CN105631904A - Eutrophic lake total algae storage remote sensing evaluation method - Google Patents
Eutrophic lake total algae storage remote sensing evaluation method Download PDFInfo
- Publication number
- CN105631904A CN105631904A CN201510996568.8A CN201510996568A CN105631904A CN 105631904 A CN105631904 A CN 105631904A CN 201510996568 A CN201510996568 A CN 201510996568A CN 105631904 A CN105631904 A CN 105631904A
- Authority
- CN
- China
- Prior art keywords
- chaohu
- ndbi
- lake
- chlorophyll
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
Abstract
The invention provides an eutrophic lake total algae storage (token state: total chlorophyll a, dimension: t) remote sensing evaluation method comprising the steps that the quantitative relation between NDBI and concentration of water surface chlorophyll a is acquired based on bio-optical model simulation and actually measured data and popularized to MODIS satellite image data through Rayleigh scattering correction; the water depth spatial distribution situation of the Chaohu Lake is determined based on the water level of the Chaohu Zhongmiao Temple and the Chaohu brake of the same day and Chaohu underwater DEM; the lake NDBI threshold is judged through screening based on actually measured profile empirical data, and the evaluation method for total storage of algae in a unit water column under the algae bloom and non-algae bloom conditions is constructed; and the evaluation method for total storage of algae in a unit image element based on MODIS satellite images is constructed. The annular and monthly change rule of eutrophic lake total algae storage and spatial distribution thereof can be accurately acquired based on the method.
Description
Technical field
The present invention relates to eutrophic lake algae total inventory remote sensing estimation method.
Background technology
The wawter bloom phenomenon that algal bloom causes is important feature (Kong Fanxiang and Gao Guang of water body in lake eutrophication, 2005), China has become one of the country that breakout of cyanobacteria blooms is the most serious, distribution is the widest in the world (Wu Qinglong etc., 2008). Satellite remote sensing has fast, on a large scale, periodic feature, become the technique means (Pan Delu and Ma high honour, 2008) that lake blue algae wawter bloom monitoring and prediction early warning is indispensable. At present, the satellite remote sensing monitoring of China's middle and lower reach of Yangtze River eutrophic lake (Taihu Lake, Chaohu etc.) blue-green alga bloom area all substantially achieves businessization and runs (horse high honour etc., 2010; Zhu Li etc., 2013), for government and water environment management department provide important decision-making foundation.
In fact, owing to blue-green algae has the special cells structure of pseudo-empty born of the same parents (Vacuole), himself there is buoyancy and regulate ability (Walsby, 1994 of buoyancy according to environmental change (illumination, water power); Kong Fanxiang and Song Lirong, 2011), cause water body top layer algal tufa area that acute variation often can occur within a few hours, the phenomenon that in the short period of time, big area blue-green alga bloom is assembled or disappeared even occur. Therefore, monitoring covers algae distribution situation in the very difficult exact representation water body of wawter bloom area separately, only obtains algae total inventory in water body, could accurate assurance blue-green algae changing condition.
Summary of the invention
It is an object of the invention to provide a kind of eutrophic lake algae total inventory remote sensing estimation method; can accurately obtain the spatial and temporal distributions of lake algae total inventory; accurately analyze the generation of algae total amount, Status of development and trend in lake; science assessment lake pollution is administered and restoration of the ecosystem effect, for the water resources management of the department such as water conservancy, environmental protection, the science decision of water environment protection provide science and technology support.
The above-mentioned purpose of the present invention is realized by the technology feature of independent claim, dependent claims by select else or favourable in the way of develop the technology feature of independent claim.
For reaching 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 estimation method t), comprise: bio-optical model simulation and measured data basis on, obtain the quantitative relationship between NDBI and water body top layer chlorophyll-a concentration, and extend to the MODIS satellite image data corrected through Rayleigh scattering; Based on water level on the same day and Chaohu DEM under water of Chaohu Zhong Miao and Chaohu lock, it is determined that the depth of water space distribution situation in Chaohu; Based on measured section rule of thumb data, screening judges lake NDBI threshold value, builds algae total inventory evaluation method in algal tufa and non-algal tufa condition lower unit water column respectively; Based on the evaluation method of algae total inventory in the unit pixel of MODIS satellite image. Based on the method for the present invention, can accurately obtain eutrophic lake algae total inventory year border, the moon border Changing Pattern and spatial distribution thereof.
As further example, the specific implementation of aforementioned method comprises:
1) on the basis of bio-optical model simulation and measured data, the quantitative relationship between NDBI and water body top layer chlorophyll-a concentration is obtained
Based on algal tufa and suspended substance spectral response characteristics, build the basic index of algae index NDBI as top layer, lake chlorophyll-a concentration; On the basis of bio-optical model, in conjunction with the measured data in Chaohu, carry out the numerical simulation under different sight, it is determined that the quantitative relationship of NDBI and chlorophyll-a concentration, utilize field measured data to build based on RrsThe NDBI of data and the quantitative relationship of top layer chlorophyll-a concentration; Simulate Chaohu Prefecture under different aerosol type and thickness, different sun high angle, satellite observing angle and position angle situation, the remote sensing reflectance R of ground monitoringrsR after correcting with the Rayleigh scattering of simulationrcBetween quantitative relationship; Top layer chlorophyll-a concentration inversion algorithm based on ground measured spectra data is applied to the MODIS satellite image data corrected through Rayleigh scattering, thus gets top layer, full waters, lake chlorophyll-a concentration spatial distribution;
2) based on water level on the same day and Chaohu DEM under water of Chaohu Zhong Miao and Chaohu lock, it is determined that the depth of water space distribution situation in Chaohu
By the waterlevel data at station, Zha Yuzhong mausoleum, Chaohu, calculate the waterlevel data in full lake; Obtain lakebed altitude figures by lakebed DEM, subtract the altitude figures dark spatial distribution data of full lake water by waterlevel data;
3) screening judges the NDBI threshold value of the non-algal tufa condition in lake, based on measured section data, builds the remote sensing estimation method of algal tufa and non-algal tufa condition following table algae total amount
The evaluation number NDBI of the non-algal tufa condition in wherein said judgement lake refers to based on algal tufa and suspended substance spectral response characteristics, using this algae index as the basic index judging algal tufa and non-algal tufa; Based on measured data, utilize CART decision tree and ground remote sensing reflectivity RrsR after correcting with Rayleigh scatteringrcBetween quantitative relationship, get NDBIRrc=0.1193 is the differentiation threshold value of non-algal tufa and algal tufa condition in satellite image; Based on Chaohu Prefecture's field section monitoring data, the remote sensing estimation method of algae total amount when constructing algal tufa and non-algal tufa respectively;
4) based on the evaluation method of algae total inventory in each picture unit of MODIS satellite image
Utilize the remote sensing estimation method of algae total amount under the quantitative relationship between water body top layer chlorophyll-a concentration and NDBI, respective conditions, based on MODIS satellite image again in conjunction with Chaohu depth of water data on the same day, obtain the algae total inventory in each picture unit water column of satellite image.
Based on abovementioned steps and method, obtain after to the satellite image processing of several time serieses the algae total inventory in the full lake of eutrophic lake year border, the moon border Changing Pattern and spatial distribution thereof.
From the above technical solution of the present invention shows that, the eutrophic lake algae total inventory MODIS satellite remote sensing evaluation method of the present invention, bio-optical model simulation and measured data basis on, obtain the quantitative relationship between NDBI and water body top layer chlorophyll-a concentration, and extend to the MODIS satellite image data corrected through Rayleigh scattering; Based on water level on the same day and Chaohu DEM under water of Chaohu Zhong Miao and Chaohu lock, it is determined that the depth of water space distribution situation in Chaohu; Based on measured section rule of thumb data, screening judges lake NDBI threshold value, builds algae total inventory evaluation method in algal tufa and non-algal tufa condition lower unit water column respectively; Based on the evaluation method of algae total inventory in the unit pixel of MODIS satellite image. Obtain the spatial and temporal distributions of full lake algae total inventory, it is possible to the spatial and temporal distributions of objective reality ground reflection lake eutrophication situation more. Lake algae total inventory remote sensing monitoring can effectively realize lake algal tufa risk and water source district is carried out Efficient Evaluation; The long-term high precision monitor of lake algae total inventory; contribute to change and the development trend thereof of algae total amount between science assessment year border; Efficient Evaluation lake pollution administers the performance with restoration of the ecosystem, is the water resources management of the department such as water conservancy, environmental protection, the science decision offer science and technology support of water environment protection.
As long as it is to be understood that aforementioned concepts and all combinations of extra design of describing in further detail below can be regarded as a part for subject matter of the present disclosure when such design is not conflicting. In addition, all combinations of claimed theme are all regarded as a part for subject matter of the present disclosure.
Foregoing and other aspect, embodiment and feature that the present invention instructs can be understood by reference to the accompanying drawings from the following description more comprehensively. Feature and/or the useful effect of other additional aspect such as illustrative embodiments of the present invention will be obvious in the following description, or by the practice of the embodiment instructed according to the present invention is learnt.
Accompanying drawing explanation
Accompanying drawing is not intended to draw in proportion. In the accompanying drawings, each illustrating in each figure be identical or approximately uniform integral part can represent with identical label. For clarity, in each figure, not each integral part is all labeled. Now, the embodiment of all respects of the present invention also will be described with reference to accompanying drawing by example, wherein:
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 type and thickness thereof, when different sun high angle, satellite observing angle and position angle, and RrsWith RrcBetween quantitative relationship.
Fig. 4 is that NDBI judges algal tufa and the CART decision tree of non-algal tufa condition.
Fig. 5 is algal tufa and the remote sensing estimation method of Chaohu algae total amount when 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 change statistical graph of a certain section of period (2003-2013) Chaohu algae total inventory.
In aforementioned diagram 1-8, as each coordinate, mark or other expressions that English form is expressed, it is known in the field, does not repeat again in this example.
Embodiment
In order to more understand 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 with reference to the accompanying drawings to describe the present invention in the disclosure, shown in the drawings of the embodiment of many explanations. Embodiment of the present disclosure must not be intended to comprise all aspects of the present invention. It is to be understood that, multiple design presented hereinbefore and embodiment, and those designs described in more detail below and enforcement mode can in many ways in any one is implemented, this should be design disclosed in this invention and embodiment is not limited to any enforcement mode. In addition, aspects more disclosed by the invention can be used alone, or uses with any appropriately combined of other aspects disclosed by the invention.
The present invention gives MODIS satellite data to the remote sensing appraising of eutrophic lake algae total inventory, above-mentioned purpose be achieved in that bio-optical model simulation and measured data basis on, obtain the quantitative relationship between NDBI and water body top layer chlorophyll-a concentration, and extend to the MODIS satellite image data corrected through Rayleigh scattering; Based on water level on the same day and Chaohu DEM under water of Chaohu Zhong Miao and Chaohu lock, it is determined that the depth of water space distribution situation in Chaohu; Based on measured section rule of thumb data, screening judges lake NDBI threshold value, builds algae total inventory evaluation method in algal tufa and non-algal tufa condition lower unit water column respectively; Based on the evaluation method of algae total inventory in the unit pixel of MODIS satellite image. Obtain the spatial and temporal distributions of full lake algae total inventory, it is possible to the spatial and temporal distributions of objective reality ground reflection lake eutrophication situation more.
The exemplarily description of property, shown in accompanying drawing, is specifically described the enforcement of aforementioned method.
Step 1, on the basis of bio-optical model simulation and measured data, obtain the quantitative relationship between NDBI and water body top layer chlorophyll-a concentration
Chlorophyll-a concentration change chlorophyll a evaluation number NDBI that is responsive and don't that affect by high suspended matter is referred to based on chlorophyll a and suspended substance spectral response characteristics, select wave band red, green and it is similar to NDVI expression-form, the disadvantageous effect that chlorophyll-a concentration is estimated by high suspended matter can be avoided, and using this algae index as chlorophyll-a concentration remote sensing monitoring index.
Specifically, based on water body optically active substance (chlorophyll a, mineral suspensions, yellow substance) fundamental surveillance principle, the spectral signature of three kinds of optically active substances in research water body, and in conjunction with the inversion algorithm of existing chlorophyll a in case �� waters, comparative analysis is relative merits separately, while selecting accurately to estimate chlorophyll-a concentration, the basic index do not monitored as blue-green alga bloom MODIS satellite by the monitoring index of mineral suspensions in water body and the impact of yellow substance, to overcome the rough sledding of other optical active matter confrontation chlorophyll-a concentrations monitoring in water body.
In this example, owing to the water body of high chlorophyll a has individual reflection peak at green light band (570nm), and cause the reflection paddy (Fig. 1) of red spectral band in the strong absorption of 665nm because of chlorophyll a, the content of chlorophyll a therefore can be estimated from the chlorophyll a characteristic wave bands that MODIS is corresponding. Fig. 1 is high chlorophyll a under MODIS band setting, the high muddy and general spectrum of water body and the difference of three, it can be seen that if taking 555nm, 645nm wave band as two ends basic point, high chlorophyll a water body and high feculent water body have maximum difference. According to this feature, it is proposed that NDBI (Normalizeddifferencebloomindex) index:
NDBIRrs=(Rrs(555)-Rrs(645))/(Rrs(555)+Rrs(645))(1)
Wherein, Rrs(��) it is the �� wavelength place water body remote sensing reflectance that ground survey obtains.
On the basis of bio-optical model, in conjunction with the measured data in Chaohu, carry out the numerical simulation under different sight, prove the quantitative relationship of NDBI with chlorophyll-a concentration theoretically, and in water body, other optical active matters this algorithm of verifying affects.
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 (��)=aw(��)+aph(��)+ad(��)+ag(��)
bb(��)=bbw(��)+bbp(��)(2)
Wherein awAnd bbwCorrespond to the uptake factor of pure water and backscattering coefficient; And aph��adAnd agBeing then the uptake factor of phytoplankton dynamic processes, mineral suspensions and yellow substance, they all also exist substantial connection, b with the amount of respective substance in water bodybpBeing the backscattering coefficient of particulate matter in water body, in the water body that algae content is not high, this coefficient and mineral suspensions have substantial connection. Wherein,
According to formula (1), between NDBI and chlorophyll-a concentration, there is following relation,
According to formula (4), there is dull relation between NDBI and chlorophyll-a concentration, also it is exactly that NDBI increases with the increase of chlorophyll-a concentration. Therefore, it is assumed that in water body, mineral suspensions concentration is 50mg/L, when ignoring yellow substance and affect, Fig. 2 is quantitative relationship between the NDBI based on bio-optical model simulation and water body top layer chlorophyll-a concentration.
According to our 2013-2014 in the spectroscopic data of the fieldwork in Chaohu and corresponding chlorophyll-a concentration data, we construct the inversion algorithm of Chaohu surface water chlorophyll a based on measured spectra data.
Chla=3.888 e15.83��NDBI(Rrs)(5)
Investigate the R of Chaohu Prefecture after the Rayleigh scattering of the remote sensing reflectance of ground monitoring and simulation is corrected by different aerosol type and thickness, different sun high angle, satellite observing angle and position anglercBetween the impact of quantitative relationship, and determine quantitative model between the two by simulated data.
In this example, the inversion algorithm obtaining chlorophyll a based on measured spectra data will being extended to satellite image data, air is corrected and be can not ignore. But still lack for high feculent water body effectively accurate air correction algorithm at present, this adopts the Rayleigh scattering of MODIS image to correct, also being exactly corrected by this kind, the optical information on atmospheric layer top eliminates the impact of Rayleigh scattering, still contains aerosol information and terrestrial information. Data after correcting based on Rayleigh scattering, NDBI is expressed as:
NDBIRrc=(Rrc(555)-Rrc(645))/(Rrc(555)+Rrc(645))(6)
Wherein, Rrc(��) reflectivity at the �� wavelength place of Rayleigh correction it is through. RrcThat MODIS data carry out Rayleigh scattering correction, then based on the research of (2004) such as Hu be converted into Rayleigh scattering correct after reflectivity:
In formula,It is the sensor radiant ratio after correcting ozone and other gas absorption effects, F0The outer solar irradiance of atmospheric sphere when being obtain data, ��0It is sun zenith angle, RrIt it is the rayleigh reflectance adopting 6S (Vermote etc., 1997) to predict.
Based on radiation transfer theory and the ocean_atmosphere system supposing a non-coupled, RrcCan be expressed as:
Rrc=Ra+t0tRtarget(8)
In formula, RaIt is aerosol reflectivity (comprising the interaction coming from aerosol particles), RtargetIt is the surface albedo of fieldwork target (algae or water body), t0Being the atmospheric transmissivity from the sun to target compound, t is the atmospheric transmissivity from target compound to satellite sensor. Due to the impact by wind and current, planktonic algae presents the form of a kind of oil slick usually, and therefore t can regard the optical transmittance of planktonic algae as.
In order to investigate different aerosol type and thickness thereof, and satellite observes the impact that causes, we according to Chaohu Prefecture at different aerosol type and thickness, different sun high angle, satellite observing angle and position angle to the R after the remote sensing reflectance of ground monitoring and the Rayleigh scattering rectification of simulationrcBetween the impact (Fig. 3) of quantitative relationship, and determine quantitative model between the two by simulated data,
NDBI(Rrc)=0.605 NDBI (Rrs)+0.023��(9)
Chlorophyll a inversion algorithm based on ground measured spectra data is applied to the satellite image data corrected through Rayleigh scattering, and based on formula (5) and formula (9), the MODIS satellite high-precision inverse model of Chaohu chlorophyll a is as follows,
Chla=1.935 e26.165��NDBI(Rrc)(10)
Correct according to the Rayleigh scattering based on MODIS image, the high precision estimation of water body top layer chlorophyll-a concentration in umbra picture can be realized in conjunction with formula (10). Idiographic flow is mainly as follows: 1. the MODIS image obtained has carried out geometric correction and radiation calibration calculating. Geometric correction adopts GeographicLat/Lon projection, corrects in conjunction with the latitude and longitude information in 1B data, and the position precision after correction reaches 0.5 pixel. ERDAS utilizes vector border, lake, extracts lake waters by mask technique, remove the impact of island vegetation, utilize nearest neighbour method, MODIS500m image data are heavily sampled as 250m; 2. MODIS image calculates it at the R of band1 (645nm) and band4 (555nm) as unit one by onercValue; 3. NDBI value is calculated as unit one by one according to formula (6); 4. the water body top layer chlorophyll a spatial distribution result then according to formula (10), after can being calculated.
2, based on water level on the same day and Chaohu DEM under water of Chaohu Zhong Miao and Chaohu lock, it is determined that the depth of water space distribution situation in Chaohu
Depth of water data subtract each other by the waterlevel data on lake region same day and lake region dem data to obtain. Have chosen the measured data of Chaohu Floodgate Station with two hydrology websites in loyal station, mausoleum in calculating, by the statistics all station datas of 2006-2013, the mean water level gradient obtaining two websites is poor. Adopt this average slope difference to obtain the water level in full lake for the number of days lacking a certain website, for the number of days comprising two websites, adopt the water level gradient difference interpolation of actual measurement to obtain the water level in full lake. Again in conjunction with lake region DEM, subtract each other with the waterlevel data of actual measurement and dem data, and then obtain the depth of water data of actual measurement;
3, screening judges the NDBI threshold value of the non-algal tufa condition in lake, based on measured section data, builds the remote sensing estimation method of algal tufa and non-algal tufa condition following table algae total amount
In order to obtain the threshold value of the NDBI of algal tufa and non-algal tufa, based on the field measured data of Chaohu, utilize CART decision tree analysis (Fig. 4), it is determined that NDBIRrs=0.24 as judging non-algal tufa condition foundation. According to Chaohu Prefecture at different aerosol type and thickness, different sun high angle, satellite observing angle and position angle to the R after the remote sensing reflectance of ground monitoring and the Rayleigh scattering rectification of simulationrcBetween quantitative relationship, when being applied to satellite image, threshold value is NDBIRrc=0.1193.
Chaohu field section enquiry data shows, Chaohu algae is in vertical difference mainly within the scope of distance 2 meters, water body top layer, and 2-3 rice scope inner chlorophyll a mean concns is 15 �� g/L, and less than 3 meters chlorophyll a mean concnss are 8 �� g/L. According to water body top layer NDBI value and top layer chlorophyll-a concentration, choose different algae total amount evaluation methods, as shown in Figure 5. Concrete grammar is as follows:
If 1. NDBIRrc< 0.1193, it is non-algal tufa condition, if top layer Chla < 15 is �� g/L, then top layer is consistent to 3m place concentration under water, and less than 3 meters chlorophyll a mean concnss are 8 �� g/L;
If 2. NDBIRrc<0.1193, for non-algal tufa condition, if top layer Chla>15 is �� g/L, then within the scope of the 40cm of top layer, concentration is remote-sensing inversion top layer Chla concentration, top layer 40cm drops to 15 �� g/Ls to 2m scope is linear under water, 2-3 rice scope inner chlorophyll a mean concns is 15 �� g/L, and less than 3 meters chlorophyll a mean concnss are 8 �� g/L;
If 3. NDBIRrc>0.1193, it is algal tufa condition, if top layer Chla<90 is �� g/L, then top layer is consistent to 2m place concentration under water, and 2-3 rice scope inner chlorophyll a mean concns is 15 �� g/L, and less than 3 meters chlorophyll a mean concnss are 8 �� g/L;
If 4. NDBIRrc> 0.1193, for algal tufa condition, if top layer Chla > 90 is �� g/L, then within the scope of the 40cm of top layer, concentration is remote-sensing inversion top layer Chla concentration, top layer 40cm drops to 15 �� g/Ls to 2m scope is linear under water, 2-3 rice scope inner chlorophyll a mean concns is 15 �� g/L, and less than 3 meters chlorophyll a mean concnss are 8 �� g/L.
4, based on the evaluation method of algae total inventory in each picture unit of MODIS satellite image
Utilize the remote sensing estimation method of algae total amount under the quantitative relationship between water body top layer chlorophyll-a concentration and NDBI, respective conditions, based on MODIS satellite image again in conjunction with Chaohu depth of water data on the same day, obtain the algae total inventory in each picture unit water column of satellite image. The spatial distribution (Fig. 6) of full lake algae total inventory can be obtained based on aforementioned method.
According to above-mentioned steps, in conjunction with the MODIS image in 2003-2013 Chaohu, the variation tendency (Fig. 7) of the chlorophyll a Time and place of Chaohu long-term sequence can be obtained. Based on aforesaid inversion algorithm method, obtain after to the satellite image processing of several time serieses eutrophic lake chlorophyll-a concentration year border, the moon border Changing Pattern and spatial distribution (Fig. 8) thereof.
Can realize the estimation of a certain MODIS image algae total inventory by aforesaid method, more objective reality ground reflection lake eutrophication situation and spatial and temporal distributions thereof. The remote sensing appraising of algae total inventory, it is possible to effectively realize lake algal tufa risk and water source district is carried out Efficient Evaluation; In addition; after MODIS history image is calculated one by one by aforesaid method; the long-term high precision monitor (such as Fig. 7) of lake algae total inventory can be realized; contribute to change and the development trend thereof of algal tufa actual strength between science assessment year border; Efficient Evaluation lake pollution administers the performance with restoration of the ecosystem, is the water resources management of the department such as water conservancy, environmental protection, the science decision offer science and technology support of water environment protection.
Although the present invention with better embodiment disclose as above, so itself and be not used to limit the present invention. Persond having ordinary knowledge in the technical field of the present invention, without departing from the spirit and scope of the present invention, when being used for a variety of modifications and variations. Therefore, protection scope of the present invention is when being as the criterion depending on those as defined in claim.
Claims (6)
1. the remote sensing estimation method of an eutrophic lake algae total inventory, it is characterised in that, the realization of the method comprises the following steps:
1) on the basis of bio-optical model simulation and measured data, the quantitative relationship between NDBI and water body top layer chlorophyll-a concentration is obtained
Based on algal tufa and suspended substance spectral response characteristics, build the basic index of evaluation number NDBI as top layer, lake chlorophyll-a concentration of algae; On the basis of bio-optical model, in conjunction with the measured data in Chaohu, carry out the numerical simulation under different sight, it is determined that the quantitative relationship of NDBI and chlorophyll-a concentration, utilize field measured data to build based on RrsThe NDBI of data and the quantitative relationship of top layer chlorophyll-a concentration; Simulate Chaohu Prefecture under different aerosol type and thickness, different sun high angle, satellite observing angle and position angle situation, the remote sensing reflectance R of ground monitoringrsR after correcting with the Rayleigh scattering of simulationrcBetween quantitative relationship; Top layer chlorophyll-a concentration inversion algorithm based on ground measured spectra data is applied to the MODIS satellite image data corrected through Rayleigh scattering, obtain being applied to the evaluation number NDBI of MODIS image, thus get top layer, full waters, lake chlorophyll-a concentration spatial distribution;
2) based on water level on the same day and Chaohu DEM under water of Chaohu Zhong Miao and Chaohu lock, it is determined that the depth of water space distribution situation in Chaohu
By the waterlevel data at station, Zha Yuzhong mausoleum, Chaohu, calculate the waterlevel data in full lake; Obtain lakebed altitude figures by lakebed DEM, subtract the altitude figures dark spatial distribution data of full lake water by waterlevel data;
3) screening judges the NDBI threshold value of the non-algal tufa condition in lake, based on measured section data, builds the remote sensing estimation method of algal tufa and non-algal tufa condition following table algae total amount
The evaluation number NDBI of the non-algal tufa condition in wherein said judgement lake refers to based on algal tufa and suspended substance spectral response characteristics, using this algae index as the basic index judging algal tufa and non-algal tufa; Based on measured data, utilize CART decision tree and ground remote sensing reflectivity RrsR after correcting with Rayleigh scatteringrcBetween quantitative relationship, get NDBIRrc=0.1193 is the differentiation threshold value of non-algal tufa and algal tufa condition in satellite image; Based on Chaohu Prefecture's field section monitoring data, build the remote sensing estimation method of algae total amount when algal tufa and non-algal tufa respectively;
4) based on the evaluation method of algae total inventory in each picture unit of MODIS satellite image
Utilize the remote sensing estimation method of algae total amount under the quantitative relationship between water body top layer chlorophyll-a concentration and NDBI, respective conditions, based on MODIS satellite image again in conjunction with Chaohu depth of water data on the same day, obtain the algae total inventory in each picture unit water column of satellite image, and obtain after to the satellite image processing of several time serieses the algae total inventory in the full lake of eutrophic lake year border, the moon border Changing Pattern and spatial distribution thereof.
2. eutrophic lake algae total inventory remote sensing estimation method according to claim 1, it is characterised in that, described step 1) in, the spectral signature of chlorophyll a and mineral suspensions comes from the spectroscopic data R of Chaohu fieldworkrs, Monitoring equipment is the two channels ground spectromonitor of ASD company of the U.S..
3. eutrophic lake algae total inventory remote sensing estimation method according to claim 1, it is characterised in that, described step 1) in, the evaluation number NDBI expression-form based on ground measured spectra data is:
(Rrs(555)-Rrs(645))/(Rrs(555)+Rrs(645))��
4. eutrophic lake algae total inventory remote sensing estimation method according to claim 1, it is characterised in that, described step 1) in, carry out the numerical simulation of different sight, specifically comprise:
First, when mineral suspensions concentration and yellow substance remain unchanged, obtain the quantitative relationship between NDBI and chlorophyll-a concentration;
Secondly, when simulation chlorophyll a and yellow substance concentration are constant, NDBI is to the response of mineral suspensions concentration;
Finally, when simulation chlorophyll a and mineral suspensions concentration remain unchanged, yellow substance change in concentration is on the impact of NDBI.
5. eutrophic lake algae total inventory remote sensing estimation method according to claim 1, it is characterized in that, described step 1) in, aerosol type is with reference to the result of the LUT of SeaDas, aerosol thickness is with reference to the long-term monitoring result scope in Chaohu Prefecture, and observation angle is then determined according to the relative position in the sun, satellite and Chaohu.
6. the MODIS satellite high-precision monitoring method of nutrition-enriched water of lake chlorophyll a according to claim 1, it is characterised in that, described step 1) in, the NDBI index expression-form being applied to MODIS image is:
(Rrc(555)-Rrc(645))/(Rrc(555)+Rrc(645))
Further, it is based upon on the basis of the radiation calibration of MODIS satellite image, geometric correction and the correction of air Rayleigh scattering.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510996568.8A CN105631904B (en) | 2015-09-21 | 2015-12-25 | A kind of eutrophic lake algae total inventory remote sensing estimation method |
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510605143 | 2015-09-21 | ||
| CN201510605143X | 2015-09-21 | ||
| CN201510996568.8A CN105631904B (en) | 2015-09-21 | 2015-12-25 | A kind of eutrophic lake algae total inventory remote sensing estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105631904A true CN105631904A (en) | 2016-06-01 |
| CN105631904B CN105631904B (en) | 2019-01-11 |
Family
ID=56046791
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510996568.8A Active CN105631904B (en) | 2015-09-21 | 2015-12-25 | A kind of eutrophic lake algae total inventory remote sensing estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105631904B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106315856A (en) * | 2016-09-07 | 2017-01-11 | 中国科学院南京地理与湖泊研究所 | MODIS (moderate resolution imaging spectroradiometer) satellite synchronous monitoring method for cyanobacterial bloom and aquatic vegetation in eutrophic lakes |
| CN106408005A (en) * | 2016-09-05 | 2017-02-15 | 中国科学院南京地理与湖泊研究所 | MODIS pigment concentration estimation-based eutrophicated lake water quality risk assessment method |
| CN108088805A (en) * | 2017-12-15 | 2018-05-29 | 中国科学院南京地理与湖泊研究所 | Algae total amount satellite remote-sensing monitoring method in eutrophic lake euphotic layer |
| CN109060614A (en) * | 2018-09-04 | 2018-12-21 | 中国科学院南海海洋研究所 | A kind of measurement method and equipment of marine atmosphere optical parameter |
| CN111324952A (en) * | 2020-02-17 | 2020-06-23 | 中国科学院水利部成都山地灾害与环境研究所 | Ice lake volume estimation method for acquiring water level based on multi-source remote sensing data |
| CN111723479A (en) * | 2020-06-11 | 2020-09-29 | 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 |
| CN112070234A (en) * | 2020-09-04 | 2020-12-11 | 中国科学院南京地理与湖泊研究所 | Ground-based remote sensing machine learning algorithm for chlorophyll and phycocyanin in water body under complex scene |
| CN112179854A (en) * | 2020-09-30 | 2021-01-05 | 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 |
| CN114049243A (en) * | 2021-10-26 | 2022-02-15 | 中山大学 | Enteromorpha accumulation amount estimation method and system and storage medium |
| CN114235748A (en) * | 2021-12-06 | 2022-03-25 | 南宁师范大学 | Satellite remote sensing monitoring method based on Yangtze river plain lake eutrophication |
| CN117390466A (en) * | 2023-12-13 | 2024-01-12 | 江西省水利科学院(江西省大坝安全管理中心、江西省水资源管理中心) | Lake steady state discrimination method based on similarity measurement |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104374713A (en) * | 2014-12-03 | 2015-02-25 | 中国科学院南京地理与湖泊研究所 | MODIS remote sensing monitoring method for vertical distribution pattern of eutrophic lake water algae |
| CN104390917A (en) * | 2014-12-03 | 2015-03-04 | 中国科学院南京地理与湖泊研究所 | High-precision satellite MODIS (Moderate-resolution Imaging Spectroradiometer) monitoring method for chlorophyll a of eutrophic lake water body |
-
2015
- 2015-12-25 CN CN201510996568.8A patent/CN105631904B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104374713A (en) * | 2014-12-03 | 2015-02-25 | 中国科学院南京地理与湖泊研究所 | MODIS remote sensing monitoring method for vertical distribution pattern of eutrophic lake water algae |
| CN104390917A (en) * | 2014-12-03 | 2015-03-04 | 中国科学院南京地理与湖泊研究所 | High-precision satellite MODIS (Moderate-resolution Imaging Spectroradiometer) monitoring method for chlorophyll a of eutrophic lake water body |
Non-Patent Citations (2)
| Title |
|---|
| 马孟枭 等: "巢湖水体组分垂向分布特征及其对水下光场的影响", 《环境科学》 * |
| 马孟枭 等: "藻类垂向分布对内陆湖泊叶绿素a反演算法影响的模拟研究", 《红外与毫米波学报》 * |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106408005B (en) * | 2016-09-05 | 2020-05-05 | 中国科学院南京地理与湖泊研究所 | Eutrophic lake water quality risk evaluation method based on MODIS pigment concentration estimation |
| CN106408005A (en) * | 2016-09-05 | 2017-02-15 | 中国科学院南京地理与湖泊研究所 | MODIS pigment concentration estimation-based eutrophicated lake water quality risk assessment method |
| CN106315856A (en) * | 2016-09-07 | 2017-01-11 | 中国科学院南京地理与湖泊研究所 | MODIS (moderate resolution imaging spectroradiometer) satellite synchronous monitoring method for cyanobacterial bloom and aquatic vegetation in eutrophic lakes |
| CN106315856B (en) * | 2016-09-07 | 2019-02-26 | 中国科学院南京地理与湖泊研究所 | The MODIS satellite synchronization monitoring method of eutrophic lake cyanobacterial bloom and aquatic vegetation |
| CN108088805A (en) * | 2017-12-15 | 2018-05-29 | 中国科学院南京地理与湖泊研究所 | Algae total amount satellite remote-sensing monitoring method in eutrophic lake euphotic layer |
| 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 |
| CN109060614B (en) * | 2018-09-04 | 2020-12-11 | 中国科学院南海海洋研究所 | Method and equipment for measuring ocean atmosphere optical parameters |
| CN109060614A (en) * | 2018-09-04 | 2018-12-21 | 中国科学院南海海洋研究所 | A kind of measurement method and equipment of marine atmosphere optical parameter |
| CN111324952B (en) * | 2020-02-17 | 2023-09-01 | 中国科学院水利部成都山地灾害与环境研究所 | Ice lake volume estimation method for acquiring water level based on multi-source remote sensing data |
| CN111324952A (en) * | 2020-02-17 | 2020-06-23 | 中国科学院水利部成都山地灾害与环境研究所 | Ice lake volume estimation method for acquiring water level based on multi-source remote sensing data |
| CN111723479A (en) * | 2020-06-11 | 2020-09-29 | 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 |
| 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 |
| CN112070234A (en) * | 2020-09-04 | 2020-12-11 | 中国科学院南京地理与湖泊研究所 | Ground-based remote sensing machine learning algorithm for chlorophyll and phycocyanin in water body under complex scene |
| 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 |
| CN112179854A (en) * | 2020-09-30 | 2021-01-05 | 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 |
| CN114049243A (en) * | 2021-10-26 | 2022-02-15 | 中山大学 | Enteromorpha accumulation amount estimation method and system and storage medium |
| CN114235748A (en) * | 2021-12-06 | 2022-03-25 | 南宁师范大学 | Satellite remote sensing monitoring method based on Yangtze river plain lake eutrophication |
| CN117390466A (en) * | 2023-12-13 | 2024-01-12 | 江西省水利科学院(江西省大坝安全管理中心、江西省水资源管理中心) | Lake steady state discrimination method based on similarity measurement |
| CN117390466B (en) * | 2023-12-13 | 2024-02-06 | 江西省水利科学院(江西省大坝安全管理中心、江西省水资源管理中心) | Lake steady state discrimination method based on similarity measurement |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105631904B (en) | 2019-01-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105631904B (en) | A kind of eutrophic lake algae total inventory remote sensing estimation method | |
| CN104820224B (en) | The MODIS satellite high-precision monitoring methods of nutrition-enriched water of lake chlorophyll a | |
| CN105203466B (en) | Algae total inventory remote sensing estimation method under the conditions of a kind of non-algal tufa of eutrophic lake | |
| CN104390917B (en) | High-precision satellite MODIS (Moderate-resolution Imaging Spectroradiometer) monitoring method for chlorophyll a of eutrophic lake water body | |
| Lim et al. | Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea | |
| Wang et al. | Estimation of suspended sediment concentrations using Terra MODIS: An example from the Lower Yangtze River, China | |
| Zhang et al. | Monitoring the river plume induced by heavy rainfall events in large, shallow, Lake Taihu using MODIS 250 m imagery | |
| Nas et al. | An application of Landsat-5TM image data for water quality mapping in Lake Beysehir, Turkey | |
| CN104374713B (en) | MODIS remote sensing monitoring method for vertical distribution pattern of eutrophic lake water algae | |
| Lihan et al. | Satellite-measured temporal and spatial variability of the Tokachi River plume | |
| CN105303030B (en) | A kind of MODIS remote sensing estimation method of eutrophic lake algae Gauss vertical characteristics structural parameters | |
| CN106315856B (en) | The MODIS satellite synchronization monitoring method of eutrophic lake cyanobacterial bloom and aquatic vegetation | |
| Zhao et al. | Making global river ecosystem health assessments objective, quantitative and comparable | |
| Ma et al. | Spectral index fusion for salinized soil salinity inversion using Sentinel-2A and UAV images in a coastal area | |
| Iqbal et al. | Seasonal effect of agricultural pollutants on coastline environment: a case study of the southern estuarine water ecosystem of the boseong county Korea. | |
| Wang et al. | Water quality analysis in Lake Hachiroko, Japan, using ALOS AVNIR‐2 data | |
| CN108088805B (en) | Satellite remote sensing monitoring method for total amount of algae in true optical layer of eutrophic lake | |
| Zablotskii et al. | Estimation of suspended sediment concentration using vnredsat–1A multispectral data, a case study in red river, hanoi, vietnam | |
| Kaiser et al. | Spectral enhancement of SPOT imagery data to assess marine pollution near Port Said, Egypt | |
| Du et al. | Using remote sensing to understand the total suspended matter dynamics in lakes across Inner Mongolia | |
| Pimwiset et al. | Water Turbidity Determination by a Satellite Imagery-Based Mathematical Equation for the Chao Phraya River: 10.32526/ennrj/20/202100237 | |
| Zhu et al. | Spatiotemporal variations of total suspended matter in complex archipelagic regions using a sigmoid model and Landsat-8 imagery | |
| Sunar et al. | How efficient can Sentinel-2 data help spatial mapping of mucilage event in the Marmara Sea? | |
| Totsuka et al. | Noise removal method for unmanned aerial vehicle data to estimate water quality of Miharu Dam Reservoir, Japan | |
| Hayes et al. | Estimating dredge-induced turbidity using drone imagery |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |