CN113762383A - Vegetation index fusion method based on multi-source data - Google Patents

Vegetation index fusion method based on multi-source data Download PDF

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CN113762383A
CN113762383A CN202111044340.0A CN202111044340A CN113762383A CN 113762383 A CN113762383 A CN 113762383A CN 202111044340 A CN202111044340 A CN 202111044340A CN 113762383 A CN113762383 A CN 113762383A
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雷添杰
李小涵
岳建伟
张平
徐瑞瑞
张保山
鲁源
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Beijing Normal University
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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 processing 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 relevant data of the research area influencing the vegetation coverage of the earth surface, and calibrating a biogeochemical model Biome-BGC based on the relevant data to obtain a daily-scale vegetation coverage grid data set; s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a Terra satellite in a research area, and carrying out 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, and realizing fusion of multi-source vegetation data; the invention solves the problems of low space-time resolution and estimation precision of the coverage of the surface vegetation.

Description

Vegetation index fusion method based on multi-source data
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 relation 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 and global and regional climate models. Therefore, the method for acquiring the earth surface vegetation coverage and the change information thereof with higher space-time resolution has important practical significance for revealing the earth surface space change rule, discussing the change driving factor 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 problems of low space-time resolution and estimation accuracy of the coverage of the surface vegetation.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: 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 processing 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 relevant data of the research area influencing the vegetation coverage of the earth surface, and calibrating a biogeochemical model Biome-BGC based on the relevant data to obtain a daily-scale vegetation coverage grid data set;
s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a Terra satellite in a research area, and carrying out 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 vegetation coverage grid data set and the ten-day vegetation coverage grid data set, and realizing fusion of multi-source vegetation data.
Further, the related data in step S2 includes: the method comprises the following steps of soil depth data, soil sand content data, soil silt content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
Further, the vegetation index fusion model in step S4 is:
Figure BDA0003250600420000021
vfcys＝ay×vfcy+βy
vfczs＝az×vfcz+βz
Figure BDA0003250600420000022
Figure BDA0003250600420000023
Figure BDA0003250600420000024
Figure BDA0003250600420000025
wherein, vfctrueCoverage for true surface vegetation, vfcxsFor normalized field vegetation coverage grid data, vfcysVegetation coverage grid data on a normalized daily scale, vfczsVegetation coverage grid data in normalized ten-day scale, vfcxGrid data for field vegetation coverage, vfcyIs vegetation coverage grid data on a daily scale, vfczIs a ten-day-scale vegetation coverage grid data, ayAnd betayVegetation coverage grid data vfc on a daily scaleyNormalized coefficient of (a)zAnd betazVegetation coverage grid data vfc for ten-day scalezThe normalized coefficient of (a) is determined,
Figure BDA0003250600420000026
coverage grid data vfc for field vegetationxThe standard deviation of (a) is determined,
Figure BDA0003250600420000027
vegetation coverage grid data vfc on a daily scaleyThe standard deviation of (a) is determined,
Figure BDA0003250600420000028
vegetation coverage grid data vfc for ten-day scalezThe standard deviation of (a) is determined,
Figure BDA0003250600420000029
coverage grid data vfc for field vegetationxThe average value of (a) of (b),
Figure BDA0003250600420000031
vegetation coverage grid data vfc on a daily scaleyThe average value of (a) of (b),
Figure BDA0003250600420000032
vegetation coverage grid data vfc for ten-day scalezMean value of, omega1Is the normalized weight, omega, of the normalized field vegetation coverage grid data2Weight, ω, of vegetation coverage grid data for normalized daily scale3Vegetation coverage grid number in normalized ten days scaleAccording to the weight.
In conclusion, the beneficial effects of the invention are as follows:
a vegetation index fusion method based on multi-source data comprises the steps of utilizing a raster data set 1 based on field investigation data, utilizing a calibrated and verified Biome-BGC model pair to generate a raster data set 2, and utilizing Terra satellite AVHRR and MODIS sensor images to generate a raster data set 3. Then, error variances of the three vegetation coverage data sets are respectively estimated by means of a Triple-Collocation 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 the satellite-ground multi-source vegetation data is achieved. The obtained result is superior to the estimation precision of a single result, the support of long-time sequence data is obtained, the space-time resolution and the estimation precision of the coverage of the surface vegetation are improved, and more accurate data support is provided for the refined regional ecological environment change and the regional climate model.
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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 the 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 it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
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 processing 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:
and (3) carrying out spatialization on the observation data of 162 sites of the inner Mongolia, sorting regional field site survey data, introducing DEM and longitude and latitude as explanatory variables, and rasterizing the regional vegetation coverage by adopting a geographic weighting regression model to obtain a field vegetation coverage grid data set.
S2, acquiring relevant data of the research area influencing the vegetation coverage of the earth surface, and calibrating a biogeochemical model Biome-BGC based on the relevant data to obtain a daily-scale vegetation coverage grid data set;
the related data in step S2 includes: the method comprises the following steps of soil depth data, soil sand content data, soil silt 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 carrying out parameter localization on the BIOME _ BGC model, wherein the required input raster meteorological data acquisition method is an APSIM (advanced persistent subscriber identity Module) interpolation method, the data source is station meteorological observation data, and the spatial resolution after interpolation is 0.5km multiplied by 0.5 km. And simulating a daily-scale vegetation coverage grid data set by using the BIOME _ BGC model after calibration and verification.
S3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a Terra satellite in a research area, and carrying out 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 (FVC) Data and Digital Elevation Model (DEM) Data of MODISs of tera satellites.
The remote sensing vegetation coverage data are obtained by training a relation model from the preprocessed reflectivity to the FVC value based on a machine learning method, and the ten-day value vegetation coverage is obtained by resampling the data through aggregation grouping. The data source is reflectivity and vegetation coverage products of AVHRR and MODIS sensors of a Terra satellite, the time resolution is 8 days, and the monitoring is carried out for 46 times all year round. The time range of the vegetation coverage remote sensing data set is 2000-2015 years, an SIN projection mode is adopted, and the spatial resolution is 0.5km multiplied by 0.5 km.
S4, constructing and solving a vegetation index fusion model according to the field vegetation coverage grid data set, the daily vegetation coverage grid data set and the ten-day vegetation coverage grid data set, and realizing fusion of multi-source vegetation data.
In this embodiment, step S4 specifically includes:
and respectively carrying out error estimation on the 3 data sets by using a Triple-similarity (TC) method, and selecting the sample number of each independent data set to be more than 100 in order to avoid the numerical problem in the error estimation process. Arranging and ordering the 3 kinds of data in time and space, enabling the data of 3 earth surface vegetation coverage degrees on grid points of the same time and the same space to exist, then constructing a vegetation index fusion model, and determining the weight omega in the vegetation index fusion model by adopting an improved least square method1、ω2And ω3。
The vegetation index fusion model in step S4 is:
Figure BDA0003250600420000051
vfcys＝ay×vfcy+βy
vfczs＝az×vfcz+βz
Figure BDA0003250600420000052
Figure BDA0003250600420000053
Figure BDA0003250600420000054
Figure BDA0003250600420000055
wherein, vfctrueCoverage for true surface vegetation, vfcxsFor normalized field vegetation coverage grid data, vfcysVegetation coverage grid data on a normalized daily scale, vfczsVegetation coverage grid data in normalized ten-day scale, vfcxGrid data for field vegetation coverage, vfcyIs vegetation coverage grid data on a daily scale, vfczIs a ten-day-scale vegetation coverage grid data, ayAnd betayVegetation coverage grid data vfc on a daily scaleyNormalized coefficient of (a)zAnd betazVegetation coverage grid data vfc for ten-day scalezThe normalized coefficient of (a) is determined,
Figure BDA0003250600420000061
coverage grid data vfc for field vegetationxThe standard deviation of (a) is determined,
Figure BDA0003250600420000062
vegetation coverage grid data vfc on a daily scaleyThe standard deviation of (a) is determined,
Figure BDA0003250600420000063
vegetation coverage grid data vfc for ten-day scalezThe standard deviation of (a) is determined,
Figure BDA0003250600420000064
coverage grid data vfc for field vegetationxThe average value of (a) of (b),
Figure BDA0003250600420000065
vegetation coverage grid data vfc on a daily scaleyThe average value of (a) of (b),
Figure BDA0003250600420000066
vegetation coverage grid data vfc for ten-day scalezMean value of, omega1Is the normalized weight, omega, of the normalized field vegetation coverage grid data2Vegetation coverage grid number for normalized daily scaleAccording to the weight, ω3And the weight of the normalized ten-day-scale vegetation coverage grid data is obtained.
The improved least squares method refers to: for the three types of vegetation coverage data, the field vegetation coverage raster data is relatively accurate, so that the least square method is not used for solving the optimal solution for the three data sets simultaneously, but the least square method is firstly used for solving the weight omega for the daily vegetation coverage raster data set and the daily vegetation coverage raster data set2And ω3To obtain omega2And omega3The image fused by the daily-scale vegetation coverage raster data set and the ten-day-scale vegetation coverage raster data set is used for solving the weight omega1And (ω)2+ω3) Finally to omega1、ω2And ω3Normalization is carried out to obtain the weight omega1、ω2And ω3The method can fuse the obtained fusion result of the satellite-ground multi-source earth surface vegetation coverage, has better data quality than the single-source vegetation coverage data, can better reflect the real earth surface vegetation coverage, and has better application prospect.

Claims (3)

1. A vegetation index fusion method based on multi-source data is characterized by comprising the following steps:
s1, acquiring a field vegetation coverage data set of a research area, and performing spatial rasterization processing 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 relevant data of the research area influencing the vegetation coverage of the earth surface, and calibrating a biogeochemical model Biome-BGC based on the relevant data to obtain a daily-scale vegetation coverage grid data set;
s3, acquiring vegetation coverage acquired by AVHRR and MODIS sensors of a Terra satellite in a research area, and carrying out 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 vegetation coverage grid data set and the ten-day vegetation coverage grid data set, and realizing fusion of multi-source vegetation data.
2. The multi-source data-based vegetation index fusion method of claim 1, wherein the related data in the step S2 comprises: the method comprises the following steps of soil depth data, soil sand content data, soil silt content data, soil clay content data, DEM data, longitude and latitude gradient data and slope direction data.
3. The multi-source data-based vegetation index fusion method of claim 1, wherein the vegetation index fusion model in the step S4 is:
Figure FDA0003250600410000011
vfcys＝ay×vfcy+βy
vfczs＝az×vfcz+βz
Figure FDA0003250600410000012
Figure FDA0003250600410000021
Figure FDA0003250600410000022
Figure FDA0003250600410000023
wherein, vfctrueCoverage for true surface vegetation, vfcxsFor normalized field vegetation coverage grid data, vfcysVegetation coverage grid data on a normalized daily scale, vfczsVegetation coverage grid data in normalized ten-day scale, vfcxGrid data for field vegetation coverage, vfcyIs vegetation coverage grid data on a daily scale, vfczIs a ten-day-scale vegetation coverage grid data, ayAnd betayVegetation coverage grid data vfc on a daily scaleyNormalized coefficient of (a)zAnd betazVegetation coverage grid data vfc for ten-day scalezThe normalized coefficient of (a) is determined,
Figure FDA0003250600410000024
coverage grid data vfc for field vegetationxThe standard deviation of (a) is determined,
Figure FDA0003250600410000025
vegetation coverage grid data vfc on a daily scaleyThe standard deviation of (a) is determined,
Figure FDA0003250600410000026
vegetation coverage grid data vfc for ten-day scalezThe standard deviation of (a) is determined,
Figure FDA0003250600410000027
coverage grid data vfc for field vegetationxThe average value of (a) of (b),
Figure FDA0003250600410000028
vegetation coverage grid data vfc on a daily scaleyThe average value of (a) of (b),
Figure FDA0003250600410000029
vegetation coverage grid data vfc for ten-day scalezMean value of, omega1Is the normalized weight of the normalized field vegetation coverage grid data,ω2weight, ω, of vegetation coverage grid data for normalized daily scale3And the weight of the normalized ten-day-scale vegetation coverage grid data is obtained.
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