CN114089371B - Method for estimating underground soil leakage quantity in karst region by utilizing laser Lidar technology - Google Patents
Method for estimating underground soil leakage quantity in karst region by utilizing laser Lidar technology Download PDFInfo
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Abstract
The invention provides a method for estimating underground soil leakage in a karst region by utilizing a laser Lidar technology, which comprises the following steps: performing first aerial photography on a research area to be monitored by using an unmanned aerial vehicle carrying a laser radar sensor, and obtaining a digital elevation model (DEM 1) of the research area according to multi-echo information of the first aerial photography laser point cloud; performing second aerial photography on the research area to be monitored, wherein a preset time is reserved between the second aerial photography and the first aerial photography, and a digital elevation model (DEM 2) of the research area is obtained according to multi-echo information of the laser point cloud of the second aerial photography; and calculating the underground soil leakage amount, wherein the underground soil leakage amount is the total erosion amount of the soil minus the erosion amount of the earth surface soil. The method can solve the technical problems that the soil erosion amount is calculated inaccurately due to the difficulty in calculating the soil leakage amount in the prior art.
Description
Technical Field
The invention relates to the technical field of karst region soil leakage calculation, in particular to a method for estimating karst region underground soil leakage by using a laser Lidar technology.
Background
The central zone of one east Asia zone of three global karst concentrated continuous distribution areas at the southwest karst landform of China, while the soil formed by erosion residues in the part of the zone is less, and the soil layer in most of the zone is thin, so that the limited soil is very valuable in karst zone. However, the rapid urban process and the interference of human activities lead to the stony desertification in the southwest karst region of China to be aggravated, meanwhile, the water and soil loss is aggravated, and a special water and soil loss mode is provided in the karst region, namely, the water and soil loss mode is shown in fig. 1, which is a schematic diagram of the water and soil loss process, and due to the limited total amount of soil in the karst region and low water conservation capacity, shallow pores (cracks 2) develop, and the atmospheric precipitation part generates slope surface flows on the slope surface, and part vertically infiltrates downwards along the soil 1, the surface karst zone and the conveyor belt of the karst envelope zone, and enters the underground river 5 system at the low-lying place through a vertical shaft, a pipeline, a water falling hole 4 and the like. Therefore, the special binary hydrologic structure and soil leakage path of the karst ecological system are created by the unique karst landform, and the extensive development of karst cracks, pores and pipelines, so that the serious threat is caused to the regional ecological environment. In the research process of enhancing the water and soil loss prevention in the karst region in southwest, the water and soil loss prevention and treatment research of the karst peak cluster depression is one of the key research contents. When estimating the water and soil leakage amount, the existing artificial simulation method is to simulate the water and soil leakage amount process of the karst region under the conditions of different rainfall intensities and different continuous rainfall times by using an artificial rainfall method through manually simulating the topography, the topography and the underground structure of karst regions, wherein the method is easy to obtain in the aspect of data monitoring during indoor simulation, but is difficult to restore the real environment of the karst region, so that certain errors exist. The field detection method has the advantages that the working difficulty is increased, great manpower and material resources are required, and the working efficiency is low; 137 The Cs tracer method is costly and requires researchers to correct the interfering factors in time. Therefore, the methods for estimating the water and soil leakage have the defects of large error, low efficiency and large field work difficulty.
Disclosure of Invention
The invention aims to at least solve one of the technical problems, and provides a method for estimating the underground soil leakage amount in a karst region by utilizing a laser Lidar (laser radar) technology, so as to solve the technical problems that the soil leakage amount is difficult to calculate and the calculation of the soil erosion amount is inaccurate in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
The method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology is characterized by comprising the following steps of:
Performing first aerial photography on a research area to be monitored by using an unmanned aerial vehicle carrying a laser radar sensor, and obtaining a digital elevation model (DEM 1) of the research area according to multi-echo information of the first aerial photography laser point cloud;
Carrying out second aerial photographing on a research area to be monitored by using an unmanned aerial vehicle carrying a laser radar sensor, wherein a preset time is reserved between the second aerial photographing and the first aerial photographing, and a digital elevation model (DEM 2) of the research area is obtained according to multiple echo information of a laser point cloud of the second aerial photographing;
Calculating the underground soil leakage amount: the soil erosion amount of the underground is obtained by subtracting the earth surface soil erosion amount from the total soil erosion amount, wherein the earth surface soil erosion amount is obtained according to a modified general soil erosion amount equation, and the total soil erosion amount is obtained according to the obtained variation of the two aerial digital elevation models.
Further, the calculation of the total erosion amount G of the soil in the research area comprises the following steps:
rasterizing the geomorphic digital elevation model of the research area obtained by the first aerial photography and the second aerial photography respectively, and keeping the spatial resolution of the DEM parameters obtained by the two aerial photography of the research area consistent during the rasterizing;
And respectively carrying out difference value operation on DEM parameters of the study area of the two aerial photographs in a GIS to obtain the variation (delta DEM) of the digital elevation model, wherein the total erosion G of the soil of the study area is as follows:
G＝∑ΔDEM×(d1+d2)/2×s×ρ
Wherein: g is the total erosion amount of soil; the delta DEM is the variation of each grid digital elevation model of laser Lidar data; d 1 is the total soil coverage of the research area before erosion; d 2 is the total soil coverage rate of the corroded research area; (d 1+d2)/2 is the average soil coverage by erosion; s is the area of the unit grid after laser Lidar data processing; ρ is the soil volume weight.
Further, the general soil loss correction equation is as follows:
E＝RUSLE＝R·K·LS·C·P
wherein: e is the erosion amount of the earth surface soil; RUSLE is the general soil loss correction, R is the rainfall erosion factor; k is a soil corrosiveness factor; l is a slope length factor; s is a gradient factor; c is vegetation coverage and management factors; p is a soil and water conservation measure factor.
Further, the calculation formula of the underground soil leakage N is as follows:
N＝G-E＝∑ΔDEM×(d1+d2)/2×s×ρ-R·K·LS·C·P。
Further, before the step of calculating the underground soil leakage amount, error analysis is carried out on the DEM generated by the two aerial photographs, and whether the elevation difference obtained by the two aerial photographs is caused by real terrain change is judged; if the elevation difference obtained by the two aerial photographs is caused by the real terrain change, the underground soil leakage is calculated.
Further, the error analysis of the DEM generated by two aerial photographs includes the following steps:
S31, calculating an error of the terrain variation through the DEM error of the two aerial photographs according to an error propagation law;
S32, distinguishing whether the elevation difference obtained by two aerial photographs is a change caused by a real terrain change or a change caused by an error under a certain confidence level according to the principle of the statistical t-test.
Further, the error δ DoD of the terrain variation is:
wherein: delta DOD is the error of the terrain variation, delta DEM1 is the error of the DEM obtained by the first aerial photography, and delta DEM2 is the error of the DEM obtained by the second aerial photography.
Further, step S32 includes the steps of:
converting the terrain variation into corresponding t statistics, and according to the t test principle, the t statistics corresponding to the terrain variation are as follows:
Wherein: z DEM1 is a digital elevation model obtained by the first aerial photography, Z DEM2 is a digital elevation model obtained by the second aerial photography, and delta DoD is an error of the terrain variation;
Obtaining a t statistic threshold value under a given confidence coefficient by referring to a t test boundary value table; when the t statistic corresponding to the terrain variation is larger than the t statistic threshold value under the given confidence coefficient, the real terrain variation is considered to occur under the given confidence coefficient level; when the t statistic corresponding to the terrain variation is smaller than or equal to the t statistic threshold value under the given confidence, the elevation difference obtained by two aerial photographs under the given confidence level is considered to be caused by errors.
Further, when the first aerial photographing and the second aerial photographing are carried out in the research area, the heading and the aerial photographing height are set on the premise that the vertical photographing and the side-to-side overlapping degree are more than 80%, and the period that the weather is clear, the ground has no continuous wind direction and the wind power is less than level 2 is selected for aerial photographing.
Further, the method for obtaining the digital elevation model (DEM 1) of the research area according to the multi-echo information of the first aerial laser point cloud and obtaining the digital elevation model (DEM 2) of the research area according to the multi-echo information of the second aerial laser point cloud comprises the following steps:
Preprocessing the original laser point cloud data of the aerial photography for two times, wherein the preprocessing step comprises point cloud denoising, point cloud filtering and point cloud classification, and the preprocessing is performed by means of python or C++;
Extracting multi-echo information of the first aerial photographing laser point cloud by using a Python language, reserving the lowest elevation data, and removing the data information returned on vegetation, so as to obtain elevation data of the real landform surface, and generating a digital elevation model (DEM 1) of a research area;
And extracting multi-echo information of the laser point cloud for the second aerial photography by using Python language, reserving the lowest elevation data, removing the data information returned on vegetation, thereby obtaining elevation data of the real landform surface, and generating a digital elevation model (DEM 2) of the research area.
By adopting the technical scheme, the invention has the following beneficial effects:
1. According to the method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology, the digital elevation model of the research area before and after soil loss and leakage can be obtained simply, conveniently and accurately by combining the modern unmanned aerial vehicle photogrammetry technology, the influence of the ground surface soil erosion amount is eliminated by estimating the underground soil leakage amount, and the accuracy of the underground soil leakage amount calculation in the karst region is further improved.
2. According to the method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology, the variation of the digital elevation model is accurately extracted by utilizing the laser Lidar data, the total soil erosion amount G of the research area is obtained through the variation of the digital elevation model, a scientific basis is provided for further estimating the soil leakage amount, so that an underground soil leakage amount equation is obtained, the calculated soil leakage amount is more accurate, the problems that the monitoring of soil erosion, water and soil loss and the like is difficult, the research efficiency is low, the quantitative estimation error is large and the like in the prior art are solved, and theoretical guidance is provided for the stony desertification control and the water and soil prevention in the karst region.
3. According to the method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology, the DEM generated by two aerial photographs is subjected to error analysis, so that the situation of terrain change caused by errors is eliminated, and on the premise that the elevation difference obtained by two aerial photographs is caused by real terrain change, the soil leakage amount is estimated, so that the estimation of the soil leakage amount is more accurate; according to the method, DEM errors of two aerial photographs are calculated into corresponding terrain variation according to an error propagation law, errors of the terrain variation are obtained, whether the elevation difference obtained by the two aerial photographs is variation caused by real terrain variation or variation caused by the errors is distinguished under a certain confidence level according to a statistical t-test principle, and a basis and a method are provided for judging whether the elevation difference obtained by the two aerial photographs is caused by real terrain variation or not.
4. The method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology combines the modern unmanned aerial vehicle photogrammetry technology to estimate the soil leakage amount in a real geographic environment, and is more real and reliable compared with the existing manual simulation method; compared with a field detection method and a 137 Cs tracing method, the laser Lidar technology is adopted to estimate the underground soil leakage quantity in the karst region, the automation degree is higher, the field work difficulty is lower, and the efficiency is higher.
Drawings
FIG. 1 is a schematic view of a water and soil loss process.
Fig. 2 is a schematic diagram of unmanned aerial vehicle photogrammetry process.
Fig. 3 is a view of unmanned aerial vehicle photogrammetry information extraction.
Fig. 4 is a detailed view of the surface processing.
FIG. 5 is a flowchart of a method for estimating the leakage of underground soil in karst regions by using laser Lidar technology according to a preferred embodiment of the present invention.
Description of the main reference signs
1-Soil, 2-cracks, 3-rocks, 4-water falling holes and 5-underground rivers.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 2 to 5, a method for estimating the leakage amount of underground soil in karst regions by using laser Lidar technology according to a preferred embodiment of the present invention includes the following steps:
S1, performing first aerial photography on a research area to be monitored by using an unmanned aerial vehicle carrying a laser radar sensor, and obtaining a digital elevation model (DEM 1) of the research area according to multi-echo information of the laser point cloud of the first aerial photography.
S2, performing second aerial photography on the research area to be monitored by using an unmanned aerial vehicle carrying a laser radar sensor, wherein a preset time is reserved between the second aerial photography and the first aerial photography, and a digital elevation model (DEM 2) of the research area is obtained according to multiple echo information of the laser point cloud of the second aerial photography.
In step S1 and step S2, aerial photographing is performed by using the laser radar sensor carried by the unmanned aerial vehicle, and the aerial photographing process mainly includes erection of a ground GPS base station, mounting and installing of the laser radar sensor carried by the unmanned aerial vehicle, and route planning and uploading of the route, which belong to the prior art, and are omitted for brevity and not described herein. In addition, when the first aerial photographing and the second aerial photographing are carried out in the research area, the time period that the weather is clear, the ground has no continuous wind direction and the wind power is less than level 2 is preferably selected, so that the obtained image is not influenced by atmospheric factors; when in aerial photography, the heading and the aerial photography height are set on the premise that vertical shooting is ensured and the side-to-side overlapping degree is more than 80%. After a period of months or years, the soil loss and leakage amount are increased due to strong karst action, water flow scouring, chemical corrosion and the like, and after a preset period of time, for example, a period of months or years, the first aerial photographing is performed for the second aerial photographing in the research area to be monitored.
In this embodiment, the method for obtaining the digital elevation model (DEM 1) of the research area according to the multi-echo information of the first aerial laser point cloud and obtaining the digital elevation model (DEM 2) of the research area according to the multi-echo information of the second aerial laser point cloud includes:
Preprocessing the original laser point cloud data of the aerial photography for two times, wherein the preprocessing step mainly comprises point cloud denoising, point cloud filtering and point cloud classification, and the preprocessing is performed by means of python or C++;
Extracting multi-echo information of the first aerial photographing laser point cloud by using a Python language, reserving the lowest elevation data, and removing the data information returned on vegetation, so that high-precision elevation data of a real landform surface is obtained, and a digital elevation model (DEM 1) of a research area is generated;
And extracting multi-echo information of the laser point cloud for the second aerial photography by using Python language, reserving the lowest elevation data, and removing the data information returned on vegetation, so that high-precision elevation data of the real landform surface is obtained, and a digital elevation model (DEM 2) of a research area is generated.
S3, performing error analysis on the DEM generated by the two aerial photographs, and judging whether the elevation difference obtained by the two aerial photographs is caused by real terrain change.
In the present embodiment, step S3 includes the steps of:
S31, obtaining an error of a terrain variation through error calculation of the DEM obtained through aerial photography twice according to an error propagation law, wherein the error delta DoD of the terrain variation is specifically:
wherein: delta DoD is the error of the terrain variation, delta DEM1 is the error of the DEM obtained by the first aerial photography, delta DEM2 is the error of the DEM obtained by the second aerial photography, and the error of the DEM in the formula is the error space distribution diagram of the DEM, which can be obtained by adopting the following method:
S311, during the first aerial photographing and the second aerial photographing, performing field control measurement on a research area by adopting a GPS-RTK mode, setting the same coordinate system for measurement control points of the two aerial photographing, determining the number of the control points by the size of the research area, selecting half of the control points as three-dimensional control points to participate in the three-dimensional process correction photogrammetry, and using the rest control points as check points to evaluate the precision of photogrammetry results to obtain the error of the DEM elevation at the check points;
s312, adopting a Monte Carlo method, and evaluating error spatial distribution of a photogrammetric result based on the known error of the DEM elevation at the check point in the step S311 to obtain an error spatial distribution map of the two-time aerial DEM.
Step 32, distinguishing whether the elevation difference obtained by two aerial photographs is a change caused by a real terrain change or a change caused by an error under a certain confidence level according to the principle of the statistical t-test.
In this embodiment, the lowest detection level, i.e., a confidence level of 68.3%, is selected to distinguish whether the difference in elevation obtained from two aerial shots is a change due to a real terrain change or an error-induced change. Therefore, in step 32, the terrain variation needs to be converted into corresponding t statistics (assuming that the errors are completely randomly distributed) and confidence level detection is performed, specifically:
converting the terrain variation into corresponding t statistics, and according to the t test principle, the t statistics corresponding to the terrain variation are as follows:
Wherein: z DEM1 is a digital elevation model obtained by the first aerial photography, Z DEM2 is a digital elevation model obtained by the second aerial photography, and delta DoD is an error of the terrain variation;
Obtaining a t statistic threshold value under a given confidence coefficient (namely 68.3%) by referring to a t test limit value table, and considering that the real terrain change occurs under a given confidence coefficient level when the t statistic corresponding to the terrain change is larger than the t statistic threshold value under the given confidence coefficient; when the t statistic corresponding to the terrain variation is smaller than or equal to the t statistic threshold value under the given confidence, the elevation difference obtained by two aerial photographs under the given confidence level is considered to be caused by errors.
S4, if the elevation difference obtained by the aerial photographing is caused by the real terrain change, calculating the underground soil leakage, wherein the underground soil leakage is obtained by subtracting the earth surface soil erosion amount from the total soil erosion amount, the earth surface soil erosion amount is obtained according to a modified general soil erosion amount equation, and the total soil erosion amount is obtained according to the obtained change amount of the digital elevation model of the aerial photographing.
Wherein, the calculation of the total erosion amount G of the soil in the research area comprises the following steps:
Rasterizing the digital elevation model of the land feature of the research area obtained by the first aerial photography and the second aerial photography respectively, wherein the spatial resolution of the DEM parameter (namely the land feature parameter) obtained by the two aerial photography of the research area is kept consistent during the rasterizing, and the size of the specific spatial resolution can be set according to actual conditions;
And (3) performing difference value operation on the geomorphic parameters of the study area of the two aerial photographs in a GIS (geographic information system) to obtain the variation (delta DEM) of the digital elevation model, wherein the total erosion G of the soil of the study area is as follows:
G＝∑ΔDEM×(d1+d2)/2×s×ρ
Wherein: g is the total erosion amount of soil; the delta DEM is the variation of each grid digital elevation model of laser Lidar data; d 1 is the total soil coverage of the research area before erosion; d 2 is the total soil coverage rate of the corroded research area; (d 1+d2)/2 is the average soil coverage by erosion; s is the area of the unit grid after laser Lidar data processing; ρ is the soil volume weight and this data is the measured data.
The total soil coverage rate d of the research area is calculated through the coverage area of each grid soil of the DEM data, and specifically comprises the following steps:
wherein: d r is the soil coverage rate of the unit grid; d ri is the covered area of the unit grid soil, i is the unit grid; s is the unit grid area.
The total soil coverage rate d of the research area is:
d＝dr×c
Wherein: d is the total soil coverage rate of the research area; c is the total number of grids in the investigation region.
The unit grid soil covered area d ri is obtained by aerial photography, and when the point cloud classification processing is performed on the original laser point cloud data, the principle that the values of the laser echo intensities of different ground objects are different is applied, so that the soil covered information is identified, and the data is obtained.
D 1 and d 2 are obtained according to the DEM data obtained by aerial photography and the method.
The general soil loss equation for correction in step S4 is:
E＝RUSLE＝R·K·LS·C·P
Wherein: e is the erosion amount of the earth surface soil; RUSLE is the general soil loss correction, R is the rainfall erosion factor; k is a soil corrosiveness factor; l is a slope length factor; s is a gradient factor; c is vegetation coverage and management factors; p is a soil and water conservation measure factor. The calculation of the equation for correcting the general soil loss belongs to the prior art, and is omitted for brevity.
The calculation formula of the underground soil leakage amount N in the step S4 is as follows:
N＝G-E＝∑ΔDEM×(d1+d2)/2×s×ρ-R·K·LS·C·P。
according to the method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology, the digital elevation model of the research area before and after soil loss and leakage can be obtained simply, conveniently and accurately by combining the modern unmanned aerial vehicle photogrammetry technology, the influence of the ground surface soil erosion amount is eliminated by estimating the underground soil leakage amount, and the accuracy of the underground soil leakage amount calculation in the karst region is further improved.
According to the method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology, the variation of the digital elevation model is accurately extracted by utilizing the laser Lidar data, the total soil erosion amount G of the research area is obtained through the variation of the digital elevation model, a scientific basis is provided for further estimating the soil leakage amount, so that an underground soil leakage amount equation is obtained, the calculated soil leakage amount is more accurate, the problems that the monitoring of soil erosion, water and soil loss and the like is difficult, the research efficiency is low, the quantitative estimation error is large and the like in the prior art are solved, and theoretical guidance is provided for the stony desertification control and the water and soil prevention in the karst region.
According to the method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology, the DEM generated by two aerial photographs is subjected to error analysis, so that the situation of terrain change caused by errors is eliminated, and on the premise that the elevation difference obtained by two aerial photographs is caused by real terrain change, the soil leakage amount is estimated, so that the estimation of the soil leakage amount is more accurate; according to the method, DEM errors of two aerial photographs are calculated into corresponding terrain variation according to an error propagation law, errors of the terrain variation are obtained, whether the elevation difference obtained by the two aerial photographs is variation caused by real terrain variation or variation caused by the errors is distinguished under a certain confidence level according to a statistical t-test principle, and a basis and a method are provided for judging whether the elevation difference obtained by the two aerial photographs is caused by real terrain variation or not.
The method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology combines the modern unmanned aerial vehicle photogrammetry technology to estimate the soil leakage amount in a real geographic environment, and is more real and reliable compared with the existing manual simulation method; compared with a field detection method and a 137 Cs tracing method, the laser Lidar technology is adopted to estimate the underground soil leakage quantity in the karst region, the automation degree is higher, the field work difficulty is lower, and the efficiency is higher.
The foregoing description is directed to the preferred embodiments of the present invention, but the embodiments are not intended to limit the scope of the invention, and all equivalent changes or modifications made under the technical spirit of the present invention should be construed to fall within the scope of the present invention.
Claims (9)
1. The method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology is characterized by comprising the following steps of:
performing first aerial photography on a research area to be monitored by using an unmanned aerial vehicle carrying a laser radar sensor, and obtaining a digital elevation model DEM 1 of the research area according to multi-echo information of the first aerial photography laser point cloud;
Carrying out second aerial photographing on a research area to be monitored by using an unmanned aerial vehicle carrying a laser radar sensor, wherein a preset time is reserved between the second aerial photographing and the first aerial photographing, and a digital elevation model DEM 2 of the research area is obtained according to multiple echo information of a laser point cloud of the second aerial photographing;
Calculating the underground soil leakage amount: the soil erosion amount of the underground is obtained by subtracting the earth surface soil erosion amount from the total soil erosion amount, wherein the earth surface soil erosion amount is obtained according to a modified general soil erosion amount equation, and the total soil erosion amount is obtained according to the obtained variation of the digital elevation model of the aerial photo for two times, and the method comprises the following steps of:
rasterizing the geomorphic digital elevation model of the research area obtained by the first aerial photography and the second aerial photography respectively, and keeping the spatial resolution of the DEM parameters obtained by the two aerial photography of the research area consistent during the rasterizing;
And respectively carrying out difference value operation on DEM parameters of a study area in two aerial photographs in a GIS to obtain the variation delta DEM of the digital elevation model, wherein the total erosion G of soil of the study area is as follows:
Wherein: g is the total erosion amount of soil; the delta DEM is the variation of each grid digital elevation model of laser Lidar data; d 1 is the total soil coverage of the research area before erosion; d 2 is the total soil coverage rate of the corroded research area; (d 1+d2)/2 is the average soil coverage by erosion; s is the area of the unit grid after laser Lidar data processing; ρ is the soil volume weight.
2. The method for estimating underground soil loss in karst regions by using laser Lidar technology as claimed in claim 1, wherein the correction general soil loss equation is:
wherein: e is the erosion amount of the earth surface soil; RUSLE is the general soil loss correction, R is the rainfall erosion factor; k is a soil corrosiveness factor; l is a slope length factor; s is a gradient factor; c is vegetation coverage and management factors; p is a soil and water conservation measure factor.
3. The method for estimating the underground soil leakage amount in karst regions by utilizing the laser Lidar technology as claimed in claim 2, wherein the calculation formula of the underground soil leakage amount N is as follows:
。
4. The method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology according to claim 1, wherein before the step of calculating the underground soil leakage amount, error analysis is further performed on the DEM generated by the two aerial photographs, and whether the elevation difference obtained by the two aerial photographs is caused by real terrain change is judged; if the elevation difference obtained by the two aerial photographs is caused by the real terrain change, the underground soil leakage is calculated.
5. The method for estimating a leakage amount of underground soil in karst regions by using a laser Lidar technique as claimed in claim 4, wherein the error analysis of the DEM generated by the two aerial photographs comprises the steps of:
S31, calculating an error of the terrain variation through the DEM error of the two aerial photographs according to an error propagation law;
S32, distinguishing whether the elevation difference obtained by two aerial photographs is a change caused by a real terrain change or a change caused by an error under a certain confidence level according to the principle of the statistical t-test.
6. The method for estimating an amount of subsurface soil leakage in karst regions using laser Lidar technology as claimed in claim 5, wherein the error δ DoD of the terrain variation is:
Wherein: delta DoD is the error of the terrain variation, delta DEM1 is the error of the DEM obtained by the first aerial photography, and delta DEM2 is the error of the DEM obtained by the second aerial photography.
7. The method for estimating an amount of subsurface soil leakage in karst regions using laser Lidar technology as claimed in claim 6, wherein step S32 comprises the steps of:
converting the terrain variation into corresponding t statistics, and according to the t test principle, the t statistics corresponding to the terrain variation are as follows:
Wherein: z DEM1 is a digital elevation model obtained by the first aerial photography, Z DEM2 is a digital elevation model obtained by the second aerial photography, and delta DoD is an error of the terrain variation;
Obtaining a t statistic threshold value under a given confidence coefficient by referring to a t test limit value table, and when the t statistic corresponding to the terrain variation is larger than the t statistic threshold value under the given confidence coefficient, considering that the real terrain variation occurs under the given confidence coefficient level; when the t statistic corresponding to the terrain variation is smaller than or equal to the t statistic threshold value under the given confidence, the elevation difference obtained by two aerial photographs under the given confidence level is considered to be caused by errors.
8. The method for estimating the underground soil leakage amount in the karst region by utilizing the laser Lidar technology according to claim 1, wherein when the first aerial photographing and the second aerial photographing are carried out in a research area, the heading and the aerial photographing height are set on the premise that vertical photographing is ensured and the side lap reaches more than 80%, and the period of clear weather, no continuous wind direction on the ground and wind power <2 level is selected for aerial photographing.
9. The method for estimating the leakage amount of underground soil in karst regions by utilizing a laser Lidar technology according to claim 1, wherein the method for obtaining the digital elevation model DEM 1 of the research area according to the multi-echo information of the laser point cloud for the first time and obtaining the digital elevation model DEM 2 of the research area according to the multi-echo information of the laser point cloud for the second time is as follows:
Preprocessing the original laser point cloud data of the aerial photography for two times, wherein the preprocessing step comprises point cloud denoising, point cloud filtering and point cloud classification, and the preprocessing is performed by means of python or C++;
Extracting multi-echo information of the first aerial photographing laser point cloud by using a Python language, reserving the lowest elevation data, and removing the data information returned on vegetation, so that elevation data of a real landform surface is obtained, and a digital elevation model DEM 1 of a research area is generated;
And extracting multi-echo information of the laser point cloud for the second aerial photography by using Python language, reserving the lowest elevation data, and removing the data information returned on vegetation, so as to obtain elevation data of the real landform surface, and generating a digital elevation model DEM 2 of the research area.
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