Quick estimation method suitable for hot melt collapse volume of Tibet Plateau

Abstract

The invention discloses a quick estimation method suitable for the hot melt slump volume of the Qinghai-Tibet Plateau, and the method comprises the following steps: S1, obtaining remote sensing data and DEM data of a target region, the remote sensing data being remote sensing data after RTS occurrence, and the DEM data comprising a baseline DEM representing before RTS occurrence and a target DEM representing after RTS occurrence; s2, performing RTS visual interpretation according to the remote sensing data to obtain an RTS mask, and calculating to obtain an RTS area; s3, performing spatial registration correction on the baseline DEM by taking the target DEM as a reference to obtain a registered baseline DEM, and resampling the registered baseline DEM to a spatial grid which is the same as the target DEM; s4, calculating to obtain the RTS volume; s5, performing regression analysis according to the RTS area and the RTS volume to obtain an A-V model of the RTS; and S6, according to the A-V model, carrying out rapid estimation on the hot melt slump volume of a to-be-estimated area. According to the method, the hot melt collapse volume of the Qinghai-Tibet Plateau can be accurately and rapidly estimated.

Description

Technical Field

[0001] The present invention relates to the technical field of geoscience and mapping remote sensing, and particularly relates to a method for rapidly estimating the volume of thermokarst slumps in the Qinghai-Tibet Plateau. Background Art

[0002] With global warming, the Qinghai-Tibet Plateau, known as the "Third Pole" of the earth, has experienced a significant warming process, leading to the accelerated degradation of its widely distributed permafrost. Retrogressive Thaw Slumps (RTS) are a severe manifestation of permafrost degradation, specifically referring to the progressive sliding and mass loss of ice-rich permafrost along slopes after melting due to gravity. This geomorphic process is becoming increasingly frequent and expanding in scale in the Qinghai-Tibet Plateau, triggering a series of severe environmental and engineering problems.

[0003] Firstly, RTS is an important erosion agent in alpine regions, capable of instantaneously eroding large amounts of soil and bedrock, significantly changing the regional geomorphic morphology, increasing the sediment content in downstream river channels, and affecting the hydrological process and water quality safety. Secondly, a huge amount of organic carbon is sealed in the permafrost layer. The occurrence of RTS exposes these originally stable carbon pools to the surface, where they rapidly decompose under aerobic conditions, releasing greenhouse gases such as carbon dioxide and methane, forming a potential "carbon-climate feedback" effect, which may accelerate the process of global warming. In addition, the expansion of RTS may also threaten the stability of infrastructure in the plateau region, such as the Qinghai-Tibet Railway, highway, and oil pipelines.

[0004] Therefore, accurately quantifying the volume of material loss caused by RTS and its spatio-temporal dynamic changes is crucial for evaluating its environmental impacts (such as soil erosion rate, river sediment flux, carbon release flux) and formulating effective disaster risk management and climate change adaptation strategies.

[0005] However, traditional methods for obtaining the volume of RTS face great challenges. Although field surveys and geodetic methods have high accuracy, they are time-consuming, laborious, and costly, and it is difficult to apply them to the vast, remote, and harsh environment of the Qinghai-Tibet Plateau for large-scale and long-term monitoring. The method based on Digital Elevation Model Differencing (DEM Differencing) is a common means for estimating volume changes, but its application highly depends on obtaining DEM data of at least two different periods, covering the same area and with sufficient accuracy. In practice, especially for RTS in historical periods (such as decades ago), high-quality baseline DEM data is often lacking, resulting in great uncertainty or even inability to estimate the volume. Even if there is data, the registration error between DEMs from different sources will significantly affect the accuracy of the differencing results. Summary of the Invention

[0006] In view of the above problems, the present invention aims to provide a rapid estimation method for the volume of thermokarst slumps in the Qinghai-Tibet Plateau.

[0007] The technical solution of the present invention is as follows:

[0008] A rapid estimation method for the volume of thermokarst slumps in the Qinghai-Tibet Plateau, comprising the following steps:

[0009] S1: Obtain remote sensing data and DEM data of the target area. The remote sensing data is the remote sensing data after the occurrence of RTS, and the DEM data includes a baseline DEM representing before the occurrence of RTS and a target DEM representing after the occurrence of RTS;

[0010] S2: Conduct visual interpretation of RTS based on the remote sensing data to obtain an RTS mask, and calculate the area of RTS;

[0011] S3: Perform spatial registration and correction of the baseline DEM with the target DEM as a reference benchmark to obtain the registered baseline DEM, and resample the registered baseline DEM to the same spatial grid as the target DEM;

[0012] S4: Differentiate the target DEM and the registered baseline DEM to calculate the volume of RTS;

[0013] S5: Conduct regression analysis based on the RTS area and the RTS volume to obtain the A-V model of RTS;

[0014] S6: Rapidly estimate the volume of thermokarst slumps in the area to be estimated according to the A-V model.

[0015] Preferably, in step S1, the target DEM uses DEM data with high spatial resolution.

[0016] Preferably, in step S3, the spatial registration and correction specifically includes the following sub-steps:

[0017] S21: Select stable areas without terrain changes in each RTS area and its surrounding areas as stable areas;

[0018] S22: Obtain a spatial affine transformation matrix through an iterative algorithm to minimize the elevation difference between the baseline DEM and the target DEM in the stable areas after transformation.

[0019] Preferably, in step S3, the registered baseline DEM is resampled to the same spatial grid as the target DEM using the bilinear interpolation method.

[0020] Preferably, in step S4, the volume of RTS is calculated by the following formula:

[0021]

[0022] Where: ΔV is the RTS volume; n is the number of grids in the slump area; h′ i is the elevation value after the slump occurs; h i is the elevation value before the slump occurs; x res and y res are the spatial resolutions of the grid in the x and y directions respectively; h′ i -h i <0 indicates that only the grids with a decrease in elevation value are considered in the calculation of ΔV.

[0023] Preferably, in step S4, after obtaining the RTS volume, the following steps are further included to ensure that the RTS has a sufficient slump depth to minimize the influence of data noise on the A-V model:

[0024]

[0025] Where: is the 98% quantile of the slump depth in the slump area; is the mean residual of the target DEM and the baseline DEM in the stable area; σ is the variance of the residual.

[0026] Preferably, in step S5, the following formula is used for regression analysis:

[0027] logV = logk + αlogA (3)

[0028] The obtained A-V model is:

[0029] V = kA α (4)

[0030] Where: V is the RTS volume; k is the proportionality coefficient; α is the exponent; A is the RTS area.

[0031] Preferably, the A-V model is:

[0032] V = (0.18 ± 0.04)A 1.20±0.02 (5).

[0033] Preferably, in step S5, after obtaining the A-V model, the following steps are further included to evaluate and / or correct the A-V model.

[0034] Preferably, the goodness of fit of the A-V model is evaluated using statistical indicators; the spatio-temporal heterogeneity of the model parameters of the A-V model under different geographical partitions, different terrain slopes, different RTS scales, and different occurrence time periods is analyzed, and correction parameters under different conditions are established according to the spatio-temporal heterogeneity analysis results.

[0035] The beneficial effects of the present invention are as follows:

[0036] (1) The present invention can overcome data limitations and achieve large-scale and long-time-series volume estimation: By establishing the area-volume conversion relationship of rock glacier thermal slumping (RTS), the present invention cleverly bypasses the hard dependence of the traditional DEM differential method on high-quality and multi-temporal DEM data. Since the area information of RTS can be relatively easily extracted from more extensive and longer time-series remote sensing images, this method enables effective estimation of the RTS volume over a large range and long time span on the Qinghai-Tibet Plateau even in the absence of accurate historical DEMs, greatly enhancing the ability to evaluate the total volume of RTS and its dynamic changes.

[0037] (2) The present invention can improve the regional estimation accuracy and reliability: By collecting local RTS sample data (area and accurate volume calculated by DEM differential) on the Qinghai-Tibet Plateau, the present invention specifically fits the power-law relationship parameters suitable for the unique environmental characteristics of this region (such as low subsurface ice content, complex terrain, etc.). This region-customized model significantly improves the accuracy and reliability of estimating the RTS volume on the Qinghai-Tibet Plateau, providing a solid data basis for more accurately evaluating subsequent environmental effects such as soil erosion and carbon release caused by it. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 It is a schematic diagram of the principle of spatial registration and correction in a specific embodiment; among them, (a) is a three-dimensional schematic diagram of an RTS, with the target DEM above and the baseline DEM below; (b) is a plan view of the corresponding RTS, taken by an unmanned aerial vehicle; (c) is a vertical cross-section of the corresponding RTS in the baseline DEM (TanDEM-X), the target DEM (UAV), and the corrected DEM (Coreg-TanDEM-X); (d) is a difference histogram of the baseline DEM before (Dh) and after (Coreg_Dh) correction and the target DEM;

[0040] Figure 2 It is a schematic diagram of the data source for establishing the A-V model in a specific embodiment;

[0041] Figure 3 It is a schematic diagram of the fitting process of the A-V model in a specific embodiment;

[0042] Figure 4 Schematic diagram of the dynamic change result of the RTS area in a specific embodiment; among them, (a) is a schematic diagram of the change in soil erosion caused by RTS in 4 regions and the entire Qinghai-Tibet Plateau in the past 30 years; (b) is a schematic diagram of the result of comparing the release of soil organic carbon (RTS) estimated from soil erosion and the important process amounts of the plateau carbon sink; (c) is a schematic diagram of the change in temperature on the Qinghai-Tibet Plateau in the past 30 years. Detailed implementation manners

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, without conflict, the embodiments in the present application and the technical features in the embodiments may be combined with each other. It should be pointed out that, unless otherwise specified, all technical and scientific terms used in the present application have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms "including" or "comprising" and the like used in the present invention disclosure mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects.

[0044] The present invention provides a method for quickly estimating the volume of thermokarst slumps on the Qinghai-Tibet Plateau, including the following steps:

[0045] S1: Obtain remote sensing data and DEM data of the target area, where the remote sensing data is the remote sensing data after the occurrence of RTS, and the DEM data includes a baseline DEM representing before the occurrence of RTS and a target DEM representing after the occurrence of RTS.

[0046] In a specific embodiment, the target DEM uses DEM data with high spatial resolution. In this embodiment, using DEM data with high spatial resolution as the target DEM can improve accuracy and timeliness. Optionally, the DEM data with high spatial resolution is GF7 satellite stereo imagery (resolution better than 1 meter), unmanned aerial vehicle (UAV) LiDAR, or centimeter-level DEM generated by photogrammetry.

[0047] It should be noted that, in addition to the baseline DEM and the target DEM, other auxiliary DEM data, such as HMADEM (~8m resolution), can also be collected for the DEM data to increase the sample size or perform cross-validation. Ensure that all DEM data have clear coordinate system, elevation datum, and timestamp information.

[0048] S2: Perform visual interpretation of RTS according to the remote sensing data to obtain an RTS mask, and calculate the RTS area.

[0049] In the present invention, the purpose of this step is to accurately identify and delineate the boundary ranges of all thermokarst slumps occurring within the target area. Based on high-resolution remote sensing images, particularly the temporal images used to generate the target DEM (such as GF7 images), detailed visual interpretation is carried out in a Geographic Information System (GIS) software (such as QGIS). The interpreter carefully delineates the influence range of each RTS according to the typical geomorphic features of RTS, such as the horseshoe-shaped headwall, deposites, and exposed thawed soil areas, to generate vector polygon data (RTS mask). This process is to supplement the RTSs that may be missed, have unclear features, or are of small scale by automated methods (such as methods based on NDVI time series), and to provide precise spatial range constraints for subsequent DEM registration and differential calculation. The interpretation results include the positions, contour boundaries of each RTS, and their corresponding unique identifiers.

[0050] S3: Using the target DEM as a reference benchmark, perform spatial registration and correction on the baseline DEM to obtain the registered baseline DEM, and resample the registered baseline DEM to the same spatial grid as the target DEM.

[0051] Since there may be systematic spatial deviations (including horizontal displacement and vertical deviation, etc.) between DEM data from different sources and different time phases, this step can eliminate these deviations to ensure that the baseline DEM and the target DEM are precisely aligned spatially.

[0052] In a specific embodiment, as Figure 1 shown, the specific steps for performing spatial registration and correction include the following sub-steps:

[0053] S21: Select stable areas without terrain changes in each RTS area and its surrounding areas (excluding the internal areas of RTS through the RTS mask) as stable areas;

[0054] S22: Obtain a spatial affine transformation matrix through an iterative algorithm to minimize the elevation difference between the transformed baseline DEM and the target DEM in the stable areas.

[0055] In the above embodiment, the spatial affine transformation matrix is obtained through the xdem.coreg.IPC.fit method of the xdem library. Through the spatial affine transformation matrix, translation errors in the X, Y, and Z directions and possible rotation errors can be corrected.

[0056] In a specific embodiment, use bilinear interpolation to resample the registered baseline DEM to the same spatial grid as the target DEM.

[0057] S4: Differentiate the target DEM and the registered baseline DEM, and calculate to obtain the RTS volume.

[0058] In a specific embodiment, the RTS volume is calculated by the following formula:

[0059]

[0060] where: ΔV is the RTS volume; n is the number of grids in the landslide area; h′ i is the elevation value after the landslide occurs; h i is the elevation value before the landslide occurs; x res and y res are the spatial resolutions of the grid in the x and y directions respectively; h′ i -h i <0 indicates that only the grids with decreased elevation values are considered in the calculation of ΔV.

[0061] In the above embodiment, by calculating the elevation difference between the target DEM and the registered baseline DEM, the topographic changes caused by RTS are quantified, especially the erosion depth and volume. Subtract the registered baseline DEM from the target DEM pixel by pixel to obtain the elevation change raster map (dh = h′ i -h i ). In this raster map, the negative value (dh < 0) area represents the elevation decrease, that is, the area where the landslide occurs; the positive value area represents the elevation increase, that is, the area where the material accumulates. To accurately calculate the erosion volume (V), only within the RTS mask interpreted in step S2, for all pixels with negative elevation changes (dh < 0), multiply the elevation change value (i.e., the erosion depth) by the area of the pixel (x res ×y res ) to obtain the total erosion volume of RTS.

[0062] In a specific embodiment, after obtaining the RTS volume, it further includes the step of using the following formula to ensure that the RTS has sufficient landslide depth to minimize the influence of data noise on the A-V model:

[0063]

[0064] where: is the 98% quantile of the landslide depth in the landslide area; is the mean residual of the target DEM and the baseline DEM in the stable area; σ is the variance of the residual.

[0065] In the above embodiment, by setting a threshold based on the distribution characteristics of the registered residual (re), those false positive results with weak erosion depth signals and likely caused by noise can be excluded, thereby further improving the reliability of the calculation results.

[0066] S5: Perform a regression analysis based on the RTS area and the RTS volume to obtain the A-V model of the RTS.

[0067] In a specific embodiment, the following formula is used for the regression analysis:

[0068] logV = logk + αlogA (3)

[0069] The obtained A-V model is:

[0070] V = kA α (4)

[0071] Where: V is the RTS volume; k is the proportionality coefficient; α is the exponent; A is the RTS area.

[0072] In a specific embodiment, linear regression analysis is performed on the log(A) and log(V) data using the least squares method or other robust regression methods to estimate the two key parameters of the power-law model shown in formula (4): the proportionality coefficient k and the exponent α. Thus, the A-V model with determined parameters is obtained. In a specific embodiment, the A-V model is:

[0073] V = (0.18 ± 0.04)A 1.20±0.02 (5).

[0074] In a specific embodiment, after obtaining the A-V model, it further includes the steps of evaluating and / or correcting the A-V model. Optionally, statistical indicators (such as the coefficient of determination R 2 , the mean absolute percentage error MAPE, the 99% quantile absolute percentage error APE_99th) are used to evaluate the goodness of fit of the A-V model; analyze the spatio-temporal heterogeneity of the model parameters of the A-V model under different geographical partitions (such as different river basins), different terrain slopes, different RTS scales, and different occurrence periods, and establish correction parameters under different conditions according to the spatio-temporal heterogeneity analysis results.

[0075] S6: Rapidly estimate the volume of thermokarst slumps in the area to be estimated according to the A-V model.

[0076] In a specific embodiment, the rapid estimation method for the volume of thermokarst slumps applicable to the Qinghai-Tibet Plateau of the present invention is used to rapidly estimate the volume of thermokarst slumps in the Qinghai-Tibet Plateau. In this embodiment, the data source for establishing the A-V model is as Figure 2As shown in the figure, a centimeter-level DEM generated using GF7 satellite stereo imagery (resolution better than 1 m), UAV LiDAR, or photogrammetry is used as the target DEM after the occurrence of RTS, and GLO-30 (~30 m resolution, applicable to RTS occurring after 2015) or AW3D30 (~30 m resolution, applicable to RTS occurring between 2011 - 2015) selected according to the occurrence time of RTS is used as the baseline DEM before the occurrence of RTS. This embodiment conducts statistical analysis based on more than 1400 RTSs (as Figure 3 shown), and the A-V model generated is as shown in Equation (5). The parameterization scheme and corresponding errors of the A-V formula under different regions and conditions are shown in Table 1:

[0077] Table 1 Parameterization scheme and corresponding errors of the A-V formula under different regions and conditions

[0078]

[0079]

[0080] As can be seen from Table 1, the A-V model (the overall estimation formula for the entire Qinghai-Tibet Plateau) obtained in the present invention has extremely high accuracy, R 2 = 0.88, p < 0.001, with an average error of 3.55%. At a 99% probability, the maximum relative error of this model is less than 12%. The correction formulas for various regions and slopes within the Qinghai-Tibet Plateau also have extremely high accuracy. The A-V model established through the present invention can obtain the RTS volume only relying on the RTS area, thereby quickly estimating the volume of thermokarst collapse in the Qinghai-Tibet Plateau.

[0081] The A-V model obtained above can estimate the historical volume that is difficult to directly measure based on the relatively easily accessible historical area information of RTS. Furthermore, combined with soil physical and chemical property data, the corresponding material erosion amount and organic carbon release amount can be estimated. The general process applied to the 30-year scale reconstruction is roughly as follows:

[0082] 1. Data source

[0083] RTS dataset: The annual area change dynamic information of RTS in the entire Qinghai-Tibet Plateau starting from 1986 reconstructed using Landsat imagery based on the time series method.

[0084] Soil physical and chemical property information:

[0085] Soil bulk density: The mass of soil per unit volume (such as g / cm 3 or kg / m 3)。This is the basis for converting the erosion volume into soil mass. It is necessary to obtain the bulk density values representing the characteristics of the active layer and the upper permafrost layer of the permafrost on the Qinghai-Tibet Plateau.

[0086] Soil Organic Carbon Content / Concentration (SOC%): The mass percentage (%) or concentration (such as g C / kg soil) of organic carbon in the soil per unit mass. This is the key to calculating the total organic carbon pool in the eroded materials. Since the carbon content varies significantly with depth (usually higher in the surface layer), ideally, the average SOC content of different depth soil layers involved in erosion needs to be considered.

[0087] Subsurface Ice Content: The erosion volume V is a mixture of soil and ice. Precise calculation requires knowing the proportion of soil in the in-situ volume. It is usually corrected by estimating the average volumetric ice content, or the bulk density and SOC content used are based on measurements under "dry soil" or specific water / ice content conditions. Here, we sampled from Beiluhe and obtained an ice content of 15% through CT scanning.

[0088] Labile Carbon Fraction: Not all SOC is easily decomposed. Understanding the proportion of easily decomposed carbon helps to more accurately estimate the actual release amount, but this is usually difficult to obtain on a large scale. Here, an approximate value of 60% is taken.

[0089] 2. Reconstruction of the Volume Dynamics of RTS

[0090] Substitute the area A of each RTS into the formula to calculate the corresponding erosion volume (V). Accumulate the estimated volumes (V) of all RTSs identified in the study area over 30 years to obtain the total mass erosion volume caused by RTSs on the 30-year scale in this area. The results are as Figure 4 shown.

[0091] 3. Estimation of Soil Organic Carbon (SOC) Release

[0092] The volume V eroded by RTS contains soil, melted subsurface ice, and the organic carbon in it. When this organic carbon originally frozen in permafrost is exposed to the surface environment, it will decompose and be released into the atmosphere (mainly CO2, and CH4 may also be released under anaerobic conditions). First, determine the volume of RTS through its area, and then substitute the data of soil bulk density, ice content in permafrost, organic carbon content, and organic carbon decomposition ratio to obtain the potential soil organic carbon release amount.

[0093] In summary, the present invention can rapidly estimate the volume of thermokarst collapse in the Qinghai-Tibet Plateau, providing technical support for more accurately evaluating subsequent environmental effects such as soil erosion and carbon release caused by it. Compared with the prior art, the present invention has made significant progress.

[0094] As described above, only the representative embodiments of the present invention are given, and there is no limitation in any form to the present invention. Any person skilled in the art, within the scope of the technical solution of the present invention, using the technical content disclosed above to make some modifications or modified embodiments are equivalent embodiments of the present invention. However, as long as the content does not depart from the technical solution of the present invention, any simple modification, equivalent change, and modification made to the above embodiments according to the technical essence of the present invention still belong to the scope of the technical solution of the present invention.

Claims

1. A rapid estimation method applicable to the volume of thermokarst slumps on the Qinghai-Tibet Plateau, characterized in that, It includes the following steps: S1: Obtain the remote sensing data and DEM data of the target area. The remote sensing data is the remote sensing data after the occurrence of RTS. The DEM data includes the baseline DEM representing before the occurrence of RTS and the target DEM representing after the occurrence of RTS; S2: Conduct visual interpretation of RTS based on the remote sensing data to obtain the RTS mask, and calculate the RTS area; S3: Perform spatial registration and correction of the baseline DEM with the target DEM as the reference benchmark to obtain the registered baseline DEM, and resample the registered baseline DEM to the same spatial grid as the target DEM; S4: Differentiate the target DEM and the registered baseline DEM to calculate the RTS volume; S5: Conduct regression analysis based on the RTS area and the RTS volume to obtain the A-V model of RTS; S6: Rapidly estimate the thermokarst collapse volume of the area to be estimated according to the A-V model.

2. The rapid estimation method for the volume of thermokarst collapse in the Qinghai-Tibet Plateau according to claim 1, characterized in that In step S1, the target DEM uses high-spatial-resolution DEM data.

3. The rapid estimation method for the volume of thermokarst collapse in the Qinghai-Tibet Plateau according to claim 1, characterized in that In step S3, the spatial registration and correction specifically includes the following sub-steps: S21: Select stable areas without terrain changes in each RTS area and its surrounding areas as stable areas; S22: Obtain the spatial affine transformation matrix through an iterative algorithm to minimize the elevation difference between the baseline DEM and the target DEM in the stable area after transformation.

4. The rapid estimation method for the volume of thermokarst collapse in the Qinghai-Tibet Plateau according to claim 1, characterized in that In step S3, use the bilinear interpolation method to resample the registered baseline DEM to the same spatial grid as the target DEM.

5. The rapid estimation method for the volume of thermokarst collapse in the Qinghai-Tibet Plateau according to claim 1, characterized in that In step S4, the RTS volume is calculated by the following formula: Where: ΔV is the RTS volume; n is the number of grids in the slump area; h ′ i is the elevation value after the slump occurs; h i is the elevation value before the slump occurs; x res and y res are the spatial resolutions of the grid in the x and y directions respectively; h ′ i -h i <0 indicates that only grids with a decrease in elevation value are considered in the calculation of ΔV.

6. The rapid estimation method for the volume of thermokarst slumps applicable to the Qinghai-Tibet Plateau according to claim 5, characterized in that, In step S4, after obtaining the RTS volume, it also includes the step of ensuring that RTS has sufficient collapse depth by the following formula to minimize the influence of data noise on the A-V model: Where: is the 98% quantile of the slumping depth in the slumping area; is the mean residual of the target DEM and the baseline DEM in the stable area; σ is the variance of the residuals.

7. The rapid estimation method for the volume of thermokarst slumps applicable to the Qinghai-Tibet Plateau according to any one of claims 1-6, characterized in that, In step S5, the following formula is used for regression analysis: logV = logk + αlogA (3) The obtained A-V model is: V = kA α (4) Where: V is the RTS volume; k is the proportionality coefficient; α is the exponent; A is the RTS area.

8. The rapid estimation method for the volume of thermokarst collapse applicable to the Qinghai-Tibet Plateau according to claim 7, characterized in that The A-V model is: V = (0.18 ± 0.04) A 1.20±0.02 (5).

9. The rapid estimation method for the volume of thermokarst collapse in the Qinghai-Tibet Plateau according to claim 1, characterized in that In step S5, after obtaining the A-V model, it also includes the step of evaluating and / or correcting the A-V model.

10. The rapid estimation method for the volume of thermokarst collapse in the Qinghai-Tibet Plateau according to claim 9, characterized in that Use statistical indicators to evaluate the goodness of fit of the A-V model; analyze the spatio-temporal heterogeneity of the model parameters of the A-V model under different geographical partitions, different terrain slopes, different RTS scales, and different occurrence time periods, and establish correction parameters under different conditions according to the spatio-temporal heterogeneity analysis results.

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