A Study on Eutrophication of Lake Based on Hyper-spectraI Remote
author: Gavin
2022-01-07
Water quality is one of the major global concerns. The prediction of water quality parameters in water bodies is an important condition for water quality assessment. Nitrogen and phosphorus are essential nutrients in the process of eutrophication of water bodies. Nitrogen is an essential element for amino acids and other organic matter. Phosphorus provides continuous material support for cell growth and division. Phosphorus is a component of nucleic acid protein. Nucleic acid is the storage place of genetic material. At the same time, phosphorus is a component of phospholipids and adenosine triphosphate. Phospholipids are components of cell biofilms. Adenosine triphosphate provides energy for plant growth and other processes. Therefore, the concentration of phosphorus has a significant effect on the nutritional status of water bodies. Great influence. Chlorophyll a is one of the representative substances that characterize phytoplankton: Chlorophyll a is the embodiment of water bloom, and the concentration of chlorophyll a determines the content of phytoplankton to a certain extent.
Principle
First, normalize and differentiate the spectral bands of lakes, and then perform correlation analysis on the traditional measured data of lakes and normalized and differential spectra to identify the sensitive bands of total phosphorus, and use the traditional measured total phosphorus data and spectral data of lakes to establish total Phosphorus model, and use the model to calculate and analyze the distribution of total phosphorus concentration. Using the GF-1 WFV2 data and the characteristics of chlorophyll a, the remote sensing image of lakes was retrieved using the spectral relationship method to retrieve the temporal and spatial distribution of chlorophyll a concentration. Finally, the MLP neural network is used to predict the concentration of each water quality parameter, and the comprehensive nutrient status index method and spatial analysis method are used to analyze the nutrient status and spatial distribution of the lake.
Solution
Spectral collection uses Optosky ATP9110-25 and ATH9010,ATP9110-25 broadband field spectroradiometer is a newportable hyperspectral fieldSpec from Optosky. Wavelength rangeof 300-2500 nm, suitable for geological research, mineralexploration, remote sensing, crop monitoring, forest research,oceanography and other fields of application.Spectral data is collected by spectrometer on the blessing and brightness of ground objects. Spectral data sampling is mainly to sample and record the spectral brightness information of the whiteboard and the water body.ATH9010 series is the 3rd generation hyperspectral imager . This new series feature compact and light.
The sampling process is as follows: ①Turn on the spectrometer first, then turn on the computer. ②Set the data storage path and test parameters for the sampled spectrum. ③The lens will be vertically compared to the standard white board for dark current monitoring. ④The lens will be vertically compared to the standard gray board, click the space Key to measure the DN value of the gray board. ⑤The lens compares the standard gray board vertically, while blocking the direct sunlight from irradiating the gray board, and measuring the reflection of the diffuse scattered light from the sky. ⑥The lens is tilted against the water and keeps the lens against the sun. The angle between the normal direction of the water surface is "35°<α<45°" to measure the DN value of the water body. ⑦Keep the instrument in the same observation plane, and then rotate the lens upward by an angle, so that the sky light radiance is equal to the observation direction day. The apex angle is equal to the observation angle α during the water surface measurement. ⑧The lens is vertically compared to the standard whiteboard to calibrate the whiteboard. ⑨Facing the sun, the lens is vertically compared to the water body to measure the apparent reflectance of the water body and the water body remote sensing reflection calculated by the tilt measurement ⑩After the test, close the spectrometer and its driver, and then close the computer.
Figure 2 The distribution of dissolved oxygen and chlorophyll concentration in Zhelin Bay, eastern Guangdong, taken by hyperspectral
Principle
First, normalize and differentiate the spectral bands of lakes, and then perform correlation analysis on the traditional measured data of lakes and normalized and differential spectra to identify the sensitive bands of total phosphorus, and use the traditional measured total phosphorus data and spectral data of lakes to establish total Phosphorus model, and use the model to calculate and analyze the distribution of total phosphorus concentration. Using the GF-1 WFV2 data and the characteristics of chlorophyll a, the remote sensing image of lakes was retrieved using the spectral relationship method to retrieve the temporal and spatial distribution of chlorophyll a concentration. Finally, the MLP neural network is used to predict the concentration of each water quality parameter, and the comprehensive nutrient status index method and spatial analysis method are used to analyze the nutrient status and spatial distribution of the lake.
Solution
Spectral collection uses Optosky ATP9110-25 and ATH9010,ATP9110-25 broadband field spectroradiometer is a newportable hyperspectral fieldSpec from Optosky. Wavelength rangeof 300-2500 nm, suitable for geological research, mineralexploration, remote sensing, crop monitoring, forest research,oceanography and other fields of application.Spectral data is collected by spectrometer on the blessing and brightness of ground objects. Spectral data sampling is mainly to sample and record the spectral brightness information of the whiteboard and the water body.ATH9010 series is the 3rd generation hyperspectral imager . This new series feature compact and light.
The sampling process is as follows: ①Turn on the spectrometer first, then turn on the computer. ②Set the data storage path and test parameters for the sampled spectrum. ③The lens will be vertically compared to the standard white board for dark current monitoring. ④The lens will be vertically compared to the standard gray board, click the space Key to measure the DN value of the gray board. ⑤The lens compares the standard gray board vertically, while blocking the direct sunlight from irradiating the gray board, and measuring the reflection of the diffuse scattered light from the sky. ⑥The lens is tilted against the water and keeps the lens against the sun. The angle between the normal direction of the water surface is "35°<α<45°" to measure the DN value of the water body. ⑦Keep the instrument in the same observation plane, and then rotate the lens upward by an angle, so that the sky light radiance is equal to the observation direction day. The apex angle is equal to the observation angle α during the water surface measurement. ⑧The lens is vertically compared to the standard whiteboard to calibrate the whiteboard. ⑨Facing the sun, the lens is vertically compared to the water body to measure the apparent reflectance of the water body and the water body remote sensing reflection calculated by the tilt measurement ⑩After the test, close the spectrometer and its driver, and then close the computer.
Test result
The initial spectral data is spectral brightness information, and the original data needs to be processed. Use software to check the validity of the collected samples and eliminate outliers. Collect 5 pieces of spectral information for each collection object (including whiteboard) during collection, take the average value of the spectral data, convert it into the original reflectance, export the spectral reflectance data, and use the whiteboard to correct the water spectral reflectance.
The initial spectral data is spectral brightness information, and the original data needs to be processed. Use software to check the validity of the collected samples and eliminate outliers. Collect 5 pieces of spectral information for each collection object (including whiteboard) during collection, take the average value of the spectral data, convert it into the original reflectance, export the spectral reflectance data, and use the whiteboard to correct the water spectral reflectance.
Fig. 1 Spatial distribution of total phosphorus concentration in Taihu Lake.
The spatial difference of total phosphorus concentration was obvious, with the highest value of 0.38mg/L and the lowest value of 0.06mg/L. (b) Monthly variation of total phosphorus concentration in different lakes.
The lake area also generally reaches its maximum phosphorus concentration between June and September. The total phosphorus concentration in Zhushan Bay, Meiliang Bay and the west bank of Taihu Lake was higher than the mean value of the whole lake from March to October of the year, and was significantly higher than that in the rest of Taihu Lake. The total phosphorus concentration in Gonghu Bay was higher than that in the whole lake only in June, and the total phosphorus concentration in the south bank of Taihu Lake and Great Taihu Lake was relatively low throughout the year.
The lake area also generally reaches its maximum phosphorus concentration between June and September. The total phosphorus concentration in Zhushan Bay, Meiliang Bay and the west bank of Taihu Lake was higher than the mean value of the whole lake from March to October of the year, and was significantly higher than that in the rest of Taihu Lake. The total phosphorus concentration in Gonghu Bay was higher than that in the whole lake only in June, and the total phosphorus concentration in the south bank of Taihu Lake and Great Taihu Lake was relatively low throughout the year.
Figure 2 The distribution of dissolved oxygen and chlorophyll concentration in Zhelin Bay, eastern Guangdong, taken by hyperspectral
Conclusion
The total phosphorus concentration of the spectral analysis and the chlorophyll a concentration retrieved from remote sensing images are analyzed for the nutritional status index. The results are close to the range of the nutritional status index of the traditional measured concentration, and the spatial distribution is also relatively consistent. Combining the comprehensive nutritional status index of the hyperspectral data and the comprehensive nutritional status index of the traditional measured data, the results are also relatively similar, which shows that the estimated concentration of the hyperspectral data is suitable for the analysis of the nutritional status index.
Related products
Handheld FieldSpec Spectroradiometer:ATP9100
FieldSpec Spectroradiometer:ATP9110-25
Airborne Hyperspectral Remote Sensing System:ATH9010
Drone Hyperspectral Imaging System:ATHL9010
Related articles
The total phosphorus concentration of the spectral analysis and the chlorophyll a concentration retrieved from remote sensing images are analyzed for the nutritional status index. The results are close to the range of the nutritional status index of the traditional measured concentration, and the spatial distribution is also relatively consistent. Combining the comprehensive nutritional status index of the hyperspectral data and the comprehensive nutritional status index of the traditional measured data, the results are also relatively similar, which shows that the estimated concentration of the hyperspectral data is suitable for the analysis of the nutritional status index.
Related products
Handheld FieldSpec Spectroradiometer:ATP9100
FieldSpec Spectroradiometer:ATP9110-25
Airborne Hyperspectral Remote Sensing System:ATH9010
Drone Hyperspectral Imaging System:ATHL9010
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