Effective aerial monitoring of cyanobacterial harmful algal blooms is dependent on understanding cellular migration

Qu, Mingzhi; Anderson, Stephen; Lyu, Pin; Malang, Yasir; Lai, Jizhou; Liu, Jianye; Jiang, Bin; Xie, Feng; Liu, Hugh H. T.; Lefebvre, Daniel D.; Wang, Yuxiang S.

doi:10.1016/j.hal.2019.101620

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…This research aims to conduct observations using photographs taken from an UAV and to be able to conduct a spatially high-resolution analysis freely without restrictions due to photographing date or cloud cover [41,42]. Using a small UAV system equipped with a consumer-grade camera, Qu et al [43] determined surface-floating cyanobacteria at a maximum detection altitude of 80 m. The small UAV can cover up to 1 km 2 per flight mission, and the short time lag between sampling and flight allows for follow-up monitoring and treatment. Guimarães et al [44] photographed a small reservoir with an NGB (near-infrared (N), green (G), and blue (B)) camera connected to an UAV and extracted NDVI from orthorectified images.…”

Section: Introductionmentioning

confidence: 99%

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

Irie,

Manabe,

Yamashita

2024

Drones

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The observation of the phytoplankton distribution with a high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to hydraulic phenomena. Photography from UAVs, which has an excellent temporal and spatial resolution, is an effective method to obtain water quality information comprehensively. In this study, we attempted to develop a method for estimating the chlorophyll concentration from aerial images using machine learning that considers brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis. The reflectance of harmful algae bloom (HAB) was different from that of phytoplankton seen under normal conditions; so, the images containing HAB were the causes of error in the estimation of the chlorophyll concentration. First, the images when the bloom occurred were extracted by the discrimination with machine learning. Then, the other images were used for the regression of the concentration. Finally, the coefficient of determination between the estimated chlorophyll concentration when no bloom occurred by the image analysis and the observed value reached 0.84. The proposed method enables the detailed depiction of the spatial distribution of the chlorophyll concentration, which contributes to the improvement in water quality management in reservoirs.

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Section: Introductionmentioning

confidence: 99%

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

Irie,

Manabe,

Yamashita

2024

Drones

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“…On the other hand, HAB observation based on the analysis of aerial images taken from Unmanned Aerial Vehicles (UAV) is superior to satellite images in terms of spatiotemporal resolution and in that it is not affected by clouds [34]. Using a small Unmanned Aerial Vehicle system equipped with a consumer-grade camera, Qu et al [35] determined surface-floating cyanobacteria at a maximum detection altitude of 80 m. The small UAV can cover up to 1 km 2 per flight mission, and the short time lag between sampling and flight allows for follow-up monitoring and treatment. Guimarães et al [36] photographed a small reservoir with an NGB (near-infrared (N), green (G), blue (B)) camera connected to a UAV, and extracted normalized differential vegetation index (NDVImod) from orthorectified images.…”

Section: Introductionmentioning

confidence: 99%

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images considering Turbid Water Distribution in a Reservoir

Irie,

Manabe,

Yamashita

2024

Preprint

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The causes of algal blooms in reservoirs are often complexly intertwined with chemical, physical, and biological factors such as the supply of nutrients. Observation of phytoplankton distribution with high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to wind and water temperature stratification. Observation from a UAV, which has excellent temporal and spatial resolution, is considered to be an effective method to obtain water quality information comprehensively. On the other hand, it is not only the growth of plankton that affects the color of the water surface but also turbidity. Furthermore, since the brightness value of passive sensors such as optical cameras changes depending on the amount of insolation, it is necessary to perform analysis after making corrections for this. In this study, we attempted to develop a method for estimating chlorophyll concentration from aerial images taken from UAVs using machine learning that takes into account brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis.

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“…The water samples were analyzed for cyanobacteria cell concentration, chlorophyll-a, phycocyanin, and phycoerythrin to calibrate the relationship and model between the in situ sample data and the UAS imagery. These water quality parameters were selected based on the current NH State cyanobacteria monitoring procedures (cell concentration) of the New Hampshire Department of Environmental Protection (NHDES), and standard monitoring practices through pigment analyses for cyanobacteria (chlorophyll-a, phycocyanin, and phycoerythrin) [10,11,13,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. The specific objectives of this study were: 1.…”

Section: Introductionmentioning

confidence: 99%

“…would help to build tie points over this homogenous surface. This challenge was also stated by many other scientists [17,24,[31][32][33][55][56][57]. Due to this issue, ten water quality sampling points were not included in the UAS spectral data to water quality parameter analyses because they occurred in the reflectance data "holes."…”

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confidence: 99%

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

Bunyon,

Fraser,

McQuaid

et al. 2023

Remote Sensing

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With the increasing occurrence of cyanobacteria blooms, it is crucial to improve our ability to monitor impacted lakes accurately, efficiently, and safely. Cyanobacteria are naturally occurring in many waters globally. Some species can release neurotoxins which cause skin irritations, gastrointestinal illness, pet/livestock fatalities, and possibly additional complications after long-term exposure. Using a DJI M300 RTK Unmanned Aerial Vehicle equipped with a MicaSense 10-band dual camera system, six New Hampshire lakes were monitored from May to September 2022. Using the image spectral data coupled with in situ water quality data, a random forest classification algorithm was used to predict water quality categories. The analysis yielded very high overall classification accuracies for cyanobacteria cell (93%), chlorophyll-a (87%), and phycocyanin concentrations (92%). The 475 nm wavelength, normalized green-blue difference index—version 4 (NGBDI_4), and normalized green-red difference index—version 4 (NGRDI_4) indices were the most important features for these classifications. Logarithmic regressions illuminated relationships between single bands/indices with water quality data but did not perform as well as the classification algorithm approach. Ultimately, the UAS multispectral data collected in this study successfully classified cyanobacteria cell, chlorophyll-a, and phycocyanin concentrations in the studied NH lakes.

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Effective aerial monitoring of cyanobacterial harmful algal blooms is dependent on understanding cellular migration

Cited by 11 publications

References 35 publications

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images considering Turbid Water Distribution in a Reservoir

Using Imagery Collected by an Unmanned Aerial System to Monitor Cyanobacteria in New Hampshire, USA, Lakes

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