Web-Based Spatial Decision Support Systems to Monitor and Manage Coastal Environments

Fiduccia, Andrea; Gugliermetti, Franco; Pagliaro, Francesca; Mattoni, Benedetta; Nardecchia, Fabio; Canu, Claudio

doi:10.1049/cp.2016.0920

Cited by 9 publications

(2 citation statements)

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“…Monitoring and analysis of coastal and marine areas is of utmost importance. Several analysis methods have been proposed as environment sensitivity [1], risk assessment [2], the use of GIS [3] and others. In this context, the use of multi sensor satellite data to detect, identify and record environmental pollution data on sea water has significantly increased in the last decade.…”

Section: Introductionmentioning

confidence: 99%

A Sediment Detection Analysis with Multi Sensor Satellites: Caspian Sea and Persian Gulf case studies

Nezhad¹,

Groppi²,

Marzialetti³

et al. 2019

World Congress on Civil, Structural, and Environmental Engineering

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Nowadays the use of multi sensor satellite data for developing and mapping the distribution of suspended particulates in coastal area, sea and ocean waters is increasing in many sciences including environmental and marine studies. This study focuses on using remote sensing satellite images to monitor and detect Suspended Sediment (SS) event of coastal areas. MODIS (or Moderate Resolution Imaging Spectroradiometer)-Terra satellite sensor data (9 Jun 2018) of the east coast of Caspian Sea (between Turkmenistan and Iran) and the north-western of Persian Gulf (between Iran, Iraq, Kuwait) were used. For detecting Suspended Sediments Event (SSE) low-spatial resolution (250-1000m) sea and ocean color satellites data can be used. Multi-spectral sensor satellite images over the Caspian Sea and Persian Gulf have been analyzed to detect, to characterize and to classify suspended sediments in this part. Moreover, this paper discusses suspended sediments detection by using multi sensor satellite images and spectrum matching has been used to identify the sediments areas. In particular through the spectrum matching the direct identification of specific materials has been carried out via the extraction of the specific spectral features. ENVI software has been used to carry out the analysis. Using this software, the suspended sediments close to the coastal area have been successfully detected.

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Section: Introductionmentioning

confidence: 99%

A Sediment Detection Analysis with Multi Sensor Satellites: Caspian Sea and Persian Gulf case studies

Nezhad¹,

Groppi²,

Marzialetti³

et al. 2019

World Congress on Civil, Structural, and Environmental Engineering

View full text Add to dashboard Cite

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“…Applicationserversareresponsibleforrequeststoandfromtheclients.Inaddition, severalserviceshaveincludedthethreetypesofapplicationsi.e.dataservers,catalog, andprocessingservers.Severalcatalogserversareemployedtosearchthemetadata regarding the stored geospatial data. It is an important component for controlling geospatialinformationincloudcomputingenvironments.Thecatalogserviceshave implementationofastandardpublish-find-bindmodel.ThismodelisdefinedbyOGC webservicearchitecture.DataserverdealswiththeWCS,WMSandWFS.Different types of processing servers offer numerous geospatial data processes which allow differentclientstosmearinWPSstandardgeospatialdata.Detailexplanationofevery processeshavedonebytheclientrequestwhichforwardedtothedesireprocessing service,providesandspecifiesthedefiniteregionandfeedbackswiththedifferent compositestandards.Thedatatierlayerhasthegeospatialdataalongwithdifferent information.Systemsutilizethedatatierlayertorecover,store,updateandmanipulate thegeospatialdataforanalysis.Datahavestoredindifferentopensourcepackagesor simplefilesystem.ThesystemarchitectureofGeospatialcloudcomputingshowsthat geospatialdataareoneofthekeycomponentsindatalayerforthehandlinggeospatial analysis.Thewidespreaduseofcloudforgeospatialdatastorageandanalysislead to generation of large amount of data from different areas (Fiduccia et al, 2016;Delipetrevetal.,2014;Lenkaetal.,2016;Bariketal.,2018).…”

Section: Introductionmentioning

confidence: 99%

FogLearn

Barik

Priyadarshini

Dubey

et al. 2018

International Journal of Fog Computing

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Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This article discusses the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This article proposes and develops a fog computing-based framework, i.e. FogLearn. This is for the application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. The proposed architecture employs machine learning on a deep learning framework for the analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results show that fog computing holds an immense promise for the analysis of medical and geospatial big data.

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TCloud

Barik

Lenka

Dubey³

et al. 2018

GIS Applications in the Tourism and Hospitality Industry

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This chapter proposes and develops a cloud-computing-based SDI model named as TCloud for sharing, analysis, and processing of spatial data particularly in the Temple City of India, Bhubaneswar. The main purpose of TCloud is to integrate all the spatial information such as tourism sites which include various temples, mosques, churches, monuments, lakes, mountains, rivers, forests, etc. TCloud can help the decision maker or planner or common users to get enough information for their further research and studies. It has used open source GIS quantum GIS for the development of spatial database whereas QGIS plugin has been linked with quantum GIS for invoking cloud computing environment. It has also discussed the various spatial overlay analysis in TCloud environment.

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Web-Based Spatial Decision Support Systems to Monitor and Manage Coastal Environments

Cited by 9 publications

References 0 publications

A Sediment Detection Analysis with Multi Sensor Satellites: Caspian Sea and Persian Gulf case studies

A Sediment Detection Analysis with Multi Sensor Satellites: Caspian Sea and Persian Gulf case studies

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