File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images

TitleRegression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images
Authors
Issue Date2007
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01431161.asp
Citation
International Journal Of Remote Sensing, 2007, v. 28 n. 24, p. 5567-5582 How to Cite?
AbstractMangrove wetlands have been rapidly diminishing because of human pressures worldwide. The Guangdong Province in South China, which has the largest area of mangrove wetlands in the nation, is under severe threat as a result of rapid urbanization and economic development. In this paper, comparisons were made between optical Landsat TM images and Radarsat fine-mode images for estimating wetland biomass. Regression and analytical models were used to establish the relationships between remote sensing data and wetland biomass. The optimal parameter values for the analytical model were determined using genetic algorithms. Experiments indicate that the models using Radarsat fine-mode images have significant accuracy improvement in terms of Root Mean-Square Error (RMSE) whereas the use of the single Normalized Difference Vegetation Index (NDVI) may produce serious errors in biomass estimation. The Radarsat images can obtain more accurate trunk information about mangrove forests because of higher resolution and side-looking geometry. The use of genetic algorithms can help to decompose backscatter into vegetation and soil backscattering, which is very useful for ecological modelling.
Persistent Identifierhttp://hdl.handle.net/10722/176291
ISSN
2015 Impact Factor: 1.64
2015 SCImago Journal Rankings: 0.751
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Xen_US
dc.contributor.authorYeh, AGOen_US
dc.contributor.authorWang, Sen_US
dc.contributor.authorLiu, Ken_US
dc.contributor.authorLiu, Xen_US
dc.contributor.authorQian, Jen_US
dc.contributor.authorChen, Xen_US
dc.date.accessioned2012-11-26T09:08:15Z-
dc.date.available2012-11-26T09:08:15Z-
dc.date.issued2007en_US
dc.identifier.citationInternational Journal Of Remote Sensing, 2007, v. 28 n. 24, p. 5567-5582en_US
dc.identifier.issn0143-1161en_US
dc.identifier.urihttp://hdl.handle.net/10722/176291-
dc.description.abstractMangrove wetlands have been rapidly diminishing because of human pressures worldwide. The Guangdong Province in South China, which has the largest area of mangrove wetlands in the nation, is under severe threat as a result of rapid urbanization and economic development. In this paper, comparisons were made between optical Landsat TM images and Radarsat fine-mode images for estimating wetland biomass. Regression and analytical models were used to establish the relationships between remote sensing data and wetland biomass. The optimal parameter values for the analytical model were determined using genetic algorithms. Experiments indicate that the models using Radarsat fine-mode images have significant accuracy improvement in terms of Root Mean-Square Error (RMSE) whereas the use of the single Normalized Difference Vegetation Index (NDVI) may produce serious errors in biomass estimation. The Radarsat images can obtain more accurate trunk information about mangrove forests because of higher resolution and side-looking geometry. The use of genetic algorithms can help to decompose backscatter into vegetation and soil backscattering, which is very useful for ecological modelling.en_US
dc.languageengen_US
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/01431161.aspen_US
dc.relation.ispartofInternational Journal of Remote Sensingen_US
dc.titleRegression and analytical models for estimating mangrove wetland biomass in South China using Radarsat imagesen_US
dc.typeArticleen_US
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_US
dc.identifier.authorityYeh, AGO=rp01033en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1080/01431160701227638en_US
dc.identifier.scopuseid_2-s2.0-37749013357en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-37749013357&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue24en_US
dc.identifier.spage5567en_US
dc.identifier.epage5582en_US
dc.identifier.isiWOS:000251751600009-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridLi, X=34872691500en_US
dc.identifier.scopusauthoridYeh, AGO=7103069369en_US
dc.identifier.scopusauthoridWang, S=35239092300en_US
dc.identifier.scopusauthoridLiu, K=35170138700en_US
dc.identifier.scopusauthoridLiu, X=14521152600en_US
dc.identifier.scopusauthoridQian, J=18936748300en_US
dc.identifier.scopusauthoridChen, X=14033772000en_US

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats