Raab, Christoph

[["dc.bibliographiccitation.firstpage","653"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","European Journal of Remote Sensing"],["dc.bibliographiccitation.lastpage","666"],["dc.bibliographiccitation.volume","52"],["dc.contributor.author","Raab, Christoph"],["dc.contributor.author","Tonn, B."],["dc.contributor.author","Meißner, M."],["dc.contributor.author","Balkenhol, Niko"],["dc.contributor.author","Isselstein, Johannes"],["dc.date.accessioned","2020-12-10T18:15:34Z"],["dc.date.available","2020-12-10T18:15:34Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1080/22797254.2019.1701560"],["dc.identifier.eissn","2279-7254"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17033"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74888"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Abteilung Wildtierwissenschaften"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Multi-temporal RapidEye Tasselled Cap data for land cover classification"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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","381"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Remote Sensing in Ecology and Conservation"],["dc.bibliographiccitation.lastpage","398"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Raab, Christoph"],["dc.contributor.author","Riesch, Friederike"],["dc.contributor.author","Tonn, Bettina"],["dc.contributor.author","Barrett, Brian"],["dc.contributor.author","Meißner, Marcus"],["dc.contributor.author","Balkenhol, Niko"],["dc.contributor.author","Isselstein, Johannes"],["dc.contributor.editor","He, Kate"],["dc.contributor.editor","Wegmann, Martin"],["dc.date.accessioned","2021-04-14T08:27:14Z"],["dc.date.available","2021-04-14T08:27:14Z"],["dc.date.issued","2020"],["dc.description.abstract","Abstract Semi‐natural grasslands represent ecosystems with high biodiversity. Their conservation depends on the removal of biomass, for example, through grazing by livestock or wildlife. For this, spatially explicit information about grassland forage quantity and quality is a prerequisite for efficient management. The recent advancements of the Sentinel satellite mission offer new possibilities to support the conservation of semi‐natural grasslands. In this study, the combined use of radar (Sentinel‐1) and multispectral (Sentinel‐2) data to predict forage quantity and quality indicators of semi‐natural grassland in Germany was investigated. Field data for organic acid detergent fibre concentration (oADF), crude protein concentration (CP), compressed sward height (CSH) and standing biomass dry weight (DM) collected between 2015 and 2017 were related to remote sensing data using the random forest regression algorithm. In total, 102 optical‐ and radar‐based predictor variables were used to derive an optimized dataset, maximizing the predictive power of the respective model. High R2 values were obtained for the grassland quality indicators oADF (R2 = 0.79, RMSE = 2.29%) and CP (R2 = 0.72, RMSE = 1.70%) using 15 and 8 predictor variables respectively. Lower R2 values were achieved for the quantity indicators CSH (R2 = 0.60, RMSE = 2.77 cm) and DM (R2 = 0.45, RMSE = 90.84 g/m²). A permutation‐based variable importance measure indicated a strong contribution of simple ratio‐based optical indices to the model performance. In particular, the ratios between the narrow near‐infrared and red‐edge region were among the most important variables. The model performance for oADF, CP and CSH was only marginally increased by adding Sentinel‐1 data. For DM, no positive effect on the model performance was observed by combining Sentinel‐1 and Sentinel‐2 data. Thus, optical Sentinel‐2 data might be sufficient to accurately predict forage quality, and to some extent also quantity indicators of semi‐natural grassland."],["dc.description.abstract","Radar (Sentinel‐1) and multispectral (Sentinel‐2) data were evaluated for mapping semi‐natural grassland forage quantity and quality indicators in Germany. The predictor dataset was optimized using permutation‐based variable importance, maximizing the predictive power of the random forest regression models. Simple ratios between the narrow near‐infrared and red‐edge region were among the most important variables. The model performance was only marginally increased by including Sentinel‐1 data. image"],["dc.description.sponsorship","Landwirtschaftliche Rentenbank"],["dc.identifier.doi","10.1002/rse2.149"],["dc.identifier.eissn","2056-3485"],["dc.identifier.issn","2056-3485"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17449"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82214"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","2056-3485"],["dc.relation.issn","2056-3485"],["dc.relation.orgunit","Zentrum für Biodiversität und Nachhaltige Landnutzung"],["dc.relation.orgunit","Abteilung Wildtierwissenschaften"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by-nc/4.0/"],["dc.title","Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]