CN111795941A - Hyperspectral identification method for algal community structure in bloom stage - Google Patents
Hyperspectral identification method for algal community structure in bloom stage Download PDFInfo
- Publication number
- CN111795941A CN111795941A CN202010798423.8A CN202010798423A CN111795941A CN 111795941 A CN111795941 A CN 111795941A CN 202010798423 A CN202010798423 A CN 202010798423A CN 111795941 A CN111795941 A CN 111795941A
- Authority
- CN
- China
- Prior art keywords
- water
- bloom
- algae
- algal
- green
- 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
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Cultivation Of Seaweed (AREA)
Abstract
The invention relates to a hyperspectral identification method for algal community structures in a bloom stage, and belongs to the field of ecological environment monitoring. Firstly, preliminarily judging the water bloom phenomenon by adopting a high-definition photo, then constructing spectral parameters for representing photosynthetic pigments for diagnosing algae communities and populations by utilizing the spectral characteristic difference of the blue-green algae, green algae and diatom populations in the water surface reflection spectrum sensitive wavelength, applying the spectral parameters to the final judgment of the water bloom phenomenon and the quantitative estimation of algae population characteristics, and finally realizing the extraction of the algae community structure in the water bloom stage by calculating the relative abundance ratio of the blue-green-diatom populations. The method can accurately judge the water bloom phenomenon and identify the algal community structure in the water bloom stage.
Description
Technical Field
The invention relates to a hyperspectral identification method for an algal community structure in a bloom stage, belongs to the field of ecological environment monitoring, and is particularly suitable for quantitative estimation of relative abundance of blue-green-diatom populations in the bloom stage.
Background
The water bloom phenomenon is a phenomenon of mass algae aggregation caused by explosive proliferation of algae in a water body. When the phenomenon of water bloom occurs in densely populated or ecologically sensitive areas and harms human and ecological health, the phenomenon is defined as harmful water bloom. Blue algae, green algae and diatoms are the most common water bloom algae in inland eutrophic water bodies, and especially the blue algae water bloom is an important ecological problem for inland lake and reservoir management and water resource development and utilization. The main manifestations of the water bloom are: a large amount of algae gathered on the surface layer of the water body can shield light from entering water and block air exchange, which is not beneficial to the photosynthesis of other non-dominant algae and causes the death of other plankton caused by the reduction of dissolved oxygen in the water body; some harmful blue algae have the potential of secreting diarrheal, paralytic and neurotoxic toxins, so that fish and shrimp die and landscape pollution are caused, and further the safety of drinking water of surrounding residents is directly endangered, and huge economic loss and negative social influence are caused. Therefore, a whole set of algal bloom phenomenon monitoring technical system needs to be established to realize the extraction of algal colony structures (relative abundance ratio of blue-green-diatom population) in the algal bloom stage and the identification of dominant population.
The conventional monitoring method of the water bloom phenomenon mainly comprises the steps of selecting a water bloom volatile area as an observation water area, measuring water body biochemical parameters of the whole processes of initial development, development and extinction of the water bloom on site, and determining the number of algae cells in a water sample and distinguishing dominant algae species by respectively utilizing an electron microscope and a phytoplankton classification fluorometer. However, the conventional monitoring method for the water bloom phenomenon may be limited by the uncertainty of the water bloom in the time-space domain, and may generate high manpower and economic cost, which is not favorable for the long-term and wide-area monitoring. Therefore, it is necessary to introduce a remote sensing monitoring technology with the characteristics of quasi-real time, high precision, large range, low cost and the like in the dynamic observation process of the water bloom phenomenon.
At present, the algal bloom phenomenon remote sensing extraction algorithm mainly achieves the purpose of estimating the algae biomass by analyzing the sensitive spectral characteristics corresponding to chlorophyll alpha. However, the structure of the algal community during the outbreak of the algal bloom is complex, and the algal bloom remote sensing extraction algorithm based on the chlorophyll alpha concentration quantitative inversion model is ineffective when estimating the biomass of the blue algae, the green algae and the diatom population, so that the potential algal toxin risk of the algal bloom cannot be evaluated. The inherent optical properties of blue light, green light and red light wave bands can visually reflect the physiological and biochemical characteristics of blue-green algae, green-green algae and diatom populations, and spectral characteristic differences are shown along with the relative abundance changes of the three types of algae, so that the relative abundance ratio of the blue-green-diatom populations in the bloom stage, namely the algae community structure, can be effectively extracted. In order to effectively identify the algal community structure in the bloom stage, the hyperspectral identification of the algal community structure in the bloom stage of an observation water area is realized by combining the spectral characteristic difference of the blue-green-diatom population and analyzing from the inherent optical quantitative angle of the algal population.
Disclosure of Invention
In view of the above, the invention provides a hyperspectral identification method for an algal community structure in a bloom stage, which can accurately judge a bloom phenomenon and identify an algal community structure (relative abundance ratio of a blue-green-diatom population) in the bloom stage by constructing spectral parameters of a characterization algal community and population diagnosis photosynthetic pigment suitable for hyperspectral data of a water surface.
In order to achieve the purpose, the invention provides the following technical scheme:
a hyperspectral identification method for algal community structures in bloom stage is realized by an in-situ digital camera and a surface feature spectrometer, and comprises the following steps:
s1: acquiring a high-definition photo by using an in-situ digital camera, preliminarily judging whether the water bloom phenomenon exists in an observation water area, if the water color is not abnormal, finally judging that the water bloom phenomenon does not occur, otherwise, turning to the step S2;
s2: acquiring water surface reflection spectrum data by using a surface feature spectrometer, and extracting a specific wavelength remote sensing reflectivity sensitive to an algae community and a population;
s3: constructing spectral parameters for representing algae communities and blue-green-diatom populations to diagnose photosynthetic pigments by utilizing remote sensing reflectivity data of sensitive wavelengths;
s4: diagnosing photosynthetic pigment spectral parameters by using the characteristic algae communities, finally judging whether the water bloom phenomenon exists in the observed water area, and if so, turning to the step S5;
s5: and (3) identifying the algal community structure and the dominant population of the algal community in the bloom stage by using the spectral parameters for representing the blue-green-diatom population diagnosis photosynthetic pigment.
Furthermore, the in-situ digital camera and the surface feature spectrometer are used for observing the same water area, are arranged back to the sun and respectively keep an included angle with the water surface at a certain angle.
Further, the step S1 specifically includes: firstly, regularly shooting high-definition photos on the water surface by using an in-situ digital camera; then, analyzing the photo by using an image recognition technology; and finally, judging whether the water body color abnormity caused by the algae accumulation water surface exists, namely the grey value of the water body reaches the set threshold value of the water bloom phenomenon, if not, finally judging that the water bloom phenomenon does not occur, otherwise, turning to the step S2.
Further, the sensitive wavelengths in step S2 are respectively four wavelengths (λ 1, λ 2, λ 3, λ 4) corresponding to characteristic reflection wave peaks (R1, R2, R3, R4) of chlorophyll α, chlorella chlorophyll b, phycocyanin, fucoxanthin and the like, and four wavelengths (λ 5, λ 6, λ 7, λ 8) corresponding to characteristic reflection wave troughs (R5, R6, R7, R8) in sequence, and the total of the sensitive wavelengths is eight wavelengths.
Further, the calculation formula for constructing the spectral parameters for characterizing the algal colony and the blue-green-diatom population to diagnose the photosynthetic pigments in step S3 is as follows:
(1) spectral parameters characterizing the diagnosis of the photosynthetic pigment chlorophyll α by the algal colony:
(2) spectral parameters characterizing the diagnosis photosynthetic pigment chlorophyll b of the green alga population:
(3) the spectral parameters for characterizing the diagnosis photosynthetic pigment phycocyanin of the blue algae population are as follows:
(4) spectral parameters characterizing the diatom population for diagnosis of the photosynthetic pigment fucoxanthin:
further, the step S4 specifically includes: headFirstly, setting a threshold parameter H of the water bloom phenomenon; then, using H in step S3aPerforming data comparison if HaIf the number is less than H, the water bloom is finally judged not to be exposed, otherwise, the water bloom phenomenon is finally judged to exist, and the step S5 is switched to.
Further, the identification of algal colony structure in bloom stage in step S5 is performed according to H in step S3b、HPAnd HFConstructing a formula of relative abundance ratio of blue-green-diatom population as follows:
blue algae, green algae and diatom are A.HP∶B·Hb∶HF
Wherein A, B is a ratio parameter.
Further, the setting of the parameter H, A, B can be obtained by training a large amount of existing water surface hyperspectral data of blue-green-diatom bloom in a deep learning neural network.
The invention has the beneficial effects that: the invention provides a hyperspectral identification method of an algal community structure in a bloom stage, which aims at a blue-green-diatom bloom phenomenon under a complex algal community structure, utilizes an in-situ digital camera and a surface feature spectrometer, and accurately judges the bloom phenomenon and identifies the relative abundance ratio of the blue-green-diatom population in the bloom stage, namely the algal community structure, by constructing a characterization algal community and population diagnosis photosynthetic pigment spectrum parameter suitable for hyperspectral data of a water surface.
Drawings
For the purpose and technical solution of the present invention, the present invention is illustrated by the following drawings:
FIG. 1 is a flow chart of a hyperspectral identification method of an algal community structure in a bloom stage;
FIG. 2 is a schematic diagram of the construction of spectral parameters of photosynthetic pigments for algae community and population diagnosis according to the embodiment of the present invention, wherein serial numbers 1 to 8 correspond to eight wavelengths λ 1 to λ 8, respectively.
Detailed Description
In order to make the purpose and technical solution of the present invention more clearly understood, the present invention will be described in detail with reference to the accompanying drawings and examples.
Example (b): in order to judge the water bloom phenomenon in a certain water area and identify the relative abundance ratio of the blue-green-diatom population in the water bloom outbreak period, the embodiment provides a hyperspectral identification method for an algae community structure in the water bloom period, which is realized by selecting an area where the water bloom easily occurs and installing an in-situ digital camera and a surface feature spectrometer.
Specifically, the installation requirements of the in-situ digital camera are as follows: more than or equal to 500 ten thousand pixels, the distance between the camera and the water surface is about 1m, the included angle between the shooting direction and the normal line of the water surface is about 40 degrees, and the shooting direction is the direction back to the sun; the installation requirement of the surface feature spectrometer is as follows: the spectral range should include 325 and 1075nm, the spectral resolution is less than or equal to 3nm, the distance between the probe and the water surface is about 1.3m, the included angle between the direction pointed by the probe and the normal of the water surface is about 40 degrees, and the direction is the direction back to the sun.
With reference to fig. 1, the method comprises the following steps:
the method comprises the following steps: and (4) acquiring the high-definition photos by using the in-situ digital camera, preliminarily judging whether the water bloom phenomenon exists in the observation water area, if not, finally judging that the water bloom phenomenon does not occur, otherwise, turning to the step two.
Firstly, regularly shooting high-definition photos on the water surface by using an in-situ digital camera; then, analyzing the photo by using an image recognition technology; and finally, judging whether the water body color abnormity caused by the algae accumulation water surface exists, namely the grey value of the water body reaches the set threshold value of the water bloom phenomenon, if not, finally judging that the water bloom phenomenon does not occur, otherwise, turning to the step two.
Step two: and (3) acquiring water surface reflection spectrum data by using a surface feature spectrometer, and extracting the remote sensing reflectivity of specific wavelength sensitive to algae community and population change.
The sensitive wavelengths are respectively eight wavelengths in sequence, namely four wavelengths (lambda 1-709 nm, lambda 2-640 nm, lambda 3-570 nm and lambda 4-460 nm) corresponding to characteristic reflection wave peaks (R1, R2, R3 and R4) of chlorophyll alpha, green algae chlorophyll b, phycocyanin and fucoxanthin and four wavelengths (lambda 5-670 nm, lambda 6-655 nm, lambda 7-625 nm and lambda 8-485 nm) corresponding to characteristic reflection wave troughs (R5, R6, R7 and R8).
Step three: and (3) constructing spectral parameters for representing the algae communities and the blue-green-diatom population to diagnose photosynthetic pigments by utilizing the remote sensing reflectivity data of the sensitive wavelength.
Respectively constructing by using the characteristic absorption valley of 650-680nm wave band:
(1) spectral parameters characterizing the diagnosis of the photosynthetic pigment chlorophyll α by the algal colony:
(2) spectral parameters characterizing the diagnosis photosynthetic pigment chlorophyll b of the green alga population:
based on the strong absorption characteristics of phycocyanin in the wave band of 620-640nm, the following components are constructed:
(3) the spectral parameters for characterizing the diagnosis photosynthetic pigment phycocyanin of the blue algae population are as follows:
aiming at the strong absorption characteristic of fucoxanthin in the 450-530nm wave band, the construction method comprises the following steps:
(4) spectral parameters characterizing the diatom population for diagnosis of the photosynthetic pigment fucoxanthin:
step four: and (5) diagnosing the photosynthetic pigment spectrum parameters by using the characteristic algae communities, finally judging whether the water bloom phenomenon exists in the observed water area, and if so, turning to the fifth step.
Firstly, learning according to big data, and setting a threshold parameter H of the water bloom phenomenon to be 0.0025; then, using H in step S3aPerforming data comparison if HaIf the water bloom is less than 0.0025, finally judging that the water bloom is not outbreak, otherwise, finally judging that the water bloom phenomenon exists, and turning to the fifth step.
Step five: according to H in step threeb、HPAnd HFAnd (3) constructing a blue-green-diatom population relative abundance ratio formula and a ratio coefficient generated by big data learning, and identifying the algal community structure in the bloom stage.
Blue algae, green algae and diatom are 3.7. HP∶1.8·Hb∶HF
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (8)
1. A hyperspectral identification method for algal community structures in bloom stage is realized by an in-situ digital camera and a surface feature spectrometer, and is characterized by comprising the following steps:
s1: acquiring a high-definition photo by using an in-situ digital camera, preliminarily judging whether the water bloom phenomenon exists in an observation water area, if the water color is not abnormal, finally judging that the water bloom phenomenon does not occur, otherwise, turning to the step S2;
s2: acquiring water surface reflection spectrum data by using a surface feature spectrometer, and extracting a specific wavelength remote sensing reflectivity sensitive to algae community and population change;
s3: constructing spectral parameters for representing algae communities and blue-green-diatom populations to diagnose photosynthetic pigments by utilizing remote sensing reflectivity data of sensitive wavelengths;
s4: diagnosing photosynthetic pigment spectral parameters by using the characteristic algae communities, finally judging whether the water bloom phenomenon exists in the observed water area, and if so, turning to the step S5;
s5: and (3) identifying the algal community structure and the dominant population of the algal community in the bloom stage by using the spectral parameters for representing the blue-green-diatom population diagnosis photosynthetic pigment.
2. The hyperspectral identification method of an algal community structure in the bloom stage according to claim 1, wherein the in-situ digital camera and the surface feature spectrometer are used for observing the same water area, are arranged in a direction opposite to the sun, and respectively keep an included angle with a water surface at a certain angle.
3. The hyperspectral identification method of an algal colony structure in the bloom stage according to claim 1, wherein the step S1 specifically comprises: firstly, regularly taking a picture of the water surface by using an in-situ digital camera; then, analyzing the photo by using an image recognition technology; and finally, judging whether the water body color abnormity caused by the algae accumulation water surface exists, namely the grey value of the water body reaches the set threshold value of the water bloom phenomenon, if not, finally judging that the water bloom phenomenon does not occur, otherwise, turning to the step S2.
4. The hyperspectral identification method of an algal colony structure in the bloom stage as claimed in claim 1, wherein the sensitive wavelengths in step S2 are respectively four wavelengths (λ 1, λ 2, λ 3, λ 4) corresponding to characteristic reflection wave peaks (R1, R2, R3, R4) and four wavelengths (λ 5, λ 6, λ 7, λ 8) corresponding to characteristic reflection waves troughs (R5, R6, R7, R8) in sequence, and the total number of the sensitive wavelengths is eight wavelengths.
5. The method for hyperspectral identification of an algal community structure in the bloom stage according to claim 1, wherein the step S3 is implemented by constructing a calculation formula for characterizing photosynthetic pigment spectrum parameters for diagnosis of an algal community and a blue-green-diatom population as follows:
(1) spectral parameters characterizing the diagnosis of the photosynthetic pigment chlorophyll a by the algal colony:
(2) spectral parameters characterizing the diagnosis photosynthetic pigment chlorophyll b of the green alga population:
(3) the spectral parameters for characterizing the diagnosis photosynthetic pigment phycocyanin of the blue algae population are as follows:
(4) spectral parameters characterizing the diatom population for diagnosis of the photosynthetic pigment fucoxanthin:
6. the hyperspectral identification method of an algal colony structure in the bloom stage according to claim 1, wherein the step S4 specifically comprises: firstly, setting a threshold parameter H of the water bloom phenomenon; then, using H in step S3aPerforming data comparison if HaIf the number is less than H, the water bloom is finally judged not to be exposed, otherwise, the water bloom phenomenon is finally judged to exist, and the step S5 is switched to.
7. The method for hyperspectral identification of algal colony structures in bloom stage according to claim 1, wherein the step S5 is implemented according to the step S3b、HPAnd HFConstructing a formula of relative abundance ratio of blue-green-diatom population as follows:
blue algae, green algae and diatom are A.HP∶B·Hb∶HF
Wherein A, B is a ratio parameter.
8. The hyperspectral identification method of an algal colony structure in the bloom stage as claimed in any one of claim 6 or claim 7, wherein the setting of the parameter H, A, B can be obtained by training a large amount of water surface hyperspectral data of the existing blue-green-diatom bloom in a deep learning neural network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010798423.8A CN111795941B (en) | 2020-08-06 | 2020-08-06 | Hyperspectral identification method for algal colony structure in water bloom stage |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010798423.8A CN111795941B (en) | 2020-08-06 | 2020-08-06 | Hyperspectral identification method for algal colony structure in water bloom stage |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111795941A true CN111795941A (en) | 2020-10-20 |
| CN111795941B CN111795941B (en) | 2023-05-16 |
Family
ID=72833918
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010798423.8A Active CN111795941B (en) | 2020-08-06 | 2020-08-06 | Hyperspectral identification method for algal colony structure in water bloom stage |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111795941B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113340825A (en) * | 2021-06-17 | 2021-09-03 | 重庆大学 | Method for measuring and calculating chlorophyll a concentration under high-turbidity background interference |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721655A (en) * | 2012-06-01 | 2012-10-10 | 天津科技大学 | Optimal distinguishing method for red tide water body and main algae thereof |
| CN103439472A (en) * | 2013-06-14 | 2013-12-11 | 北京工商大学 | Lake-reservoir cyanobacteria water bloom recognition method based on remote sensing monitoring and evidence fusion technology improvement |
| CN103760112A (en) * | 2014-01-23 | 2014-04-30 | 中国科学院南京地理与湖泊研究所 | Lake water body blue-green algae abundance estimation method based on remote sensing |
| CN103868901A (en) * | 2014-03-14 | 2014-06-18 | 中国科学院合肥物质科学研究院 | Discrete three-dimensional fluorescence spectrum-based phytoplankton identification and measurement method and discrete three-dimensional fluorescence spectrum-based phytoplankton identification and measurement device |
| CN104569306A (en) * | 2014-12-24 | 2015-04-29 | 中国科学院重庆绿色智能技术研究院 | Method for prejudging succession and water blooms of algae species |
| CN104596448A (en) * | 2015-02-11 | 2015-05-06 | 中国科学院南京地理与湖泊研究所 | Eutrophic water aquatic vegetation remote sensing extraction method based on alga index frequency method |
| CN105138994A (en) * | 2015-08-31 | 2015-12-09 | 中国科学院遥感与数字地球研究所 | Water bloom identification method and device based on hyperspectral remote sensing image |
| CN106290389A (en) * | 2016-09-05 | 2017-01-04 | 中国科学院南京地理与湖泊研究所 | The algal tufa of a kind of eutrophic lake MODIS image and non-algal tufa condition classification method |
| CN106399080A (en) * | 2016-09-08 | 2017-02-15 | 河海大学 | Integrated measuring device and method for algae population biomass in lake and reservoir shallow-water areas |
| CN109270007A (en) * | 2018-10-30 | 2019-01-25 | 中国科学院南京地理与湖泊研究所 | Eutrophic lake bloom blue algae monoid quantitative identification method |
| CN110702628A (en) * | 2019-10-26 | 2020-01-17 | 山东科技大学 | Spectral index model of chlorophyll content of vegetation leaf based on continuous wavelet analysis |
| CN111239050A (en) * | 2020-03-12 | 2020-06-05 | 高永飞 | River and lake water ecological environment monitoring method based on Internet of things |
-
2020
- 2020-08-06 CN CN202010798423.8A patent/CN111795941B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721655A (en) * | 2012-06-01 | 2012-10-10 | 天津科技大学 | Optimal distinguishing method for red tide water body and main algae thereof |
| CN103439472A (en) * | 2013-06-14 | 2013-12-11 | 北京工商大学 | Lake-reservoir cyanobacteria water bloom recognition method based on remote sensing monitoring and evidence fusion technology improvement |
| CN103760112A (en) * | 2014-01-23 | 2014-04-30 | 中国科学院南京地理与湖泊研究所 | Lake water body blue-green algae abundance estimation method based on remote sensing |
| CN103868901A (en) * | 2014-03-14 | 2014-06-18 | 中国科学院合肥物质科学研究院 | Discrete three-dimensional fluorescence spectrum-based phytoplankton identification and measurement method and discrete three-dimensional fluorescence spectrum-based phytoplankton identification and measurement device |
| CN104569306A (en) * | 2014-12-24 | 2015-04-29 | 中国科学院重庆绿色智能技术研究院 | Method for prejudging succession and water blooms of algae species |
| CN104596448A (en) * | 2015-02-11 | 2015-05-06 | 中国科学院南京地理与湖泊研究所 | Eutrophic water aquatic vegetation remote sensing extraction method based on alga index frequency method |
| CN105138994A (en) * | 2015-08-31 | 2015-12-09 | 中国科学院遥感与数字地球研究所 | Water bloom identification method and device based on hyperspectral remote sensing image |
| CN106290389A (en) * | 2016-09-05 | 2017-01-04 | 中国科学院南京地理与湖泊研究所 | The algal tufa of a kind of eutrophic lake MODIS image and non-algal tufa condition classification method |
| CN106399080A (en) * | 2016-09-08 | 2017-02-15 | 河海大学 | Integrated measuring device and method for algae population biomass in lake and reservoir shallow-water areas |
| CN109270007A (en) * | 2018-10-30 | 2019-01-25 | 中国科学院南京地理与湖泊研究所 | Eutrophic lake bloom blue algae monoid quantitative identification method |
| CN110702628A (en) * | 2019-10-26 | 2020-01-17 | 山东科技大学 | Spectral index model of chlorophyll content of vegetation leaf based on continuous wavelet analysis |
| CN111239050A (en) * | 2020-03-12 | 2020-06-05 | 高永飞 | River and lake water ecological environment monitoring method based on Internet of things |
Non-Patent Citations (1)
| Title |
|---|
| 马万泉: "微囊藻生物光学特性与遥感识别研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113340825A (en) * | 2021-06-17 | 2021-09-03 | 重庆大学 | Method for measuring and calculating chlorophyll a concentration under high-turbidity background interference |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111795941B (en) | 2023-05-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hu et al. | Remote detection of Trichodesmium blooms in optically complex coastal waters: Examples with MODIS full-spectral data | |
| Pennekamp et al. | Implementing image analysis in laboratory‐based experimental systems for ecology and evolution: a hands‐on guide | |
| CN110987955B (en) | Urban black and odorous water body grading method based on decision tree | |
| CN110060245A (en) | A kind of gastric cancer lymph node dyeing pathological image automatic identifying method based on deep neural network | |
| CN110533583B (en) | Self-adaptive image augmentation system based on cervical fluid-based cells | |
| CN114781537B (en) | Sea entry and drainage port suspected pollution discharge identification method based on high-resolution satellite image | |
| CN104036257A (en) | Citrus vein phloem degeneration detection and classification method based on D-S theory through multi-source data fusion | |
| CN111105389A (en) | Detection method for pavement crack by fusing Gabor filter and convolutional neural network | |
| Liu et al. | Trophic state assessment of optically diverse lakes using Sentinel-3-derived trophic level index | |
| CN111795941B (en) | Hyperspectral identification method for algal colony structure in water bloom stage | |
| CN109191467B (en) | Method and device for predicting autophagy phenotype of cell | |
| CN115187865A (en) | Unmanned aerial vehicle image near-infrared band simulation method and medium based on ground feature spectrum | |
| Reichardt et al. | Spectroradiometric detection of competitor diatoms and the grazer Poteriochromonas in algal cultures | |
| Zhou et al. | Marine microalgae detection in microscopy images: A new dataset | |
| CN115578553B (en) | Formaldehyde rapid detection method based on time sequence image sequence | |
| Park et al. | Detection of non-O157 Shiga toxin-producing Escherichia coli (STEC) serogroups with hyperspectral microscope imaging technology | |
| MacDonald et al. | Multispectral imaging of degraded parchment | |
| CN114965300B (en) | Lake turbidity drawing method for constructing BP-TURB based on optical water body type and BP neural network algorithm | |
| CN114998724B (en) | Lake blue algae bloom remote sensing monitoring method for resisting cloud interference | |
| CN116167964A (en) | Tumor classification method and system based on tumor hyperspectral image | |
| Fan et al. | Method to characterize color of biochar and its prediction with biochar yield as model property | |
| CN111091601B (en) | PM2.5 index estimation method for real-time daytime outdoor mobile phone image | |
| Ahmad et al. | Haze effects on satellite remote sensing imagery and their corrections | |
| Davari et al. | Sketch layer separation in multi-spectral historical document images | |
| Wei et al. | A study of image processing on identifying cucumber disease |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |