CN106290389A - The algal tufa of a kind of eutrophic lake MODIS image and non-algal tufa condition classification method - Google Patents
The algal tufa of a kind of eutrophic lake MODIS image and non-algal tufa condition classification method Download PDFInfo
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Abstract
The invention discloses the algal tufa of a kind of eutrophic lake MODIS image and non-algal tufa condition classification method, based on the artificial interpretation of MODISRGB image and field measured data analysis, the MODIS image of eutrophic lake is divided into algal tufa image and non-algal tufa image；The blue-green alga bloom discrimination index that aerosol type and thickness are insensitive is used for judging algal tufa pixel by screening；Last set pixel threshold value distinguishes algal tufa and non-algal tufa condition MODIS image.The method using the present invention can effectively be distinguished high suspended matter and dominate water body image and algal tufa dominates water body image, solves the erroneous judgement problem of two kinds of images, can more Accurate Analysis eutrophic lake change of water quality feature, provide science and technology support for the estimation of lake pigment concentration.
Description
Technical field
The present invention relates to eutrophic lake eutrophic lake " algal tufa and non-algal tufa condition " MODIS image classification side
Method.
Background technology
The advantages such as satellite remote sensing technology has quasi real time, the strongest, wide coverage, low cost, for Water Environment In Lakes
The early warning of monitoring and blue-green alga bloom provides the foundation and foundation.But find in Yan Jiu, often occur that high suspended matter dominates water body
Image and algal tufa dominate the phenomenon of water body image erroneous judgement, it is therefore desirable to classify image, more reasonably to analyze lake not
The situation of change of commaterial concentration of component.
The basis of image classification is to be identified blue-green alga bloom region exactly, identifies that blue-green alga bloom region depends on water
Volume reflectivity information, water body Remote Sensing Reflectance can reflect the spatial diversity of different material concentration of component, blue-green alga bloom in water body
The difference of region and non-wawter bloom region spectrally maximum is near infrared band: blue-green alga bloom region has the most significantly to be reflected
Peak, near infrared band forms lifting, rather than wawter bloom region does not then have such rule, and this utilizes remote sensing monitoring blue-green alga bloom
Theoretical basis.At present, more common blue-green alga bloom region recognition algorithm mainly has visual analysis, single band method, ratio method, index
Method etc. 4 kinds.Wrigley RC utilizes near infrared image to interpret the breakout of cyanobacteria blooms region of California Clear Lake,
It it is the science record relatively early about blue-green alga bloom identification.Gower etc. propose MCI index, utilize phycocyanobilin 709nm wave band feature
Peak extracts blue-green alga bloom.Landsat series data spatial resolution is higher, and Li Xuwen etc. is by than relatively large Landsat TM
Image proposes CBI blue-green alga bloom intensity index.Oyama etc., based on Landsat TM/ETM+ image, utilize VCI and FAI index
Blue-green alga bloom is monitored in conjunction with ETM+ the 3rd wave band.But Landsat satellite revisiting period needs 16 days, it is difficult to meet blue-green alga bloom
Monitoring requirements.Alawadi F is for the 1st, 2,3,4 four wave band datas of MODIS, it is proposed that a kind of new detection microalgae
Superficial water bloom index SABI (Surface Algal Bloom Index, SABI), the change of environmental condition is had relatively by the method
Good stability.But the data of said method are based on water body Remote Sensing Reflectance data Rrs, obtain water body Remote Sensing Reflectance data
Key technology is atmospheric correction, and so far, the atmospheric correction of water body in lake is also not carried out businessization and runs.Atmospheric correction side
Method be used mostly based on radiation transmission analogue model (Dekker et al., 2001；Ammenberg et al.,2002；、Duan
Et al., 2008), but traditional bearing calibration based on radiative transfer model needs the atmospheric parameter of real-time on-site, the most still
Business can not be realized.And " bright image unit " atmospheric correction algorithm (i.e. near infrared band is zero from water radiation) of being used widely
(Zhao and Nakajima,1997；Arnone et al.,1998；Ruddick et al.,2000；Hu et al.,
2000；Lavender et al.,2005；Vidot and Sante, 2005), owing to China's inland lake aerosol changes relatively
For strongly, and inland lake spoke brightness is also not zero near infrared band；Additionally, lake exists large-area optics shallow water,
From water radiation except comprising from addition to the contribution of water body, also comprise the contribution from lakebed substrate, therefore " bright image unit " atmospheric correction
Algorithm is the most applicable.Hu utilizes the reflectivity data Rrc through Rayleigh correction, it is proposed that extract blue algae water with FAI index
China, it is to avoid the error that atmospheric correction brings, the most insensitive to aerosol type and thickness, and can be used for long-term sequence Taihu Lake
Cyanophyta algal bloom monitoring research, have become as the more ripe extraction that can be used for China's eutrophic lake blue-green alga bloom region
Method.
It is contemplated that select the planktonic algae index (FAI) the most insensitive to aerosol type and thickness, know at algal tufa
On the basis of other, by visually distinguishing 2000-2014 MODIS image, statistics obtains " algal tufa and non-algal tufa condition "
MODIS image classification pixel threshold value, classifies to the MODIS image of different characteristic.By to " algal tufa and non-algal tufa condition "
MODIS image is classified, and more fully can analyze Water Quality of Lake Chaohu situation comprehensively, carries for the estimation of water body pigment concentration
For technical support.
List of references:
Alawadi F.Detection of surface algal blooms using the newly developed
algorithm surface algal bloom index(SABI)[C]//Remote Sensing.International
Society for Optics and Photonics,2010:782506-782506-14.
Ammenberg P,Flink P,Lindell T,Pierson,D,Strombeck N.Biooptical
modelling combined with remote sensing to assess water quality.International
Journal of Remote Sensing.2002,23:1621-1638；
Arnone R A,Martinolich P,Gould R W,Stumpf R,Ladner S.Coastal Optical
Properties Using SeaWiFS,Ocean Optics XIV Kailua-Kona Hawaii,SPIE-the
Internation Society for Optical Engineering.November 10-13,1998；
Dekker A G,Vos R J,Peters S W M.Comparison of remote sensing data,
model results and in situ data for total suspended matter(TSM)in the southern
Frisian lakes.The Science of the Total Environment.2001,268:197-214；
Duan H,Zhang Y,Zhang B,Song K,Wang Z,Liu D,Li F.Estimation of
chlorophyll-a concentration and trophic states for inland lakes in Northeast
China from Landsat TM data and field spectral measurements.International
Journal of Remote Sensing.2008,29(3)；
Gower J,King S,Borstad G,et al.Detection of intense plankton blooms
using the 709nm band of the MERIS imaging spectrometer[J].International
Journal of Remote Sensing,2005,26(9):2005-2012.
Hu C.M.A novel ocean color index to detect floating algae in the
global oceans.Remote sensing of environment,2009,113(10):2118-2129；
Hu C.M.,Li D.Q.,Chen C.S.,et al.On the recurrent Ulvaprolifera blooms
in the Yellow Sea and East China Sea.Journal of Geophysical Research,2010a,
115,C05017；
Lavender S J,Pinkerton M H,Moore G F.Modification to the atmospheric
correction of SeaWiFS ocean color images over turbid waters.Continental Shelf
Research.2005,25:539-555；
Oyama Y,Fukushima T,Matsushita B,et al.Monitoring levels of
cyanobacterial blooms using the visual cyanobacteria index(VCI)and floating
algae index(FAI)[J].International Journal of Applied Earth Observation and
Geoinformation,2015,38:335-348.
Ruddick K G,Ovidio F,Rijkeboer M.Atmospheric correction of SeaWiFS
imagery for turbid coastal and inland Waters.Applied Optics.2000,39(6):897-
912；
Vidot J,Santer R.Atmospheric correction for inland waters-application
to SeaWiFS.International Journal of Remote Sensing.2005,26(17):3663-3682；
Wrigley RC.Remote sensing and lake eutrophication[J].Nature,1974,250:
213-214.
Zhao Fengsheng,Nakajima T.Simultaneous determination of water-leaving
reflectanceand aerosol optical thickness from Coastal Zone Color Scanner
measurements.Applied Optics.1997,36(27):6949-6956；
Li Xuwen, Niu Zhichun, Jiang Sheng, etc. Taihu Lake based on satellite image blue-green alga bloom remote sensing intensity index and grade are drawn
Divide algorithm design [J]. environment monitoring management and technology, 2011 (5): 23-30.
Summary of the invention
The algal tufa and the non-algal tufa condition image that it is an object of the invention to provide a kind of eutrophic lake MODIS image divide
Class method, can effectively classify to nutrition-enriched water of lake image, with more fully to eutrophic lake water quality situation
Analyze comprehensively, provide technical support for the estimation of water body pigment concentration.
The above-mentioned purpose of the present invention is realized by the technical characteristic of independent claims, and dependent claims is with alternative or has
The mode of profit develops the technical characteristic of independent claims.
For reaching above-mentioned purpose, the technical solution adopted in the present invention is as follows:
The algal tufa of a kind of eutrophic lake MODIS image and non-algal tufa condition classification method, including: based on MODIS
The artificial interpretation of RGB image and field measured data analysis, the MODIS image of eutrophic lake is divided into " algal tufa image " and
" non-algal tufa image "；Screening extracts the index (FAI) of blue-green alga bloom in order to judge algal tufa pixel；Set rational pixel threshold zone
Divide algal tufa and non-algal tufa image.
As further example, implementing of preceding method includes:
1) based on the MODIS artificial interpretation of RGB image and field measured data analysis, by eutrophic lake Chaohu
MODIS image is divided into " algal tufa image " and " non-algal tufa image ".
Chaohu mainly comprises three kinds of typical water bodys: clean water body, high suspended matter dominates water body and algal tufa dominates water body, uphangs
Float region and algal tufa region belong to strong signaling zone, often occur that high suspended matter dominates water body and algal tufa dominates showing of water body erroneous judgement
As；
2) screening extracts the index (FAI) of blue-green alga bloom in order to judge algal tufa pixel:
It is similar that cleaning water body dominates water body exemplary spectrum feature with high suspended matter, and cleaning water body, high suspended matter are dominated water
Body is classified as " non-algal tufa image ", and algal tufa is dominated water body and is classified as " algal tufa image ".In conjunction with the image data of eutrophic lake, carry out
Index simulation under the conditions of different images, determines the algal tufa pixel number choosing index FAI in order to judge image；
3) set rational pixel threshold value and distinguish " algal tufa image " and " non-algal tufa image ":
Count the pixel number that every scape MODIS " non-algal tufa image " is affected by boundary effect, by affected for every scape image
Pixel number makes rectangular histogram, calculates this histogrammic meansigma methods and standard deviation as distinguishing MODIS image " algal tufa " and " non-algae
China " uniform threshold of condition；
From the technical scheme invented above, the nutrition-enriched water of lake algal tufa of the present invention and non-algal tufa condition image
Sorting technique, can effectively classify to nutrition-enriched water of lake image, effectively distinguishes high suspended matter image and algal tufa image,
Solve algal tufa image and the problem of high suspended matter image erroneous judgement, in order to more fully Water Quality of Lake Chaohu situation is carried out classification point
Analysis, provides technical support for the estimation of water body pigment concentration.
As long as should be appreciated that all combinations of aforementioned concepts and the extra design described in greater detail below are at this
A part for the subject matter of the disclosure is can be viewed as in the case of the design of sample is the most conflicting.It addition, required guarantor
All combinations of the theme protected are considered as a part for the subject matter of the disclosure.
Foregoing and other aspect, the reality that present invention teach that can be more fully appreciated with from the following description in conjunction with accompanying drawing
Execute example and feature.The feature of other additional aspect such as illustrative embodiments of the present invention and/or beneficial effect will be below
Description in obvious, or by learning according in the practice of detailed description of the invention that present invention teach that.
Accompanying drawing explanation
Accompanying drawing is not intended to drawn to scale.In the accompanying drawings, each identical or approximately uniform group illustrated in each figure
One-tenth part can be indicated by the same numeral.For clarity, in each figure, the most each ingredient is the most labeled.
Now, by by example embodiment that various aspects of the invention are described in reference to the drawings, wherein:
Fig. 1 is that high SPM dominates water body and algal tufa dominates the first mode distribution of water body image.
Fig. 2 is the typical water body in three kinds of Chaohu and spectral signature thereof.
Fig. 3 is Chaohu MODIS RGB image and FAI product on April 27th, 2013.
Fig. 4 is the different lake region pixel number by land and water effects.
Fig. 5 is " algal tufa image " and the pixel threshold decision of " non-algal tufa image ".
Fig. 6 be by MODIS image after unified method judges, carry out the classification of image.
In aforementioned diagram 1-5, each coordinate, mark or other expressions expressed as English form, it is this area institute public
Know, repeat the most again.
Detailed description of the invention
In order to know more about the technology contents of the present invention, especially exemplified by specific embodiment and coordinate institute's accompanying drawings to be described as follows.
Each side the most with reference to the accompanying drawings to describe the present invention, the embodiment illustrated shown in the drawings of many.
Embodiment of the disclosure and must not be intended to include all aspects of the invention.Should be appreciated that multiple design presented hereinbefore and reality
Execute example, and those designs of describing in more detail below and embodiment can in many ways in any one comes real
Executing, this is to should be design disclosed in this invention and embodiment is not limited to any embodiment.It addition, disclosed by the invention one
A little aspects can be used alone, or otherwise any appropriately combined uses with disclosed by the invention.
The present invention carries out image based on MODIS satellite data to nutrition-enriched water of lake " algal tufa and non-algal tufa condition " and divides
Class, above-mentioned purpose is achieved in that based on the MODIS artificial interpretation of RGB image and field measured data analysis, by eutrophy
The MODIS image changing lake is divided into " algal tufa and non-algal tufa condition "；Screening extracts the index (FAI) of blue-green alga bloom in order to judge algae
China's pixel；Set rational pixel threshold value and distinguish the MODIS image under " algal tufa and non-algal tufa condition ".Selected MODIS defends
Star image is through radiation calibration, the correction of air Rayleigh scattering and the satellite image of geometric correction, the resolution of satellite image used
Rate is 250nm.
As exemplary description, shown below in conjunction with the accompanying drawings, as a example by Chaohu, the enforcement to preceding method is carried out specifically
Explanation.
Step 1, the MODIS image in eutrophic lake Chaohu is divided into " algal tufa image " and " non-algal tufa image "；
Based on the MODIS artificial interpretation of RGB image and field measured data analysis, Chaohu mainly comprises three kinds of typical water bodys:
Clean water body, high suspended matter dominates water body and algal tufa dominates water body.In EOF algorithm application process, high suspended matter often occurs
Water body and the phenomenon of algal tufa water body erroneous judgement, directly enter high suspended matter water body and algal tufa water body both of these case Rrc image data
Row EOF analyzes, first mode result display algal tufa region and high suspended matter area score value the highest (Fig. 1)；Go out with algal tufa
Existing region is identical, and the region that float is high occupies all of Mode variation signal, if not classifying image, then two class
Water body there will be the situation of erroneous judgement.
2, screening extracts the index (FAI) of blue-green alga bloom in order to judge algal tufa pixel；
It addition, relative analysis cleaning water body and high suspended matter dominate water body and algal tufa dominates the spectral signature (figure of water body
2), find that high suspended matter region point position (S2) Rrc spectrum is significantly higher than cleaning water body point position S1, but spectral shape is similar to, therefore
Cleaning water body, high suspended matter can be dominated water body and be classified as a class image (I class).And water body, algal tufa region are dominated for algal tufa
The reflectance of some position (S3) near infrared band (859nm) dramatically increases, and great changes will take place for spectral shape, the most herein by algal tufa
Image is individually divided into another kind of (II class).
Numerous for the index of the extraction of blue-green alga bloom, doctor Hu Chuanmin propose planktonic algae index (FAI,
Floating algae index), for littoral zone and inland lake water body, there is good stability, can effectively extract indigo plant
Algae algal tufa.FAI index is with 645nm and 1240nm wave band as baseline, and the difference calculating 859nm and baseline judges algal tufa, specifically
Computing formula is:
FAI=Rrc'(859)-Rrc (859) (1)
Rrc'(859)=Rrc (645)-[Rrc (1240)-Rrc (645)] * (859-645)/(1240-645) (2)
Wherein, Rrc (λ) is the reflectance at the λ wavelength of Rayleigh correction, Rrc ' (859) be based on 645nm and
The 859nm wave band relative reflectance that 1240nm wave band linear interpolation obtains.
3, set rational pixel threshold value and distinguish " algal tufa image " and " non-algal tufa image "；
According to FAI index and dependent thresholds, Chaohu image is divided into two classes: " non-algal tufa image " (I class) and " algal tufa shadow
Picture " (II class).FAI is the most sensitive to high suspended matter, and selecting pure algal tufa pixel FAI=0.02 is threshold value, can effectively distinguish " non-
Algal tufa image ".But finding in Practical Calculation, " non-algal tufa image " is due to by flood boundaries effect, band and small pieces algal tufa etc.
The impact of three kinds of situations, easily causes and " non-algal tufa image " is mistaken for " algal tufa image ", particularly flood boundaries effect causes
Mixed point of situation is most, accounts for more than 80%.
Randomly choose the scape image by flood boundaries effects, as a example by April 27th, 2013 image (Fig. 3 a), mesh
Result depending on analyzing shows that this image belongs to " non-algal tufa image ", but FAI result is by flood boundaries effects (Fig. 3 b).At random
Have selected the zones of different (shown in Fig. 3 b dotted line frame) of three lake regions, each region comprises 17*13 effectively pixel, effective picture
The unit i.e. pixel of non-null value.The pixel on Inland waterbody border is designated as 0, and land pixel is designated as bearing, and water body pixel is just designated as, and each cuts open
Face amounts to 13 pixels.Each pixel FAI value is the average of 17 pixel FAI values on vertical direction.
Fig. 4 illustrates the different lake region pixel number (as shown in Fig. 4 black surround) by flood boundaries effects, the West Lake
Two, district pixel is affected (FAI > 0.02) by boundary effect, one, middle lake region pixel, two, Donghu District pixel.If selected at random
Select other regions, will be changed by the pixel number of flood boundaries effects, zones of different affected pixel number
Mesh is different and does not determine rule.Former algorithm assume in it is considered that there is FAI > pixel of 0.02 is then considered " algal tufa shadow
Picture ", if ignoring the impact of boundary effect, then within 2013, then not having " non-algal tufa image ", this does not obviously meet objective fact.
Therefore consider flood boundaries effect, set distinguish " algal tufa image ", " non-algal tufa image " pixel threshold value the most necessary.
" algal tufa image " is distinguished very well by visual interpretation, and images all to 2000-2014 screen, will be all
After " algal tufa image " rejects, count the pixel number that every scape " non-algal tufa image " is affected by boundary effect.Every scape image is subject to
The pixel number of impact makes rectangular histogram (Fig. 5), and in figure, N is image sum.This rectangular histogram includes 1182 images, calculates this straight
The meansigma methods of side's figure and standard deviation, average is 102.59, and standard deviation is 91.02.Rectangular histogram is through the data detection of SPSS, this point
Cloth meets normal distribution.The computational methods of pixel threshold value are: meansigma methods+2* standard deviation, calculate the pixel threshold of the present embodiment accordingly
It is worth about 285 pixels, i.e. algal tufa area and need to be more than 17.80km2, this threshold value is distinguished non-algal tufa (I class) and algae as this research
The uniform threshold of China's (II class) image.The pixel threshold value of aforementioned image classification method uses statistics 3Sigma principle, and one group just
Meansigma methods+2* the standard deviation of state distributed data covers the data volume of 95%.
Can be realized MODIS image after unified method judges by said method, carry out image classification (as
Fig. 6), high suspended matter image and algal tufa image during the method for the present invention can effectively distinguish eutrophic lake MODIS image, it is to avoid
The erroneous judgement of two kinds of images.Contribute to all sidedly eutrophic lake water quality situation being analyzed comprehensively, for water body pigment concentration
Estimation provides technical support.
Although the present invention is disclosed above with preferred embodiment, so it is not limited to the present invention.Skill belonging to the present invention
Art field has usually intellectual, without departing from the spirit and scope of the present invention, when being used for a variety of modifications and variations.Cause
This, protection scope of the present invention is when being as the criterion depending on those as defined in claim.
Claims (4)
1. the algal tufa of an eutrophic lake MODIS image and non-algal tufa condition classification method, it is characterised in that: include as follows
Step:
(1) based on the MODIS artificial interpretation of RGB image and field measured data analysis, by the MODIS image of eutrophic lake
It is divided into algal tufa image and non-algal tufa image；
Eutrophic lake mainly comprises three kinds of typical water bodys: clean water body, high suspended matter dominates water body and algal tufa dominates water body,
It is similar that cleaning water body dominates water body exemplary spectrum feature with high suspended matter, cleaning water body, high suspended matter is dominated water body and is classified as non-
Algal tufa image, algal tufa is dominated water body and is classified as algal tufa image；
(2) the blue-green alga bloom discrimination index that aerosol type and thickness are insensitive is used for judging algal tufa pixel by screening；
In conjunction with the MODIS image data of eutrophic lake, carry out the index simulation under the conditions of different images, select aerosol
Type and the insensitive algae index FAI of thickness thereof are in order to judge the algal tufa pixel number of image；
(3) set pixel threshold value and distinguish algal tufa and non-algal tufa condition MODIS image；
Adding up the pixel number that every scape non-algal tufa image is affected by boundary effect, with this pixel number for single width threshold value, every scape is non-
The corresponding threshold value of algal tufa image；Do meansigma methods and the standard deviation of all single width of histogram calculation non-algal tufa image threshold value, calculate
Every scape image affected pixel number is made rectangular histogram by the uniform threshold of non-algal tufa image, obtain this histogrammic meansigma methods and
Image, as distinguishing algal tufa image and the uniform threshold of non-algal tufa image, is classified by standard deviation, obtains algal tufa image and non-
Algal tufa image；
Wherein, threshold calculations mode is: meansigma methods+2* standard deviation.
Method the most according to claim 1, it is characterised in that in described step (2), index FAI that classification is selected is expressed
For:
FAI=Rrc'(859)-Rrc (859) (1)
Rrc'(859)=Rrc (645)-[Rrc (1240)-Rrc (645)] * (859-645)/(1240-645) (2)
Wherein, Rrc (λ) is the reflectance at the λ wavelength of Rayleigh correction, and Rrc ' (859) is based on 645nm and 1240nm ripple
The 859nm wave band relative reflectance that section linear interpolation obtains.
Method the most according to claim 1, it is characterised in that in described step (3), the pixel threshold value of image classification method
Using statistics 3Sigma principle, the meansigma methods+2* standard deviation of one group of normal distribution data covers the data volume of 95%.
Method the most according to claim 1, it is characterised in that selected MODIS satellite image is set up through overshoot
On the basis of calibration, the correction of air Rayleigh scattering and geometric correction.
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| CN111795941A (en) * | 2020-08-06 | 2020-10-20 | 中国科学院重庆绿色智能技术研究院 | Hyperspectral identification method for algal community structure in bloom stage |
| CN112989692A (en) * | 2021-02-10 | 2021-06-18 | 中国科学院南京地理与湖泊研究所 | Lake eutrophication inversion method based on remote sensing data |
| CN112989281A (en) * | 2021-02-20 | 2021-06-18 | 中国科学院南京地理与湖泊研究所 | Algal bloom prediction method based on total amount of remote sensing algae |
| CN117745754A (en) * | 2023-12-18 | 2024-03-22 | 中国科学院空天信息创新研究院 | Automatic monitoring method and system for cyanobacteria bloom |
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| CN111795941A (en) * | 2020-08-06 | 2020-10-20 | 中国科学院重庆绿色智能技术研究院 | Hyperspectral identification method for algal community structure in bloom stage |
| CN112989692A (en) * | 2021-02-10 | 2021-06-18 | 中国科学院南京地理与湖泊研究所 | Lake eutrophication inversion method based on remote sensing data |
| CN112989281A (en) * | 2021-02-20 | 2021-06-18 | 中国科学院南京地理与湖泊研究所 | Algal bloom prediction method based on total amount of remote sensing algae |
| CN112989281B (en) * | 2021-02-20 | 2023-09-12 | 中国科学院南京地理与湖泊研究所 | Algal bloom prediction method based on total amount of remote sensing algae |
| CN117745754A (en) * | 2023-12-18 | 2024-03-22 | 中国科学院空天信息创新研究院 | Automatic monitoring method and system for cyanobacteria bloom |
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