CN113762383B - Vegetation index fusion method based on multi-source data - Google Patents
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Abstract
The invention discloses a vegetation index fusion method based on multi-source data, which comprises the following steps: s1, performing spatial rasterization on a field vegetation coverage data set by adopting a geographic regression weighting model to obtain the field vegetation coverage raster data set; s2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set; s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set; s4, constructing and solving a vegetation index fusion model based on the 3 data sets to realize fusion of multi-source vegetation data; the method solves the problem that the space-time resolution and the estimation accuracy of the surface vegetation coverage are not high.
Description
Technical Field
The invention relates to the technical field of ecological remote sensing, in particular to a vegetation index fusion method based on multi-source data.
Background
The vegetation coverage is an important index for representing the coverage degree of the surface vegetation, has close relations with the coverage degree of the surface vegetation, water and soil loss, land desertification, global climate change and the like, and is an important parameter of ecological environment change, global climate model and regional climate model. Therefore, the method for acquiring the surface vegetation coverage and the change information thereof with higher space-time resolution has important practical significance for revealing the surface space change rule, discussing the changed driving factors and analyzing and evaluating the regional ecological environment.
Disclosure of Invention
Aiming at the defects in the prior art, the vegetation index fusion method based on the multi-source data solves the problem that the space-time resolution and estimation accuracy of the surface vegetation coverage are not high.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a vegetation index fusion method based on multi-source data comprises the following steps:
s1, acquiring a field vegetation coverage data set of a research area, and performing spatial rasterization on the field vegetation coverage data set by adopting a geographic regression weighting model to obtain a field vegetation coverage raster data set;
s2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set;
s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set;
s4, constructing and solving a vegetation index fusion model according to the field vegetation coverage grid data set, the daily-scale vegetation coverage grid data set and the ten-day-scale vegetation coverage grid data set, so as to realize fusion of the multi-source vegetation data.
Further, the relevant data in step S2 includes: soil depth data, soil sand content data, soil powder content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
Further, in step S4, the vegetation index fusion model is:
vfc ys =a y ×vfc y +β y
vfc zs =a z ×vfc z +β z
therein, vfc true Vfc for true surface vegetation coverage xs For normalized field vegetation coverage raster data vfc ys Vegetation coverage raster data for normalized daily scale vfc zs Vegetation coverage raster data for the late ten-day scale of normalization vfc x Raster data for field vegetation coverage vfc y Vegetation coverage raster data for daily scale vfc z Raster data of vegetation coverage for ten-day scale, a y And beta y Vegetation coverage raster data vfc for daily scale y A) normalized coefficient of a z And beta z Vegetation coverage raster data vfc for ten-day scale z Is used for the normalization coefficient of (a),raster data vfc for field vegetation coverage x Standard deviation of>Vegetation coverage raster data vfc for daily scale y Is set in the standard deviation of (2),vegetation coverage raster data vfc for ten-day scale z Standard deviation of>Raster data vfc for field vegetation coverage x Mean value of->Vegetation coverage raster data vfc for daily scale y Mean value of->Vegetation coverage raster data vfc for ten-day scale z Mean, omega 1 Normalized weight, ω, for normalized field vegetation coverage raster data 2 Weights, ω, for normalized day scale vegetation coverage raster data 3 Weights for vegetation coverage raster data for the late ten-day scale are normalized.
In summary, the invention has the following beneficial effects:
a vegetation index fusion method based on multi-source data utilizes a grid data set 1 based on field investigation data, utilizes a calibrated and verified Biome-BGC model pair to generate a grid data set 2, and utilizes Terra satellite AVHRR and MODIS sensor images to generate a grid data set 3. Then, error variances of three vegetation coverage data sets are estimated respectively by means of an uncertainty estimation method (Triple-allocation) method, fusion analysis is carried out on the three data sets on the basis of an improved least square method principle, and data fusion of satellite-ground multi-source vegetation data is achieved. The obtained result is superior to the estimation precision of a result used independently, the support of long-time sequence data is obtained, the space-time resolution and the estimation precision of the surface vegetation coverage are improved, and more accurate data support is provided for the refined regional ecological environment change and the regional climate model thereof.
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FIG. 1 is a flow chart of a vegetation index fusion method based on multi-source data.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a vegetation index fusion method based on multi-source data includes the following steps:
s1, acquiring a field vegetation coverage data set of a research area, and performing spatial rasterization on the field vegetation coverage data set by adopting a geographic regression weighting model to obtain a field vegetation coverage raster data set;
in this embodiment, step S1 specifically includes:
the method comprises the steps of spatially classifying observation data of 162 sites of inner mongolia, sorting regional field site investigation data, introducing DEM and longitude and latitude as explanatory variables, and rasterizing regional vegetation coverage by adopting a geographic weighted regression model to obtain a field vegetation coverage grid data set.
S2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set;
the relevant data in step S2 includes: soil depth data, soil sand content data, soil powder content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
In this embodiment, step S2 specifically includes:
and (3) carrying out parameter localization on the BIOME_BGC model, wherein the grid meteorological data acquisition method required to be input is an APSIM interpolation method, the data source is site meteorological observation data, and the spatial resolution after interpolation is 0.5km multiplied by 0.5km. And simulating a vegetation coverage grid data set of a daily scale by using the calibrated and verified BIOME_BGC model.
S3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set;
in this embodiment, the data sources of the vegetation coverage in step S3 are: vegetation coverage (Fractional Vegetation Cover, FVC) data and digital elevation (Data Elevation model, DEM) data for the MODIS of the tera satellite.
The remote sensing vegetation coverage data is obtained by training a relation model from the preprocessed reflectivity to the FVC value based on a machine learning method, and the data is resampled through aggregation grouping to obtain the ten-day-value vegetation coverage. The data source is the reflectivity and vegetation coverage products of AVHRR and MODIS sensors of Terra satellite, the time resolution is 8 days, and the total monitoring is 46 times a year. The vegetation coverage remote sensing data set time range is 2000-2015 years, and the spatial resolution is 0.5km multiplied by 0.5km by adopting the SIN projection mode.
S4, constructing and solving a vegetation index fusion model according to the field vegetation coverage grid data set, the daily-scale vegetation coverage grid data set and the ten-day-scale vegetation coverage grid data set, so as to realize fusion of the multi-source vegetation data.
In this embodiment, step S4 specifically includes:
error estimation is carried out on 3 data sets respectively by using a Triple-collocation (TC) method, and in order to avoid numerical problems in the error estimation process, the number of samples of each independent data set is selected to be more than 100. After 3 kinds of data are sorted in time and space, the data of 3 surface vegetation coverage on the same space grid point at the same time are all presentReconstructing a vegetation index fusion model, and determining the weight omega in the vegetation index fusion model by adopting an improved least square method 1 、ω 2 And omega 3 。
The vegetation index fusion model in the step S4 is as follows:
vfc ys =a y ×vfc y +β y
vfc zs =a z ×vfc z +β z
therein, vfc true Vfc for true surface vegetation coverage xs For normalized field vegetation coverage raster data vfc ys Vegetation coverage raster data for normalized daily scale vfc zs Vegetation coverage raster data for the late ten-day scale of normalization vfc x Raster data for field vegetation coverage vfc y Vegetation coverage raster data for daily scale vfc z Raster data of vegetation coverage for ten-day scale, a y And beta y Vegetation coverage raster data vfc for daily scale y A) normalized coefficient of a z And beta z Vegetation coverage for ten-day scaleRaster data vfc z Is used for the normalization coefficient of (a),raster data vfc for field vegetation coverage x Standard deviation of>Vegetation coverage raster data vfc for daily scale y Is set in the standard deviation of (2),vegetation coverage raster data vfc for ten-day scale z Standard deviation of>Raster data vfc for field vegetation coverage x Mean value of->Vegetation coverage raster data vfc for daily scale y Mean value of->Vegetation coverage raster data vfc for ten-day scale z Mean, omega 1 Normalized weight, ω, for normalized field vegetation coverage raster data 2 Weights, ω, for normalized day scale vegetation coverage raster data 3 Weights for vegetation coverage raster data for the late ten-day scale are normalized.
The modified least square method means that: for three vegetation coverage data, wherein the field vegetation coverage raster data is accurate, so that the optimal solution is not obtained by using a least square method for three data sets at the same time, but the weight omega is solved by using a least square method for the daily-scale vegetation coverage raster data set and the ten-day-scale vegetation coverage raster data set 2 And omega 3 Obtaining omega 2 And omega 3 Re-using a daily scale vegetation coverage grid dataset and a ten-day scale vegetation coverageThe image after the fusion of the grid data sets is then covered with the field vegetation grid data, and the weight omega is solved 1 Sum (omega) 2 +ω 3 ) Finally to omega 1 、ω 2 And omega 3 Normalized to obtain the weight omega 1 、ω 2 And omega 3 The method can fuse the obtained fusion result of the star-ground multi-source surface vegetation coverage, has better data quality than the vegetation coverage data quality of a single source, can better reflect the real surface vegetation coverage, and has better application prospect.
Claims (2)
1. The vegetation index fusion method based on the multi-source data is characterized by comprising the following steps of:
s1, acquiring a field vegetation coverage data set of a research area, and performing spatial rasterization on the field vegetation coverage data set by adopting a geographic regression weighting model to obtain a field vegetation coverage raster data set;
s2, acquiring related data of the research area affecting the surface vegetation coverage, and calibrating a bio-geochemical model Biome-BGC based on the related data to obtain a daily-scale vegetation coverage grid data set;
s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a terrra satellite in a research area, and performing scale conversion on the vegetation coverage to obtain a ten-day scale vegetation coverage grid data set;
s4, constructing and solving a vegetation index fusion model according to the field vegetation coverage grid data set, the daily-scale vegetation coverage grid data set and the ten-day-scale vegetation coverage grid data set to realize fusion of multi-source vegetation data;
the vegetation index fusion model in the step S4 is as follows:
vfc ys =a y ×vfc y +β y
vfc zs =a z ×vfc z +β z
therein, vfc true Vfc for true surface vegetation coverage xs For normalized field vegetation coverage raster data vfc ys Vegetation coverage raster data for normalized daily scale vfc zs Vegetation coverage raster data for the late ten-day scale of normalization vfc x Raster data for field vegetation coverage vfc y Vegetation coverage raster data for daily scale vfc z Raster data of vegetation coverage for ten-day scale, a y And beta y Vegetation coverage raster data vfc for daily scale y A) normalized coefficient of a z And beta z Vegetation coverage raster data vfc for ten-day scale z Is used for the normalization coefficient of (a),raster data vfc for field vegetation coverage x Standard deviation of>Vegetation coverage raster data vfc for daily scale y Standard deviation of>Vegetation coverage raster data vfc for ten-day scale z Standard deviation of>Raster data vfc for field vegetation coverage x Mean value of->Vegetation coverage raster data vfc for daily scale y Mean value of->Vegetation coverage raster data vfc for ten-day scale z Mean, omega 1 Normalized weight, ω, for normalized field vegetation coverage raster data 2 Weights, ω, for normalized day scale vegetation coverage raster data 3 Weights for vegetation coverage raster data for the late ten-day scale are normalized.
2. The method of claim 1, wherein the related data in step S2 includes: soil depth data, soil sand content data, soil powder content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
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