CN108387525A - A kind of year GPP evaluation method and system based on EVI2 seasonal variations curves - Google Patents
A kind of year GPP evaluation method and system based on EVI2 seasonal variations curves Download PDFInfo
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- CN108387525A CN108387525A CN201810076612.7A CN201810076612A CN108387525A CN 108387525 A CN108387525 A CN 108387525A CN 201810076612 A CN201810076612 A CN 201810076612A CN 108387525 A CN108387525 A CN 108387525A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
Abstract
The present invention provides a kind of based on EVI2 seasonal variations curves year GPP evaluation method and system, including：Obtain annual complete time sequence remote sensing image, quantification processing is carried out to remote sensing image, obtain time series earth's surface reflectivity data, time series Reflectivity for Growing Season data are cut, extract Reflectivity for Growing Season data, to the near infrared band and red spectral band progress band math in earth's surface reflectivity data, the EVI2 time series datas of each phase research area's remote sensing image are calculated pixel-by-pixel, EVI2 time series datas are filtered, filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtain ∑ EVI2, based on vegetation pattern, extract the appraising model between ∑ EVI2 and the GPP sample pre-established, the year GPP within the scope of research area is estimated based on ∑ EVI2 and the appraising model.The present invention can be based on vegetation EVI2 whole year time-serial positions and integrate, and for different vegetation types, effectively GPP total to region year is estimated.
Description
Technical field
The invention belongs to gross primary productivity computing technique fields, more particularly to one kind being based on EVI2 (Enhanced
Vegetation Index 2, two wave band enhancement mode meta files) seasonal variations curve year GPP (Gross Primary
Productivity, gross primary productivity) evaluation method and system.
Background technology
The 25%-30% for the CO2 that vegetation in terrestrial ecosystems is discharged mankind's activity by photosynthesis fixes storage
It deposits, and the amount that the carbon of biomass was fixed on unit interval unit area by photosynthesis and be converted into vegetation is referred to as
Vegetation gross primary productivity (Gross Primary Production, GPP).GPP is commonly used for estimated crops, simultaneously
It is also the important parameter that terrestrial ecosystems Carbon budget calculates, significant role is played in the research to global carbon.Area
The GPP of domain and Global Scale range estimations help to monitor and predict vegetation growth situation, understand that terrestrial ecosystems carbon follows
The variation of ring and to prediction Future Climate Change situation it is all significant.Since the 1990s, it is based on website
Related (Eddy Covariance, EC) method of vorticity association of scale start to become the exchange of monitoring heat, moisture and CO2 and
Track the important tool of greenhouse gases.Net interchange by the CO2 observed website, so by ecosystem respiration and
GPP is calculated in relationship between the temperature of soil.But since the quantity of flux tower website and contribution area are limited, so making
Limitation and difficulty are still had to the estimation of region GPP with data are observed based on website.
Vegetation index is not the intrinsic physical quantity of vegetation, but because itself and vegetation itself physical-property parameter have strong correlation
Property, it is widely used in describing vegetative coverage and with its inverting such as vegetation photosynthetically active radiation contribution ratios, leaf area index
The parameters such as (leaf area index, LAI), biomass.Most common vegetation index is normalized differential vegetation index
(Normalized Differential Vegetation Index, NDVI), but with the growth of vegetation, in Vegetation canopy
Chlorophyll absorption feux rouges and the red spectral band of vegetation spectrum tend to be saturated, sensibility also can in high vegetation coverage area
It reduces, these factors result in NDVI cannot efficiently differentiate vegetation in high vegetation coverage area, and cause to utilize NDVI
Estimation error in the vegetation parameter in high vegetation coverage area increases.
Therefore, how effectively to estimate that a year gross primary productivity is a urgent problem to be solved.
Invention content
In view of this, the present invention provides a kind of year GPP evaluation method based on EVI2 seasonal variations curves, can be based on
Vegetation EVI2 whole year time-serial positions integrate, and for different vegetation types, effectively GPP total to region year is estimated.
To achieve the goals above, the present invention provides the following technical solutions：
A kind of year GPP evaluation method based on EVI2 seasonal variations curves, including：
Obtain annual complete time sequence remote sensing image, wherein include near infrared band and feux rouges in the remote sensing image
Wave band；
Quantification processing is carried out to the remote sensing image, obtains time series earth's surface reflectivity data；
The time series earth's surface reflectivity data is cut, the Reflectivity for Growing Season number within the scope of research area is extracted
According to；
To the near infrared band and red spectral band progress wave band fortune in the Reflectivity for Growing Season data within the scope of the research area
It calculates, calculates the EVI2 time series datas for studying area's remote sensing image described in each phase pixel-by-pixel；
The EVI2 time series datas are filtered；
Filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtain ∑ EVI2；
Based on vegetation pattern, the appraising model between ∑ EVI2 and the GPP sample pre-established is extracted；
The year GPP within the scope of the research area is estimated based on the ∑ EVI2 and the appraising model.
Preferably, to it is described research area within the scope of Reflectivity for Growing Season data near infrared band and red spectral band carry out
Band math, the EVI2 time series datas for calculating research area's remote sensing image described in each phase pixel-by-pixel include：
Based on formulaCalculate the EVI2 time series datas, wherein ρNIRFor
The reflectivity of near infrared band, ρREDFor the reflectivity of red spectral band.
Preferably, described that filtered treated EVI2 time series datas are integrated by pixel calculated curve, it obtains
∑ EVI2 includes：
Based on formulaCalculate ∑ EVI2, wherein L is the length of entire time series, f
(xi) be 1 year in xthiIt, N is the number for resolving into the shaded area under filtered time-serial position.
Preferably, it is described to the EVI2 time series datas be filtered including：
The EVI2 time series datas are filtered based on S-G filters.
Preferably, described that quantification processing is carried out to the remote sensing image, obtain time series earth's surface reflectivity data packet
It includes：
Ortho-rectification, geometric correction, radiation calibration and atmospheric correction are carried out to the remote sensing image, obtain time series
Reflectivity for Growing Season data.
A kind of year GPP estimating system based on EVI2 seasonal variations curves, including：
Acquisition module, for obtaining annual complete time sequence remote sensing image, wherein comprising close red in the remote sensing image
Wave section and red spectral band；
Processing module obtains time series earth's surface reflectivity data for carrying out quantification processing to the remote sensing image；
Module is cut, for being cut to the time series earth's surface reflectivity data, is extracted within the scope of research area
Reflectivity for Growing Season data；
Computing module, for the near infrared band and feux rouges wave in the Reflectivity for Growing Season data within the scope of the research area
Duan Jinhang band maths calculate the EVI2 time series datas that area's remote sensing image is studied described in each phase pixel-by-pixel；
Filter module, for being filtered to the EVI2 time series datas；
Integration module is obtained for being integrated by pixel calculated curve to filtered treated EVI2 time series datas
To ∑ EVI2；
Extraction module extracts the estimation mould between ∑ EVI2 and the GPP sample pre-established for being based on vegetation pattern
Type；
Estimation block, for estimating the year within the scope of the research area based on the ∑ EVI2 and the appraising model
GPP。
Preferably, the computing module is specifically used for：
Based on formulaCalculate the EVI2 time series datas, wherein ρNIRFor
The reflectivity of near infrared band, ρREDFor the reflectivity of red spectral band.
Preferably, the integration module is specifically used for：
Based on formulaCalculate ∑ EVI2, wherein L is the length of entire time series, f
(xi) be 1 year in xthiIt, N is the number for resolving into the shaded area under filtered time-serial position.
Preferably, the filter module is specifically used for：
The EVI2 time series datas are filtered based on S-G filters.
Preferably, the processing module is specifically used for：
Ortho-rectification, geometric correction, radiation calibration and atmospheric correction are carried out to the remote sensing image, obtain time series
Reflectivity for Growing Season data.
It can be seen from the above technical proposal that the present invention provides a kind of year GPP based on EVI2 seasonal variations curves to estimate
Calculation method obtains annual complete time sequence remote sensing image, wherein distant first when needing to estimation year gross primary productivity
It includes near infrared band and red spectral band to feel in image, then carries out quantification processing to remote sensing image, obtains time-sequentially
Table reflectivity data cuts time series Reflectivity for Growing Season data, extracts the Reflectivity for Growing Season within the scope of research area
Data, to the near infrared band and red spectral band progress band math in the Reflectivity for Growing Season data within the scope of research area, by picture
Element calculates the EVI2 time series datas that area's remote sensing image is studied described in each phase, and is filtered to EVI2 time series datas
Wave processing integrates filtered treated EVI2 time series datas by pixel calculated curve, obtains ∑ EVI2, last base
In vegetation pattern, the appraising model between ∑ EVI2 and the GPP sample pre-established is extracted, is based on ∑ EVI2 and appraising model
Estimate the year GPP within the scope of the research area.It can be based on vegetation EVI2 whole year time-serial positions to integrate, for difference
Vegetation pattern, effectively to region year, always GPP is estimated.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 be it is disclosed by the invention it is a kind of based on EVI2 seasonal variations curves year GPP evaluation method embodiment 1 method
Flow chart；
Fig. 2 be it is disclosed by the invention it is a kind of based on EVI2 seasonal variations curves year GPP evaluation method embodiment 2 method
Flow chart；
Fig. 3 be it is disclosed by the invention it is a kind of based on EVI2 seasonal variations curves year GPP estimating system embodiment 1 structure
Schematic diagram；
Fig. 4 be it is disclosed by the invention it is a kind of based on EVI2 seasonal variations curves year GPP estimating system embodiment 2 structure
Schematic diagram.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, being a kind of year GPP evaluation method embodiment based on EVI2 seasonal variations curves disclosed by the invention
1 flow chart, the method may include following steps：
S101, annual complete time sequence remote sensing image is obtained, wherein include near infrared band and feux rouges in remote sensing image
Wave band；
When needing to estimate year GPP, the annual complete time sequence remote sensing image that obtains first, wherein remote sensing
It needs to include near infrared band and red spectral band in image.In addition, the temporal resolution of remote sensing image data need to be as high as possible, and
Suitable spatial resolution is selected according to survey region size.For example, the time of MODIS remotely-sensed datas used by research experiment
Resolution ratio is 16 days.
S102, quantification processing is carried out to remote sensing image, obtains time series earth's surface reflectivity data；
After getting annual complete time sequence remote sensing image, a year and a day of acquisition full-time sequence remote sensing image is determined
Quantification treatment obtains time series earth's surface reflectivity data.If experiment is using 16 days vegetation index products of MODSI
MOD13A1, wherein included near infrared band and red spectral band, then need not carry out atmospheric correction to data.
S103, time series Reflectivity for Growing Season data are cut, extracts the Reflectivity for Growing Season within the scope of research area
Data；
Then, the sequential reflectivity data of research on utilization area vector file, acquisition is cut, within the scope of extraction research area
Reflectivity for Growing Season data.Wherein, research experiment can be tested using northwest-north-northeast China networks of shelterbelts region as survey region.
S104, to research area within the scope of Reflectivity for Growing Season data near infrared band and red spectral band carry out wave band fortune
It calculates, calculates the EVI2 time series datas of each phase research area's remote sensing image pixel-by-pixel；
Then, to the near infrared band and red spectral band progress wave band fortune in the Reflectivity for Growing Season data within the scope of research area
It calculates, the EVI2 time series datas for calculating each phase research area's remote sensing image pixel-by-pixel.
S105, EVI2 time series datas are filtered；
Since there is likely to be the influence of air, sensor or cloud, the annual sequential EVI2 of structure for time series data
There are irregular fluctuations for curve, so the present invention is reconstructed EVI2 time series datas by filtering.Filtered processing
Afterwards, can improve due to curve abnormality wave phenomenon caused by cloud or other reasons.
S106, filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtains ∑ EVI2；
Then filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtains ∑ EVI2.
S107, it is based on vegetation pattern, extracts the appraising model between ∑ EVI2 and the GPP sample pre-established；
Then, according to vegetation pattern data, the ∑ EVI2 and GPP samples of all kinds of vegetation patterns is extracted, that is, is extracted in advance
Appraising model between ∑ EVI2 and the GPP sample of foundation, different vegetation types have different estimation equations.
S108, the year GPP studied within the scope of area is estimated based on ∑ EVI2 and appraising model.
Finally, obtained ∑ EVI2 is substituted into the estimation mould between ∑ EVI2 and the GPP sample pre-established extracted
The year GPP within the scope of research area is calculated in type.
In conclusion in the above-described embodiments, when needing to estimation year gross primary productivity, obtaining first annual complete
Time series remote sensing image, wherein include near infrared band and red spectral band in remote sensing image, then remote sensing image is determined
Quantification treatment obtains time series earth's surface reflectivity data, is cut to time series Reflectivity for Growing Season data, extract and grind
The Reflectivity for Growing Season data within the scope of area are studied carefully, to the near infrared band and feux rouges in the Reflectivity for Growing Season data within the scope of research area
Wave band carries out band math, calculates the EVI2 time series datas that area's remote sensing image is studied described in each phase pixel-by-pixel, and right
EVI2 time series datas are filtered, to filtered treated EVI2 time series datas by pixel calculated curve
Integral, obtains ∑ EVI2, is finally based on vegetation pattern, extract the estimation mould between ∑ EVI2 and the GPP sample pre-established
Type estimates the year GPP within the scope of the research area based on ∑ EVI2 and appraising model.It can be based on the vegetation EVI2 annual times
Sequence curve integrates, and for different vegetation types, effectively GPP total to region year is estimated.
As shown in Fig. 2, being a kind of year GPP evaluation method embodiment based on EVI2 seasonal variations curves disclosed by the invention
2 flow chart, the method may include following steps：
S201, annual complete time sequence remote sensing image is obtained, wherein include near infrared band and feux rouges in remote sensing image
Wave band；
When needing to estimate year GPP, the annual complete time sequence remote sensing image that obtains first, wherein remote sensing
It needs to include near infrared band and red spectral band in image.In addition, the temporal resolution of remote sensing image data need to be as high as possible, and
Suitable spatial resolution is selected according to survey region size.For example, the time of MODIS remotely-sensed datas used by research experiment
Resolution ratio is 16 days.
S202, ortho-rectification, geometric correction, radiation calibration and atmospheric correction are carried out to remote sensing image, obtains time sequence
Row Reflectivity for Growing Season data；
After getting annual complete time sequence remote sensing image, a year and a day of acquisition full-time sequence remote sensing image is determined
Quantification treatment e.g. carries out the processing of the quantification such as ortho-rectification, geometric correction, radiation calibration and atmospheric correction, obtains time sequence
Row Reflectivity for Growing Season data.If experiment is using 16 days vegetation index product MOD13A1 of MODSI, wherein included close
Infrared band and red spectral band then need not carry out atmospheric correction to data.
S203, time series Reflectivity for Growing Season data are cut, extracts the Reflectivity for Growing Season within the scope of research area
Data；
Then, the sequential reflectivity data of research on utilization area vector file, acquisition is cut, within the scope of extraction research area
Reflectivity for Growing Season data.Wherein, research experiment can be tested using northwest-north-northeast China networks of shelterbelts region as survey region.
S204, to research area within the scope of Reflectivity for Growing Season data near infrared band and red spectral band carry out wave band fortune
It calculates, calculates the EVI2 time series datas of each phase research area's remote sensing image pixel-by-pixel；
Then, to the near infrared band and red spectral band progress wave band fortune in the Reflectivity for Growing Season data within the scope of research area
It calculates, the EVI2 time series datas for calculating each phase research area's remote sensing image pixel-by-pixel.
Specifically, in the Reflectivity for Growing Season data within the scope of research area near infrared band and red spectral band into traveling wave
Section operation can be based on formula when calculating the EVI2 time series datas of each phase research area's remote sensing image pixel-by-pixelCalculate ∑ EVI2, wherein L is the length of entire time series, f (xi) be in 1 year the
xiIt, N is the number for resolving into the shaded area under filtered time-serial position.
S205, EVI2 time series datas are filtered based on S-G filters；
Since there is likely to be the influence of air, sensor or cloud, the annual sequential EVI2 of structure for time series data
There are irregular fluctuations for curve, so the present invention is reconstructed EVI2 time series datas by filtering.Filtered processing
Afterwards, can improve due to curve abnormality wave phenomenon caused by cloud or other reasons.Specifically, using Savitzky-
EVI2 time series datas are reconstructed in Golay filtering.Using TIMESAT time series data analysis software to the time sequence of EVI2
Row carry out S-G and are filtered, 5 × 5 pixel of least square window selection, after S-G is filtered, can improve due to cloud or
Curve abnormality wave phenomenon caused by person's other reasons.
S206, filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtains ∑ EVI2；
Then filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtains ∑ EVI2.
Specifically, being based on formulaCalculate ∑ EVI2, wherein L is the length of entire time series, f
(xi) be 1 year in xthiIt, N is the number for resolving into the shaded area under filtered time-serial position.
S207, it is based on vegetation pattern, extracts the appraising model between ∑ EVI2 and the GPP sample pre-established；
Then, according to vegetation pattern data, the ∑ EVI2 and GPP samples of all kinds of vegetation patterns is extracted, that is, is extracted in advance
Appraising model between ∑ EVI2 and the GPP sample of foundation, different vegetation types have different estimation equations.As shown in the table：
1 different vegetation types year of table total GPP estimation equations
S208, the year GPP studied within the scope of area is estimated based on ∑ EVI2 and appraising model.
Finally, obtained ∑ EVI2 is substituted into the estimation mould between ∑ EVI2 and the GPP sample pre-established extracted
The year GPP within the scope of research area is calculated in type.
In conclusion in the above-described embodiments, when needing to estimation year gross primary productivity, obtaining first annual complete
Time series remote sensing image, wherein include near infrared band and red spectral band in remote sensing image, then remote sensing image is carried out just
Correction, geometric correction, radiation calibration and atmospheric correction are penetrated, time series earth's surface reflectivity data is obtained, to time-sequentially
Table reflectivity data is cut, and the Reflectivity for Growing Season data within the scope of research area are extracted, to the earth's surface within the scope of research area
Near infrared band and red spectral band in reflectivity data carry out band math, and it is distant to calculate research area described in each phase pixel-by-pixel
Feel the EVI2 time series datas of image, and the EVI2 time series datas are filtered based on S-G filters, it is right
Filtered treated EVI2 time series datas are integrated by pixel calculated curve, are obtained ∑ EVI2, are finally based on vegetation class
Type extracts the appraising model between ∑ EVI2 and the GPP sample pre-established, institute is estimated based on ∑ EVI2 and appraising model
State the year GPP within the scope of research area.Vegetation EVI2 whole year time-serial positions can be based on to integrate, for different vegetation types,
Effectively GPP total to region year is estimated.
As shown in figure 3, being a kind of year GPP estimating system embodiment based on EVI2 seasonal variations curves disclosed by the invention
1 structural schematic diagram, the system may include：
Acquisition module 301, for obtaining annual complete time sequence remote sensing image, wherein include in the remote sensing image
Near infrared band and red spectral band；
When needing to estimate year GPP, the annual complete time sequence remote sensing image that obtains first, wherein remote sensing
It needs to include near infrared band and red spectral band in image.In addition, the temporal resolution of remote sensing image data need to be as high as possible, and
Suitable spatial resolution is selected according to survey region size.For example, the time of MODIS remotely-sensed datas used by research experiment
Resolution ratio is 16 days.
Processing module 302 obtains time series earth's surface reflectivity data for carrying out quantification processing to remote sensing image；
After getting annual complete time sequence remote sensing image, a year and a day of acquisition full-time sequence remote sensing image is determined
Quantification treatment obtains time series earth's surface reflectivity data.If experiment is using 16 days vegetation index products of MODSI
MOD13A1, wherein included near infrared band and red spectral band, then need not carry out atmospheric correction to data.
Module 303 is cut, for being cut to time series Reflectivity for Growing Season data, is extracted within the scope of research area
Reflectivity for Growing Season data；
Then, the sequential reflectivity data of research on utilization area vector file, acquisition is cut, within the scope of extraction research area
Reflectivity for Growing Season data.Wherein, research experiment can be tested using northwest-north-northeast China networks of shelterbelts region as survey region.
Computing module 304, for the near infrared band and feux rouges wave in the Reflectivity for Growing Season data within the scope of research area
Duan Jinhang band maths calculate the EVI2 time series datas of each phase research area's remote sensing image pixel-by-pixel；
Then, to the near infrared band and red spectral band progress wave band fortune in the Reflectivity for Growing Season data within the scope of research area
It calculates, the EVI2 time series datas for calculating each phase research area's remote sensing image pixel-by-pixel.
Filter module 305, for being filtered to EVI2 time series datas；
Since there is likely to be the influence of air, sensor or cloud, the annual sequential EVI2 of structure for time series data
There are irregular fluctuations for curve, so the present invention is reconstructed EVI2 time series datas by filtering.Filtered processing
Afterwards, can improve due to curve abnormality wave phenomenon caused by cloud or other reasons.
Integration module 306, for being integrated by pixel calculated curve to filtered treated EVI2 time series datas,
Obtain ∑ EVI2；
Then filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtains ∑ EVI2.
Extraction module 307 extracts estimating between ∑ EVI2 and the GPP sample pre-established for being based on vegetation pattern
Calculate model；
Then, according to vegetation pattern data, the ∑ EVI2 and GPP samples of all kinds of vegetation patterns is extracted, that is, is extracted in advance
Appraising model between ∑ EVI2 and the GPP sample of foundation, different vegetation types have different estimation equations.
Estimation block 308, for estimating the year GPP within the scope of research area based on ∑ EVI2 and appraising model.
Finally, obtained ∑ EVI2 is substituted into the estimation mould between ∑ EVI2 and the GPP sample pre-established extracted
The year GPP within the scope of research area is calculated in type.
In conclusion in the above-described embodiments, when needing to estimation year gross primary productivity, obtaining first annual complete
Time series remote sensing image, wherein include near infrared band and red spectral band in remote sensing image, then remote sensing image is determined
Quantification treatment obtains time series earth's surface reflectivity data, is cut to time series Reflectivity for Growing Season data, extract and grind
The Reflectivity for Growing Season data within the scope of area are studied carefully, to the near infrared band and feux rouges in the Reflectivity for Growing Season data within the scope of research area
Wave band carries out band math, calculates the EVI2 time series datas that area's remote sensing image is studied described in each phase pixel-by-pixel, and right
EVI2 time series datas are filtered, to filtered treated EVI2 time series datas by pixel calculated curve
Integral, obtains ∑ EVI2, is finally based on vegetation pattern, extract the estimation mould between ∑ EVI2 and the GPP sample pre-established
Type estimates the year GPP within the scope of the research area based on ∑ EVI2 and appraising model.It can be based on the vegetation EVI2 annual times
Sequence curve integrates, and for different vegetation types, effectively GPP total to region year is estimated.
As shown in figure 4, being a kind of year GPP estimating system embodiment based on EVI2 seasonal variations curves disclosed by the invention
2 structural schematic diagram, the system may include：
Acquisition module 401, for obtaining annual complete time sequence remote sensing image, wherein comprising close red in remote sensing image
Wave section and red spectral band；
When needing to estimate year GPP, the annual complete time sequence remote sensing image that obtains first, wherein remote sensing
It needs to include near infrared band and red spectral band in image.In addition, the temporal resolution of remote sensing image data need to be as high as possible, and
Suitable spatial resolution is selected according to survey region size.For example, the time of MODIS remotely-sensed datas used by research experiment
Resolution ratio is 16 days.
Processing module 402, for carrying out ortho-rectification, geometric correction, radiation calibration and atmospheric correction to remote sensing image,
Obtain time series earth's surface reflectivity data；
After getting annual complete time sequence remote sensing image, a year and a day of acquisition full-time sequence remote sensing image is determined
Quantification treatment e.g. carries out the processing of the quantification such as ortho-rectification, geometric correction, radiation calibration and atmospheric correction, obtains time sequence
Row Reflectivity for Growing Season data.If experiment is using 16 days vegetation index product MOD13A1 of MODSI, wherein included close
Infrared band and red spectral band then need not carry out atmospheric correction to data.
Module 403 is cut, for being cut to time series Reflectivity for Growing Season data, is extracted within the scope of research area
Reflectivity for Growing Season data；
Then, the sequential reflectivity data of research on utilization area vector file, acquisition is cut, within the scope of extraction research area
Reflectivity for Growing Season data.Wherein, research experiment can be tested using northwest-north-northeast China networks of shelterbelts region as survey region.
Computing module 404, for the near infrared band and feux rouges wave in the Reflectivity for Growing Season data within the scope of research area
Duan Jinhang band maths calculate the EVI2 time series datas of each phase research area's remote sensing image pixel-by-pixel；
Then, to the near infrared band and red spectral band progress wave band fortune in the Reflectivity for Growing Season data within the scope of research area
It calculates, the EVI2 time series datas for calculating each phase research area's remote sensing image pixel-by-pixel.
Specifically, in the Reflectivity for Growing Season data within the scope of research area near infrared band and red spectral band into traveling wave
Section operation can be based on formula when calculating the EVI2 time series datas of each phase research area's remote sensing image pixel-by-pixelCalculate ∑ EVI2, wherein L is the length of entire time series, f (xi) be in 1 year the
xiIt, N is the number for resolving into the shaded area under filtered time-serial position.
Filter module 405 is filtered the EVI2 time series datas for being based on S-G filters；
Since there is likely to be the influence of air, sensor or cloud, the annual sequential EVI2 of structure for time series data
There are irregular fluctuations for curve, so the present invention is reconstructed EVI2 time series datas by filtering.Filtered processing
Afterwards, can improve due to curve abnormality wave phenomenon caused by cloud or other reasons.Specifically, using Savitzky-
EVI2 time series datas are reconstructed in Golay filtering.Using TIMESAT time series data analysis software to the time sequence of EVI2
Row carry out S-G and are filtered, 5 × 5 pixel of least square window selection, after S-G is filtered, can improve due to cloud or
Curve abnormality wave phenomenon caused by person's other reasons.
Integration module 406, for being integrated by pixel calculated curve to filtered treated EVI2 time series datas,
Obtain ∑ EVI2；
Then filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtains ∑ EVI2.
Specifically, being based on formulaCalculate ∑ EVI2, wherein L is the length of entire time series, f
(xi) be 1 year in xthiIt, N is the number for resolving into the shaded area under filtered time-serial position.
Extraction module 407 extracts estimating between ∑ EVI2 and the GPP sample pre-established for being based on vegetation pattern
Calculate model；
Then, according to vegetation pattern data, the ∑ EVI2 and GPP samples of all kinds of vegetation patterns is extracted, that is, is extracted in advance
Appraising model between ∑ EVI2 and the GPP sample of foundation, different vegetation types have different estimation equations.As shown in the table：
1 different vegetation types year of table total GPP estimation equations
Estimation block 408, for estimating the year GPP within the scope of research area based on ∑ EVI2 and appraising model.
Finally, obtained ∑ EVI2 is substituted into the estimation mould between ∑ EVI2 and the GPP sample pre-established extracted
The year GPP within the scope of research area is calculated in type.
In conclusion in the above-described embodiments, when needing to estimation year gross primary productivity, obtaining first annual complete
Time series remote sensing image, wherein include near infrared band and red spectral band in remote sensing image, then remote sensing image is carried out just
Correction, geometric correction, radiation calibration and atmospheric correction are penetrated, time series earth's surface reflectivity data is obtained, to time-sequentially
Table reflectivity data is cut, and the Reflectivity for Growing Season data within the scope of research area are extracted, to the earth's surface within the scope of research area
Near infrared band and red spectral band in reflectivity data carry out band math, and it is distant to calculate research area described in each phase pixel-by-pixel
Feel the EVI2 time series datas of image, and the EVI2 time series datas are filtered based on S-G filters, it is right
Filtered treated EVI2 time series datas are integrated by pixel calculated curve, are obtained ∑ EVI2, are finally based on vegetation class
Type extracts the appraising model between ∑ EVI2 and the GPP sample pre-established, institute is estimated based on ∑ EVI2 and appraising model
State the year GPP within the scope of research area.Vegetation EVI2 whole year time-serial positions can be based on to integrate, for different vegetation types,
Effectively GPP total to region year is estimated.
In order to more specifically emphasize that the independence implemented, this specification are related to number of modules or unit.For example, mould
Block or unit can be realized that the hardware circuit includes special VLSI circuits or gate array, such as logic chip, crystal by hardware circuit
Pipe or other components.Module or unit can also be realized in programmable computer hardware, for example field is imitated programmable gate array, can be compiled
Journey array logic, programmable logic device etc..
Module or unit can also be realized in by the software performed by various forms of processors.Such as one can hold
Row code module may include that one or more entities or logic computer instruction block, the block are formed into, such as,
Object, program or function.However, the executable part of identification module or unit need not physically be put together, but can be by
The different instruction for being stored in different location is formed, and when combining in logic, is formed module or unit and is reached the module
Or the purpose required by unit.
In fact, executable code module or unit can be a single instruction or multiple instruction, it might even be possible to which distribution is in place
In several different code sections of different programs, and across several storage devices.Similarly, operation data can be identified and
It is shown in this module or unit, and can implement in any suitable form and in any suitable data structure form
Tissue.Operation data can assemble single data set, or can be distributed in the different positions with different storage devices, and
Only it is present in a system or network in a manner of electronic signal at least partly.
" embodiment " or similar term mentioned by this specification indicate characteristic related with embodiment, structure or feature,
It is included in at least embodiment of the present invention.Therefore, this specification occurs term " in one embodiment " " is being implemented
In example " and similar to term possibility but it is not necessarily all direction identical embodiment.
Furthermore characteristic of the present invention, structure or feature can in any way combine in one or more embodiments.
Explanation will provide many specific details below, for example programming, software module, user's selection, network trading, database are looked into
The examples such as inquiry, database structure, hardware module, hardware circuit, hardware chip, to provide the understanding to the embodiment of the present invention.So
And those of ordinary skill in the related art will be seen that the present invention, though wherein one or more specific details are not utilized, or profit
Can also it be implemented with other methods, component, material etc..On the other hand, it is the present invention that avoids confusion, well known structure, material or behaviour
It does not have a detailed description.
Each embodiment is described by the way of progressive in this specification, the highlights of each of the examples are with other
The difference of embodiment, just to refer each other for identical similar portion between each embodiment.For device disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so description is fairly simple, related place is said referring to method part
It is bright.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, depends on the specific application and design constraint of technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond the scope of this invention.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest range caused.
Claims (10)
1. a kind of year GPP evaluation method based on EVI2 seasonal variations curves, which is characterized in that including：
Obtain annual complete time sequence remote sensing image, wherein include near infrared band and red spectral band in the remote sensing image；
Quantification processing is carried out to the remote sensing image, obtains time series earth's surface reflectivity data；
The time series earth's surface reflectivity data is cut, the Reflectivity for Growing Season data within the scope of research area are extracted；
To it is described research area within the scope of Reflectivity for Growing Season data near infrared band and red spectral band carry out band math, by
Pixel calculates the EVI2 time series datas that area's remote sensing image is studied described in each phase；
The EVI2 time series datas are filtered；
Filtered treated EVI2 time series datas are integrated by pixel calculated curve, obtain ∑ EVI2；
Based on vegetation pattern, the appraising model between ∑ EVI2 and the GPP sample pre-established is extracted；
The year GPP within the scope of the research area is estimated based on the ∑ EVI2 and the appraising model.
2. according to the method described in claim 1, it is characterized in that, in the Reflectivity for Growing Season data within the scope of the research area
Near infrared band and red spectral band carry out band math, calculate pixel-by-pixel described in each phase study area's remote sensing image EVI2
Time series data includes：
Based on formulaCalculate the EVI2 time series datas, wherein ρNIRIt is close red
The reflectivity of wave section, ρREDFor the reflectivity of red spectral band.
3. according to the method described in claim 1, it is characterized in that, described to filtered treated EVI2 time series numbers
It is integrated according to by pixel calculated curve, obtaining ∑ EVI2 includes：
Based on formulaCalculate ∑ EVI2, wherein L is the length of entire time series, f (xi)
For the xth in 1 yeariIt, N is the number for resolving into the shaded area under filtered time-serial position.
4. according to the method described in claim 1, it is characterized in that, described be filtered place to the EVI2 time series datas
Reason includes：
The EVI2 time series datas are filtered based on S-G filters.
5. according to the method described in claim 1, it is characterized in that, it is described to the remote sensing image carry out quantification processing, obtain
The time series earth's surface reflectivity data is taken to include：
Ortho-rectification, geometric correction, radiation calibration and atmospheric correction are carried out to the remote sensing image, obtain time series earth's surface
Reflectivity data.
6. a kind of year GPP estimating system based on EVI2 seasonal variations curves, which is characterized in that including：
Acquisition module, for obtaining annual complete time sequence remote sensing image, wherein include near-infrared wave in the remote sensing image
Section and red spectral band；
Processing module obtains time series earth's surface reflectivity data for carrying out quantification processing to the remote sensing image；
Module is cut, for being cut to the time series earth's surface reflectivity data, extracts the ground within the scope of research area
Table reflectivity data；
Computing module, for it is described research area within the scope of Reflectivity for Growing Season data near infrared band and red spectral band into
Row band math calculates the EVI2 time series datas that area's remote sensing image is studied described in each phase pixel-by-pixel；
Filter module, for being filtered to the EVI2 time series datas；
Integration module obtains ∑ for being integrated by pixel calculated curve to filtered treated EVI2 time series datas
EVI2；
Extraction module extracts the appraising model between ∑ EVI2 and the GPP sample pre-established for being based on vegetation pattern；
Estimation block, for estimating the year GPP within the scope of the research area based on the ∑ EVI2 and the appraising model.
7. system according to claim 6, which is characterized in that the computing module is specifically used for：
Based on formulaCalculate the EVI2 time series datas, wherein ρNIRIt is close red
The reflectivity of wave section, ρREDFor the reflectivity of red spectral band.
8. system according to claim 6, which is characterized in that the integration module is specifically used for：
Based on formulaCalculate ∑ EVI2, wherein L is the length of entire time series, f (xi)
For the xth in 1 yeariIt, N is the number for resolving into the shaded area under filtered time-serial position.
9. system according to claim 6, which is characterized in that the filter module is specifically used for：
The EVI2 time series datas are filtered based on S-G filters.
10. system according to claim 9, which is characterized in that the processing module is specifically used for：
Ortho-rectification, geometric correction, radiation calibration and atmospheric correction are carried out to the remote sensing image, obtain time series earth's surface
Reflectivity data.
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