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    • 专利摘要:
    A f1ne-scale delineation method for assessing land sensitivity to desertification includes the following steps: obtaining basic data of a study area and pre-processing the basic data, calculating spatial distributions of a wind erosion factor, a soil moisture factor, 5 a soil texture factor and a vegetation coverage factor in the study area based on the basic data, and comparing and grading with standard values to obtain a sensitivity grade value Wfi of the wind erosion factor, a sensitivity grade value SW1 of the soil moisture factor, a sensitivity grade value K of the soil texture factor and a sensitivity grade value Ci of the vegetation coverage factor, and performing a spatial analysis on sensitivity grading 10 results of each single factor to obtain the land sensitivity to desertification in the study area. The present invention greatly improves the spatial accuracy for assessment results of the land sensitivity to desertification and enhances the ecological space management and control level. 1
    公开号:NL2027951A
    申请号:NL2027951
    申请日:2021-04-13
    公开日:2021-07-13
    发明作者:Zhang Hui；Hu Mengtian；Gao Jixi；Pei Wenming；Qiu Kuanbiao；Wang Yansong；Qiao Yajun；Qu Jining；Gong Long；Ma Mengxiao；Zhang Hongling
    申请人:Nanjing Institute Of Environmental Sciences Mini Of Ecological Environment；Univ Nanjing Information Science & Tech；Shanghai Municipal Bureau Of Ecology And Env；Wang Yansong；
    IPC主号:
    专利说明:

    -1- FINE-SCALE DELINEATION METHOD FOR ASSESSING LANDSENSITIVITY TO DESERTIFICATION
    TECHNICAL FIELD The present invention belongs to the technical field of information technology application, and in particular, to a fine-scale delineation method for assessing land sensitivity to desertification.
    BACKGROUND Due to the huge population pressure and long-term unreasonable exploitation and utilization, the ecosystem and ecosystem services in China degenerate obviously. The resulting problems, such as vegetation degradation and land desertification, have become increasingly intense. Natural disasters, such as droughts and sandstorms, occur frequently. The ecological environment in arid and semi-arid regions tends to rapidly deteriorate, and grasslands in arid and semi-arid regions and oases on the edge of deserts have suffered from degradation and desertification. The ecosystem has shown the inclination to change from structural damage to functional disorder. Some construe this as natural retaliation and punishment for human disturbance and destruction. As a result, human beings have paid increasing attention to the sensitivity to land desertification in the ecosystem. Since the 1990s, with land desertification seriously increasing, research on the land sensitivity to desertification has been continuously increasing. A method for assessing land sensitivity to desertification (e.g., MEDALUS method) proposed by the European Union is one of the most well-known methods. The MEDALUS method considers climate, vegetation, soil and land management as the assessment factors of land sensitivity to desertification, and has been widely used in geographical areas, including the Mediterranean, Iran, Brazil, Africa and other places. The research on land sensitivity to desertification in China originated from Zhiyun Ouyang’s general assessment method of land sensitivity to desertification. This method has also been applied to the assessment of land sensitivity to desertification in the “Interim Regulation of Ecological Function Zoning” and “Guidelines for Ecological Protection Redline Delineation”. After these works, Chinese scholars used this method to carry
    -2- out a series of studies at watershed, regional and nationwide scales. The number of windy days and the aridity index used in this method, however, are mostly obtained by statistical interpolation of meteorological data from more than 2,400 meteorological stations in China, yet the spatial distribution of meteorological stations is uneven, and there are very few stations in Northwest China. This makes the assessment results of the land sensitivity to desertification full of errors and inaccuracies. Therefore, how to spatially refine the assessment results of the land sensitivity to desertification is of great significance to accurate identification of desertification sensitive areas, reasonable delineation of the ecological protection redlines for the land sensitivity to desertification, and regulation of land desertification prevention and control countermeasures.
    SUMMARY The technical problem to be solved by the present invention is to provide a fine- scale delineation method for assessing sensitivity to land desertification in view of the shortcomings of the prior art. In order to realize the above technical objective, the technical solution adopted by the present invention is as follows. A fine-scale delineation method for assessing sensitivity to land desertification, including the following steps: step S1: obtaining basic data of a study area and pre-processing the basic data; step S2: calculating spatial distributions of a wind erosion factor, a soil moisture factor, a soil texture factor and a vegetation coverage factor in the study area based on the basic data, and grading according to different standard values to obtain a sensitivity grade value wf; of the wind erosion factor, a sensitivity grade value SW; of the soil moisture factor, a sensitivity grade value K; of the soil texture factor and a sensitivity grade value C; of the vegetation coverage factor; step S3: performing a spatial analysis on sensitivity grading results of each single factor to obtain the land sensitivity to desertification in the study area. The specific measures taken to optimize the above technical solution include the following steps. Further, in step S1, the basic data of topography and geomorphology includes
    -3- normalized difference vegetation index (NDVI) data, elevation data, land surface temperature data, wind speed data, soil spatial data, and a desertified land distribution data set, and then the data is processed by format conversion, projection conversion, clipping, re-sampling and re-classification. Further, in step S2, original soil texture factor data is used as the soil texture factor.
    Based on the NDVI data, the vegetation coverage factor is calculated as follows: Cz JV MV, ADVE NOL in where, C is the vegetation coverage factor; NDVI is the normalized difference vegetation index; NDVlain is a minimum normalized difference vegetation index of a pure soil coverage pixel; and NDVlmax is a maximum normalized difference vegetation index of a vegetation coverage factor pixel.
    Based on the NDVI data and the land surface temperature data, the soil moisture factor is calculated as follows: sw = 2a “LST, (2); LST a - LST, where, SW is the soil moisture factor; LST; is a land surface temperature of i assessment area, LSTay is a highest land surface temperature corresponding to NDVI of the assessment area, i.e. the dry edge; and LSTye 1s a lowest land surface temperature corresponding to NDVI of the assessment area, i.e. the wet edge.
    Based on an annual average wind speed and a threshold wind speed, the wind erosion factor wf is calculated as follows: wh =u, ~u, (3); where, wf is the wind erosion factor, 4,is the annual average wind speed with a unit of m/s, and 4, is the threshold wind speed with a unit of m/s.
    According to National Centers for Environmental Prediction (NCEP) monthly average wind speed data and the elevation data, a high-precision monthly average wind speed is calculated by a terrain weight interpolation method, and the annual average wind speed 4, is obtained after averaging, where, a weight formula is as follows:
    -4- Wr, h) = ee (4), r Pex Ts where, W (r, h) is terrain weight; 7 is a distance between a point in a grid and adjacent nodes of an NCEP grid, Tnax 1s a maximum distance between the point in the grid and four adjacent nodes of the NCEP grid; #4 is an elevation of a point in the NCEP grid, hg is an elevation of a highest point of the four adjacent nodes of the NCEP grid; and the monthly average wind speed of the point is a weighted average of the monthly average wind speed of the four adjacent nodes of the NCEP grid.
    Based on a soil type and the NDVI data, the threshold wind speed +, is calculated as follows: u, =u, xe“ (3), u, = v60.818 x d + 8.554 x ev (6); where, #7 is the threshold wind speed; us is a critical erosion wind speed of a bare land surface; a is a vegetation parameter, which is 0.97514; C is the vegetation coverage factor; d is a soil particle size; and SW is the soil moisture factor.
    The present invention has the following advantages.
    In the present invention, the soil moisture factor and wind speed data that are obtained by the remote sensing quantitative inversion method are used to replace the two factors, i.e. the number of windy days and the aridity index, of meteorological station interpolated data in the general assessment of land sensitivity to desertification.
    The soil texture factor and vegetation coverage factor rate are combined to accurately divide the sensitivity grade of land desertification. The assessment result avoids the “bull’s eye” phenomenon caused by rough space and interpolation. The present invention greatly improves the spatial accuracy for assessment results of the sensitivity to land desertification and enhances the ecological space management and control level.
    BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart showing a spatial fine-scale delineation method for assessing sensitivity to land desertification according to an embodiment of the present invention;
    -5- FIG. 2 is a diagram showing a spatial distribution pattern of soil texture factor grades; FIG. 3 is a diagram showing a spatial distribution pattern of vegetation coverage factor grades, FIG. 4 is a diagram showing a spatial distribution pattern of soil moisture factor grades according to the embodiment of the present invention; FIG. 5 is a diagram showing a spatial distribution pattern of wind erosion factor grades according to the embodiment of the present invention; FIG. 6 1s a diagram showing a spatial distribution pattern of assessment results of the land sensitivity to desertification; and FIG. 7 is a comparison diagram showing the spatial distribution patterns of the sensitivity assessment results of the present invention and a general assessment result of the land sensitivity to desertification.
    DETAILED DESCRIPTION OF THE EMBODIMENTS The following is a further detailed description of the embodiment of the present invention in combination with the drawings.
    The preferred embodiment of the invention is described in detail hereinafter in combination with the drawings, in which the drawings form part of this application and are used together with the embodiment of the present invention to explain the principle of the present invention.
    According to a specific embodiment of the present invention, taking the northwest of China in 2010 as an example, a spatial fine-scale delineation method for assessing sensitivity to land desertification is disclosed, as shown in FIG. 1, which specifically includes the following steps.
    Step SI: basic data of a study area is obtained and then pre-processed.
    A. The basic data includes NDVI data, land surface temperature data, wind speed data, elevation data, soil spatial data and a desertified land distribution data set covering the study area.
    In this embodiment, the NDVI data in winter and spring of 2010 is used. The NDVI data is from SPOT/VEGETATION NDVI satellite data with a spatial resolution of 1 km. The monthly NDVI data is synthesized by the maximum synthesis method.
    -6- The surface temperature data comes from MOD21A2 data product with a spatial resolution of 1 km. The monthly average surface temperature is synthesized by the average method. The dry/wet-edge equations of each month are generated by fitting according to the monthly average NDVI and monthly average surface temperature data. The soil moisture factor of each month is calculated according to the calculation formula of soil moisture factor, and the average soil moisture factor of winter and spring 1s synthesized by the average method.
    The wind speed data comes from National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalyzed wind speed data at a height of 10 m above the ground. The spatial resolution is 0.5° = 0.5° and the annual average wind speed data is synthesized by the average method. The data range covers the whole region of China, and the data is stored in NetCDF format.
    The elevation data in this embodiment adopts the elevation DEM data with an image spatial resolution of 90 m, or adopts ASTER GDEM with a spatial resolution of 30m.
    The soil spatial data comes from the data center of sciences in cold and arid regions, including soil sand content, silt content, clay content, organic matter content, soil sectional thickness and soil classification information, covering the whole region of China. The data is stored in shape file format (SHP).
    The data of desertified land distribution in China comes from the 1:100,000 desert distribution data set of China produced by the Environmental and Ecological Science Data Center for West China (WESTDC). The data set includes the geographical distribution, area size, fluidity and fixed degree of sand dunes of the desert in China. The data is stored in shp format.
    B. Data pre-processing is the process of format conversion, projection conversion, clipping, re-sampling and re-classification. After pre-processing, all data are unified into a coordinate system with the same image resolution and clipped into the areas of the same size, so as to facilitate superposition and calculation in the subsequent steps.
    Step S2: spatial distributions of a wind erosion factor, a soil moisture factor, a soil texture factor and a vegetation coverage factor in the study area are calculated based on the basic data, and compared with standard values to obtain a sensitivity grade value wf; of the wind erosion factor, a sensitivity grade value SW; of the soil moisture factor, a sensitivity grade value K; of the soil texture factor and a sensitivity grade value C; of
    -7- the vegetation coverage factor.
    Firstly, the soil texture factor K, the vegetation coverage factor C, the soil moisture factor SW and the wind erosion factor wf are calculated.
    Original soil texture factor data is used as the soil texture factor.
    Based on the NDVI data, the vegetation coverage factor is calculated as follows: CAV DI, 0: NDVI — NDVI. where, C is the vegetation coverage factor; NDVI is the normalized difference vegetation index; NDVlmin is a minimum normalized difference vegetation index of a pure soil coverage pixel, and NDVlImax is a maximum normalized difference vegetation index of a vegetation coverage factor pixel.
    Based on the NDVI data and the land surface temperature data, the soil moisture factor is calculated as follows: sw Lln DSL 2); LST, = LST, where, SW is the soil moisture factor; LST; is a land surface temperature of 7 assessment area; LSTay is a highest land surface temperature corresponding to NDVI of the assessment area, i.e. the dry edge; and LSTwe is a lowest land surface temperature corresponding to NDVI of the assessment area, i.e. the wet edge.
    Based on wind speed data and a threshold wind speed, the wind erosion factor is calculated as follows: wi =U U, (3) where, wfis the wind erosion factor, #2 is the annual average wind speed (m/s), and 2; 1s the threshold wind speed (m/s). The calculation rules of +2 value are as follows: based on the NCEP wind speed data and elevation data, the high-precision monthly average wind speed is calculated by the terrain weight interpolation method, and the annual average wind speed «>is obtained after averaging.
    This method mainly considers the elevation of an unknown point and the distance between the unknown point and nodes of an NCEP grid, and then uses the maximum method for dimensionless processing.
    The weight formula is as follows:
    -8- i 1 Wir, h) = —— (4); r Pra — xX — Lax h where, W (r, h) is terrain weight; 7 is the distance between the point in a grid and adjacent nodes of the NCEP grid, 73,4, 1s a maximum distance between the point in the grid and four adjacent nodes of the NCEP grid; #4 is an elevation of a point in the NCEP grid, hg is an elevation of a highest point of the four adjacent nodes of the NCEP grid; and the monthly average wind speed of the point is a weighted average of the monthly average wind speed of the four adjacent nodes of the NCEP grid.
    The calculation rules of ui value are as follows: based on a soil type and the NDVI data, the threshold wind speed is calculated according to different soil types and the NDVI data.
    The critical erosion wind speed of the bare land surface is related to soil quality and water content, and is assumed to be +. The relationship between the threshold wind speed (critical erosion wind speed) and the vegetation coverage (C) is as follows: _ axC . U =u, xe (5); u, = V60. 818 x d + 8.554 x &" (6), where, 2; is the threshold wind speed with a unit of m/s; #5 is the critical erosion wind speed of the bare land surface with a unit of m/s; a is a vegetation parameter, and is 0.97514; C is the vegetation coverage factor; 1s a soil particle size with a unit of m; and SW is the soil moisture factor.
    As shown in Table 1, the soil texture factor, vegetation coverage factor, soil moisture factor and wind erosion factor are graded according to the standard values.
    Table 1 Wind Soil Soil Vegetation ~~ Grading Grading Index erosion moisture texture . - / factor assighment standard factor factor factor Insensitive <0.3 >=0.65 Bedrock Dense 1 1.0-2.0 Mild sensitive ~~ 0.3-0.8 0.55-0.65 Clayey Moderate 3 2.1-4.0 Moderate 0.8-1.5 0.45-0.55 Gravelly Less 5 4.1-6.0 sensitive ” High sensitive 1.5-2.0 0.35-0.45 Loamy Sparse 7 6.1-8.0 Extreme 52.0 <035 Sandy Naked 9 >8.0 sensitive Step S3: a spatial analysis is performed on sensitivity grading results of each
    -9- single factor to obtain a sensitivity index of land desertification in the assessment area.
    Specifically, the sensitivity index of land desertification in the assessment area is obtained by multiplying the sensitivity grading results of each single factor and raising to the fourth power.
    The formula is as follows: = wt, x SW 2 K, XC, (7% where, Dj is a comprehensive sensitivity index of desertification; wf: is the grade value of wind erosion factor in the / assessment area; SW: is the grade value of soil moisture factor in the 7 assessment area; K; is the grade value of soil texture factor in the 7 assessment area; Ci is the sensitivity grade value of vegetation coverage factor in the; assessment area.
    The sensitivity index value of land desertification is divided into 5 grades according to Table 1, and the sensitivity to land desertification grading map is made, as shown in FIG. 6. The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment.
    All technical solutions under the idea of the present invention belong to the protection scope of the present invention.
    It should be noted that for ordinary technicians in the technical field, all improvements and modifications without departing from the principle of the present invention shall be regarded as the protection scope of the present invention.
    权利要求:
    Claims (5)
    [1]
    A fine-scale delineation method for assessing the sensitivity to land desertification, comprising the following steps: step SI: obtaining base data of a study area and preprocessing the base data; step S2: calculating spatial distributions of a wind erosion factor, a soil moisture factor, a soil texture factor and a vegetation coverage factor in the study area based on the base data, and comparing and categorizing with standard values to obtain a sensitivity class value wf2 of the wind erosion factor, a sensitivity class value SW; of the soil moisture factor, obtain a sensitivity class value Ki of the soil texture factor and a sensitivity class value Ci of the vegetation coverage factor; and step S3: performing a spatial analysis of the sensitivity categorization results of each individual factor to obtain the land sensitivity to desertification in the study area.
    [2]
    The fine-scale delineation method for assessing the sensitivity to land desertification according to claim 1, wherein in step S1, the base data comprises monthly average normalized difference vegetation index data, elevation data, monthly mean land surface temperature data, monthly mean wind speed data, bottom space data, and desert land distribution data. , and then processing each data by format conversion, projection conversion, clipping, resampling, and reclassification.
    [3]
    The fine-scale delineation method for assessing the sensitivity to land desertification according to claim 2, wherein the monthly mean wind speed data is the monthly mean wind speed data at a height of 10 m above the ground; the soil space data includes a soil sand content, a soil silt content, a soil clay content, a soil organic matter content, a soil cross-sectional thickness and a
    _11- soil classification include; and desert land distribution data include a geographic distribution of desert, the desert area size, a fluidity and a fixed extent of sand dunes.
    [4]
    The fine-scale delineation method for assessing the sensitivity to land desertification, according to claim 3, wherein in step S2, the original soil texture factor data is used as the soil texture factor: based on the NDVI data, the vegetation coverage factor is calculated as follows: ADVE - ADFT where C is the factor is for vegetation cover; NDVI the normalized difference of the vegetation index 1s; NDVImin a minimum normalized difference in vegetation index of a pure soil cover pixel; and ND VImax is a maximum normalized difference vegetation index of a vegetation coverage factor pixel; based on the NDVI data and the land surface temperature data, the soil moisture factor is calculated as follows: sn SB LST, IST, where SW is the soil moisture factor; LST; is a land surface temperature of assessment area i; LSTay is a highest land surface temperature corresponding to NDVI of the assessment area, i.e. a dry edge; and LST law is a lowest land surface temperature corresponding to NDVI of the assessment area, i.e. a wet edge; based on an annual mean wind speed and a threshold wind speed, the wind erosion factor wf is calculated as follows: wi =u, ~u, (3x where, wf is the wind erosion factor, U is the annual mean wind speed and ui is the threshold wind speed; according to National Centers for Environmental Prediction (NCEP) monthly mean wind speed data and the altitude data, a very accurate monthly mean wind speed calculated by a terrain weight interpolation method and the annual mean wind speed u2 obtained
    S12 - becomes after averaging, where a terrain weight formula is as follows: Wir, hi = bo 4): Fo & where, W(r, h) is terrain weight; + is a distance between a point in a grid and adjacent nodes of an NCEP grid, "max is a maximum distance between the point in the grid and four adjacent nodes of the NCEP grid; / an increment of a point in the NCEP grid, max is a height of a highest point of the four adjacent nodes of the NCEP grid; and the monthly average wind speed of the point is a weighted average of the monthly average wind speed of the four adjacent nodes of the NCEP grid where, based on a bottom type and the NDVI data, the threshold of wind speed ui is calculated as follows: u, =u, xe" (5): u, = VOO 818 x or +8,554 x 6 (6): where, 47 is the threshold wind speed; 14 is a critical erosion wind speed of a bare land surface; a is a vegetation parameter, wherein the vegetation parameter is 0.97514; C is the vegetation coverage factor 1s; d is a soil particle size, and SW is the soil moisture factor.
    [5]
    The fine-scale delineation method for evaluating land desertification sensitivity according to claim 4, wherein the land desertification sensitivity is calculated as follows: wherein, Di; is a comprehensive sensitivity index of desertification; and wft, SW, Ki and Ci are sensitivity class values of the wind erosion factor, the soil moisture factor, the soil texture factor and the vegetation coverage factor in the assessment area 7, respectively.
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    同族专利:
    公开号 | 公开日
    NL2027951B1|2022-02-21|
    CN111539608B|2021-01-22|
    CN111539608A|2020-08-14|
    引用文献:
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    CN108053072A|2017-12-22|2018-05-18|中国科学院地理科学与资源研究所|The structure of desertification dynamic simulation model and application|
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    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    CN202010300020.6A|CN111539608B|2020-04-16|2020-04-16|Method for finely dividing soil desertification sensitivity evaluation|
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