Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/23067
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGhosh, Manoj Kumeren
dc.contributor.authorKumar, Laliten
dc.coverage.temporal1974 to 2017en
dc.date.accessioned2018-05-22T17:43:00Z
dc.date.issued2018-05-22-
dc.identifier.urihttps://hdl.handle.net/1959.11/23067en
dc.descriptionAccess to the Thesis for this Dataset can be found here: https://hdl.handle.net/1959.11/30278en
dc.description.abstractGround-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.en
dc.format769 Files. Shape and project files. Document files. Spreadsheets.en
dc.languageenen
dc.relation.urihttps://hdl.handle.net/1959.11/30278en
dc.rightsAttribution 3.0 AU*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/au*
dc.titleMapping Long Term Changes in Mangrove Cover and Predictions of Future Change under Different Climate Change Scenarios in the Sundarbans, Bangladeshen
dc.typeDataseten
dcterms.accessRightsMediateden
dcterms.rightsHolderUniversity of New Englanden
dc.subject.keywordsSundarbansen
dc.subject.keywordsBangladeshen
dc.subject.keywordsMangroveen
dc.subject.keywordsRemote Sensingen
dc.identifier.datasetidGhoshManoj_20180522en
dc.rights.accessMediateden
local.contributor.firstnameManoj Kumeren
local.contributor.firstnameLaliten
local.format.size25.6 GBen
local.date.recorded2018-05-22en
local.date.retentionend2023-05-22en
local.identifier.cloudGhoshManoj_20180522en
local.access.embargoedto2018-05-22en
local.subject.for2008090905 Photogrammetry and Remote Sensingen
local.subject.for2008050206 Environmental Monitoringen
local.subject.for2008050209 Natural Resource Managementen
local.subject.seo2008960503 Ecosystem Assessment and Management of Coastal and Estuarine Environmentsen
local.subject.seo2008960501 Ecosystem Assessment and Management at Regional or Larger Scalesen
local.subject.seo2008960505 Ecosystem Assessment and Management of Forest and Woodlands Environmentsen
local.identifier.epublicationsune:23251en
local.dcrelation.publicationClimate Variability and Mangrove Cover Dynamics at Species Level in the Sundarbans, Bangladesh https://doi.org/10.3390/su9050805en
local.dcrelation.publicationMapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans https://doi.org/10.3390/f7120305en
local.dcrelation.statusPublisheden
local.dcrelation.sourceUSGS Global Visualization Viewer (GloVis) https://glovis.usgs.gov/en
local.dcrelation.sourceBangladesh Meteorological Department http://www.bmd.gov.bd/en
local.dcrelation.sourceSurvey Of Bangladesh http://www.sob.gov.bd/en
local.dcrelation.sourceCopernicus Open Access Hub https://scihub.copernicus.eu/en
local.dcrelation.sourceWater Resources Planning Organisation http://www.warpo.gov.bd/en
dcterms.RightsStatementContact the Chief Investigator to request acccess.en
local.profile.schoolSchool of Environmental & Rural Scienceen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailmghosh@myune.edu.auen
local.profile.emaillkumar@une.edu.auen
local.output.categoryXen
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeArmidale, NSW, Australiaen
local.contributor.lastnameGhoshen
local.contributor.lastnameKumaren
dc.identifier.profilemghoshen
dc.identifier.staffune-id:mghoshen
dc.identifier.staffune-id:lkumaren
local.profile.orcid0000-0002-5279-272Xen
local.profile.orcid0000-0002-9205-756Xen
local.profile.roleauthoren
local.profile.rolesupervisoren
local.identifier.unepublicationidune:1959.11/23067en
dc.date.deposit2018-05-22en
dc.identifier.academiclevelStudenten
dc.identifier.academiclevelAcademicen
local.title.maintitleMapping Long Term Changes in Mangrove Cover and Predictions of Future Change under Different Climate Change Scenarios in the Sundarbans, Bangladeshen
local.output.categorydescriptionX Dataseten
local.description.statisticsepubsVisitors: 22<br />Views: 160<br />Downloads: 0en
local.search.authorGhosh, Manoj Kumeren
local.search.supervisorKumar, Laliten
dcterms.rightsHolder.managedbySchool of Environmental & Rural Scienceen
local.datasetcontact.nameManoj Ghoshen
local.datasetcontact.emailmanojkumer@gmail.comen
local.datasetcustodian.nameManoj Ghoshen
local.datasetcustodian.emailmanojkumer@gmail.comen
local.datasetcontact.detailsManoj Ghosh - manojkumer@gmail.comen
local.datasetcustodian.detailsManoj Ghosh - manojkumer@gmail.comen
dcterms.ispartof.projectMapping Long Term Changes in Mangrove Cover and Predictions of Future Change under Different Climate Change Scenarios in the Sundarbans, Bangladeshen
dcterms.source.datasetlocationUniveristy of New Englanden
local.uneassociationUnknownen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2018en
local.subject.for2020401304 Photogrammetry and remote sensingen
local.subject.for2020undefineden
local.subject.for2020410406 Natural resource managementen
local.subject.seo2020180601 Assessment and management of terrestrial ecosystemsen
local.subject.seo2020180403 Assessment and management of Antarctic and Southern Ocean ecosystemsen
local.subject.seo2020180301 Assessment and management of freshwater ecosystemsen
dc.coverage.placeBangladesh, Sundarbans Reserved Foresten
Appears in Collections:Dataset
School of Environmental and Rural Science
Show simple item record

Page view(s)

13,984
checked on Dec 1, 2021
Google Media

Google ScholarTM

Check

WEB OF SCIENCETM
Citations

 

This item is licensed under a Creative Commons License Creative Commons