Automatic glacier lake water amount calculation method based on ICESat-2 laser point cloud and lake boundary

Abstract

The invention provides a glacier lake water volume automatic calculation method based on ICESat-2 laser point cloud and lake boundaries. The method comprises the following steps: data preparation: acquiring ICESat-2 ATL03-level photon data of a target glacier lake area; meanwhile, lake boundary information of the region is obtained; data processing and photon screening; fitting a photon orbital equation and constructing a vertical section; lakebed parabola fitting and topographic point generation: obtaining a large amount of lakebed topographic point cloud data covering the whole lakebed basin; and S4, lakebed DEM generation and water volume calculation: taking all lakebed topographic point clouds generated in the step S4 as interpolation source data, and generating a lake underwater digital elevation model by adopting a spatial interpolation algorithm. And the automation degree and the space precision of water volume calculation are improved. And the water volume of the glacier lake which is difficult to reach can be accurately monitored in a high-efficiency, low-cost and large-area manner.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of water quantity monitoring, in particular to a glacial lake water quantity automatic calculation method based on ICESat-2 laser point cloud and lake boundaries. BACKGROUND

[0002] Glacial lake water quantity change is a key indicator for studying global climate change, glacial-lake interaction and evaluating glacial lake outburst flood (GLOF) risk. At present, the technical means for obtaining glacial lake water quantity mainly include the following ways: first, field measurement adopts a ship-mounted ultrasonic depth finder and other equipment for measurement, although high-precision data can be obtained, the cost is huge, the operation is dangerous, and it is limited to the lake area accessible to personnel, and cannot be applied to most glacial lakes in remote high mountain areas such as the Himalayas.

[0003] Secondly, a single optical remote sensing inversion technology uses Landsat, Sentinel-2 and other multi-spectral images to establish an empirical or semi-empirical model (such as band ratio method, machine learning model) of water body spectral characteristics and water depth to retrieve water depth. However, this method has obvious limitations, that is, it must rely on a large amount of measured water depth data for model correction, which greatly limits the accuracy and reliability in areas lacking measured data. In addition, single laser altimetry data application technology, such as ICESat-2 satellite data, can provide high-precision lake surface elevation data, but only discrete point elevation values on the orbit can be obtained, and the underwater topographic information cannot be directly obtained, so it is difficult to accurately calculate the volume of the entire lake. Although researchers have proposed many lake water quantity estimation models, the universality of the ideal static estimation model is low. In summary, the data preprocessing, model correction and water quantity calculation of the existing technology often require a large amount of manual intervention, resulting in a cumbersome and inefficient process that cannot meet the needs of large-scale and business monitoring. SUMMARY

[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide a glacial lake water quantity automatic calculation method based on ICESat-2 laser point cloud and lake boundaries to solve the problems raised in the background art. The present application avoids the high dependence on field measurement of water depth data when estimating glacial lake water quantity in the glacial area, and improves the automation degree and spatial accuracy of water quantity calculation. It can efficiently, cost-effectively and large-area monitor the precise water quantity of inaccessible glacial lakes.

[0005] In order to achieve the above-mentioned purpose, the present application is realized by the following technical solution: a glacial lake water quantity automatic calculation method based on ICESat-2 laser point cloud and lake boundaries, comprising the following steps:

[0006] S1, data preparation: obtain ICESat-2 ATL03 level photon data of the target glacial lake area; at the same time, obtain the lake boundary information of the area;

[0007] S2, data processing and photon screening: denoising processing is performed on the ICESat-2 ATL03 data, and signal photons of the lake surface and the lake bottom are accurately extracted;

[0008] S3, photon orbit equation fitting and vertical profile construction: a lake basin vertical profile perpendicular to the satellite orbit is determined, which is defined by two lake shore points and one lake bottom photon point;

[0009] S4, lake bottom parabolic fitting and terrain point generation: a large number of lake bottom terrain point cloud data covering the entire lake basin are obtained;

[0010] S5, lake bottom DEM generation and water volume calculation: all the lake bottom terrain point clouds generated in S4 are used as source data for interpolation, and a continuous and high-resolution underwater digital elevation model of the lake is generated by using a spatial interpolation algorithm.

[0011] Further, the lake boundary information in S1 includes water body boundary vectors extracted from high-resolution multispectral remote sensing images.

[0012] Further, the lake boundary information in S1 also includes direct import of existing lake vector databases.

[0013] Further, in S2, the processed photon data is spatially superimposed with the lake boundary to screen all the photon points falling inside the lake.

[0014] Further, S3 includes the following process:

[0015] The calm lake surface is taken as the XY plane, the positive east direction is taken as the X-axis positive direction, and the elevation is taken as the z-axis. Based on the screened lake interior photon points L1, the ICESat-2 satellite photons are projected on the lake surface A, and a spatial curve fitting algorithm is used to fit the L2 orbit equation.

[0016] Further, for each signal photon point in the lake, a plane equation passing through the point and perpendicular to the above orbit equation and the lake surface is solved; two intersection points of the plane and the lake vector boundary are obtained, and the intersection points include p3 and p4.

[0017] Further, S4 includes the following process:

[0018] On each vertical profile constructed in step S3, a parabolic equation is fitted to simulate the lakebed morphology at that location, using two shore points and one lakebed photon point as constraints. Along the fitted parabola, sampling is performed at a set fixed distance interval to generate a series of discrete lakebed topographic points with three-dimensional coordinates.

[0019] Furthermore, each ICESat-2 signal photon point within the lake is processed traversally, and S4 is repeated until a large dataset of lakebed topographic points covering the entire lake basin is obtained; the elevations of the two lake shore points are taken from the average elevation of ICESat-2 lake surface photons.

[0020] Furthermore, S5 includes the following process:

[0021] The known lake surface elevation is treated as a constant value, and a digital elevation model of the lake surface is constructed. By calculating the volume difference between the digital elevation model of the lake surface and the digital elevation model of the lake bottom, the total water storage of the glacial lake is automatically output.

[0022] Furthermore, the formula for calculating the total water storage capacity is as follows:

[0023] in, For the total water volume, the first The lake surface elevation and lake bottom elevation at each pixel are respectively , , No. The pixel area at each pixel is , The total number of pixels, The range of values ​​is Positive integers. Beneficial effects of the present invention

[0024] 1. This automated method for calculating the water volume of glacial lakes based on ICESat-2 laser point clouds and lake boundaries efficiently expands discrete photon points into continuous lake bottom surfaces through a series of purely geometric operations, such as fitting photon orbits, constructing vertical profiles, and solving parabolic equations. Ultimately, it achieves high-precision automated water volume calculation without any field measurement data.

[0025] 2. This automated method for calculating glacial lake water volume based on ICESat-2 laser point clouds and lake boundaries overcomes the high dependence of existing technologies on field-measured water depth data when estimating glacial lake water volume in glacial areas, thus improving the automation and spatial accuracy of water volume calculation. It enables efficient, low-cost, and large-area accurate water volume monitoring of inaccessible glacial lakes. Attached Figure Description

[0027] Figure 1The lake bottom parabolic fitting graph in the automatic calculation method of glacier lake water volume based on ICESat-2 laser point cloud and lake boundary. DETAILED DESCRIPTION

[0029] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, the present application will be further described below in conjunction with specific embodiments.

[0030] Please refer to Figure 1 The present application provides the following technical solutions: an automatic calculation method of glacier lake water volume based on ICESat-2 laser point cloud and lake boundary, which does not rely on water optical properties, but directly reconstructs the lake bottom surface by analyzing the spatial geometric relationship of ICESat-2 photon data, and specifically includes the following steps:

[0031] (1) Data preparation

[0032] ICESat-2 ATL03 level photon data of the target glacier lake area is obtained; at the same time, lake boundary information of the area is obtained, which can come from water body boundary vector extracted from high-resolution multispectral remote sensing image (such as Landsat), or directly imported into an existing lake vector database.

[0033] (2) Data processing and photon screening

[0034] ICESat-2 ATL03 data is denoised to accurately extract signal photons of the lake surface and the lake bottom; the processed photon data is spatially superimposed with the lake boundary to screen all photon points falling inside the lake.

[0035] (3) Photon orbit equation fitting and vertical profile construction

[0036] The calm lake surface is taken as the XY plane, the positive east direction is taken as the positive direction of the X axis, and the elevation is taken as the z axis. Based on the screened lake interior photon points L1, the ICESat-2 satellite photons are projected on the lake surface A (i.e. line L2), a spatial curve fitting algorithm is used to fit the L2 orbit equation; for each signal photon point (such as p1) in the lake, a plane equation (i.e. plane S) passing through the point and perpendicular to the above orbit equation and the lake surface is solved; two intersection points (p3, p4) of the plane and the lake vector boundary are solved, so as to determine a lake basin vertical profile perpendicular to the satellite orbit, which is defined by two lake shore points and a lake bottom photon point.

[0037] (4) Lake bottom parabolic fitting and terrain point generation

[0038] On each vertical profile constructed in step (3), a parabolic equation (L3) for simulating the lake bottom morphology at the place is fitted with two lake shore points (whose elevations are taken from the ICESat-2 lake surface photon elevation average) and one lake bottom photon point as constraint conditions; along the fitted parabola, a series of discrete lake bottom terrain points with three-dimensional coordinates are generated by sampling at a set fixed distance interval; each ICESat-2 signal photon point in the lake is processed iteratively, and the above process is repeated, and finally a large number of lake bottom terrain point cloud data sets covering the entire lake basin are obtained.

[0039] (5) Lake bottom DEM generation and water volume calculation

[0040] All the lake bottom terrain point clouds generated in step (4) are taken as interpolation source data, and a continuous and high-resolution underwater digital elevation model (DEM) of the lake is generated by using a spatial interpolation algorithm; the known lake surface elevation is taken as a constant value to construct a lake surface digital elevation model; the total water storage of the glacier lake is automatically output by calculating the volume difference between the lake surface digital elevation model and the lake bottom digital elevation model. The calculation formula is:

[0041]

[0042] Wherein, is the total water volume, the lake surface elevation and the lake bottom elevation at the i th pixel are , , , the pixel area at the i th pixel is , , is the total number of pixels, and n is a positive integer with a value range of .

[0043] The application sets a lake bottom terrain reconstruction method based on the geometric characteristics of the ICESat-2 photon orbit. Through a series of pure geometric operations such as fitting the photon orbit, constructing the vertical profile and solving the parabolic equation, the discrete photon points are efficiently expanded into a continuous lake bottom surface, and finally the high-precision automatic calculation of the water volume without any field measurement data is realized. The method provides a new technical path for glacier lake monitoring.

[0044] The basic principles and main features of the application and the advantages of the application are shown and described above, and it is obvious for those skilled in the art that the application is not limited to the details of the above exemplary embodiments, and the application can be realized in other specific forms without departing from the spirit or essential characteristics of the application.

[0045] ​Furthermore, it should be understood that although the specification is described in terms of embodiments, not every embodiment includes every feature described. The specification can include implicit combinations of explicitly mentioned features and / or explicit combinations of implicitely mentioned features. Each embodiment depends on the explicit combinations of features and / or the implicit combinations of features made specifically within that embodiment, and each such embodiment can be combined with every other such embodiment to create further embodiments.

Claims

1. An automated method for calculating glacial lake water volume based on ICESat-2 laser point clouds and lake boundaries, characterized in that, Includes the following steps: S1. Data Preparation: Acquire ICESat-2 ATL03 level photon data of the target glacial lake region; simultaneously acquire lake boundary information of the region. S2. Data processing and photon filtering: Denoising the ICESat-2 ATL03 data to accurately extract signal photons from the lake surface and bottom; S3. Photon orbit equation fitting and vertical profile construction: Determine a lake basin vertical profile that is perpendicular to the satellite orbit, defined by two shore points and one lake bottom photon point. S4. Lake Bottom Parabolic Fitting and Topographic Point Generation: Acquire a large amount of lake bottom topographic point cloud data covering the entire lake basin; S5. Lake Bottom DEM Generation and Water Calculation: Using all the lake bottom topographic point clouds generated in S4 as interpolation source data, a continuous, high-resolution underwater digital elevation model of the lake is generated using a spatial interpolation algorithm.

2. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 1, characterized in that: The lake boundary information in S1 includes water body boundary vectors extracted from high-resolution multispectral remote sensing images.

3. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 2, characterized in that: The lake boundary information in S1 also includes directly importing existing lake vector databases.

4. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 1, characterized in that: In step S2, the processed photon data is spatially superimposed with the lake boundary to filter out all photon points falling inside the lake.

5. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 1, characterized in that, S3 includes the following process: Using the calm lake surface as the XY plane, with due east as the positive X-axis and elevation as the z-axis, based on the selected photon point L1 inside the lake, ICESat-2 satellite photons are projected onto the lake surface A, and the L2 orbit equation is fitted using a spatial curve fitting algorithm.

6. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 5, characterized in that: For each signal photon point in the lake, solve for a plane equation that passes through the point and is perpendicular to the above orbit equation and the lake surface; find the two intersection points of this plane with the lake vector boundary, including p3 and p4.

7. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 1, characterized in that: S4 includes the following process: On each vertical profile constructed in step S3, a parabolic equation is fitted to simulate the lakebed morphology at that location, using two shore points and one lakebed photon point as constraints. Along the fitted parabola, sampling is performed at a set fixed distance interval to generate a series of discrete lakebed topographic points with three-dimensional coordinates.

8. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 7, characterized in that: The process iterates through each ICESat-2 signal photon point in the lake, repeating step S4 until a large dataset of lakebed topographic points covering the entire lake basin is obtained; the elevations of the two lake shore points are taken from the average elevation of ICESat-2 lake surface photons.

9. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 1, characterized in that, The S5 process includes the following steps: The known lake surface elevation is treated as a constant value, and a digital elevation model of the lake surface is constructed. By calculating the volume difference between the digital elevation model of the lake surface and the digital elevation model of the lake bottom, the total water storage of the glacial lake is automatically output.

10. The automated calculation method for glacial lake water volume based on ICESat-2 laser point cloud and lake boundary as described in claim 9, characterized in that, It also includes the formula for calculating the total water storage: ； in, For the total water volume, the first The lake surface elevation and lake bottom elevation at each pixel are respectively , , No. The pixel area at each pixel is , The total number of pixels, The range of values ​​is Positive integers.

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