CN111504424A - Lake water storage variable quantity monitoring method based on remote sensing - Google Patents
Lake water storage variable quantity monitoring method based on remote sensing Download PDFInfo
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- CN111504424A CN111504424A CN202010552880.9A CN202010552880A CN111504424A CN 111504424 A CN111504424 A CN 111504424A CN 202010552880 A CN202010552880 A CN 202010552880A CN 111504424 A CN111504424 A CN 111504424A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F23/00—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm
- G01F23/22—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water
- G01F23/28—Indicating or measuring liquid level or level of fluent solid material, e.g. indicating in terms of volume or indicating by means of an alarm by measuring physical variables, other than linear dimensions, pressure or weight, dependent on the level to be measured, e.g. by difference of heat transfer of steam or water by measuring the variations of parameters of electromagnetic or acoustic waves applied directly to the liquid or fluent solid material
- G01F23/284—Electromagnetic waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9027—Pattern recognition for feature extraction
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
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- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
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- Computer Vision & Pattern Recognition (AREA)
- Thermal Sciences (AREA)
- Fluid Mechanics (AREA)
- Measurement Of Levels Of Liquids Or Fluent Solid Materials (AREA)
Abstract
The invention discloses a lake water storage variation monitoring method based on remote sensing, which comprises the following steps of collecting and analyzing remote sensing data, extracting lake water body information based on an object-oriented method, extracting lake water level based on synthetic aperture radar height measurement data, establishing a relation model based on remote sensing lake area variation-water level variation, and estimating and researching long-time sequence water volume variation of lakes.
Description
Technical Field
The invention relates to the technical field of monitoring of lake water storage variation, in particular to a lake water storage variation monitoring method based on remote sensing.
Background
Climate change is currently a major scientific problem internationally, and water resources are one of the most direct and important areas affected by climate change. The climate influences the distribution and change of water resources by influencing a series of important links of water circulation such as precipitation, evaporation, runoff and the like. The lake is an important carrier of surface water resources, the climate generally has the trend of warm humidification and warm drying development in recent decades, and the form and the water quantity of the lake are greatly changed.
The satellite remote sensing technology provides a technical means for monitoring the change of the lake water amount. In the prior art, the lake area and the water level are indispensable parameters in water volume change monitoring, and in the existing research, the lake water volume change estimation based on the satellite remote sensing area-satellite remote sensing water level is finally performed. The conventional lake water storage capacity change monitoring method needs to combine multi-source radar satellite water level data, but the on-orbit time of the satellite radar water level data is limited, so that the time length of water volume change research cannot be extended; some of the systems use single optical satellite data, and the time length of the water volume change of the lake cannot be expanded to the maximum extent.
Disclosure of Invention
The invention aims to overcome the existing defects and provides a lake water storage variation monitoring method based on remote sensing, which is independent of multi-source water level data, estimates the water storage variation of the lake in the past fifty years based on single satellite radar height measurement data (Sentinel-3) in a limited time period on the basis of long-time sequence lake area extracted by two kinds of optical remote sensing data of L andsat and Keyhole satellites, provides a new idea for long-time sequence water volume variation monitoring of the lake and can effectively solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention provides a lake water storage variable quantity monitoring method based on remote sensing, which comprises the following steps:
s1: collecting and analyzing remote sensing data;
s2: extracting lake water body information based on an object-oriented method;
s3: extracting lake water level based on synthetic aperture radar height measurement data;
s4: establishing a relation model based on the remote sensing lake area change-water level change;
s5: and (3) estimating and researching the water quantity change of the lake in a long time sequence.
As a preferable scheme, in step S1, the remote sensing data collection and analysis includes:
the remote sensing image data are obtained through sorting, and the remote sensing image data obtained through sorting comprise L andsat optical satellite data of a long time sequence, American keyhole series satellites and rising satellite radar height measurement data Sentinel-3 data of recent years.
As a preferable scheme, in step S1, the remote sensing data collection and analysis further includes:
combining the two optical image lake color expressions to determine the lake non-icing period, screening and arranging the images, and screening and arranging the satellite height measurement data of the lake non-icing period.
As a preferable scheme, in step S2, the extraction of the lake water body information based on the object-oriented method includes:
according to the two optical images, image preprocessing is carried out, then lake water body extraction is carried out by utilizing a normalized water body index method and an object-oriented method respectively, L andsat water body with banding phenomenon is repaired through manual inspection, the precision of the two methods is contrasted and analyzed, and the optimal non-icing-period lake boundary vector data in nearly fifty years is obtained through analysis.
As a preferable scheme, in step S3, the synthetic aperture radar elevation data-based lake level extraction includes:
defining characteristics of 2016-plus-2019 lake Sentinel-3 synthetic aperture radar satellite transit data, carrying out error correction on the transit satellite height measurement data by considering influences of atmosphere and terrain based on a satellite height measurement principle, screening satellite height measurement data points on the lake surface by combining areas of synchronous remote sensing lakes, and carrying out averaging processing on abnormal value elimination and synchronous water level data points to obtain water level data of each transit time; and carrying out trial lake verification based on the actually measured water level data.
As a preferable scheme, in step S4, the building of the relational model based on remote sensing lake area change-water level change includes:
analyzing the annual change characteristics of the water level based on 2016-plus-2019 lake satellite water level data; analyzing the consistency of the lake area and the water level change based on 2016-plus 2019 remote sensing lake non-ice period area data; selecting satellite water level data close to the lake area time, and establishing a stable relation model of water level variation and area variation.
As a preferable scheme, in step S5, the long time series water volume change estimation study of lakes includes:
assuming that the lake topography is not obviously changed in the last fifty years of research, estimating the lake water level change in the last fifty years based on the lake area change data of fifty years according to the lake water level change-area change relation model; and further estimating the water volume change of the lake in the last fifty years based on the water volume change model.
As a preferable scheme, the water amount variation model is:
assuming that the lake water volume is changed into an irregular platform body, calculating the water volume change under the water level change according to the following formula:
in the formula, SaIs the area of the upper surface of the table bodyCorresponding to the area of a lake in high water, SbThe area of the lower surface of the platform body corresponds to the area of the lake when the water is low, and h is a water level change value.
As a preferable scheme, the lake area is obtained by optical remote sensing, and the lake water level is obtained by radar height measurement data.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
1. on the basis of long-time sequence lake areas extracted by two kinds of optical remote sensing data of L andsat and Keyhole satellites without depending on multi-source water level data, the water storage capacity change of the lake in the past fifty years is estimated based on single satellite radar height measurement data (Sentinel-3) in a limited time period, and a new idea is provided for long-time sequence water volume change monitoring of the lake.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example (b):
the embodiment provides a lake water storage variation monitoring method based on remote sensing, which comprises the following steps:
(1) remote sensing data collection and analysis
The method comprises the steps of sorting and obtaining remote sensing image data, wherein the remote sensing image data comprises long-time sequence L andsat optical satellite data, American Keyhole (Keyhole) series satellites, rising satellite radar height measurement data Sentinel-3(Sentinel-3A/3B) data in recent years, determining the non-icing period of lakes by combining two optical image lake color expressions, screening and sorting images, and screening and sorting satellite height measurement data in the non-icing period of lakes.
(2) Lake water body information extraction based on object-oriented method
According to the two optical images, image preprocessing is carried out, then lake water body extraction is carried out by utilizing a normalized water body index method and an object-oriented method respectively, L andsat water body with banding phenomenon is repaired through manual inspection, the precision of the two methods is contrasted and analyzed, and the optimal non-icing-period lake boundary vector data in nearly fifty years is obtained through analysis.
(3) Lake water level extraction based on synthetic aperture radar height measurement data
Defining characteristics of 2016-plus-2019 lake Sentinel-3 synthetic aperture radar satellite transit data, carrying out error correction on the transit satellite height measurement data by considering influences of atmosphere and terrain based on a satellite height measurement principle, screening satellite height measurement data points on the lake surface by combining areas of synchronous remote sensing lakes, and carrying out averaging processing on abnormal value elimination and synchronous water level data points to obtain water level data of each transit time; and carrying out trial lake verification based on the actually measured water level data.
(4) Relation model establishment based on remote sensing lake area change-water level change
Analyzing the annual change characteristics of the water level based on 2016-plus-2019 lake satellite water level data; analyzing the consistency of the lake area and the water level change based on 2016-plus 2019 remote sensing lake non-ice period area data; selecting satellite water level data close to the lake area time, and establishing a stable relation model of water level variation and area variation.
(5) Study on estimation of water volume change of long-time sequence of lakes
Assuming that the lake topography is not obviously changed in the last fifty years of research, estimating the lake water level change in the last fifty years based on the lake area change data of fifty years according to the lake water level change-area change relation model; and further estimating the water volume change of the lake in the last fifty years based on the water volume change model. Wherein the water volume change model is as follows:
assuming that the lake water amount is changed into an irregular stage, the water amount change under the water level change is calculated according to the following formula (Taube, 2000):
in the formula, SaIs the area of the upper surface of the platform body corresponding to the area of the lake when the water is high, SbThe area of the lower surface of the platform body corresponds to the area of the lake when the water is low, and h is a water level change value.The lake area is obtained by optical remote sensing, and the lake water level is obtained by radar height measurement data.
The method is independent of multi-source water level data, and on the basis of long-time sequence lake areas extracted by two optical remote sensing data of L andsat and Keyhole satellites, the water storage capacity change of the lake in the past fifty years is estimated based on single satellite radar height measurement data (Sentinel-3) in a limited time period, so that a new idea is provided for long-time sequence water volume change monitoring of the lake.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A lake water storage variable quantity monitoring method based on remote sensing is characterized by comprising the following steps: the method comprises the following steps:
s1: collecting and analyzing remote sensing data;
s2: extracting lake water body information based on an object-oriented method;
s3: extracting lake water level based on synthetic aperture radar height measurement data;
s4: establishing a relation model based on the remote sensing lake area change-water level change;
s5: and (3) estimating and researching the water quantity change of the lake in a long time sequence.
2. The lake impounded water variation monitoring method based on remote sensing of claim 1, characterized in that: in step S1, the remote sensing data collection and analysis includes:
the remote sensing image data are obtained through sorting, and the remote sensing image data obtained through sorting comprise L andsat optical satellite data of a long time sequence, American keyhole series satellites and rising satellite radar height measurement data Sentinel-3 data of recent years.
3. The lake impounded water variation monitoring method based on remote sensing of claim 2, characterized in that: in step S1, the remote sensing data collection and analysis further includes:
combining the two optical image lake color expressions to determine the lake non-icing period, screening and arranging the images, and screening and arranging the satellite height measurement data of the lake non-icing period.
4. The lake impounded water variation monitoring method based on remote sensing of claim 1, characterized in that: in step S2, the extraction of lake water information based on the object-oriented method includes:
according to the two optical images, image preprocessing is carried out, then lake water body extraction is carried out by utilizing a normalized water body index method and an object-oriented method respectively, L andsat water body with banding phenomenon is repaired through manual inspection, the precision of the two methods is contrasted and analyzed, and the optimal non-icing-period lake boundary vector data in nearly fifty years is obtained through analysis.
5. The lake impounded water variation monitoring method based on remote sensing of claim 1, characterized in that: in step S3, the lake level extraction based on the synthetic aperture radar height measurement data includes:
defining characteristics of 2016-plus-2019 lake Sentinel-3 synthetic aperture radar satellite transit data, carrying out error correction on the transit satellite height measurement data by considering influences of atmosphere and terrain based on a satellite height measurement principle, screening satellite height measurement data points on the lake surface by combining areas of synchronous remote sensing lakes, and carrying out averaging processing on abnormal value elimination and synchronous water level data points to obtain water level data of each transit time; and carrying out trial lake verification based on the actually measured water level data.
6. The lake impounded water variation monitoring method based on remote sensing of claim 1, characterized in that: in step S4, the building of the relationship model based on remote sensing lake area change-water level change includes:
analyzing the annual change characteristics of the water level based on 2016-plus-2019 lake satellite water level data; analyzing the consistency of the lake area and the water level change based on 2016-plus 2019 remote sensing lake non-ice period area data; selecting satellite water level data close to the lake area time, and establishing a stable relation model of water level variation and area variation.
7. The lake impounded water variation monitoring method based on remote sensing of claim 1, characterized in that: in step S5, the study of estimating water volume changes of long time series of lakes includes:
assuming that the lake topography is not obviously changed in the last fifty years of research, estimating the lake water level change in the last fifty years based on the lake area change data of fifty years according to the lake water level change-area change relation model; and further estimating the water volume change of the lake in the last fifty years based on the water volume change model.
8. The lake impounded water variation monitoring method based on remote sensing of claim 7, characterized in that: the water quantity change model is as follows:
assuming that the lake water volume is changed into an irregular platform body, calculating the water volume change under the water level change according to the following formula:
in the formula, SaIs the area of the upper surface of the platform body corresponding to the area of the lake when the water is high, SbThe area of the lower surface of the platform body corresponds to the area of the lake when the water is low, and h is a water level change value.
9. The lake impounded water variation monitoring method based on remote sensing of claim 1, characterized in that: the lake area is obtained by optical remote sensing, and the lake water level is obtained by radar height measurement data.
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| CN202010552880.9A CN111504424A (en) | 2020-06-17 | 2020-06-17 | Lake water storage variable quantity monitoring method based on remote sensing |
| AU2021100306A AU2021100306A4 (en) | 2020-06-17 | 2021-01-18 | Remote Sensing-Based Lake Water Storage Change Monitoring Method |
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Cited By (7)
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| CN112200050A (en) * | 2020-09-30 | 2021-01-08 | 同济大学 | Lake water volume space-time change analysis method and device based on multi-source satellite data |
| CN112945352A (en) * | 2021-02-03 | 2021-06-11 | 成都鼎安华智慧物联网股份有限公司 | Extraction method based on remote sensing data water level abnormal information |
| CN113011740A (en) * | 2021-03-18 | 2021-06-22 | 长江水资源保护科学研究所 | Method for constructing lake wetland ecology-water level gradient response relation |
| CN113256101A (en) * | 2021-05-20 | 2021-08-13 | 东南大学 | Key driving force analysis method for water storage capacity change of urban and rural lakes |
| US11391569B2 (en) * | 2018-11-08 | 2022-07-19 | Tsinghua University | Method and device for monitoring water volume change, computer device and storage medium |
| CN114858987A (en) * | 2022-03-30 | 2022-08-05 | 河海大学 | River and lake water quantity and quality monitoring and management system based on Internet of things |
| CN115077656A (en) * | 2022-08-16 | 2022-09-20 | 清华大学 | Reservoir water reserve retrieval method and device |
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| CN114152302B (en) * | 2022-02-07 | 2022-06-21 | 中国科学院地理科学与资源研究所 | Lake water storage variation estimation method, system, electronic device and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US11391569B2 (en) * | 2018-11-08 | 2022-07-19 | Tsinghua University | Method and device for monitoring water volume change, computer device and storage medium |
| CN112200050A (en) * | 2020-09-30 | 2021-01-08 | 同济大学 | Lake water volume space-time change analysis method and device based on multi-source satellite data |
| CN112945352A (en) * | 2021-02-03 | 2021-06-11 | 成都鼎安华智慧物联网股份有限公司 | Extraction method based on remote sensing data water level abnormal information |
| CN113011740A (en) * | 2021-03-18 | 2021-06-22 | 长江水资源保护科学研究所 | Method for constructing lake wetland ecology-water level gradient response relation |
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| CN113256101A (en) * | 2021-05-20 | 2021-08-13 | 东南大学 | Key driving force analysis method for water storage capacity change of urban and rural lakes |
| CN113256101B (en) * | 2021-05-20 | 2022-10-28 | 东南大学 | Key driving force analysis method for water storage capacity change of urban and rural lakes |
| CN114858987A (en) * | 2022-03-30 | 2022-08-05 | 河海大学 | River and lake water quantity and quality monitoring and management system based on Internet of things |
| CN114858987B (en) * | 2022-03-30 | 2024-06-11 | 河海大学 | River and lake water quantity and quality monitoring and management system based on Internet of things |
| CN115077656A (en) * | 2022-08-16 | 2022-09-20 | 清华大学 | Reservoir water reserve retrieval method and device |
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