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.

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.

scholarly journals Prediction of Algal Blooms in the Great Lakes through a Convolution Neural Network of Remote Sensing Data

2018 ◽  
Author(s):  
Karthik Srinivasan ◽  
Vikram Duvvur ◽  
Daniel Hess
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Remote Sensing Data ◽  
Convolution Neural Network ◽  
Physical Processes ◽  
Significant Information ◽  
Sensing Data ◽  
Percent Accuracy

AbstractHarmful algal blooms (HABs) are the proliferation of algae due to eutrophication and have severe repercussions to the ecological balance in many water bodies, due to the toxins the algae produce. Additionally, the identification and prediction of these HABs has been a challenge in the scientific community due to the interactions between both biological and physical processes that cause the HABs. Here, we used remote sensing data to bypass these issues; remote sensing data provides significant information about the coverage of chlorophyll which can be used to locate HABs. Using this indicator of HABs, we trained a Convolution Neural Network (CNN) to identify nine types of algal blooms, using 25 epochs of 900 images, which can predict algal bloom shapes with an 80 percent accuracy. This approach of HAB identification can easily be applied to other aquatic ecosystems where remote sensing data is present.

scholarly journals IDENTIFICATION OF HIGH POTENTIAL BAYS FOR HABs OCCURRENCE IN PENINSULAR MALYSIA USING PALSAR REMOTE SENSING DATA

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 97-101 ◽  
Author(s):  
A. B. Pour ◽  
M. Hashim
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Integrated Management ◽  
Remote Sensing Data ◽  
High Potential ◽  
Population Pressure ◽  
Kuala Lumpur ◽  
Coastal Geology ◽  
Future Challenges

Increasing frequency, intensity, and geographic distribution of Harmful algal blooms (HABs) poses a serious threat to the coastal fish/shellfish aquaculture and fisheries in Malaysian bays. Rising in sea level, shoreline erosion, stresses on fisheries, population pressure, interference of land-use and lack of institutional capabilities for integrated management make major challenges. Recent investigations and satellite observations indicate HABs originated from specific coast that have favourable geographic, geomorphic and coastal geology conditions to bring the green macro algae from the coast offshore. Therefore, the identification of high HABs frequented bays using remote sensing and geology investigations in Malaysian waters is required to reduce future challenges in this unique case. This research implemented comprehensive geomorphic and coastal geology investigations combined with remote sensing digital image processing approach to identify Malaysian bays frequented with HABs occurrence in Malaysian waters territory. The landscape and geomorphological features of the Malaysian bays were constructed from the Phased Array type L-band Synthetic Aperture Radar (PALSAR) remote sensing satellite data combined with field observations and surveying. The samples for laboratory analysis were collected from the sediment stations with different distance across shorelines features and watersheds of the Johor Bahru estuary. This research identified that semi-enclosed bays such as Kuala Lumpur and Johor Bahru bays with connection to estuaries have high potential to be frequented with HABs occurrence.

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 Current and Future Remote Sensing of Harmful Algal Blooms in the Chesapeake Bay to Support the Shellfish Industry

2020 ◽  
Vol 7 ◽  
Author(s):  
Jennifer L. Wolny ◽  
Michelle C. Tomlinson ◽  
Stephanie Schollaert Uz ◽  
Todd A. Egerton ◽  
John R. McKay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chesapeake Bay ◽  
Harmful Algal Blooms ◽  
Algal Blooms

Automated Multi-Sensor Near-Real Time Flood Monitoring in the Lower Mekong

2020 ◽  
Author(s):  
Amanda Markert ◽  
Kel Markert ◽  
Timothy Mayer ◽  
Farrukh Chisthie ◽  
Biplov Bhandari Bhandari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Southeast Asia ◽  
Real Time ◽  
Disaster Response ◽  
Monsoon Season ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Cost Effective ◽  
Google Earth ◽  
Flood Maps

<p>Floods and water-related disasters impact local populations across many regions in Southeast Asia during the annual monsoon season.  Satellite remote sensing serves as a critical resource for generating flood maps used in disaster efforts to evaluate flood extent and monitor recovery in remote and isolated regions where information is limited.  However, these data are retrieved by multiple sensors, have varying latencies, spatial, temporal, and radiometric resolutions, are distributed in different formats, and require different processing methods making it difficult for end-users to use the data.  SERVIR-Mekong has developed a near real-time flood service, HYDRAFloods, in partnership with Myanmar’s Department of Disaster Management that leverages Google Earth Engine and cloud computing to generate automated multi-sensor flood maps using the most recent imagery available of affected areas. The HYDRAFloods application increases the spatiotemporal monitoring of hydrologic events across large areas by leveraging optical, SAR, and microwave remote sensing data to generate flood water extent maps.  Beta testing of HYDRFloods conducted during the 2019 Southeast Asia monsoon season emphasized the importance of multi-sensor observations as frequent cloud cover limited useable imagery for flood event monitoring. Given HYDRAFloods’ multi-sensor approach, cloud-based resources offer a means to consolidate and streamline the process of accessing, processing, and visualizing flood maps in a more cost effective and computationally efficient way. The HYDRAFlood’s cloud-based approach enables a consistent, automated methodology for generating flood extent maps that are made available through a single, tailored, mapviewer that has been customized based on end-user feedback, allowing users to switch their focus to using data for disaster response.</p>

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