A study on the evaluation of water-bloom using image processing

Choo, Yeonwoo; Kang, Guyoung; Kim, Dong-Min; Lee, Sungjong

doi:10.1007/s11356-018-3578-6

Cited by 21 publications

(24 citation statements)

References 22 publications

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“…As a result, the difference between the FHL-derived CHL and in-situ CHL value was less than 20%, although a CHL image with a high spatial resolution could not be generated. Choo et al (2018) [17] used the NDVI to calculate the CHL calculated in their study.…”

Section: Discussionmentioning

confidence: 99%

“…Various attempts have recently been made to overcome the limitations of satellite observations. Among them, studies are being conducted using a UAV, which can be easily applied at a lower cost with breakthrough hardware and software, allowing us to obtain higher spatial resolutions than in satellites images [16][17][18][19][20][21]. Such an observation system is called a low-altitude remote sensing (LARS) system and collects optical information through sensors mounted on a UAV flying below the clouds.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, recent studies have attempted to estimate the CHL by acquiring high-resolution images using an UAV. Choo et al (2018) [17] attempted to measure the CHL quantitatively using a multispectral camera attached to a UAV. A total of 44 images were synthesized, creating an orthophoto around a river.…”

Section: Introductionmentioning

confidence: 99%

“…The square of the correlation coefficient (R 2 ) of the calculated and in-situ CHLs was 0.7031, indicating a high correlation and the possibility of a quantitative CHL analysis through images captured by a multispectral camera attached to a UAV. However, as Choo et al (2018) [17] estimated the CHL through the NDVI calculated based on the radiance of the sea surface, rather than R rs observed using a multispectral camera, the calculated data varied according to different light intensities.…”

Section: Introductionmentioning

confidence: 99%

“…The radiance and irradiance (Wm −2 nm −1 ) observed using the multispectral camera were calculated to obtain the values of R rs and were compared with those observed by a hyperspectral radiometer to determine the sensitivity of the sensor. [17] attempted to measure the CHL quantitatively using a multispectral camera attached to a UAV. A total of 44 images were synthesized, creating an orthophoto around a river.…”

Section: Introductionmentioning

confidence: 99%

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A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

Baek

Kim

et al. 2019

Remote Sensing

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In this study, a low-altitude remote sensing (LARS) observation system was employed to observe a rapidly changing coastal environment-owed to the regular opening of the sluice gate of the Saemangeum seawall-off the west coast of South Korea. The LARS system uses an unmanned aerial vehicle (UAV), a multispectral camera, a global navigation satellite system (GNSS), and an inertial measurement unit (IMU) module to acquire geometry information. The UAV system can observe the coastal sea surface in two dimensions with high temporal (1 s−1) and spatial (20 cm) resolutions, which can compensate for the coarse spatial resolution of in-situ measurements and the low temporal resolution of satellite observations. Sky radiance, sea surface radiance, and irradiance were obtained using a multispectral camera attached to the LARS system, and the remote sensing reflectance (Rrs) was accordingly calculated. In addition, the hyperspectral radiometer and in-situ chlorophyll-a concentration (CHL) measurements were obtained from a research vessel to validate the Rrs observed using the multispectral camera. Multi-linear regression (MLR) was then applied to derive the relationship between Rrs of each wavelength observed using the multispectral sensor on the UAV and the in-situ CHL. As a result of applying MLR, the correlation and root mean square error (RMSE) between the remotely sensed and in-situ CHLs were 0.94 and ~0.8 μg L−1, respectively; these results show a higher correlation coefficient and lower RMSE than those of other, previous studies. The newly derived algorithm for the CHL estimation enables us to survey 2D CHL images at high temporal and spatial resolutions in extremely turbid coastal oceans.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

Baek

Kim

et al. 2019

Remote Sensing

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Commercially available unoccupied aerial systems for monitoring harmful algal blooms: A comparative study

Fernandez-Figueroa

Wilson

Rogers

2021

Limnology & Ocean Methods

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Reliable remote sensing platforms and methods for monitoring phytoplankton are needed for mitigating the detrimental impacts of cyanobacterial harmful algal blooms on small inland waterbodies. Commercial unoccupied aerial systems (UASs) present an affordable high-resolution solution for the rapid assessment of cyanobacterial abundance in small aquatic systems by recording the reflectance of photosynthetic pigments found in all phytoplankton (i.e., chlorophyll a [Chl a]) and those related to cyanobacteria (i.e., phycocyanin). This study evaluates the performance of four sensors, including visible light spectra (red, green, blue -[RGB]) sensors on the Phantom 4 and Phantom 4 Professional platforms, the MAPIR Survey3W modified multispectral (i.e., near-infrared, green, blue) sensor, and the Parrott Sequoia multispectral (i.e., green, red, near-infrared, red-edge) sensor for estimating cyanobacterial abundance. The performance of each sensor was determined by comparing 26 vegetation indices to Chl a and phycocyanin measurements of 54 ponds that varied in size and productivity. Vegetation indices that included the red and near-infrared wavelengths generated from Parrot Sequoia aerial images provided the best Chl a (i.e., Normalized Difference Vegetation Index, r 2 = 0.79, p < 0.0001) and phycocyanin (i.e., Green Normalized Difference Vegetation Index, r 2 = 0.64, p < 0.0001) estimates. The RGB sensors were moderately effective for estimating Chl a, whereas the MAPIR Survey3W generated poor estimates of both pigments due to differences in recorded wavelengths. Results suggest commercial multiband multispectral UAS sensors provide a low-cost, plug-and-play alternative for managers and researchers interested in integrating remote sensing tools for quantitatively estimating phytoplankton abundance in small inland systems.Small inland waterbodies are ubiquitous and provide important ecosystems services, including, but not limited to, drinking water supply, food production, aquatic recreation, and climate regulation (Biggs et al. 2017). Blooms dominated by cyanobacterial harmful algal blooms (CyanoHABs) are becoming more frequent and intense as a result of climate change and cultural eutrophication (Huisman et al. 2018). CyanoHABs impair freshwater systems through the production of toxins (i.e., cyanotoxins) linked to illness and fatalities in livestock, pets, and humans, as well as off-flavor compounds (e.g., geosmin and 2-methylisoborneol) that impart a musty scent and flavor on drinking water and farm-raised fish leading to significant economic losses to aquaculture and drinking water industries (Merel et al. 2013;Tucker and Schrader 2020). CyanoHABs are traditionally monitored via cell counts or by measuring the concentration of photosynthetic pigments found in all phytoplankton (i.e., chlorophyll a [Chl a]) relative to accessory pigments abundant in cyanobacteria (i.e., phycocyanin). However, traditional methods are constrained by processing times, training, cost, and may not be representative of the entire waterbody (...

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A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere

Adkins,

Li,

Blasko

et al. 2024

Landsc Ecol

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Context Understanding the movement of bioaerosols, such as spores and pollen, through the atmosphere is important for a broad spectrum of landscape research, including agricultural fungal outbreaks and pollen threats to public health. As spores and pollen can be transported in the air over large distances, the use of aircraft has historically played a role in detecting and mapping their presence in the lower atmosphere. Objectives We present a simple alternative to costly and specialized aircraft and associated equipment that are typically used in the study of spores and pollen in the atmosphere. Methods We use 3D printable components and common lab supplies mounted on an uncrewed aircraft (UA). Conveniently, this setup does not require additional electronic components to control collection during flight, using the UA landing gear mechanism instead. Results We demonstrate that this apparatus can collect fungal spores in the atmosphere and describe potential impacts by the environment and experimental protocol on collection efficiency. These include the effects of: (1) competing airflows from UA rotors, flight trajectories, and wind, (2) flight altitude, and (3) particle size and Petri dish collection medium. Conclusions Complex biological mechanisms and atmospheric dynamics dictate the release, transport, and deposition of bioaerosols. Economical methods to sample bioaerosols in the lower atmosphere can increase the amount and type of data collected and unlock new understanding. The methodology presented here provides an economical method to sample bioaerosols that can help improve landscape-level understanding of the dispersal of bioaerosols.

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A study on the evaluation of water-bloom using image processing

Cited by 21 publications

References 22 publications

A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System

Commercially available unoccupied aerial systems for monitoring harmful algal blooms: A comparative study

A simple mechanism for uncrewed aircraft bioaerosol sampling in the lower atmosphere

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