CN105631904B - A kind of eutrophic lake algae total inventory remote sensing estimation method - Google Patents
A kind of eutrophic lake algae total inventory remote sensing estimation method Download PDFInfo
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Abstract
The present invention provides a kind of eutrophic lake algae total inventory (token state: chlorophyll a total amount, dimension: remote sensing estimation method t), it include: on the basis of bio-optical model simulation and measured data, the quantitative relationship between NDBI and water body surface layer chlorophyll-a concentration is obtained, and extends to the MODIS satellite image data corrected by Rayleigh scattering；DEM under water level on the same day and water of Chaohu Lake based on Middle Temple on Nest Lake and Chaohu lock determines the depth of water space distribution situation in Chaohu；Based on measured section empirical data, screening judges lake NDBI threshold value, constructs algae total inventory evaluation method in algal tufa and non-algal tufa condition lower unit water column respectively；The evaluation method of algae total inventory in unit pixel based on MODIS satellite image.Based on method of the invention, can accurately obtain eutrophic lake algae total inventory year border, moon border changing rule and its spatial distribution.
Description
Technical field
The present invention relates to eutrophic lake algae total inventory remote sensing estimation methods.
Background technique
Wawter bloom phenomenon caused by algal bloom be water body in lake eutrophication important feature (Kong Fanxiang and bloom,
2005), China has become one of the country that breakout of cyanobacteria blooms most serious, distribution are most wide in the world (Wu Qinglong etc., 2008).
Satellite remote sensing has the characteristics that quick, a wide range of, periodic, it has also become the monitoring of lake blue algae wawter bloom and prediction and warning are indispensable
Technological means (Pan Delu and Ma high honour, 2008).Currently, China middle and lower reach of Yangtze River eutrophic lake (Taihu Lake, Chaohu etc.)
The Satellite Remote Sensing of cyanobacterial bloom area realized substantially businessization operation (horse high honour etc., 2010；Zhu Li etc.,
2013), important decision-making foundation is provided for government and water environment management department.
In fact, since cyanobacteria has the special cells structure of pseudo- ghost (Vacuole), its own with buoyancy and according to
Environmental change (illumination, hydrodynamic force) adjust buoyancy ability (Walsby, 1994；Kong Fanxiang and Song Lirong, 2011), lead to water body
Acute variation often occurs within a few hours for surface layer algal tufa area, in addition occur in the short time aggregation of large area cyanobacterial bloom or
The phenomenon that disappearance.Therefore, it is separately monitored covering wawter bloom area and is difficult exact representation Measures of Algae in Water Body distribution situation, only acquisition water
Internal algae total inventory, could accurately hold cyanobacteria changing condition.
Summary of the invention
The purpose of the present invention is to provide a kind of eutrophic lake algae total inventory remote sensing estimation methods, can accurately obtain
The spatial and temporal distributions of lake algae total inventory, it is accurate to analyze the generation of algae total amount, state of development and trend, Scientific evaluation lake in lake
Pollution control and Effect of Ecological Restoration provide for the water resources management of the departments such as water conservancy, environmental protection, the science decision of water environment protection
Science and technology support.
Above-mentioned purpose of the invention realizes that dependent claims are to select else or have by the technical characteristic of independent claims
The mode of benefit develops the technical characteristic of independent claims.
To reach above-mentioned purpose, the technical solution adopted in the present invention is as follows:
The present invention provides a kind of eutrophic lake algae total inventory (token state: chlorophyll a total amount, dimension: remote sensing t)
Evaluation method, comprising: on the basis of bio-optical model simulation and measured data, obtain NDBI and water body surface layer chlorophyll a
Quantitative relationship between concentration, and extend to the MODIS satellite image data corrected by Rayleigh scattering；Based on Middle Temple on Nest Lake and nest
DEM under the water level on the same day and water of Chaohu Lake of Hu Zha determines the depth of water space distribution situation in Chaohu；Based on measured section empirical data,
Screening judges lake NDBI threshold value, constructs algae total inventory evaluation method in algal tufa and non-algal tufa condition lower unit water column respectively；
The evaluation method of algae total inventory in unit pixel based on MODIS satellite image.Based on method of the invention, can accurately obtain
Eutrophic lake algae total inventory year border, moon border changing rule and its spatial distribution.
As further example, the specific implementation of preceding method includes:
1) on the basis of bio-optical model simulation and measured data, NDBI and water body surface layer chlorophyll-a concentration are obtained
Between quantitative relationship
Based on algal tufa and suspended matter spectral response characteristics, it is dense as lake surface layer chlorophyll a to construct algae index NDBI
The cardinal index of degree；On the basis of bio-optical model, in conjunction with the measured data in Chaohu, the Numerical-Mode under different scenes is carried out
It is quasi-, it determines the quantitative relationship of NDBI and chlorophyll-a concentration, is based on R using field measured data buildingrsThe NDBI of data and surface layer
The quantitative relationship of chlorophyll-a concentration；Chaohu Prefecture is simulated in different aerosol types and thickness, different solar elevations, satellite
In the case of view angle and azimuth, the remote sensing reflectance R of ground monitoringrsWith the R after the Rayleigh scattering correction of simulationrcBetween
Quantitative relationship；Surface layer chlorophyll-a concentration inversion algorithm based on situ measurements of hyperspectral reflectance data is applied to by Rayleigh scattering
The MODIS satellite image data of correction, to get the full waters surface layer chlorophyll-a concentration spatial distribution in lake；
2) DEM under the water level on the same day and water of Chaohu Lake based on Middle Temple on Nest Lake and Chaohu lock, determines the depth of water spatial distribution in Chaohu
Situation
By the waterlevel data at Chaohu mausoleum Zha Yuzhong station, the waterlevel data in full lake is calculated；Lakebed is obtained by lakebed DEM
Altitude data subtracts the full lake water depth spatial distribution data of altitude data by waterlevel data；
3) screening judges the NDBI threshold value of the non-algal tufa condition in lake, is based on measured section data, constructs algal tufa and non-algal tufa
The remote sensing estimation method of condition following table algae total amount
Wherein the evaluation number NDBI of the judgement non-algal tufa condition in lake refers to is rung based on algal tufa and suspended matter spectrum
Feature is answered, using the algae index as the cardinal index for judging algal tufa and non-algal tufa；Based on measured data, CART decision tree is utilized
And ground remote sensing reflectivity RrsWith the R after Rayleigh scattering correctionrcBetween quantitative relationship, get NDBIRrc=0.1193
For the differentiation threshold value of algal tufa non-in satellite image and algal tufa condition；Based on Chaohu Prefecture's field section monitoring data, construct respectively
The remote sensing estimation method of algae total amount under the conditions of algal tufa and non-algal tufa；
4) in each pixel based on MODIS satellite image algae total inventory evaluation method
Using between water body surface layer chlorophyll-a concentration and NDBI quantitative relationship, under respective conditions algae total amount remote sensing appraising
Method, the bathymetric data based on MODIS satellite image in conjunction with Chaohu on the same day obtain the algae in each pixel water column of satellite image
Class total inventory.
Based on abovementioned steps and method, acquisition eutrophic lake is complete after the satellite image processing to several time serieses
The algae total inventory in lake year border, moon border changing rule and its spatial distribution.
From the above technical solution of the present invention shows that, eutrophic lake algae total inventory MODIS satellite of the invention is distant
Feel evaluation method, on the basis of bio-optical model simulation and measured data, obtains NDBI and water body surface layer chlorophyll-a concentration
Between quantitative relationship, and extend to by Rayleigh scattering correct MODIS satellite image data；Based on Middle Temple on Nest Lake and Chao Huzha
Water level on the same day and water of Chaohu Lake under DEM, determine the depth of water space distribution situation in Chaohu；Based on measured section empirical data, screening
Judge lake NDBI threshold value, constructs algae total inventory evaluation method in algal tufa and non-algal tufa condition lower unit water column respectively；It is based on
The evaluation method of algae total inventory in the unit pixel of MODIS satellite image.The spatial and temporal distributions for obtaining full lake algae total inventory, can
Reflect the spatial and temporal distributions of lake eutrophication situation with more objective reality.Lake algae total inventory remote sensing monitoring can be effective
Realization effectively assessed to lake algal tufa risk and to watershed；The long-term high precision monitor of lake algae total inventory,
Facilitate the variation and its development trend of algae total amount between Scientific evaluation year border, effectively assesses lake pollution and administer and restoration of the ecosystem
Performance, provide science and technology support for the water resources management of the departments such as water conservancy, environmental protection, the science decision of water environment protection.
It should be appreciated that as long as aforementioned concepts and all combinations additionally conceived described in greater detail below are at this
It can be viewed as a part of the subject matter of the disclosure in the case that the design of sample is not conflicting.In addition, required guarantor
All combinations of the theme of shield are considered as a part of the subject matter of the disclosure.
Can be more fully appreciated from the following description in conjunction with attached drawing present invention teach that the foregoing and other aspects, reality
Apply example and feature.The features and/or benefits of other additional aspects such as illustrative embodiments of the invention will be below
Description in it is obvious, or learnt in practice by the specific embodiment instructed according to the present invention.
Detailed description of the invention
Attached drawing is not intended to drawn to scale.In the accompanying drawings, identical or nearly identical group each of is shown in each figure
It can be indicated by the same numeral at part.For clarity, in each figure, not each component part is labeled.
Now, example will be passed through and the embodiments of various aspects of the invention is described in reference to the drawings, in which:
Fig. 1 is the basic principle schematic of NDBI Index Monitoring algal tufa.
Fig. 2 is quantitative relationship between NDBI and chlorophyll-a concentration under theoretical modeling.
Fig. 3 is different aerosol types and its thickness, when different solar elevations, moonscope angle and azimuth, Rrs
With RrcBetween quantitative relationship.
Fig. 4 is the CART decision tree that NDBI judges algal tufa Yu non-algal tufa condition.
Fig. 5 is the remote sensing estimation method of Chaohu algae total amount under the conditions of algal tufa and non-algal tufa.
Fig. 6 is MODIS satellite high-precision monitoring spatial distribution result (on December 4th, 2010) of Chaohu chlorophyll a.
Fig. 7 is the spatial and temporal distributions result summary view of a certain section of period (2003-2013) Chaohu algae total inventory.
Fig. 8 is the variation statistical chart of a certain section of period (2003-2013) Chaohu algae total inventory.
It is that this field institute is public as each coordinate of English form expression, mark or other expressions in aforementioned diagram 1-8
Know, does not repeat again in this example.
Specific embodiment
In order to better understand the technical content of the present invention, special to lift specific embodiment and institute's accompanying drawings is cooperated to be described as follows.
Various aspects with reference to the accompanying drawings to describe the present invention in the disclosure, shown in the drawings of the embodiment of many explanations.
It is not intended to cover all aspects of the invention for embodiment of the disclosure.It should be appreciated that a variety of designs and reality presented hereinbefore
Those of apply example, and describe in more detail below design and embodiment can in many ways in any one come it is real
It applies, this is to should be conception and embodiment disclosed in this invention to be not limited to any embodiment.In addition, disclosed by the invention one
A little aspects can be used alone, or otherwise any appropriately combined use with disclosed by the invention.
The present invention gives MODIS satellite data to the remote sensing appraising of eutrophic lake algae total inventory, and above-mentioned purpose is
It is achieved: on the basis of bio-optical model simulation and measured data, obtaining NDBI and water body surface layer chlorophyll-a concentration
Between quantitative relationship, and extend to by Rayleigh scattering correct MODIS satellite image data；Based on Middle Temple on Nest Lake and Chao Huzha
Water level on the same day and water of Chaohu Lake under DEM, determine the depth of water space distribution situation in Chaohu；Based on measured section empirical data, screening
Judge lake NDBI threshold value, constructs algae total inventory evaluation method in algal tufa and non-algal tufa condition lower unit water column respectively；It is based on
The evaluation method of algae total inventory in the unit pixel of MODIS satellite image.The spatial and temporal distributions for obtaining full lake algae total inventory, can
Reflect the spatial and temporal distributions of lake eutrophication situation with more objective reality.
It is shown with reference to the accompanying drawing as illustrative description, the implementation of preceding method is specifically described.
Step 1, in bio-optical model simulation and on the basis of measured data, obtain NDBI and water body surface layer chlorophyll a
Quantitative relationship between concentration
Chlorophyll a evaluation number NDBI that is sensitive and not influenced by high suspended matter is changed on chlorophyll-a concentration and refers to base
In chlorophyll a and suspended matter spectral response characteristics, selects red, green wave band and be similar to NDVI expression-form, can avoid height
The adverse effect that suspended matter estimates chlorophyll-a concentration, and using the algae index as chlorophyll-a concentration remote sensing monitoring index.
Specifically, the fundamental surveillance based on water body optically active substance (chlorophyll a, mineral suspensions, yellow substance) is former
Reason studies the spectral signature of three kinds of optically active substances in water body, and inverting of the existing chlorophyll a in case Ⅱ waters is combined to calculate
Method, the respective advantage and disadvantage of comparative analysis, while selecting accurate estimation chlorophyll-a concentration, not by mineral suspensions in water body and
Cardinal index of the Monitoring Index of the influence of yellow substance as cyanobacterial bloom MODIS satellite monitoring, to overcome other in water body
The rough sledding of optical active matter confrontation chlorophyll-a concentration monitoring.
In this example, since the water body of high chlorophyll a has a reflection peak in green light band (570nm), and because chlorophyll a exists
Strong absorb of 665nm and the reflection paddy (Fig. 1) for causing red spectral band, therefore can be from the corresponding chlorophyll a characteristic wave bands of MODIS
To estimate the content of chlorophyll a.Fig. 1 is high chlorophyll a under MODIS band setting, the spectrum of high muddy and general water body with
And the difference of three, it can be seen that if using 555nm, 645nm wave band as both ends basic point, high chlorophyll a water body and high muddy water
Body has maximum difference.According to this feature, proposes NDBI (Normalized difference bloom index) and refer to
Number:
NDBIRrs=(Rrs(555)-Rrs(645))/(Rrs(555)+Rrs(645)) (1)
Wherein, Rrs(λ) is water body remote sensing reflectance at the λ wavelength of ground survey acquisition.
On the basis of bio-optical model, in conjunction with the measured data in Chaohu, carry out the numerical simulation under different scenes, from
Theoretically prove in the quantitative relationship and water body of NDBI and chlorophyll-a concentration that other optical active matters are verified algorithm influence.
In this example, for general water body, the remote sensing reflectance of water body and the inherent optics attribute of water body are proportional,
A (λ)=aw(λ)+aph(λ)+ad(λ)+ag(λ)
bb(λ)=bbw(λ)+bbp(λ) (2)
Wherein awAnd bbwCorrespond to the absorption coefficient and backscattering coefficient of pure water；And aph、adAnd agIt is then phytoplankton
The absorption coefficient of pigment, mineral suspensions and yellow substance, they all with the amount of respective substance in water body there is substantial connection,
bbpIt is the backscattering coefficient of particulate matter in water body, in the not high water body of algae content, which has with mineral suspensions
Substantial connection.Wherein,
According to formula (1), there are following relationship between NDBI and chlorophyll-a concentration,
According to formula (4), there is monotonic relationshis between NDBI and chlorophyll-a concentration, that is, NDBI is with chlorophyll-a concentration
Increase and increases.Therefore it is presumed that mineral suspensions concentration is 50mg/L in water body, in the case where ignoring yellow substance influences,
Fig. 2 is based on quantitative relationship between the bio-optical model NDBI simulated and water body surface layer chlorophyll-a concentration.
According to our 2013-2014 the fieldwork in Chaohu spectroscopic data and corresponding chlorophyll-a concentration number
According to we construct the inversion algorithm of Chaohu surface water chlorophyll a based on measured spectra data.
Chla=3.888e15.83×NDBI(Rrs) (5)
Chaohu Prefecture is investigated in different aerosol types and thickness, different solar elevations, moonscope angle and orientation
Angle is to the R after the remote sensing reflectance of ground monitoring and the Rayleigh scattering correction of simulationrcBetween quantitative relationship influence, and pass through
Analogue data determines quantitative model between the two.
In this example, the inversion algorithm for obtaining chlorophyll a based on measured spectra data is extended into satellite image data,
Atmosphere correction be can not ignore.But still lack be directed to high turbid water body effectively accurate atmosphere correction algorithm at present, this is adopted
It is corrected with the Rayleigh scattering of MODIS image, that is, by this correction, the optical information on atmosphere top eliminates Rayleigh scattering
Influence, still include aerosol information and terrestrial information.Based on the data after Rayleigh scattering correction, NDBI expression are as follows:
NDBIRrc=(Rrc(555)-Rrc(645))/(Rrc(555)+Rrc(645)) (6)
Wherein, Rrc(λ) is the reflectivity at the λ wavelength by Rayleigh correction.RrcIt is that MODIS data carry out Rayleigh scattering
Correction, the research for being then based on Hu etc. (2004) are converted into the reflectivity after Rayleigh scattering correction:
In formula,It is the sensor radiation rate corrected after ozone and other gettering effects, F0It is big when obtaining data
The outer solar irradiance of air ring, θ0It is solar zenith angle, RrIt is the rayleigh reflectance using 6S (Vermote etc., 1997) prediction.
Based on radiation transfer theory and assume a non-coupled ocean-Atmosphere System, RrcIt can be expressed as:
Rrc=Ra+t0tRtarget (8)
In formula, RaIt is aerosol reflectivity (including the interaction from aerosol particles), RtargetIt is fieldwork
The surface reflectivity of target (algae or water body), t0It is the atmospheric transmissivity from the sun to object, t is from object to defending
The atmospheric transmissivity of star sensor.Due to the influence of wind-engaging and water flow, planktonic algae typically exhibits a kind of form of oil slick,
Therefore t is considered as the light transmission of planktonic algae.
It is influenced caused by different aerosol types and its thickness and moonscope to investigate, we are according to Chaohu
Remote sensing of the area in different aerosol types and thickness, different solar elevations, moonscope angle and azimuth to ground monitoring
R after reflectivity and the correction of the Rayleigh scattering of simulationrcBetween quantitative relationship influence (Fig. 3), and pass through analogue data determine
Quantitative model between the two,
NDBI(Rrc)=0.605NDBI (Rrs)+0.023。 (9)
Retrieving Chlorophyll-a Concentration algorithm based on situ measurements of hyperspectral reflectance data is applied to the satellite shadow corrected by Rayleigh scattering
As data, it is based on formula (5) and formula (9), the MODIS satellite high-precision inverse model of Chaohu chlorophyll a is as follows,
Chla=1.935e26.165×NDBI(Rrc) (10)
According to the Rayleigh scattering correction based on MODIS image, water body surface layer in total image can be realized in conjunction with formula (10)
The high-precision of chlorophyll-a concentration is estimated.Detailed process is mainly as follows: 1. to the MODIS image of acquisition carried out geometric correction and
Radiation calibration calculates.Geometric correction is projected using Geographic Lat/Lon, is carried out in conjunction with the latitude and longitude information in 1B data
Correction, the position precision after correction reach 0.5 pixel.Vector boundary in lake is utilized in ERDAS, is extracted by mask technique
Lake waters remove the influence of island vegetation, are 250m by MODIS 500m image data resampling using nearest neighbor method；②
Pixel calculates it in the R of band1 (645nm) and band4 (555nm) one by one in MODIS imagercValue；3. one by one according to formula (6)
Pixel calculates NDBI value；4. the water body surface layer chlorophyll a spatial distribution knot after calculating can be obtained then according to formula (10)
Fruit.
2, DEM under the water level on the same day and water of Chaohu Lake based on Middle Temple on Nest Lake and Chaohu lock, determines the depth of water spatial distribution in Chaohu
Situation
Bathymetric data is to subtract each other to obtain with lake region dem data by the waterlevel data on the day of lake region.Chaohu is had chosen in calculating
The measured data of two hydrology websites in lock station and loyal mausoleum station obtains two stations by counting all station datas of 2006-2013
The mean water algebraic difference between adjacent gradients of point.Number of days for lacking a certain website obtains the water level in full lake using the mean inclination difference, for
Number of days including two websites obtains the water level in full lake using the water level algebraic difference between adjacent gradients interpolation of actual measurement.In conjunction with lake region DEM, with reality
The waterlevel data of survey subtracts each other with dem data, and then the bathymetric data surveyed；
3, screening judges the NDBI threshold value of the non-algal tufa condition in lake, is based on measured section data, constructs algal tufa and non-algal tufa
The remote sensing estimation method of condition following table algae total amount
It is determined based on the field measured data of Chaohu using CART to obtain the threshold value of the NDBI of algal tufa and non-algal tufa
Plan tree analyzes (Fig. 4), determines NDBIRrs=0.24 conduct judges non-algal tufa condition foundation.According to Chaohu Prefecture in different aerosols
The remote sensing reflectance and simulation of type and thickness, different solar elevations, moonscope angle and azimuth to ground monitoring
R after Rayleigh scattering correctionrcBetween quantitative relationship, when being applied to satellite image threshold value be NDBIRrc=0.1193.
Chaohu field section survey data shows Chaohu algae in Vertical Difference mainly apart from 2 meters of water body surface layer range
Interior, 2-3 meters of range inner chlorophyll a mean concentrations are 15 μ g/L, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L.According to water
Surface layer NDBI value and surface layer chlorophyll-a concentration choose different algae total amount evaluation methods, as shown in Figure 5.Specific method is such as
Under:
1. if NDBIRrc< 0.1193, it is non-algal tufa condition, if the μ g/L of surface layer Chla < 15, concentration at surface layer to underwater 3m
Unanimously, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L；
2. if NDBIRrc<0.1193, it is non-algal tufa condition, if concentration within the scope of surface layer Chla>15 μ g/L, surface layer 40cm
For remote-sensing inversion surface layer Chla concentration, linear to drop to g/L, 2-3 meters of range internal lobes of 15 μ green by surface layer 40cm to underwater 2m range
Plain a mean concentration is 15 μ g/L, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L；
3. if NDBIRrc>0.1193, it is algal tufa condition, if the μ g/L of surface layer Chla<90, concentration one at surface layer to underwater 2m
It causes, 2-3 meters of range inner chlorophyll a mean concentrations are 15 μ g/L, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L；
4. if NDBIRrc> 0.1193, it is algal tufa condition, if concentration is within the scope of surface layer Chla > 90 μ g/L, surface layer 40cm
Remote-sensing inversion surface layer Chla concentration, surface layer 40cm drop to g/L, 2-3 meters of range inner chlorophylls of 15 μ to underwater 2m range is linear
A mean concentration is 15 μ g/L, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L.
4, in each pixel based on MODIS satellite image algae total inventory evaluation method
Using between water body surface layer chlorophyll-a concentration and NDBI quantitative relationship, under respective conditions algae total amount remote sensing appraising
Method, the bathymetric data based on MODIS satellite image in conjunction with Chaohu on the same day obtain the algae in each pixel water column of satellite image
Class total inventory.The spatial distribution (Fig. 6) of full lake algae total inventory can be obtained based on preceding method.
Chaohu long-term sequence can be obtained in conjunction with the MODIS image in the Chaohu 2003-2013 according to above-mentioned steps
The variation tendency (Fig. 7) of chlorophyll a time and space.Based on inversion algorithm method above-mentioned, defended to several time serieses
Obtained after star image processing eutrophic lake chlorophyll-a concentration year border, moon border changing rule and its spatial distribution (Fig. 8).
It can be realized by the above method and a certain MODIS image algae total inventory estimated, more reflect to objective reality
Lake eutrophication situation and its spatial and temporal distributions.The remote sensing appraising of algae total inventory can be realized effectively to lake algal tufa wind
Nearly and watershed is effectively assessed；In addition, after MODIS history image is calculated one by one by the above method, Ji Keshi
The long-term high precision monitor (such as Fig. 7) of existing lake algae total inventory, facilitates the change of algal tufa actual strength between Scientific evaluation year border
Change and its development trend, effectively assessment lake pollution administer the performance with restoration of the ecosystem, is the water resource of the departments such as water conservancy, environmental protection
Management, the science decision of water environment protection provide science and technology support.
Although the present invention has been disclosed as a preferred embodiment, however, it is not to limit the invention.Skill belonging to the present invention
Has usually intellectual in art field, without departing from the spirit and scope of the present invention, when can be used for a variety of modifications and variations.Cause
This, the scope of protection of the present invention is defined by those of the claims.
Claims (6)
1. a kind of remote sensing estimation method of eutrophic lake algae total inventory, which is characterized in that the realization of this method include with
Lower step:
1) it on the basis of bio-optical model simulation and measured data, obtains between NDBI and water body surface layer chlorophyll-a concentration
Quantitative relationship
Based on algal tufa and suspended matter spectral response characteristics, the evaluation number NDBI of algae is constructed as lake surface layer chlorophyll a
The cardinal index of concentration；On the basis of bio-optical model, in conjunction with the measured data in Chaohu, the Numerical-Mode under different scenes is carried out
It is quasi-, it determines the quantitative relationship of NDBI and chlorophyll-a concentration, is based on R using field measured data buildingrsThe NDBI of data and surface layer
The quantitative relationship of chlorophyll-a concentration；Chaohu Prefecture is simulated in different aerosol types and thickness, different solar elevations, satellite
In the case of view angle and azimuth, the remote sensing reflectance R of ground monitoringrsWith the R after the Rayleigh scattering correction of simulationrcBetween
Quantitative relationship；Surface layer chlorophyll-a concentration inversion algorithm based on situ measurements of hyperspectral reflectance data is applied to by Rayleigh scattering
The MODIS satellite image data of correction obtain the evaluation number NDBI applied to MODIS image, to get lake Quan Shui
Domain surface layer chlorophyll-a concentration spatial distribution；
2) DEM under the water level on the same day and water of Chaohu Lake based on Middle Temple on Nest Lake and Chaohu lock, determines the depth of water space distribution situation in Chaohu
By the waterlevel data at Chaohu mausoleum Zha Yuzhong station, the waterlevel data in full lake is calculated；Lakebed elevation is obtained by lakebed DEM
Data subtract the full lake water depth spatial distribution data of altitude data by waterlevel data；
3) screening judges the NDBI threshold value of the non-algal tufa condition in lake, is based on measured section data, constructs algal tufa and non-algal tufa condition
The remote sensing estimation method of following table algae total amount
Wherein the evaluation number NDBI of the judgement non-algal tufa condition in lake refers to special based on algal tufa and suspended matter spectral response
Sign, using the algae index as the cardinal index for judging algal tufa and non-algal tufa；Based on measured data, using CART decision tree and
Ground remote sensing reflectivity RrsWith the R after Rayleigh scattering correctionrcBetween quantitative relationship, get NDBIRrc=0.1193 is satellite
The differentiation threshold value of non-algal tufa and algal tufa condition in image；Based on Chaohu Prefecture's field section monitoring data, construct respectively algal tufa and
The remote sensing estimation method of algae total amount under the conditions of non-algal tufa；
According to water body surface layer NDBI value and surface layer chlorophyll-a concentration, different algae total amount evaluation methods is chosen, as follows:
1. if NDBIRrc< 0.1193, it is non-algal tufa condition, if the μ g/L of surface layer Chla < 15, concentration one at surface layer to underwater 3m
It causes, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L；
2. if NDBIRrc<0.1193, it is non-algal tufa condition, if concentration is distant within the scope of surface layer Chla>15 μ g/L, surface layer 40cm
Feel inverting surface layer Chla concentration, surface layer 40cm drops to g/L, 2-3 meters of range inner chlorophyll a of 15 μ to underwater 2m range is linear
Mean concentration is 15 μ g/L, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L；
3. if NDBIRrc>0.1193, it is algal tufa condition, if the μ g/L of surface layer Chla<90, concentration is consistent at surface layer to underwater 2m,
2-3 meters of range inner chlorophyll a mean concentrations are 15 μ g/L, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L；
4. if NDBIRrc> 0.1193, it is algal tufa condition, if concentration is remote sensing within the scope of surface layer Chla > 90 μ g/L, surface layer 40cm
Inverting surface layer Chla concentration, surface layer 40cm to linear g/L, the 2-3 meters of range inner chlorophyll a of 15 μ that drop to of underwater 2m range are put down
Equal concentration is 15 μ g/L, and 3 meters or less chlorophyll a mean concentrations are 8 μ g/L；
4) in each pixel based on MODIS satellite image algae total inventory evaluation method
Using between water body surface layer chlorophyll-a concentration and NDBI quantitative relationship, under respective conditions algae total amount remote sensing estimation method,
Bathymetric data based on MODIS satellite image in conjunction with Chaohu on the same day, the algae obtained in each pixel water column of satellite image are total
Storage, and to several time serieses satellite image processing after obtain the full lake of eutrophic lake algae total inventory year
Border, moon border changing rule and its spatial distribution.
2. a kind of remote sensing estimation method of eutrophic lake algae total inventory according to claim 1, which is characterized in that
In the step 1), the spectroscopic data R of the spectral signatures of chlorophyll a and mineral suspensions from Chaohu fieldworkrs,
Monitoring instrument is the binary channels ground spectromonitor of U.S. ASD company.
3. a kind of remote sensing estimation method of eutrophic lake algae total inventory according to claim 1, which is characterized in that
In the step 1), the evaluation number NDBI expression-form based on situ measurements of hyperspectral reflectance data are as follows:
(Rrs(555)-Rrs(645))/(Rrs(555)+Rrs(645))
Rrs(λ) is water body remote sensing reflectance at the λ wavelength of ground survey acquisition.
4. a kind of remote sensing estimation method of eutrophic lake algae total inventory according to claim 1, which is characterized in that
In the step 1), the numerical simulation of different scenes is carried out, is specifically included:
Firstly, being obtained between NDBI and chlorophyll-a concentration in the case where mineral suspensions concentration and yellow substance remain unchanged
Quantitative relationship；
Secondly, when simulation chlorophyll a and constant yellow substance concentration, response of the NDBI to mineral suspensions concentration；
Finally, yellow substance concentration changes the influence to NDBI when simulation chlorophyll a and mineral suspensions concentration remain unchanged.
5. a kind of remote sensing estimation method of eutrophic lake algae total inventory according to claim 1, which is characterized in that
In the step 1), LUT as a result, aerosol thickness referring to Chaohu Prefecture throughout the year monitor knot of the aerosol type referring to SeaDas
Fruit range, observation angle are then determined according to the sun, satellite and the relative position in Chaohu.
6. a kind of remote sensing estimation method of eutrophic lake algae total inventory according to claim 1, which is characterized in that
NDBI index expression-form in the step 1), applied to MODIS image are as follows:
(Rrc(555)-Rrc(645))/(Rrc(555)+Rrc(645))
Also, it establishes on the basis of the correction of the radiation calibration of MODIS satellite image, geometric correction and atmosphere Rayleigh scattering；Rrc
(λ) is the reflectivity at the λ wavelength by Rayleigh correction.
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| CN108088805B (en) * | 2017-12-15 | 2020-04-03 | 中国科学院南京地理与湖泊研究所 | Satellite remote sensing monitoring method for total amount of algae in true optical layer of eutrophic lake |
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| CN111723479B (en) * | 2020-06-11 | 2023-02-28 | 中国科学院南京地理与湖泊研究所 | Process-oriented eutrophic lake algae total quantity remote sensing estimation method |
| CN112070234B (en) * | 2020-09-04 | 2024-01-30 | 中国科学院南京地理与湖泊研究所 | Water chlorophyll and phycocyanin land-based remote sensing machine learning algorithm under complex scene |
| CN112179854B (en) * | 2020-09-30 | 2022-05-27 | 中国科学院南京地理与湖泊研究所 | Eutrophic lake cyanobacterial bloom remote sensing monitoring method based on P-FUI water color index |
| CN114049243B (en) * | 2021-10-26 | 2023-09-29 | 中山大学 | Enteromorpha accumulation amount estimation method, system and storage medium |
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