CN113313296A - Regional soil erosion quantitative evaluation method based on RUSLE - Google Patents

Regional soil erosion quantitative evaluation method based on RUSLE Download PDF

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CN113313296A
CN113313296A CN202110547287.XA CN202110547287A CN113313296A CN 113313296 A CN113313296 A CN 113313296A CN 202110547287 A CN202110547287 A CN 202110547287A CN 113313296 A CN113313296 A CN 113313296A
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黄发明
陈佳武
叶舟
潘李含
陈世轩
严俊
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Abstract

The invention relates to the technical field of regional soil erosion quantitative evaluation, in particular to a regional soil erosion quantitative evaluation method based on RUSLE, which comprises the following steps: collecting multi-source data, extracting seasonal rainfall erosion force by multi-source data preprocessingR i Factor, seasonal vegetation coverage and management factorC i Factor and slope-considered water and soil conservation measuresP SUSLE Factors and other soil erosion evaluation factors; constructing a SUSLE model based on the RUSLE model, and outputting a soil erosion grading map; the invention reflects the soil erosion amount under different seasonal characteristics by considering seasonal changes of rainfall and vegetation coverage in the year and the influence of the slope on water and soil conservation measure factors so as to effectively improve the accuracy of regional soil erosion quantitative evaluation.

Description

Regional soil erosion quantitative evaluation method based on RUSLE
Technical Field
The invention relates to the technical field of regional soil erosion quantitative evaluation, in particular to a regional soil erosion quantitative evaluation method based on RUSLE.
Background
Soil erosion is one of the most serious environmental problems facing the world. Soil provides a material base for various species in an ecological system, and the soil is corroded and damaged to cause a series of problems of ecological environment and the like, such as reduction of land productivity, deterioration of water quality, aggravation of flood disasters and the like, so that the sustainable development of human beings is severely restricted. Therefore, it is very necessary to develop quantitative prediction research of regional soil erosion.
Currently, widely used empirical soil erosion evaluation models include the Universal Soil Loss Equation (USLE) and the modified universal soil loss equation (rusel). However, when quantitative prediction of soil erosion is carried out by using the USLE/RUSLE model, the rainfall erosion force factor is calculated by the model according to the total annual rainfall, and the problem of non-uniformity of rainfall in each season is not considered; in calculating the vegetation coverage factor, the annual average or specific day vegetation coverage value is often used without taking into account the changing characteristics of vegetation coverage over seasonal changes. In addition, for the water and soil conservation measure factor, the common empirical model mostly adopts some empirical values. But the gradient factor obviously has an important influence on the effect of the water and soil conservation measure factor. Therefore, it becomes a current concern to consider the influence of seasonal characteristic changes and gradients on each soil erosion evaluation factor of the RUSLE model so as to improve the regional soil erosion evaluation effect.
Disclosure of Invention
In order to solve the problems in the RUSLE model, the invention provides a regional soil erosion quantitative evaluation method based on the RUSLE. The method comprises the following steps:
s1: collecting multi-source data related to the research area;
s2: preprocessing multi-source data and extracting seasonal rainfall erosion force RiFactor, seasonal vegetation coverage and management factor CiFactor and slope-considered water and soil conservation measures PSUSLEFactors and other soil erosion evaluation factors, and unifying the evaluation factors in the ARCGIS softwareThe spatial resolution of the sub-field;
s3: and constructing a SUSLE model based on each evaluation factor and the RUSLE model, acquiring soil erosion amount of each grid unit in the research area, dividing the research area into five erosion grades of ultra-low, medium, high and ultra-high, and acquiring a soil erosion grading diagram.
Further, the multi-source data in step S1 includes daily rainfall vector data of a rainfall site in the research area, physical property vector data of soil, Digital Elevation Model (DEM) grid data, and Landsat TM remote sensing images.
Further, in step S2, seasonal rainfall erosion force R is extracted by preprocessing the multi-source dataiFactor, seasonal vegetation coverage and management factor CiFactor and slope-considered water and soil conservation measures PSUSLEFactors and other soil erosion evaluation factors. The method comprises the following specific steps:
s21: the rainfall erosion force R of each season under each rainfall site is obtained according to a rainfall erosion force R value calculation formula based on the annual average rainfall and provided by Wischmeier and the like by preprocessing daily rainfall vector data of the rainfall sites in the research areaiObtaining rainfall erosion force R in each season by an inverse distance weight interpolation method in ARCGIS softwareiFactor graph. The R value calculation formula proposed by Wischmeier et al is as follows:
Figure BDA0003074015140000021
in formula (1): r is the average rainfall erosiveness factor for many years; i is the month; p is a radical ofiIs the ith monthly mean rainfall; p is the annual average rainfall.
In addition, the inverse distance weight interpolation method in the ARCGIS software means that the attribute of the evaluated cell block is related to the attribute of a known point within a certain distance around the evaluated cell block, and the relation is inversely proportional to the nth power of the distance from the known point to the center point of the evaluated cell block. The corresponding calculation formula is as follows:
Figure BDA0003074015140000022
Figure BDA0003074015140000023
in formula (2): zoRepresenting an estimated value; z is a radical ofiThe attribute value of the ith (i ═ 1, 2, 3 · · n) sample; p is the power of the distance, which significantly affects the result of the interpolation, and its selection criterion is the minimum mean absolute error; diTo evaluate the distance between a cell block and a cell block to be evaluated.
S22: preprocessing Landsat remote sensing image data of a research area, selecting representative Landsat remote sensing images of all seasons, and obtaining normalized vegetation index (NDVI) maps of all seasons in ENVI 5.3 software. Then, the vegetation cover and management C in each season is obtained by normalizing the vegetation index (NDVI) chartiFactor graph. The formula for NDVI is as follows:
NDVI＝(NIR-R)/(NIR+R) (4)
in formula (4): NIR and R represent the near infrared and red bands, respectively, of the Landsat TM image.
The calculation formula of the vegetation coverage and management factor C is as follows:
Figure BDA0003074015140000024
fc＝(NDVI-NDVImin)/(NDVImax-NDVImin) (6)
in formulae (5) and (6): f. ofcVegetation coverage; NDVI represents the vegetation coverage in a unit pixel, the vegetation growth condition and the like; NDVImin,NDVImaxThe NDVI values for bare terrain or no vegetation coverage and for fully covered vegetation are indicated, respectively.
S23: preprocessing Landsat (TM) remote sensing image data of a research area, mainly performing geometric precision correction and registration on the obtained Landsat (TM) remote sensing image, performing remote sensing classification extraction and manual interpretation to obtain the Landsat (TM) remote sensing image dataThe soil utilization map of the research area. The influence of the gradient on the water and soil conservation measures is considered by combining the gradient and the land utilization map, and the land utilization map is assigned, so that the water and soil conservation measures P are obtainedSUSLEFactor graph.
S24: an LS factor graph is obtained by preprocessing raster data of a Digital Elevation Model (DEM) in a research area and mainly combining gradient factors (S) of a McCool gentle slope and a Liubao element steep slope and a calculation formula of a slope length factor (L). The expression is as follows:
Figure BDA0003074015140000031
L＝(λ/22.13)m (8)
Figure BDA0003074015140000032
in formulae (7), (8), and (9): s is a gradient factor; θ is the slope value (°); l is a slope length factor; λ is the slope length (m); m is a variable slope length index.
S25: by preprocessing the physical property vector data of the soil in the research area, the soil erodibility K value can be calculated according to an erosion-productivity evaluation model (EPIC), and a soil erodibility K factor graph is obtained by using an inverse distance weight interpolation method in ARCGIS software. The expression of the erosion-productivity evaluation model (EPIC) is as follows:
Figure BDA0003074015140000033
in formula (10): SAN-sand content (%); SIL-powder content (%); CLA-cosmid content (%); c-organic carbon content (%), organic content divided by 1.724; SNI is 1-SAN/100.
Further, the rusel model in step S3 is as follows:
A＝R×C×K×L×S×PRUSLE (11)
the SUSLE model that considers seasonal characteristics and grade effects is as follows:
Figure BDA0003074015140000034
in formulae (11) and (12): a is annual soil erosion amount [ t/(ha-year)](ii) a R and RiRespectively, the annual average rainfall erosion force factor [ (MJ. mm)/(ha. h. year)](ii) a C and CiYear and season vegetation coverage and management factors; k is a soil erodability factor [ (t-ha-h)/(MJ-ha-mm)](ii) a LS is a slope length and gradient factor; pRUSLEAnd PSUSLEWater and soil conservation measure factors without considering and considering the gradient respectively; wherein LS and Ci、PSUSLEThe factors are dimensionless.
The invention has the beneficial effects that: in the traditional soil erosion quantitative evaluation process, the influence of seasonal change characteristics of rainfall and vegetation coverage and gradient factors on water and soil conservation measures is considered, and the soil erosion evaluation accuracy can be effectively improved.
Drawings
FIG. 1 is a flow chart of the method for quantitatively evaluating regional soil erosion based on RUSLE according to the present invention;
FIG. 2 is a SUSLE soil erosion grading map of the RUSLE-based regional soil erosion quantitative evaluation method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, a specific embodiment of the present invention will be further described with reference to fig. 1.
The embodiment of the invention provides a regional soil erosion quantitative evaluation method based on RUSLE, which comprises the following steps:
s1: and collecting and organizing multi-source data related to a research area, wherein the research area in the specific embodiment is Ningdu county of Jiangxi Ganzhou city in China. The multi-source data includes: 1998-year rainfall vector data of 8 rainfall sites near Ningdu county and periphery, physical property vector data of 194 sampling point soil of a surface soil layer of Jiangxi province, 30m spatial resolution Digital Elevation Model (DEM) grid data, and 4-pair Landsat TM remote sensing images of 3, 12, 7, 28, 10, 3 and 12, 19 and the like of 2017.
S2: extraction of seasonal rainfall erosive power R by preprocessing multi-source dataiFactor, seasonal vegetation coverage and management factor CiFactor and slope-considered water and soil conservation measures PSUSLEFactors and other soil erosion evaluation factors, and unifying the spatial resolution of each soil erosion evaluation factor in the ARCGIS software to be 30 m. The method comprises the following specific steps:
s21: based on the 1998-plus 2017-year daily rainfall vector data of 8 rainfall sites in Ningdu county and nearby places provided by the China meteorological data center, the rainfall erosion force R value calculation formula provided by Wischmeier and the like is utilized to calculate the rainfall erosion force R in each season under each rainfall siteiValue, wherein the erosive power R of rainfall is in each seasoniThe value is the sum of rainfall erosive power of corresponding months in each season in the formula (1). Then, interpolating rainfall erosion force R of each season under each rainfall site by adopting an inverse distance weight interpolation method in ARCGIS software to obtain rainfall erosion force R of each season under a SUSLE model in a research areaiFactor graph.
The R value calculation formula proposed by Wischmeier et al is as follows:
Figure BDA0003074015140000041
in formula (1): r is the average rainfall erosiveness factor for many years; i is the month; p is a radical ofiIs the ith monthly mean rainfall; p is the annual average rainfall.
The inverse distance weight interpolation method in the ARCGIS software is as follows:
Figure BDA0003074015140000042
Figure BDA0003074015140000043
in formula (2): zoRepresenting an estimated value; z is a radical ofiIs the ith (i)Attribute values of 1, 2, 3 · n) samples; p is the power of the distance, which significantly affects the result of the interpolation, and its selection criterion is the minimum mean absolute error; diTo evaluate the distance between a cell block and a cell block to be evaluated.
S22: based onhttp://ids.ceode.ac.cn/index.aspx4 sets of Landsat remote sensing images such as 3-12 days in 2017, 28 days in 7, 3 days in 10 and 19 days in 12 are acquired by a website, and normalized vegetation index (NDVI) maps in all seasons are acquired in ENVI 5.3 software. Then, a grid calculator tool of ARCGIS software is utilized to obtain vegetation coverage and management C in each seasoniFactor graph. The formula for NDVI is as follows:
NDVI＝(NIR-R)/(NIR+R) (4)
in formula (4): NIR and R represent the near infrared and red bands, respectively, of the Landsat TM image.
The calculation formula of the vegetation coverage and management factor C is as follows:
Figure BDA0003074015140000051
fc＝(NDVI-NDVImin)/(NDVImax-NDVImin) (6)
in formulae (5) and (6): f. ofcVegetation coverage; NDVI represents the vegetation coverage in a unit pixel, the vegetation growth condition and the like; NDVImin,NDVImaxThe NDVI values for bare terrain or no vegetation coverage and for fully covered vegetation are indicated, respectively.
S23: based on the Landsat remote sensing image shot in 19 days 12 and 12 months in 2017, geometric precision correction and registration are carried out on the obtained Landsat remote sensing image, remote sensing classification extraction and manual interpretation are carried out, and a soil utilization map of a research area is obtained. The land utilization is mainly divided into five types of woodland, farmland, construction land, bare grassland and water area. The water and soil conservation measures P under the corresponding gradient condition are assigned by considering the influence of the gradient on the water and soil conservation measuresSUSLEThe values are shown in Table 1.
TABLE 1 land utilization classes with different slopesType water and soil conservation measure factor PSUSLE
Figure BDA0003074015140000052
S24: based on 30m spatial resolution DEM data acquired by Google Earth 7.1.8.3036(32-bit), a study region slope length LS factor graph is acquired in ARCGIS 10.2 software by using a formula proposed by McCool and Liu Bao Yuan. The corresponding calculation formula is as follows:
Figure BDA0003074015140000053
L＝(λ/22.13)m (8)
Figure BDA0003074015140000054
in formulae (7), (8), and (9): s is a gradient factor; θ is the slope value (°); l is a slope length factor; λ is the slope length (m); m is a variable slope length index.
S25: based on the physical property information of 194 sampling point soils acquired by dinoflagellate in the topsoil layer of Jiangxi province, including the sand grain content, the particle content, the clay grain content, the organic carbon content and the like of each sampling point soil, the K values of the soil erodibility of 194 sampling points of Jiangxi province are calculated by using an erosion-productivity evaluation model (EPIC), and the K factor graph of the soil erodibility of the research area is obtained by combining an inverse distance weight interpolation method in ARCGIS software, Ningcity county boundaries and mask extraction in a space analysis tool. The expression of the erosion-productivity evaluation model (EPIC) is as follows:
Figure BDA0003074015140000061
in formula (10): SAN-sand content (%); SIL-powder content (%); CLA-cosmid content (%); c-organic carbon content (%), organic content divided by 1.724; SNI is 1-SAN/100.
S3: and acquiring a soil erosion grading map under the SUSLE model. And performing regional soil erosion quantitative evaluation by combining the SUSLE model based on the 5 soil erosion evaluation factors to obtain the soil erosion amount corresponding to each grid unit. According to the technical standard of water and soil conservation (SL190-2007), the soil erosion amount of a research area is divided into five types of erosion grades such as extremely low (less than 5t/ha/year), low (5-25 t/ha/year), medium (25-50 t/ha/year), high (50-80 t/ha/year) and extremely high (more than 80t/ha/year), grid units corresponding to the soil erosion grades are named as extremely low, medium, high and extremely high erosion areas, and the final soil erosion grading graph is shown in FIG. 2.
The features of the embodiments and embodiments described herein above may be combined with each other without conflict. The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (4)

1. A regional soil erosion quantitative evaluation method based on RUSLE is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1: collecting multi-source data related to the research area;
s2: preprocessing multi-source data and extracting seasonal rainfall erosion force RiFactor, seasonal vegetation coverage and management factor CiFactor and slope-considered water and soil conservation measures PSUSLEFactors and other soil erosion evaluation factors, and unifying the spatial resolution of each soil erosion evaluation factor in the ARCGIS software;
s3: and constructing a SUSLE model based on each soil erosion evaluation factor and the RUSLE model, acquiring the soil erosion amount of each grid unit in the research area, dividing the research area into five erosion grades such as extremely low, medium, high and extremely high, and acquiring a soil erosion grading diagram.
2. The method for quantitatively evaluating regional soil erosion based on RUSLE according to claim 1, wherein the method comprises the following steps: the multi-source data in the step S1 comprises daily rainfall vector data of rainfall stations in the research area, physical property vector data of soil, Digital Elevation Model (DEM) grid data and Landsat TM remote sensing images.
3. The method for quantitatively evaluating regional soil erosion based on RUSLE according to claim 1, wherein the method comprises the following steps: extracting seasonal rainfall erosion force R by preprocessing multi-source data in step S2iFactor, seasonal vegetation coverage and management factor CiFactor and slope-considered water and soil conservation measures PSUSLEFactors and other soil erosion evaluation factors, the steps are as follows:
1) the rainfall erosion force R of each season under each rainfall site is obtained according to a rainfall erosion force R value calculation formula based on the annual average rainfall and provided by Wischmeier and the like by preprocessing daily rainfall vector data of the rainfall sites in the research areaiObtaining rainfall erosion force R in each season by an inverse distance weight interpolation method in ARCGIS softwareiA factor graph; the R value calculation formula proposed by Wischmeier et al is as follows:
Figure FDA0003074015130000011
in formula (1): r is the average rainfall erosiveness factor for many years; i is the month; p is a radical ofiIs the ith monthly mean rainfall; p is annual average rainfall; in addition, the inverse distance weight interpolation method in the ARCGIS software indicates that the attribute of the evaluated cell block is related to the attribute of a known point within a certain distance around the evaluated cell block, and the relation is inversely proportional to the nth power of the distance from the known point to the central point of the evaluated cell block; the corresponding calculation formula is as follows:
Figure FDA0003074015130000012
Figure FDA0003074015130000013
in formula (2): zoRepresenting an estimated value; z is a radical ofiThe attribute value of the ith (i ═ 1, 2, 3 · · n) sample; p is the power of the distance, which significantly affects the result of the interpolation, and its selection criterion is the minimum mean absolute error; diFor evaluating the distance between the unit block to be evaluated and the unit block;
2) preprocessing Landsat remote sensing image data of a research area, selecting representative Landsat remote sensing images of all seasons, and acquiring normalized vegetation index (NDVI) maps of all seasons in ENVI 5.3 software; then, the vegetation cover and management C in each season is obtained by normalizing the vegetation index (NDVI) chartiA factor graph; the formula for NDVI is as follows:
NDVI＝(NIR-R)/(NIR+R) (4)
in formula (4): NIR and R respectively represent the near infrared band and the red band of the Landsat TM image;
the calculation formula of the vegetation coverage and management factor C is as follows:
Figure FDA0003074015130000021
fc＝(NDVI-NDVImin)/(NDVImax-NDVImin) (6)
in formulae (5) and (6): f. ofcVegetation coverage; NDVI represents the vegetation coverage in a unit pixel, the vegetation growth condition and the like; NDVImin,NDVImaxNDVI values representing bare earth or no vegetation coverage and fully vegetation coverage, respectively;
3) preprocessing Landsat TM remote sensing image data of a research area, mainly performing geometric precision correction and registration on the obtained Landsat TM remote sensing image, performing remote sensing classification extraction and manual interpretation, and obtaining a soil utilization map of the research area; the influence of the gradient on the water and soil conservation measures is considered by combining the gradient and the land utilization map, and the land utilization map is assigned, so that the water and soil conservation measures P are obtainedSUSLEFactor(s)A drawing;
4) preprocessing raster data of a Digital Elevation Model (DEM) in a research area, and mainly combining gradient factors (S) of a McCool gentle slope and a Liubao element steep slope and a calculation formula of a slope length factor (L) to obtain an LS factor graph; the expression is as follows:
Figure FDA0003074015130000022
L＝(λ/22.13)m (8)
Figure FDA0003074015130000031
in formulae (7), (8), and (9): s is a gradient factor; θ is the slope value (°); l is a slope length factor; λ is the slope length (m); m is a variable slope length index;
5) the method comprises the steps of preprocessing physical property vector data of soil in a research area, calculating a soil erodibility K value according to an erosion-productivity evaluation model (EPIC), and obtaining a soil erodibility K factor graph by using an inverse distance weight interpolation method in ARCGIS software; the expression of the erosion-productivity evaluation model (EPIC) is as follows:
Figure FDA0003074015130000032
in formula (10): SAN-sand content (%); SIL-powder content (%); CLA-cosmid content (%); c-organic carbon content (%), organic content divided by 1.724; SNI is 1-SAN/100.
4. The method for quantitatively evaluating regional soil erosion based on RUSLE according to claim 1, wherein the method comprises the following steps: the RUSLE model in step S3 is as follows:
A＝R×C×K×L×S×PRUSLE (11)
the SUSLE model that considers seasonal characteristics and grade effects is as follows:
Figure FDA0003074015130000033
in formulae (11) and (12): a is annual soil erosion amount [ t/(ha-year)](ii) a R and RiRespectively, the annual average rainfall erosion force factor [ (MJ. mm)/(ha. h. year)](ii) a C and CiYear and season vegetation coverage and management factors; k is a soil erodability factor [ (t-ha-h)/(MJ-ha-mm)](ii) a LS is a slope length and gradient factor; pRUSLEAnd PSUSLEWater and soil conservation measure factors without considering and considering the gradient respectively; wherein LS and Ci、PSUSLEThe factors are dimensionless.
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