CN113450348B - Soil erosion quantitative estimation method based on high-resolution stereopair image - Google Patents
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Abstract
The invention relates to a soil erosion quantitative estimation method based on a high-resolution stereopair image, which comprises the following steps: step S1, acquiring regional reference DEM data and a high-resolution stereopair satellite image, and constructing a high-resolution DEM to obtain high-resolution DEM data; step S2: calculating a slope length factor L from the high resolution DEM datayGradient factor Sy(ii) a S3, performing fusion processing on the front-view full-color and multi-spectral images in the high-resolution DEM data, performing land utilization classification, and calculating vegetation coverage FVC and a vegetation coverage factor B; step S4, calculating a monthly rainfall erosion force factor Rm，m =1,2 … … 12 and an annual rainfall erosive power factor R; step S5, calculating soil erodability factor K, engineering factor E and cultivation factor T; and step S6, further acquiring the unit soil loss according to the data obtained by calculation in the steps S2-S5. The invention can effectively improve the soil erosion detection precision.
Description
Technical Field
The invention belongs to the field of land erosion monitoring, and particularly relates to a soil erosion quantitative estimation method based on high-resolution stereopair images.
Background
In a quantitative production and construction project water and soil loss monitoring seed, the earth surface is inevitably damaged due to the construction of the production and construction project, so that the terrain changes, and serious water and soil loss is caused. Therefore, how to effectively monitor the loss caused by the change of the terrain in the construction process is the key of quantitative estimation of water and soil loss of production and construction projects.
The acquisition of the topographic data of traditional monitoring comes from on-site actual measurement and unmanned aerial vehicle aerial photography, is difficult to carry out continuous construction monitoring effectively on a large scale, therefore how to obtain continuous topographic data in construction area on a large scale effectively is especially important to monitoring production construction project soil erosion and water loss.
Disclosure of Invention
In view of the above, the present invention provides a method for quantitatively estimating soil erosion based on a high-resolution stereopair image, and particularly aims at quantitatively estimating soil erosion of a production construction project to overcome the defects in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a soil erosion quantitative estimation method based on a high-resolution stereopair image comprises the following steps:
step S1, acquiring reference DEM data of the area to be detected and a high-resolution stereopair satellite image, and constructing a high-resolution DEM to obtain high-resolution DEM data;
step S2: calculating a slope length factor L according to high-resolution DEM data obtained by the high-resolution stereopair to the satellite imageyGradient factor Sy；
S3, performing fusion processing on the front-view full-color and multi-spectral images in the high-resolution DEM data, performing land utilization classification, and calculating vegetation coverage FVC and a vegetation coverage factor B;
step S4, calculating a monthly rainfall erosion force factor Rm，m＝1,2 … … 12 and an annual rainfall erosive power factor R;
step S5, calculating soil erodability factor K, engineering factor E and cultivation factor T;
and step S6, further acquiring the unit soil loss according to the data obtained by calculation in the steps S2-S5.
Further, the step S1 is specifically:
step S11: acquiring regional reference DEM data and high-resolution stereopair satellite images during construction, wherein the high-resolution stereopair satellite images comprise front-view panchromatic images, front-view and rear-view panchromatic images and front-view multispectral image data;
step S12: loading an orthographic panchromatic image on the left image by utilizing a DEM Extraction module in ENVI software; loading a rear-view full-color image or a front-view full-color image on the right image;
step S13: extracting the image pair elevation, selecting an automatic searching connection point, setting related parameters, and checking and adjusting the connection points one by one according to the sequence of result errors from large to small, wherein the control error is within a preset range;
step S14: setting DEM extraction parameters including output projection parameters, background values, terrain fineness and output pixel size, and generating high-resolution DEM data;
step S15: and using the reference DEM data as an elevation control inspection basis, checking abnormal values, roads, cloud areas and water parts of the generated high-resolution DEM data with strong tendency, and generating the final high-resolution DEM data by means of human-computer interaction editing.
Further, the step S2 is specifically:
calculating a slope length factor L of the production construction project according to the high-resolution DEM data generated by the stereopairy；
λ＝λxcosθ
In the formula, lambda is the horizontal projection slope length of the calculation unit; theta is the calculating unit gradient; m is a slope length index; l isyCalculating the slope length of the unit;
calculating a gradient factor Sy:
In the formula, e is a natural logarithm base.
Further, the step S3 is specifically:
step S31: performing fusion processing on the visible full-color and multi-spectral images, performing land utilization classification, and calculating an NDMVI index;
step S32: calculating vegetation coverage FVC by adopting a pixel dichotomy model and utilizing NMDVI;
step S33: according to the land utilization classification result and the vegetation coverage FVC, B factor reference values under different vegetation coverage nodes are constructed with a given step length based on B factors of front and rear nodes to construct a linear change function of an interval, the B factor of a arbor forest land is obtained through linear interpolation according to standard requirements, the classification result is combined, 1 is obtained for cultivated land, and the water body and the impervious surface are both 0 to obtain a vegetation coverage factor B;
linear variation function:
in the formula (I), the compound is shown in the specification,
the linear change value of the factor B in the ith interval; a isiThe linear change coefficient of the factor B in the ith interval; b isiThe ith interval B factor reference value; i is 0, 5 … 100.
Further, the monthly rainfall erosion force factor RmThe calculation method of the annual rainfall erosive power factor R is as follows:
Rm＝0.183*Pm 1.995
in the formula, RmIs the rainfall erosive power factor of the m-th month; pmThe rainfall in the mth month.
Further, the concrete steps of calculating the engineering measure factor E, the soil erodibility factor K and the cultivation factor T are as follows:
step S51: acquiring data of whether water and soil conservation measures are adopted before disturbance of surface production construction projects in a research area;
step S52: assigning values according to the E factor assignment tables under different water conservation measures, and if no water and soil conservation measure is adopted on the ground surface, assigning the engineering measure factors to be 1;
calculating a soil erodibility factor K based on an erosion-productivity influence calculation model EPIC by using soil attribute data:
in the formula: SAN is sand content, SIL is silt content, CLA is clay content, and c is organic carbon content; SN (service provider)1＝1-SAN/100；
Providing a T factor calculation method according to farming measures and rotation systems in different areas, and meanwhile, setting the value of T to be 1 when a calculation unit does not cultivate land;
T＝T1T2
in the formula: t is1Preparing soil and planting mode factors; t is2Is used as a rotation system factor.
Further, the unit soil loss calculation formula is as follows:
Myz＝RKLySyBETA
in the formula, MyzCalculating unit soil loss for a vegetation damage type general disturbance earth surface; r isIs rainfall erosion force factor; k is a soil erodability factor; l isyIs a slope length factor; syIs a gradient factor; b is vegetation coverage factor; e is an engineering measure factor; t is a cultivation measure factor; a is the horizontal plane projected area of the computing unit.
Compared with the prior art, the invention has the following beneficial effects:
the multi-temporal DEM constructed based on the high-resolution stereopair images is introduced into the quantitative estimation of water and soil loss, so that the soil erosion detection precision is improved, the method is applied to the soil detection of production and construction projects, and the water and soil loss caused by the terrain change in the construction process can be effectively monitored.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a comparison of DEM data constructed from stereo image pairs and 16m reference data in accordance with an embodiment of the present invention;
FIG. 3 is a water loss and soil erosion intensity distribution chart according to an embodiment of the present invention;
FIG. 4 is a construction disturbance map of a production construction project in an embodiment of the present invention;
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a soil erosion quantitative estimation method based on high resolution stereopair images
Step S1: acquiring regional reference DEM data of a production construction project and a high-resolution stereopair satellite image of resource No. three during construction, wherein the high-resolution stereopair satellite image comprises a front-view panchromatic image, a rear-view panchromatic image, a front-view panchromatic image and a multispectral image, and the panchromatic images with different viewing angles are utilized to construct a high-resolution DEM with strong situational performance;
the specific steps of step S1 are as follows:
step S11: collecting reference DEM data of a production and construction project area and resource No. three high-resolution stereopair satellite images during construction, wherein the images comprise front-view and back-view panchromatic images, front-view panchromatic images and multispectral image data;
step S12: loading an orthographic panchromatic image on the left image by utilizing a DEM Extraction module in ENVI software; loading a rear-view full-color image or a front-view full-color image on the right image;
step S13: extracting the elevation of the image pair, selecting automatic searching connection points, setting relevant parameters (such as the number of the connection points, a search window, a mobile window, a point correlation threshold value and the like), and checking and adjusting the connection points one by one according to the sequence of result errors from large to small, wherein the control errors are within an allowable range;
step S14: and (4) setting DEM extraction parameters including output projection parameters, background values, terrain fineness and output pixel size to generate high-resolution DEM data with strong situational property.
Step S15: according to the obtained corresponding resource third DEM data, using 16m resolution reference DEM data and the preprocessed multispectral image as an elevation control inspection basis, carrying out geographic space registration, and ensuring the space consistency of the constructed DEM and the actual image. The abnormal value, the road, the cloud area, the water body part and the like are checked, and high-resolution DEM data are generated by means of human-computer interaction editing and the like;
step S2: calculating a slope length factor L according to the DEM data obtained in the step S1yGradient factor Sy；
The specific steps of step S2 are as follows:
step S21: calculating the slope length factor L of the production construction project according to the resource No. three DEM data obtained in the step S1 and the method provided by the production construction project soil loss measurement guide ruley；
Slope length factor LyAnd (3) calculating:
λ＝λxcos θ equation 2
Wherein, lambda is the horizontal projection slope length m of the calculation unit, (when lambda is less than or equal to 100m, the calculation is carried out according to the actual value, and when lambda is more than 100m, the calculation is carried out according to 100 m); θ is the calculation unit slope, (°); m is a slope length index; l isyThe unit slope length is calculated.
Step S22: calculating a gradient factor S of the production construction project according to the DEM data obtained in the step S1 and a method provided by the production construction project soil loss measurement guide ruley；
Gradient factor SyAnd (3) calculating:
in the formula, e is a natural logarithm base, and can be 2.72.
Step S3: performing fusion processing on front-view full-color and multi-spectral images in the stereo image pair, performing land utilization classification, and calculating vegetation coverage FVC and vegetation coverage factor B;
the specific steps of step S3 are as follows:
step S31: performing fusion processing on front-view full-color and multi-spectral images in the stereoscopic image pair, performing land utilization classification, and calculating an NDMVI index;
step S32: calculating vegetation coverage FVC by adopting a pixel dichotomy model and utilizing NMDVI;
step S33: according to the land utilization classification result and the vegetation coverage FVC, B factor reference values under different vegetation coverage nodes (0, 5 percent, 10 percent.. 100 percent) provided by the production and construction project soil loss measurement and calculation guide rule are utilized, a linear change function of an interval is constructed according to a given step length (5 percent) and based on B factors of front and rear nodes, and the B factor of the arbor forest land is obtained through linear interpolation according to standard requirements. And combining the classification result, taking 1 of farmland, and obtaining a vegetation coverage factor B by using the water body and the impervious surface which are both 0.
Linear variation function:
in the formula (I), the compound is shown in the specification,
the linear change value of the factor B in the ith interval; a isiThe linear change coefficient of the factor B in the ith interval; biThe ith interval B factor reference value; i is 0, 5 … 100.
Step S4: calculating a monthly rainfall erosion force factor Rm(m ═ 1,2 … … 12) and an annual rainfall erosive power factor R;
the specific steps of step S4 are as follows:
Rm＝0.183*Pm 1.995equation 6
In the formula, RmRainfall erosive power factor (MJ. mm/(hm) at month m2·h))；PmThe rainfall (mm) at month m.
Step S5: calculating an engineering measure factor E, a soil erodability factor K and a cultivation factor T;
the specific steps of step S5 are as follows:
step S51: collecting data of whether water and soil conservation measures are adopted before disturbance of surface production construction projects in a research area;
step S52: and assigning according to an E factor assignment table under different water conservation measures provided by production and construction project soil loss measurement and guidance rules, and if no water and soil conservation measure is adopted on the surface of the ground, assigning the engineering measure factors to be 1.
TABLE 1E-factor assignment table under different water conservation measures
Calculating a soil erodibility factor K based on an erosion-productivity influence calculation model EPIC by using soil attribute data:
in the formula: SAN is sand content (%); SIL is silt content (%); CLA is the cosmid content (%); c is the organic carbon content (%); SN (service provider)1＝1-SAN/100。
A T factor calculation method is provided according to farming measures and rotation systems of different areas, and a T sub-value is set to be 1 when a calculation unit does not cultivate land.
T＝T1T2Equation 9
In the formula: t is a unit of1The soil preparation and planting mode factors are dimensionless; t is2Is a rotation system factor without dimension.
Step S6: calculating annual soil erosion modulus M caused by production and construction projectsyz；
The specific steps of step S6 are as follows:
respectively calculating the soil erosion modulus of each year according to a vegetation damage type disturbed land surface calculation unit soil loss calculation formula, wherein the concrete formula is as follows:
Myz＝RKLySyBETA equation 10
In the formula, MyzCalculating unit soil loss, t, for a vegetation-damaged general disturbed surface; r is rainfall erosion force factor MJ.mm/(hm)2H); k is soil erodability factor, t.hm2·h/(hm2·MJ·mm)；LyIs a slope length factor and has no dimension; syThe gradient factor is a dimensionless gradient factor; b is a vegetation coverage factor without dimension; e is an engineering measure factor and is dimensionless; t is a cultivation measure factor and is dimensionless; a is the horizontal plane projection area of the computing unit, hm2。
In this embodiment, in order to more clearly express quantitative water and soil loss at different construction stages, resource three-dimensional satellite stereopair image data of 2016, 2018 and 2019 years are respectively obtained, and soil erosion moduli of the three years are respectively calculated according to the above steps. As shown in fig. 2, when the DEM data constructed by the resource three-dimensional pairs in the research areas 2016, 2018, and 2019 are compared with the 16m reference data, it can be seen that the DEM constructed by the resource three-dimensional pairs in different years is consistent with the reference image in overall relief and mountain trend distribution, but has finer spatial texture in detail. For DEM changes at different time phases, the detail comparison shows that the DEM constructed by the stereopair can better reflect the surface changes caused by the production construction process relative to the reference DEM at different production construction periods, and the appearance is better. The constructed DEM shows sensitivity to terrain change, and is beneficial to subsequent calculation of more accurate water and soil loss of production construction projects. Fig. 3 is a spatial distribution diagram of soil erosion moduli generated by calculation in 2016, 2018 and 2019 of the Changtian county major production construction project generated by calculation, and is obtained by classifying and classifying erosion strengths according to the soil erosion classification standard (SL190-2007) and comparing with a soil erosion modulus diagram calculated by referring to DEM data.
In the embodiment, the aspects of field investigation, statistical data, result space-time distribution and the like are utilized to evaluate the aspects of the precision, the rationality and the like of the improved method. As can be seen from fig. 4, in the early stage of construction of the line road project (fig. 42016), because the project construction is just started, there are partially exposed disturbed ground surfaces on both sides of the road, which are main loss areas. In the construction period (fig. 42018), there are exposed mountain soil slopes, which are affected by the disturbed terrain and soil, and the slope protection area will suffer serious water and soil loss under the action of external force, and the slope protection during the construction period is mainly manifested as severe loss in the figure. In the later stage of construction (fig. 42019), the slope protection area is because implemented ecological slope protection technology, and soil erosion basically does not take place, and the erosion strength mainly runs off for a short time, shows the protective effect of ecological slope protection, and is unanimous with actual conditions.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (2)
1. A soil erosion quantitative estimation method based on a high-resolution stereopair image is characterized by comprising the following steps:
step S1, acquiring reference DEM data and a high-resolution stereopair satellite image of the area to be detected, and constructing a high-resolution DEM to obtain high-resolution DEM data;
step S2: calculating a slope length factor L according to high-resolution DEM data obtained by the high-resolution stereopair to the satellite imageyGradient factor Sy；
S3, performing fusion processing on the front-view full-color and multi-spectral images in the high-resolution DEM data, performing land utilization classification, and calculating vegetation coverage FVC and a vegetation coverage factor B;
step S4, calculating a monthly rainfall erosion force factor RmM is 1,2 … … 12 and an annual rainfall erosive power factor R;
step S5, calculating soil erodability factor K, engineering factor E and cultivation factor T;
step S6, further acquiring unit soil loss according to the data obtained by calculation in the steps S2-S5;
the step S2 specifically includes:
calculating a slope length factor L of the production construction project according to the high-resolution DEM data generated by the stereopairy；
λ＝λxcosθ
In the formula, λxCalculating the slope length of the unit, wherein lambda is the horizontal projection slope length of the unit; theta is the calculating unit gradient; m is a slope length index; l isyCalculating the slope length of the unit;
calculating a gradient factor Sy:
In the formula, e is a natural logarithm base;
the step S3 specifically includes:
step S31: performing fusion processing on the visible full-color and multi-spectral images, performing land utilization classification, and calculating an NDMVI index;
step S32: calculating vegetation coverage FVC by adopting a pixel dichotomy model and utilizing NMDVI;
step S33: according to the land utilization classification result and the vegetation coverage FVC, B factor reference values under different vegetation coverage nodes are constructed with a given step length based on B factors of front and rear nodes to construct a linear change function of an interval, the B factor of a arbor forest land is obtained through linear interpolation according to standard requirements, the classification result is combined, 1 is obtained for cultivated land, and the water body and the impervious surface are both 0 to obtain a vegetation coverage factor B;
linear variation function:
in the formula (I), the compound is shown in the specification,
the linear change value of the factor B in the ith interval; a is aiThe linear change coefficient of the factor B in the ith interval; b isiThe ith interval B factor reference value;
the monthly rainfall erosive power factor RmAnd the calculation method of the annual rainfall erosive power factor R is as follows:
Rm＝0.183*Pm 1.995
in the formula, RmIs the rainfall erosive power factor of the m-th month; pmThe rainfall in the mth month;
the concrete steps of calculating the engineering measure factor E, the soil erodibility factor K and the cultivation factor T are as follows:
step S51: acquiring data of whether water and soil conservation measures are adopted before disturbance of surface production construction projects in a research area;
step S52: assigning values according to the E factor assignment tables under different water conservation measures, and if no water and soil conservation measure is adopted on the ground surface, assigning the engineering measure factors to be 1;
calculating a soil erodibility factor K based on an erosion-productivity influence calculation model EPIC by using soil attribute data:
in the formula: SAN is sand content, SIL is silt content, CLA is clay content, and c is organic carbon content; SN (service provider)1＝1-SAN/100；
Providing a T factor calculation method according to farming measures and rotation systems in different areas, and meanwhile, setting the value of T to be 1 when a calculation unit does not cultivate land;
T＝T1T2
in the formula: t is1Preparing soil and planting mode factors; t is2Is a rotation system factor;
the unit soil loss calculation formula is as follows:
Myz＝RKLySyBETA
in the formula, MyzCalculating unit soil loss for a vegetation damage type general disturbance earth surface; r is rainfall erosion force factor; k is a soil erodability factor; l isyIs a slope length factor; syIs a gradient factor; b is vegetation coverage factor; e is an engineering measure factor; t is a cultivation measure factor; and A is the horizontal plane projection area of the calculation unit.
2. The method for quantitatively estimating soil erosion based on high-resolution stereopair images according to claim 1, wherein said step S1 is specifically:
step S11: acquiring reference DEM data of a region to be detected and a high-resolution stereopair satellite image during construction, wherein the high-resolution stereopair satellite image comprises an orthographic panchromatic image, a forward-looking and backward-looking panchromatic image and orthographic multispectral image data;
step S12: loading an orthographic panchromatic image on the left image by utilizing a DEM Extraction module in ENVI software; loading a rear-view full-color image or a front-view full-color image on the right image;
step S13: extracting the image pair elevation, selecting an automatic searching connection point, setting related parameters, and checking and adjusting the connection points one by one according to the sequence of result errors from large to small, wherein the control error is within a preset range;
step S14: setting DEM extraction parameters including output projection parameters, background values, terrain fineness and output pixel size, and generating high-resolution DEM data;
step S15: and using the reference DEM data as an elevation control inspection basis, checking abnormal values, roads, cloud areas and water parts of the generated high-resolution DEM data with strong tendency, and generating the final high-resolution DEM data by means of human-computer interaction editing.
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| CN103940974A (en) * | 2014-02-19 | 2014-07-23 | 西北农林科技大学 | Spatio-temporal dynamic analysis method of soil erosion in meso-scale watershed based on GIS |
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| CN110346329A (en) * | 2018-08-19 | 2019-10-18 | 福州大学 | A kind of soil erosion modulus quantitative estimation method of integrated multiresolution remotely-sensed data |
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| CN105004725A (en) * | 2015-08-04 | 2015-10-28 | 珠江水利委员会珠江水利科学研究院 | Method for quantitatively monitoring soil erosion change amount in real time for water and soil conservation comprehensive treatment |
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