scholarly journals Minimizing Economical Losses with the Help of “Real-Time” Algal Surveillance

2017 ◽  
Author(s):  
Edna Granéli ◽  
Christina Esplund ◽  
Elin Lindehoff ◽  
Andreas Brutemark
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Algal Blooms ◽  
Warning System ◽  
Tourism Industry ◽  
Cyanobacterial Blooms ◽  
Public Confidence ◽  
The Public ◽  
Western Side ◽  
Economical Losses

Cyanobacterial blooms covering almost the entire Baltic Sea is a yearly feature during July-August. For the tourism industry at Öland island, SE Sweden, the economical losses during the summer 2005 amounted to 17-23 million euros. Remote sensing satellite images show that all the Öland beaches are covered with decomposing algae. In reality, these blooms rarely reach the western side of the island. To more accurately inform the public on the quality of the water for swimming, with the help of volunteers, a daily real-time surveillance of the algal densities on the beaches was performed. The volunteers (from 15 years old to pensioners) were trained at the Linnaeus University, from simple laboratory techniques, to more complicated ones such as identification and enumeration of the toxic cyanobacteria species. By latest 9.00 a.m., the public had access to information on the algal situation on 17 beaches. We could show that: 1) although remote sensing images showed Öland being surrounded by the blooms, our surveillance showed no algal accumulations on the beaches 2) that the real-time warning system boosted public confidence in the local water quality and during the first “Miss Algae”-summer 2006, the economical losses by the tourism industry turned in profits, the gain amounting to 17 million euros, 3) this kind of real-time surveillance is economical feasible due to low-costs involved, but also, the project has a great social value for the volunteers who mostly were pensioners. The volunteers who participated in “Miss Algae” had a good knowledge about the area they monitored (as their houses are located nearby) and could disseminate knowledge to the public in these areas. This kind of project also render a lot of interest regional, national and international, and can be used in advertising campaigns to increase tourism in the areas affected by algal blooms.

scholarly journals Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes

2016 ◽  
Vol 76 (s1) ◽  
Author(s):  
Mariano Bresciani ◽  
Claudia Giardino ◽  
Rosaria Lauceri ◽  
Erica Matta ◽  
Ilaria Cazzaniga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Algal Blooms ◽  
Remote Sensing Data ◽  
Late Summer ◽  
Eutrophic Lake ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Negative Effects ◽  
Eutrophic Lakes ◽  
Chl A

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.

scholarly journals Quantifying Scales of Spatial Variability of Cyanobacteria in a Large, Eutrophic Lake Using Multiplatform Remote Sensing Tools

2021 ◽  
Vol 9 ◽  
Author(s):  
Samantha L. Sharp ◽  
Alexander L. Forrest ◽  
Keith Bouma-Gregson ◽  
Yufang Jin ◽  
Alicia Cortés ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Temporal Dynamics ◽  
Eutrophic Lake ◽  
Hyperspectral Data ◽  
Cyanobacterial Blooms ◽  
Satellite Monitoring ◽  
Sampling Effort

Harmful algal blooms of cyanobacteria are increasing in magnitude and frequency globally, degrading inland and coastal aquatic ecosystems and adversely affecting public health. Efforts to understand the structure and natural variability of these blooms range from point sampling methods to a wide array of remote sensing tools. This study aims to provide a comprehensive view of cyanobacterial blooms in Clear Lake, California — a shallow, polymictic, naturally eutrophic lake with a long record of episodic cyanobacteria blooms. To understand the spatial heterogeneity and temporal dynamics of cyanobacterial blooms, we evaluated a satellite remote sensing tool for estimating coarse cyanobacteria distribution with coincident, in situ measurements at varying scales and resolutions. The Cyanobacteria Index (CI) remote sensing algorithm was used to estimate cyanobacterial abundance in the top portion of the water column from data acquired from the Ocean and Land Color Instrument (OLCI) sensor on the Sentinel-3a satellite. We collected hyperspectral data from a handheld spectroradiometer; discrete 1 m integrated surface samples for chlorophyll-a and phycocyanin; multispectral imagery from small Unmanned Aerial System (sUAS) flights (∼12 cm resolution); Autonomous Underwater Vehicle (AUV) measurements of chlorophyll-a, turbidity, and colored dissolved organic matter (∼10 cm horizontal spacing, 1 m below the water surface); and meteorological forcing and lake temperature data to provide context to our cyanobacteria measurements. A semivariogram analysis of the high resolution AUV and sUAS data found the Critical Scale of Variability for cyanobacterial blooms to range from 70 to 175 m, which is finer than what is resolvable by the satellite data. We thus observed high spatial variability within each 300 m satellite pixel. Finally, we used the field spectroscopy data to evaluate the accuracy of both the original and revised CI algorithm. We found the revised CI algorithm was not effective in estimating cyanobacterial abundance for our study site. Satellite-based remote sensing tools are vital to researchers and water managers as they provide consistent, high-coverage data at a low cost and sampling effort. The findings of this research support continued development and refinement of remote sensing tools, which are essential for satellite monitoring of harmful algal blooms in lakes and reservoirs.

scholarly journals The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 60 ◽  
Author(s):  
Igor Ogashawara
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Lake Erie ◽  
Water Governance ◽  
Spectral Band ◽  
Cyanobacterial Blooms ◽  
Chl A ◽  
Cyanobacterial Harmful Algal Blooms ◽  
New Algorithms

Cyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed.

scholarly journals Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1708 ◽  
Author(s):  
Miguel Martin-Abadal ◽  
Ana Ruiz-Frau ◽  
Hilmar Hinz ◽  
Yolanda Gonzalez-Cid
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Warning System ◽  
Cost Effective ◽  
Marine Species ◽  
Global Scale ◽  
Tourism Industry ◽  
Commercial Fishing ◽  
Anthropogenic Pressures ◽  
Effective Manner

During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans.

scholarly journals Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida

2018 ◽  
Vol 10 (10) ◽  
pp. 1656 ◽  
Author(s):  
Sita Karki ◽  
Mohamed Sultan ◽  
Racha Elkadiri ◽  
Tamer Elbayoumi
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Multivariate Regression ◽  
Algal Blooms ◽  
Wildlife Conservation ◽  
River Mouth ◽  
Warning System ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Color Processing

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Real time observations of coastal algal blooms by an early warning system

2005 ◽  
Vol 65 (1-2) ◽  
pp. 172-190 ◽  
Author(s):  
J.H.W. Lee ◽  
I.J. Hodgkiss ◽  
K.T.M. Wong ◽  
I.H.Y. Lam
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Algal Blooms ◽  
Warning System

scholarly journals Mapping and forecasting onsets of harmful algal blooms using MODIS data over coastal waters surrounding Charlotte County, Florida

2018 ◽  
Author(s):  
Sita Karki ◽  
Mohamed Sultan ◽  
Racha Elkadiri ◽  
Tamer Elbayoumi
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Multivariate Regression ◽  
Algal Blooms ◽  
Wildlife Conservation ◽  
River Mouth ◽  
Warning System ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Color Processing

Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

A successful person, society and civic activism

2020 ◽  
Vol 16 (10) ◽  
pp. 1800-1816
Author(s):  
G.B. Kozyreva ◽  
T.V. Morozova ◽  
R.V. Belaya
Keyword(s):  
Social Capital ◽  
Political Life ◽  
Public Confidence ◽  
Russian Society ◽  
The Public ◽  
Civic Activism ◽  
The Social ◽  
Human And Social Capital ◽  
Person Model ◽  
Social Disparity

Subject. The article provides considerations on the formation and development of a successful person model in the modern Russian society. Objectives. The study is an attempt to model a successful person in the Russian society, when the ideological subsystem of the institutional matrix is changing. Methods. The study relies upon the theory of institutional matrices by S. Kirdina, theories of human and social capital. We focus on the assumption viewing a person as a carrier of social capital, which conveys a success, socio-economic position, social status, civic activism, doing good to your family and the public, confidence in people and association with your region. The empirical framework comprises data of the sociological survey of the Russian population in 2018. The data were processed through the factor analysis. Results. We devised a model of a successful person in today's Russian society, which reveals that a success, first of all, depends on the economic wellbeing and has little relation to civic activism. The potential involvement (intention, possibility, preparedness) in the social and political life significantly dominates the real engagement of people. The success has a frail correlation with constituents of the social capital, such as confidence in people and doing good to the public. Conclusions and Relevance. Based on the socio-economic wellbeing, that is consumption, the existing model of a successful person proves to be ineffective. The sustainability of socio-economic wellbeing seriously contributes to the social disparity of opportunities, which drive a contemporary Russian to a success in life.

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

Sign in / Sign up

Export Citation Format

Share Document