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Abstract
Qiandao Lake is located in the northern edge of subtropics, and its water body is thermally stratified in summer. It is of great scientific significance to study the vertical physical and chemical indexes and phytoplankton characteristics of the Qiandao Lake to reveal the aquatic ecosystem structure of the thermally stratified lake. Conventional observation uses in-situ profile instruments, which is time consuming and labor intensive. In recent years, lidar has shown increasing oceanic applications; however, it has not yet been extensively applied in inland water. There are no studies using lidar for detecting subsurface plankton layer in Qiandao Lake. In this study, we investigated the applicability of this technology for identifying subsurface plankton layer. A simple and fast phytoplankton layer detection method was introduced. The lidar-detected layer was found to well correspond with that of the in-situ measured subsurface chlorophyll maximum layer (SCML) and phycocyanin maximum layer. Primary results show that lidar and our detection method are effective for subsurface phytoplankton layer detection. They can serve as a good monitoring tool for studying inland water stratification.
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Qiandao Lake (also known as Xin'anjiang Reservoir) is in Chun ‘an City, at the border of western Zhejiang province and southern Anhui province (118°34’ −119 ° 15 ‘E, 29°22’ −29°50'N). It is located in the subtropical northern rim, a southeast coastal monsoon region, which has warm climate and abundant rainfall; the annual average temperature is 16.90°C. The water level of the lake is above sea level elevation of 108 m, and the surface area is 580 km2, with a capacity of 178.4 × 108 m3; the average water depth is 31 m. Qiandao Lake, as the “ecological source” of the Qiantang River, is an important source of drinking water in the Yangtze River Delta region. In recent years, with the rapid development of social economy and unreasonable use of resources, the pollution load of Qiandao Lake water has gradually increased, eutrophication has gradually appeared in some waters, and the quality of water environment has gradually decreased.
According to the data of the state of the environment bulletin of China [1,2], it shows that the water quality of Qiandao Lake decreased from Class I in 2000 to Class III in 2017: the total phosphorus increased from 0.018 mg·L−1 to 0.037 mg·L−1, the total nitrogen rose from 0.8 mg·L−1 to 1.5 mg·L−1, and chlorophyll-a rose from 2 µg·L−1 in 6 µg·L−1. There are frequent outbreaks of cyanobacterial blooms in local waters. However, past studies on Qiandao Lake focused on changes in water quality or only on floating algae changes; water quality study associated with phytoplankton stratification is rarely reported. There is a lack of studies on the vertical distribution of phytoplankton community, especially on the measurement of some important physiological parameters in the process of photosynthesis of algae.
In the deep-water reservoir such as Qiandao Lake, the vertical temperature difference due to water stratification is large and lasts for a long time. The vertical distribution of water temperature shows that there are different degrees of thermocline and temperature stratification in all four seasons, among which the stratification of water temperature in front of the dam with the deepest water depth is the most obvious. The water temperature gradient exceeds 0. 6 °C / m in spring, summer and autumn [3]. The phenomenon of seasonal stratification affects the physical characteristics, chemical processes and biological activities. It is of great scientific significance to study the vertical physical and chemical indexes and phytoplankton characteristics of the Qiandao Lake to reveal the aquatic ecosystem structure of the thermally stratified lake. Conventional observation method uses in-situ profile of water quality instrument, which is time consuming and labor intensive. In contrast, lidar remote sensing has the advantage of rapid and range-resolved acquisition, hence providing large-scale detection for vertical structure information of phytoplankton [4]. Increasing, lidars are more and more widely used, including mapping bathymetry [5,6], optical properties profiling [7–11], also for finding schools of fish [12,13], dissolved organic matter [14,15], bubbles [16], internal waves [17], and so on [18–20]. Nowadays, there are more and more shipborne lidars developed for different applications [21–24]. Recently, some researchers detected subsurface plankton layers in sea water using lidar for rapid and wide range observations [25–27], but none of these studies focused on inland water, which is more complex and has different driving mechanism of water stratification.
In this paper, observations of subsurface plankton layers in Qiandao Lake using shipborne lidar technology are described. The lidar experiments were carried on in June 2019. Synchronous conventional measurements were carried out to validate the lidar measurements. We introduced a simple and fast phytoplankton layer detection method. Then, an example result of step-by-step processing is described, and validation of shipborne lidar measurements by in situ measurements is presented. Finally, vertical profile distributions of lidar-detected layer mapped along ship travel tracks are analyzed.
2. Materials and methods
2.1 Lidar system
The shipborne lidar was developed by the Shanghai Institute of Technical Physics (SITP), Chinese Academy of Sciences. The lidar system adopts coaxial cage structure design to make it more compact. It employs a Q-switched laser (G-08E, Brillouin Laser Technology Inc., Shanghai, China) at $532\, \textrm{nm}$ with pulse repetition rate (PRF) of 20 kHz. The pulse width is less than 0.9 ns, and single pulse energy is 8 µJ. The detection module uses a metal package PMT with Gate Function (H11526-20-NF, Hamamatsu Photonics Inc., Hamamatsu, Japan). The combination of built-in metal package PMT and gate circuit makes this module compact yet still provides excellent characteristics: 100 ns minimum gate width, 10 kHz repetition rate. This module also contains a high-voltage power supply so that PMT gain can be varied by simply adjusting the control voltage. The internal protection monitor issues an error signal if high-intensity light enters the module. The data acquisition module utilizes a time-correlated single-photon counting system with four channels (FT1040, Star Second Science and Technology Inc., Shanghai, China). Each channel can achieve a time resolution of 64 ps, a dead time of less than 10 ns and a saturation count rate of up to 100 Mcps (million counts per second); and it supports a total event transmission rate of 40M Events/s. A beam splitting prism separate the received signal into two channels with orthogonal polarizations, thus the depolarization signal can be obtained. Detailed system parameters are given in Table 1. Figure 1 shows a block diagram of the lidar system and a photo of real products.
Table 1. Shipborne lidar system parameters
2.2 Study area
The experiments were carried out on June 3-5, 2019. During the investigation, the lidar was installed at about 10 m above water surface and was mounted to point 30°off nadir. Simultaneously, conventional measurements using profile chlorophyll fluorescent probe (RBR XR-420, RBR Ltd., Canada) and 6-Channel backscattering sensor and fluorometer (HydroScat-6, HOBI Labs Ltd., Bellevue, USA) were carried out to validate the lidar measurements. XR-420 is a self-contained underwater instrument for measuring chlorophyll fluorescence or concentration in natural waters, which has small volume and high precision, and can be used for water quality investigation and monitoring for lake, river and port, marine environment monitoring and ecological investigation. HydroScat-6 is a self-contained underwater device for measuring optical backscattering at six different wavelengths in natural waters. Its unique optical geometry also provides measurements of fluorescence. It includes a depth transducer, rechargeable batteries, a data logger with real-time clock, and an external switch for controlling logging. The sampling and measurement principles and methods are mainly based on the Ocean Optics Protocols for the Satellite Ocean Color Sensor Validation from the NASA [28]. Figure 2 shows the shipborne lidar travel tracks (thin yellow lines).
Fig. 2. Study area in Qiandao Lake. Thin yellow lines are shipborne lidar running routes on June 3-5, 2019.
2.3 A simple and fast detection method
A Mie scattering lidar signal can be expressed by the quasi-single-scattering equation:
The range-corrected lidar signal S can be simplified as described by Eq. (2) [30].
In this study, we introduce a simple and fast phytoplankton layer detection method (see Fig. 3). The basic idea is that the original lidar signal S0 can be taken as the sum of background lidar signal SB and phytoplankton scattering layer lidar signal SL. SB is assumed as the lidar signal in vertically uniform phytoplankton water.Fig. 3. Schematic diagram of subsurface phytoplankton layer detection method (reproduced and modified from our previous study in Ref. [26]).
Due to water’s exponential decay function on Lidar background signal, it can be estimated as an exponential regression:
Thus, the logarithmic form of SB is in fact a linear fitting function. where SB(0) is the background lidar signal at the depth of water surface.Thus, signal SL of phytoplankton layer from the original signal S0 can be obtained by subtracting the regression and correcting for the background attenuation.
3. Results and discussion
3.1 An example result of step-by-step processing
Figure 4 shows an example result of step-by-step processing. Figure 4(a) shows the raw lidar echo signal S0, and Fig. 4(b) shows the range corrected signal by Eq. (2). The lidar signal was firstly preprocessed before layer detecting procedure, the multi-pulse average and background-noise reduction technology was used to improve signal to noise (SNR) of measurements and reject random noise peaks that might be misidentified as layers. Each fifty pulse echoes were averaged and the signal from ambient noise light was calculated and subtracted by the average of the last one hundred samples of the pulse. Then the noise-subtracted signal was logarithmically transformed to obtain lidar preprocessing measured signal. It seems that the lidar echo signal decreased sharply within 1.5 m depth, when the lidar light just entered the lake surfacewater. That is due to water surface’s strong reflection. We can see that the subsurface layer slight bulge signal could be found at the depth range from 5 m to 15 m in Fig. 4(b). The bulge in the lidar signal was caused by the scattering of subsurface layer. If there is no subsurface layer, the lidar echo signal will decrease as depth increases due to water attenuation. Based on this principle, we can obtain the subsurface layer through finding the abnormal bulge in the lidar signal. Figure 4(c) shows the background lidar signal SB in logarithmic form by a linear fitting function and range-corrected S0 in logarithmic form. SB is assumed as the lidar signal in vertical uniform phytoplankton water, which follows a exponential decay function. Hence, a linear fitting function is appropriate for obtaining Log(SB). The red line in Fig. 4(c) shows the linear fitting result from Eq. (5) and the Least-squares fit polynomial method was employed in our study. Then, the subsurface layer signal SL can be obtained by subtracting regression Log(SB) from Log(S0) (see Fig. 4(d)). Because of the high reflection of the sea surface, the upper-ocean data at 2 m are larger than the theoretical value. In order to avoid the sea surface effect, we set the value below 2 m into zero. It shows that the subsurface layer signal is obviously detected by our method, which is at the depth range from 7.5 to 15.5 m, and the maximum depth is at about 10 m. Here, the subsurface layer thickness is calculated by the full width at half maximum of the signal, which is near the depth range from 8 to 12 m in Fig. 4(d).
Fig. 4. An example result of step-by-step processing. (a) Origin signal S0; (b) range-corrected signal; (c) Logarithmic form of S0 and SB by linear fitting; (d) detected subsurface layer SL.
3.2 Validation
Figure 5 shows the validation results by comparing shipborne lidar measurements and in situ measurements by RBR XR-420 at the station (119.183°E, 29.567°N). The stratification of phytoplankton, water temperature, physical and chemical indexes is obvious in the study area. The black line is the lidar-detected layers, the red line is the vertical distribution of chlorophyll concentration, the cyan line is the vertical distribution of phycocyanin concentration, the carmine line is the temperature vertical distribution, the green line is the vertical distribution of DO (dissolved oxygen), the blue line is the vertical distribution of pH, and the dotted black line is the thermocline depth. The subsurface chlorophyll maximum layer (SCML) and phycocyanin maximum layer are both near the depth of 10 m (red line and cyan line in Fig. 5). The lidar-detected layer is found to well correspond to the SCML and phycocyanin maximum layer, which indicates that our method is effective for subsurface phytoplankton layer detection. Meanwhile, the clines of temperature, pH and dissolved oxygen are also all near 10 m and correspond well with the SCML. We suspect that temperature, pH and dissolved oxygen are the main driving factors for the generation of SCML in Qiandao Lake. Besides, the lidar-detected layer also well corresponds to the physical and chemical indicators, such as thermocline depth, DO-cline and pH-cline. It reveals that the lidar technology and our detection method can be used to monitor subsurface biophysical chemistry indicators for lake waters, which saves a lot of manpower and material resources, compared with conventional observation methods. Until now, there is a lack of studies on the vertical distribution of phytoplankton community in lake water, especially on the measurement of some important physiological parameters in the process of photosynthesis of algae. Hence, lidar technology combined with our detection method can serve as a good monitoring tool.
Fig. 5. Comparison of shipborne lidar measurements and in situ measurements at the station (119.183°E, 29.567°N). The black line is the lidar-detected layers, the red line is the vertical distribution of chlorophyll concentration, the cyan line is the vertical distribution of phycocyanin concentration, the carmine line is the temperature vertical distribution, the green line is the vertical distribution of DO (dissolved oxygen), the blue line is the vertical distribution of pH, the purple line is the volume scattering function of 140°at 510 nm, and the dotted black line is the thermocline depth.
3.3 Vertical profile distribution mapped along lidar tracks
Figure 6 shows the vertical profile distribution of lidar-detected layer mapped along ship tracks at different locations. Figure 6(a) is the vertical profile distribution measured by lidar for 10 minutes near (118.800°E, 29.667°N), and Fig. 6(b) is vertical profile distribution measured by lidar for 30 minutes near (119.200°E, 29.500°N). During measurements, the ship traveled forward at almost a constant speed. It seems that the lidar echo amplitude values were very high within 1.5 m depth, when the lidar light just entered into the lake surface water. That was due to water surface’s strong reflection. We can see both subsurface layers near the two locations. The signal amplitude of subsurface scattering layer is about twice as large than the uniform water signal. The subsurface layer vertical structure distribution varied as the ship traveled forward to different locations. Figure 7 shows the lidar-detected layer’s maximum depth and thickness along the ship tracks. Figure 7(a) is the lidar-detected layer’s maximum depth and thickness when ship traveled near (118.800°E, 29.667°N), and Fig. 7(b) is the lidar-detected layer’s maximum depth and thickness when ship traveled near (119.200°E, 29.500°N). It shows that the layer’s maximum depth was at the range from 8.7 to 8.9 m and the layer thickness was at the range from 1.2 to 1.5 m near (118.800°E, 29.667°N). The layer’s maximum depth was at the range from 10 to 12 m and the layer thickness was at the range from 1 to 2.5 m near (118.800°E, 29.667°N). The layer’s maximum depth and thickness show small changes as the ship traveled forward. We can see that the change of layer’s maximum depth near (118.800°E, 29.667°N) was more obvious than that near (119.200°E, 29.500°N), and its maximum depth was shallower than that near (119.200°E, 29.500°N) (Figs. 7(a) and (b)). However, the layer thickness at (118.800°E, 29.667°N) was larger than that at (119.200°E, 29.500°N). The discrepancy may be due to different biophysical chemistry factors at the two locations. In this study, the lidar-detected subsurface layer’s maximum depth agrees well with that in previous studies on stratification in summer in Qiandao Lake [31,32]. It demonstrates our shipborne lidar has the ability to survey and characterize lake phytoplankton structure.
Fig. 6. Vertical distribution of lidar-detected layer mapped along ship tracks at different locations. (a) Vertical distribution measured by lidar for 10 min when ship traveled near (118.800°E, 29.667°N), and (b) vertical distribution measured by lidar for 30 min when ship traveled near (119.200°E, 29.500°N).
Fig. 7. Lidar-detected layer’s maximum depth and thickness along ship tracks at different locations. Lidar-detected layer’s maximum depth and thickness when ship traveled near (118.800°E, 29.667°N) (a), and (119.200°E, 29.500°N) (b).
4. Conclusions
In this study, we proposed a simple and fast method for subsurface layer detection, by finding the abnormal bulge in lidar signal and fitting the uniform water signal. The lidar-detected layer is found to well correspond to the subsurface chlorophyll maximum layer, phycocyanin maximum layer, and the clines of temperature, pH and dissolved oxygen, which indicates that our method is effective for subsurface phytoplankton layer detection. Until now, there is a lack of studies on the vertical distribution of phytoplankton community in lake water, especially the measurements of some important physiological parameters in the process of photosynthesis of algae. Hence, lidar technology together with our detection method can serve as a good monitoring tool. More field experiments of quantitative inversion study are needed in the future.
Funding
The National Key Research and Program of China (2016YFC1400902); Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources (QNYC1803); National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (41901305); Zhejiang Natural Science Foundation (LQ19D060003).
Acknowledgments
The shipborne lidar data were supported by the Shanghai Institute of Technical Physics (SITP), Chinese Academy of Sciences. The in situ optical data were funded by the State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources. The authors are grateful for all the anonymous reviewers whose suggestions significantly improved the quality of the paper.
Disclosures
The authors declare no conflicts of interest.
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