Raab, Christoph

[["dc.bibliographiccitation.firstpage","212"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Remote Sensing in Ecology and Conservation"],["dc.bibliographiccitation.lastpage","231"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Barrett, Brian"],["dc.contributor.author","Raab, Christoph"],["dc.contributor.author","Cawkwell, Fiona"],["dc.contributor.author","Green, Stuart"],["dc.date.accessioned","2019-07-31T06:50:54Z"],["dc.date.available","2019-07-31T06:50:54Z"],["dc.date.issued","2016"],["dc.description.abstract","Uplands represent unique landscapes that provide a range of vital benefits tosociety, but are under increasing pressure from the management needs of adiverse number of stakeholders (e.g. farmers, conservationists, foresters, govern-ment agencies and recreational users). Mapping the spatial distribution ofupland vegetation could benefit management and conservation programmesand allow for the impacts of environmental change (natural and anthropogenic)in these areas to be reliably estimated. The aim of this study was to evaluatethe use of medium spatial resolution optical and radar satellite data, togetherwith ancillary soil and topographic data, for identifying and mapping uplandvegetation using the Random Forests (RF) algorithm. Intensive field survey datacollected at three study sites in Ireland as part of the National Parks and Wild-life Service (NPWS) funded survey of upland habitats was used in the calibra-tion and validation of different RF models. Eight different datasets wereanalysed for each site to compare the change in classification accuracy depend-ing on the input variables. The overall accuracy values varied from 59.8% to94.3% across the three study locations and the inclusion of ancillary datasetscontaining information on the soil and elevation further improved the classifi-cation accuracies (between 5 and 27%, depending on the input classificationdataset). The classification results were consistent across the three differentstudy areas, confirming the applicability of the approach under different envi-ronmental contexts."],["dc.identifier.doi","10.1002/rse2.32"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16313"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62220"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation.issn","2056-3485"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Upland vegetation mapping using Random Forests with optical and radar satellite data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","784"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Remote Sensing Letters"],["dc.bibliographiccitation.lastpage","793"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Raab, Christoph"],["dc.contributor.author","Barrett, Brian"],["dc.contributor.author","Cawkwell, Fiona"],["dc.contributor.author","Green, Stuart"],["dc.date.accessioned","2019-07-31T06:59:55Z"],["dc.date.available","2019-07-31T06:59:55Z"],["dc.date.issued","2015"],["dc.description.abstract","Accurate atmospheric correction is an important preprocessing step for studies of multi-temporal land-cover mapping using optical satellite data. Model-based surface reflectance predictions (e.g. 6S – Second Simulation of Satellite Signal in the Solar Spectrum) are highly dependent on the adjustment of aerosol optical thickness (AOT) data. For regions with no or insufficient spatial and temporal coverage of meteorological ground measurements, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived AOT data are a valuable alternative, especially with regard to the dynamics of atmospheric conditions. In this study, atmospheric correction strategies were assessed based on the change in standard deviation (σ) compared to the raw data and also by machine learning land-cover classification accuracies. For three Landsat 8 OLI (acquired in 2013) and two RapidEye (acquired in 2010 and 2014) scenes, seven different correction strategies were tested over an agricultural area in southeast Ireland. Visibility calculated from daily spatial averaged TERRA-MODIS estimates (1° × 1° Aerosol Product) served as input for the atmospheric correction. In almost all cases the standard deviation of the raw data is reduced after incorporation of terrain correction, compared to the atmospheric-corrected data. ATCOR®-IDL-based correction decreases the standard deviation almost consistently (ranging from −0.3 to −26.7). The 6S implementation in GRASS GIS showed a tendency of increasing the variation in the data, especially for the RapidEye data. No major differences in overall accuracies (OAs) and kappa values were observed between the three machine learning classification approaches. The results indicate that the ATCOR®-IDL-based correction and MODIS parameterization methods are able to decrease the standard deviation and are therefore an appropriate approach to approximate the top-of-canopy reflectance."],["dc.identifier.doi","10.1080/2150704X.2015.1076950"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62221"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.issn","2150-704X"],["dc.relation.issn","2150-7058"],["dc.title","Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land-cover accounting and monitoring in Ireland"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]