High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Pyo, Juncheol; Ligaray, Mayzonee; Kwon, Yeo‐Jung; Ahn, Myoung Hwan; Kim, Kyunghyun; Lee, Hyuk; Ting, Kang; Cho, Seong Been; Park, Yongeun

doi:10.3390/rs10081180

Cited by 47 publications

(26 citation statements)

References 79 publications

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“…Atmospheric correction models such as MODTRAN, LOWTRAN, and 6S are widely used. The MODTRAN code can solve the radiation transfer function through generating the physical parameters of atmospheric correction [24]. The fast line-of-sight atmospheric analysis of the spectral hypercube (FLAASH) atmospheric correction method incorporates the MODTRAN 4 radiation transfer code.…”

Section: Remote-sensing Image Preprocessingmentioning

confidence: 99%

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Ding

Chao

et al. 2019

Remote Sensing

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Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.water-quality measurement, the cost is high, limiting the usage of in situ sampling and measurement. Optimization of a monitoring network is considered a trade-off between water-monitoring precision and cost [2]. A comprehensive and low-cost water-quality monitoring method was our goal.Remote-sensing monitoring of water quality and its parameters is considered as a promising and cost-effective monitoring method. There are many free-of-charge remote-sensing data, such as Landsat and Sentinel open-access data. Remote sensing provides a synoptic, repetitive, and consistent view of a water body. Water-quality-related variables, such as surface temperature, chlorophyll-a (Chl-a), turbidity and suspended solids (TSS) can be estimated from remote-sensing images according to their reflectance from the water surface [3]. The accuracy and stability of the remote-sensing inversion of surface-reflectance variables (Chl-a and SS) have improved after several years [4].Nonoptically active variables related to water pollution, such as total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and dissolved oxygen (DO), cannot be directly sensed from water-surface reflectance because of their weak optical characteristics and low signal-to-noise ratio [3]. These nonoptically active variables are closely related to optically active parameters such as Chl-a, TSS, and colored dissolved organic matter (CDOM) [3,5]. Different kinds of regression models have been introduced to establish the relation between nonoptically active variables and optical active variabl...

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Section: Remote-sensing Image Preprocessingmentioning

confidence: 99%

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Ding

Chao

et al. 2019

Remote Sensing

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“…Agent-based models have been used to observe the 2D and 3D transport trajectories of cyanobacteria. These models are coupled with an Eulerian model, which allows the cyanobacteria to drift in the model [81][82][83].…”

Section: Deterministic Versus Probabilisticmentioning

confidence: 99%

An Overview of Cyanobacteria Harmful Algal Bloom (CyanoHAB) Issues in Freshwater Ecosystems

Benayache¹,

Nguyen-Quang²,

Hushchyna³

et al. 2019

Limnology - Some New Aspects of Inland Water Ecology

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This chapter will present an overview of cyanobacterial harmful algal blooms (cyano-HABs) and biotic and abiotic factors, as well as various aspects associated with these worldwide ecological bursts. The exact causes of the cyanoHABs are still not well defined, but eutrophication and climate change (temperature increase, light intensity variation, etc.) are the two assumed main factors that may promote the proliferation and expansion of cyanobacterial blooms. However, these premises need to be profoundly investigated as the optimal combination of all factors such as increased nutrient loading, physiological characteristics of cyanobacterial species, and climate effects which could lead to the blooming pattern will require robust modeling approaches to predict the phenomena. Negative issues associated with cyanoHABs are diverse including the toxic products (cyanotoxins) released by certain taxa which can damage the health of humans and animal habitats around the related watershed as well as generate a huge water quality problem for aquatic industries.

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“…Models applied to airborne HSI have been evaluated for determination of the spatial distribution of water quality parameters including CHL. Pyo et al [43] monitored the concentrations of phycocyanin and chlorophyll a using airborne HSI with high spatial resolution. Four bio-optical indexes were calibrated for concentration estimation, and then a concentration map was generated.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Margalefidinium polykrikoides Blooms along the South Coast of Korea Using Airborne Hyperspectral Imagery

Shin

Kim

et al. 2020

Remote Sensing

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The red tide bloom-forming dinoflagellate Margalefidinium polykrikoides is well known for its harmful effects on marine organisms, and for killing fish in aquaculture cages via gill clogging at a high cell abundance. To minimize the damage caused by red tide blooms, it is essential to understand their detailed spatial distribution with high accuracy. Airborne hyperspectral imagery (HSI) is useful for quantifying red tide cell abundance because it provides substantial information on optical features related to red tide species. However, because published red tide indexes were developed for multichannel ocean color sensors, there are some limitations to applying them directly to HSI. In this study, we propose a new index for quantifying M. polykrikoides blooms along the south coast of Korea and generate a M. polykrikoides cell abundance map using HSI. A new index for estimating cell abundance was proposed using the pairs of M. polykrikoides cell abundances and in situ spectra. After optimization of the published red tide indexes and band correlation analyses, the green-to-fluorescence ratio (GFR) index was proposed based on red tide spectral characteristics. The GFR index was computed from the green (524 and 583 nm) and fluorescence wavelength bands (666 and 698 nm) and converted into red tide cell abundance using a second-order polynomial regression model. The newly proposed GFR index showed the best performance, with a coefficient of determination (R2) of 0.52, root mean squared error (RMSE) of 877.98 cells mL−1, and mean bias error (MBE) of −18.42 cells mL−1, when applied to atmospherically corrected HSI. The M. polykrikoides cell abundance map generated from the GFR index provides precise spatial distribution information and allowed us to estimate a wide range of cell abundance up to 5000 cells mL−1. This study indicates the potential of the GFR index for quantifying M. polykrikoides cell abundance from HSI with a reasonably high level of accuracy.

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High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

Cited by 47 publications

References 79 publications

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

An Overview of Cyanobacteria Harmful Algal Bloom (CyanoHAB) Issues in Freshwater Ecosystems

Quantification of Margalefidinium polykrikoides Blooms along the South Coast of Korea Using Airborne Hyperspectral Imagery

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