the pixel with the maximum nNDWI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image most possibly;
step five: inlaying the image sets obtained in the third step based on nNDVI, and taking the pixel with the largest nNDVI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image with the least possibility;
step six: setting an NDWI threshold (ideally, the threshold is 0; in practical application, some algorithms can be selected for statistics, and then adjustment is performed by combining a visual preview effect) for the most possible surface water coverage image obtained in the fourth step and the least possible surface water coverage image obtained in the fifth step, extracting all pixels higher than the set threshold, and obtaining a most possible surface water coverage range and a least possible surface water coverage range, as shown in FIG. 4 and FIG. 5, black parts except boundary lines.
Comparing fig. 4 and 5, it can be seen that the maximum possible surface water coverage of the Yanghu region in 2018 is significantly more than the minimum possible surface water coverage, indicating that the seasonal dry-out condition of Yanghu is very significant; in addition to Poyang lake, the small area of water in FIG. 4 is also significantly larger than that in FIG. 5, indicating that there is more surface water present seasonally in this area; further, the smallest possible surface water coverage in fig. 5 can be considered as an area covered by surface water all year round in 2018.
The embodiment of the invention also provides a system for rapidly extracting the largest and smallest possible surface water body ranges based on the Sentinel-2 image, which comprises the following modules:
the system comprises a first module, a second module and a third module, wherein the first module is used for obtaining image sets of all Sentinel-2MSI in a target area within a specified time range;
the second module is used for removing the image with larger cloud coverage degree in all the images in the image set;
a third module, configured to calculate a New normalized Difference Water Index (ndwi) and a New normalized Difference Vegetation Index (ndvi) of each pixel of each image in the image set, and merge the ndwi value and the ndvi value into the original image as New bands, respectively;
the fourth module is used for inlaying the image set obtained by the third module based on nNDWI, and taking the pixel with the maximum nNDWI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image which is most possible;
the fifth module is used for inlaying the image set obtained by the third module based on the nNDVI, and taking the pixel with the maximum nNDVI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image with the least possibility;
and the sixth module is used for setting an nNDWI threshold value for the most possible surface water coverage image obtained by the fourth module and the least possible surface water coverage image obtained by the fifth module, extracting all pixels higher than the set threshold value and obtaining a most possible surface water coverage range and a least possible surface water coverage range.
In the third module, the calculation formulas of nNDWI and nNDVI are as follows;
nNDWI＝(PGreen-PnarrowNIR)/(PGreen+PnarrowNIR)
in the formula, PGreenAnd PnarrowNIRRespectively represent a green band and a narrow bandReflectance of the infrared band;
nNDWI＝(PnarrowNIR–PRed)/(PnarrowNIR+PRed)
in the formula, PnarrowNIRAnd PRedRespectively representing the reflectivity of the narrow-wave near-infrared band and the red band.
The technical scheme of the invention is not limited to the time range and the space region listed above, and also comprises other time ranges and space ranges with various scales.

Claims (6)

1. The method for rapidly extracting the maximum and minimum possible surface water body ranges based on the Sentinel-2 image is characterized by comprising the following steps of:
step one, obtaining an image set of all Sentinel-2MSI images in a target area within a specified time range;
removing the images with larger cloud coverage degree in all the images in the image set;
step three, calculating a New Normalized Difference Water Index (nNDWI) and a New Normalized Difference Vegetation Index (nNDVI) of each pixel of each image in the image set, and respectively taking the nNDWI value and the nNDVI value as New wave bands to be merged into the original image;
nNDWI and nNDVI are calculated as follows;
nNDWI＝(PGreen-PnarrowNIR)/(PGreen+PnarrowNIR)
in the formula, PGreenAnd PnarrowNIRRespectively representing the reflectivities of the green band and the narrow band near infrared band, P in the Sentinel-2MSI imageGreenCorresponding wave band is B3, PnarrowNIRThe corresponding wave band is B8A;
nNDWI＝(PnarrowNIR–PRed)/(PnarrowNIR+PRed)
in the formula, PnarrowNIRAnd PRedRespectively representing the reflectivities of the narrow-band near infrared band and the red band, P in the Sentinel-2MSI imagenarrowNIRThe corresponding wave band is B8A, PRedThe corresponding wave band is B4;
step four, inlaying the image sets obtained in the step three based on nNDWI, and taking the pixel with the maximum nNDWI value in all the image sets at the same spatial pixel position to obtain the most possible surface water coverage images;
step five, inlaying the image sets obtained in the step three based on nNDVI, and taking the pixel with the maximum nNDVI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image with the least possibility;
and step six, setting a nNDWI threshold value for the most possible surface water coverage image obtained in the step four and the least possible surface water coverage image obtained in the step five, and extracting all pixels higher than the set threshold value to obtain a most possible surface water coverage range and a least possible surface water coverage range.
2. The method for rapidly extracting the maximum and minimum possible surface water body range based on the Sentinel-2 image as claimed in claim 1, wherein: and filtering out images with the cloud coverage attribute value higher than 5%.
3. The method for rapidly extracting the maximum and minimum possible surface water body range based on the Sentinel-2 image as claimed in claim 1, wherein: in step six, the nNDWI threshold is set to 0.
4. The system for rapidly extracting the maximum and minimum possible surface water body ranges based on the Sentinel-2 image is characterized by comprising the following modules:
the system comprises a first module, a second module and a third module, wherein the first module is used for obtaining image sets of all Sentinel-2MSI in a target area within a specified time range;
the second module is used for removing the image with larger cloud coverage degree in all the images in the image set;
a third module, configured to calculate a New Normalized Difference Water Index (ndwi) and a New Normalized Difference Vegetation Index (ndvi) of each pixel of each image in the image set, and merge the ndwi value and the ndvi value into the original image as New bands, respectively;
nNDWI and nNDVI are calculated as follows;
nNDWI＝(PGreen-PnarrowNIR)/(PGreen+PnarrowNIR)
in the formula, PGreenAnd PnarrowNIRRespectively representing the reflectivity of a green wave band and a narrow-wave near-infrared wave band;
nNDWI＝(PnarrowNIR–PRed)/(PnarrowNIR+PRed)
in the formula, PnarrowNIRAnd PRedRespectively representing the reflectivity of a narrow-wave near-infrared band and a red band;
the fourth module is used for inlaying the image set obtained by the third module based on nNDWI, and taking the pixel with the maximum nNDWI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image which is most possible;
the fifth module is used for inlaying the image set obtained by the third module based on the nNDVI, and taking the pixel with the maximum nNDVI value in all the image sets at the same spatial pixel position to obtain the surface water coverage image with the least possibility;
and the sixth module is used for setting an nNDWI threshold value for the most possible surface water coverage image obtained by the fourth module and the least possible surface water coverage image obtained by the fifth module, extracting all pixels higher than the set threshold value and obtaining a most possible surface water coverage range and a least possible surface water coverage range.
5. The system for rapidly extracting the maximum and minimum possible surface water body range based on the Sentinel-2 image of claim 4, wherein: and filtering out images with the cloud coverage attribute value higher than 5% in the second module.
6. The system for rapidly extracting the maximum and minimum possible surface water body range based on the Sentinel-2 image of claim 4, wherein: the ndwi threshold is set to 0 in the sixth block.
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