Gerold, Gerhard

[["dc.bibliographiccitation.artnumber","1161"],["dc.bibliographiccitation.firstpage","1161"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","de Souza Mendes, Flávia"],["dc.contributor.author","Baron, Daniel"],["dc.contributor.author","Gerold, Gerhard"],["dc.contributor.author","Liesenberg, Veraldo"],["dc.contributor.author","Erasmi, Stefan"],["dc.date.accessioned","2019-07-09T11:51:29Z"],["dc.date.accessioned","2020-05-11T13:22:28Z"],["dc.date.available","2019-07-09T11:51:29Z"],["dc.date.available","2020-05-11T13:22:28Z"],["dc.date.issued","2019"],["dc.description.abstract","Mapping vegetation types through remote sensing images has proved to be e ective, especially in large biomes, such as the Brazilian Cerrado, which plays an important role in the context of management and conservation at the agricultural frontier of the Amazon. We tested several combinations of optical and radar images to identify the four dominant vegetation types that are prevalent in the Cerrado area (i.e., cerrado denso, cerradão, gallery forest, and secondary forest). We extracted features from both sources of data such as intensity, grey level co-occurrence matrix, coherence, and polarimetric decompositions using Sentinel 2A, Sentinel 1A, ALOS-PALSAR 2 dual/full polarimetric, and TanDEM-X images during the dry and rainy season of 2017. In order to normalize the analysis of these features, we used principal component analysis and subsequently applied the Random Forest algorithm to evaluate the classification of vegetation types. During the dry season, the overall accuracy ranged from 48 to 83%, and during the dry and rainy seasons it ranged from 41 up to 82%. The classification using Sentinel 2A images during the dry season resulted in the highest overall accuracy and kappa values, followed by the classification that used images from all sensors during the dry and rainy season. Optical images during the dry season were su cient to map the di erent types of vegetation in our study area."],["dc.identifier.doi","10.3390/rs11101161"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16135"],["dc.identifier.scopus","2-s2.0-85066761706"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59955"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65039"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-85066761706&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.relation.issn","2072-4292"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","550"],["dc.title","Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado/Amazon Ecotone of Nova Mutum—Mato Grosso"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]