scholarly journals A Simple Red Tide Monitoring Method using Sentinel-2 Data for Sustainable Management of Brackish Lake Koyama-ike, Japan

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1044 ◽  
Author(s):  
Yuji Sakuno ◽  
Akihiro Maeda ◽  
Akihiro Mori ◽  
Shuji Ono ◽  
Akihiro Ito
Keyword(s):  
Spectral Reflectance ◽  
Sustainable Management ◽  
Field Data ◽  
Red Tide ◽  
Red Tides ◽  
Brackish Lake ◽  
Monitoring Method ◽  
Proposed Model ◽  
Data Points ◽  
Sentinel 2

We proposed and validated a method for monitoring red tides in the brackish Lake Koyama-ike, Japan, using Sentinel-2 Multispectral Instrument (MSI) data with a 10 m spatial resolution. To achieve this objective, we acquired 36 spectral reflectance/Chla data points in the field from 2012 to 2018. We obtained a high correlation of Chla (R2 = 0.83) using the proposed red tide model (RIKY = [MSI Band 5 – MSI Band 4]/[MSI Band 5 + MSI Band 4]) and field data. Based on our results, the proposed model was also validated using five Sentinel-2/Chla datasets from April to August 2017. Chla and red tide distribution characteristics estimated from Sentinel-2 data hardly appeared from April to July, and then spread rapidly throughout the lake (more than 70%) in August. Thus, Sentinel-2 data proved to be a very powerful tool in monitoring red tides in Lake Koyama-ike.

scholarly journals A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China

2020 ◽  
Vol 12 (11) ◽  
pp. 1884 ◽  
Author(s):  
Fugen Jiang ◽  
Andrew R. Smith ◽  
Mykola Kutia ◽  
Guangxing Wang ◽  
Hua Liu ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Prediction Accuracy ◽  
Field Data ◽  
Vegetation Indices ◽  
Arid Areas ◽  
Vegetation Canopy ◽  
Area Index ◽  
Red Edge ◽  
Semi Arid ◽  
Sentinel 2

As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.

An Algorithm for Red Tides Species’ Identification Based on Spectral Characteristics and Wavelet Analysis

2015 ◽  
Vol 719-720 ◽  
pp. 1063-1067
Author(s):  
Juan Zhang ◽  
Bing Wang ◽  
He Meng Yang
Keyword(s):  
Wavelet Analysis ◽  
Spectral Reflectance ◽  
Wavelet Decomposition ◽  
Red Tide ◽  
Spectral Characteristics ◽  
Red Tides ◽  
Spectral Curves ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
False Recognitions

Hyperspectral remote sensing technology provides a new way to identify red tides types, but many existing methods can’t take full advantage of the spectral reflectance characteristics and often yield false recognitions. So, on the premise of perfect spectral curves library of red tides to be referred, this paper proposes an algorithm based on spectral reflectance characteristics and wavelet decomposition for red tides recognition. The algorithm identify the red tide species by applying wavelet analysis to a certain wavelength range limited by the spectral features. To compare and prove the effect of this algorithm, do simulate experiments with both the proposed method and the traditional SAM method. The results show that, compared with SAM method, the algorithm put forward in this paper can better indentify the species of red tides.

Algae Migration Characteristics in Fresh Water Red Tide

1993 ◽  
Vol 28 (7) ◽  
pp. 197-201 ◽  
Author(s):  
Dunchun Wang ◽  
Isao Somiya ◽  
Shigeo Fujii
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Fresh Water ◽  
Field Data ◽  
Field Survey ◽  
Red Tide ◽  
Bottom Layer ◽  
Total Biomass ◽  
Quality Indices ◽  
Vertical Distributions

To understand the algae migration characteristics in the fresh water red tide, we performed a field survey in the Shorenji Reservoir located in Nabari City, Japan. From the analysis of the field data, it is found that the patterns of vertical distributions of the indices representing biomass are very different in the morning and the afternoon. Since some water quality indices have reverse fluctuations between the surface and the bottom layer in respect of the time series changes and the total biomass of the vertical water column is relatively constant, it is concluded that vertical and daily biomass variation of red tide alga is caused by its daily migration, that is the movement from the bottom layer to the surface in the morning and the reverse movement in the afternoon.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

scholarly journals Feeding diverse prey as an excellent strategy of mixotrophic dinoflagellates for global dominance

2021 ◽  
Vol 7 (2) ◽  
pp. eabe4214
Author(s):  
Hae Jin Jeong ◽  
Hee Chang Kang ◽  
An Suk Lim ◽  
Se Hyeon Jang ◽  
Kitack Lee ◽  
...  
Keyword(s):  
Genetic Factor ◽  
Red Tide ◽  
Biogeochemical Cycles ◽  
Evolutionary Strategy ◽  
Distribution Data ◽  
Red Tides ◽  
Trade Off ◽  
Moderate Growth ◽  
Prey Diversity ◽  
Standing Question

Microalgae fuel food webs and biogeochemical cycles of key elements in the ocean. What determines microalgal dominance in the ocean is a long-standing question. Red tide distribution data (spanning 1990 to 2019) show that mixotrophic dinoflagellates, capable of photosynthesis and predation together, were responsible for ~40% of the species forming red tides globally. Counterintuitively, the species with low or moderate growth rates but diverse prey including diatoms caused red tides globally. The ability of these dinoflagellates to trade off growth for prey diversity is another genetic factor critical to formation of red tides across diverse ocean conditions. This finding has profound implications for explaining the global dominance of particular microalgae, their key eco-evolutionary strategy, and prediction of harmful red tide outbreaks.

scholarly journals High Spatial-Resolution Red Tide Detection in the Southern Coast of Korea Using U-Net from PlanetScope Imagery

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4447
Author(s):  
Jisun Shin ◽  
Young-Heon Jo ◽  
Joo-Hyung Ryu ◽  
Boo-Keun Khim ◽  
Soo Mee Kim
Keyword(s):  
Deep Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Red Tide ◽  
Learning Approaches ◽  
Red Tides ◽  
Southern Coast ◽  
Color Sensors ◽  
Deep Learning Model ◽  
Bloom Water

Red tides caused by Margalefidinium polykrikoides occur continuously along the southern coast of Korea, where there are many aquaculture cages, and therefore, prompt monitoring of bloom water is required to prevent considerable damage. Satellite-based ocean-color sensors are widely used for detecting red tide blooms, but their low spatial resolution restricts coastal observations. Contrarily, terrestrial sensors with a high spatial resolution are good candidate sensors, despite the lack of spectral resolution and bands for red tide detection. In this study, we developed a U-Net deep learning model for detecting M. polykrikoides blooms along the southern coast of Korea from PlanetScope imagery with a high spatial resolution of 3 m. The U-Net model was trained with four different datasets that were constructed with randomly or non-randomly chosen patches consisting of different ratios of red tide and non-red tide pixels. The qualitative and quantitative assessments of the conventional red tide index (RTI) and four U-Net models suggest that the U-Net model, which was trained with a dataset of non-randomly chosen patches including non-red tide patches, outperformed RTI in terms of sensitivity, precision, and F-measure level, accounting for an increase of 19.84%, 44.84%, and 28.52%, respectively. The M. polykrikoides map derived from U-Net provides the most reasonable red tide patterns in all water areas. Combining high spatial resolution images and deep learning approaches represents a good solution for the monitoring of red tides over coastal regions.

scholarly journals MANAGEMENT EFFECTS ON GROUND COVER <q>CLUMPINESS</q>: SCALING FROM FIELD TO SENTINEL-2 COVER ESTIMATES

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 183-188 ◽  
Author(s):  
P. Scarth ◽  
R. Trevithick
Keyword(s):  
Water Quality ◽  
Spatial Resolution ◽  
Field Data ◽  
Temporal Dynamics ◽  
Management Practice ◽  
Spatial Arrangement ◽  
Practice Change ◽  
Grazing Land ◽  
Land Condition ◽  
Sentinel 2

Significant progress has been made in the development of cover data and derived products based on remotely sensed fractional cover information and field data across Australia, and these cover data sets are now used for quantifying and monitoring grazing land condition. The availability of a dense time-series of nearly 30 years of cover data to describe the spatial and temporal patterns in landscape changes over time can help with monitoring the effectiveness of grazing land management practice change. With the advent of higher spatial resolution data, such as that provided by the Copernicus Sentinel 2 series of satellites, we can look beyond reporting purely on cover amount and more closely at the operational monitoring and reporting on spatial arrangement of cover and its links with land condition. We collected high spatial resolution cover transects at 20&amp;thinsp;cm intervals over the Wambiana grazing trials in the Burdekin catchment in Queensland, Australia. Spatial variance analysis was used to determine the cover autocorrelation at various support intervals. Coincident Sentinel-2 imagery was collected and processed over all the sites providing imagery to link with the field data. We show that the spatial arrangement and temporal dynamics of cover are important indicators of grazing land condition for both productivity and water quality outcomes. The metrics and products derived from this research will assist land managers to prioritize investment and practice change strategies for long term sustainability and improved water quality, particularly in the Great Barrier Reef catchments.

scholarly journals Design of Vibration Frequency Method with Fine-Tuned Factor for Fault Detection of Three Phase Induction Motor

2021 ◽  
Vol 3 (1) ◽  
pp. 52-65
Author(s):  
Thomas Amanuel ◽  
Amanuel Ghirmay ◽  
Huruy Ghebremeskel ◽  
Robel Ghebrehiwet ◽  
Weldekidan Bahlibi
Keyword(s):  
Induction Motor ◽  
Vibration Frequency ◽  
Vibration Monitoring ◽  
Slight Variation ◽  
Frequency Method ◽  
Monitoring Method ◽  
Stator Current ◽  
Research Article ◽  
Proposed Model ◽  
Fault Parameters

This research article focuses on industrial applications to demonstrate the characterization of current and vibration analysis to diagnose the induction motor drive problems. Generally, the induction motor faults are detected by monitoring the current and proposed fine-tuned vibration frequency method. The stator short circuit fault, broken rotor bar fault, air gap eccentricity, and bearing fault are the common faults that occur in an induction motor. The detection process of the proposed method is based on sidebands around the supply frequency in the stator current signal and vibration. Moreover, it is very challenging to diagnose the problem that occur due to the complex electromagnetic and mechanical characteristics of an induction motor with vibration measures. The design of an accurate model to measure vibration and stator current is analyzed in this research article. The proposed method is showing how efficiently the root cause of the problem can be diagnosed by using the combination of current and vibration monitoring method. The proposed model is developed for induction motor and its circuit environment in MATLAB is verified to perform an accurate detection and diagnosis of motor fault parameters. All stator faults are turned to turn fault; further, the rotor-broken bar and eccentricity are structured in each test. The output response (torque and stator current) is simulated by using a modified winding procedure (MWP) approach by tuning the winding geometrical parameter. The proposed model in MATLAB Simulink environment is highly symmetrical, which can easily detect the signal component in fault frequencies that occur due to a slight variation and improper motor installation. Finally, this research article compares the other existing methods with proposed method.

A Red Tide of Gyrod Inium Aurelum in Sea Lochs of the Firth of Clyde and Associated Mortality of Pond-Reared Salmon

1982 ◽  
Vol 62 (4) ◽  
pp. 771-782 ◽  
Author(s):  
K. J. Jones ◽  
P. Ayres ◽  
A. M. Bullock ◽  
R. J. Roberts ◽  
P. Tett
Keyword(s):  
Osmotic Shock ◽  
Red Tide ◽  
Clinical Signs ◽  
Cellular Damage ◽  
Fish Ponds ◽  
Red Tides ◽  
Gymnodinium Breve ◽  
The North ◽  
Cell Densities ◽  
Gyrodinium Aureolum

Red tides of the naked dinoflagellate Gyrodinium aureolum Hulburt occurred in sealochs in the north of the Firth of Clyde, Scotland, during late September 1980. Greatestconcentrations of the organism were found in the top 1 m layer of the water column, which was stabilized, and probably also enriched with nutrients, by freshwater input fromland drainage. In addition vertical and horizontal concentration must be postulated toexplain Gyrodinium cell densities of 2 x to7 cells I"1 and chlorophyll concentrations of 2228 mg m“”3 near the shore at Otter Ferry, Loch Fyne.On 28 September 1980, water containing the red tide at Otter Ferry was unintentionally pumped into fish ponds at a shore-based salmon farm and resulted in the death, in one pond, of 3000 salmon each weighing about 1 kg and of 200–300 smolts in another when water was transferred to it from the affected pond. Pathological investigation of affected salmon showed that death was likely to have resulted from asphyxiation and osmotic shock as a result of extensive cellular damage to gills and guts. Results of mouse bioassays, using acidic and ether extracts of flesh and guts from affected salmon, suggest that necrotizing toxin(s) was associated with the cells of Gyrodinium aureolum during the bloom. The clinical signs exhibited by mice injected with toxin extracts were, however, unlike those caused by paralytic shellfish poison or toxins of the Gymnodinium breve type.

scholarly journals Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery

2020 ◽  
Author(s):  
Christina Corbane ◽  
Vasileios Syrris ◽  
Filip Sabo ◽  
Panagiotis Politis ◽  
Michele Melchiorri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Human Settlements ◽  
Modeling Methodology ◽  
Proposed Model ◽  
Reference Layer ◽  
Sentinel 2 ◽  
Core Features

Abstract Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 × 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural–urban continuum.

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