Raab, Christoph

[["dc.bibliographiccitation.firstpage","913"],["dc.bibliographiccitation.seriesnr","23"],["dc.contributor.author","Tonn, B."],["dc.contributor.author","Hüppe, C."],["dc.contributor.author","Kunze, N."],["dc.contributor.author","Noll, C."],["dc.contributor.author","Raab, C."],["dc.contributor.author","Isselstein, J."],["dc.contributor.editor","Horan, B."],["dc.contributor.editor","Hennessy, D."],["dc.contributor.editor","O’Donovan, M."],["dc.contributor.editor","Kennedy, E."],["dc.contributor.editor","McCarthy, B."],["dc.contributor.editor","Finn, J. A."],["dc.contributor.editor","O’Brien, B."],["dc.date.accessioned","2018-12-03T13:43:34Z"],["dc.date.available","2018-12-03T13:43:34Z"],["dc.date.issued","2018"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57017"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","EGF"],["dc.publisher.place","Ireland"],["dc.relation.conference","27th General Meeting of the European Grassland Federation"],["dc.relation.crisseries","Grassland Science in Europe"],["dc.relation.eventend","2018-06-21"],["dc.relation.eventlocation","Cork, Ireland"],["dc.relation.eventstart","2018-06-17"],["dc.relation.isbn","978-1-84170-643-6"],["dc.relation.isbn","978-1-84170-644-3"],["dc.relation.ispartof","Sustainable meat and milk production from grasslands"],["dc.relation.ispartofseries","Grassland Science in Europe;23"],["dc.title","Nutrient transfer by cattle through different spatial patterns of grazing and non-grazing behaviour"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["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","901"],["dc.bibliographiccitation.lastpage","903"],["dc.bibliographiccitation.seriesnr","23"],["dc.contributor.author","Raab, C."],["dc.contributor.author","Riesch, F."],["dc.contributor.author","Tonn, B."],["dc.contributor.author","Meißner, M."],["dc.contributor.author","Balkenhol, N."],["dc.contributor.author","Isselstein, J."],["dc.contributor.editor","Horan, B."],["dc.contributor.editor","Hennessy, D."],["dc.contributor.editor","O’Donovan, M."],["dc.contributor.editor","Kennedy, E."],["dc.contributor.editor","McCarthy, B."],["dc.contributor.editor","Finn, J. A."],["dc.contributor.editor","O’Brien, B."],["dc.date.accessioned","2018-12-03T13:06:43Z"],["dc.date.available","2018-12-03T13:06:43Z"],["dc.date.issued","2018"],["dc.description.abstract","Spatially explicit mapping of grassland forage quality is of major interest for sustainable grazing management of NATURA 2000 areas, especially if those are large or have limited accessibility. Therefore, this study is concerned with the estimation of crude protein (CP) and organic acid detergent fiber (oADF) content at regional scale using Sentinel-2 and Landsat 8 remote sensing data. Field data were collected in the Grafenwoehr military training area in Bavaria, Germany. Different combinations of predictor variables were applied using cross-validated random forest regression, linear regression with lasso penalty and linear regression with ridge penalty models. The red-edge band of Sentinel-2, centered at 705 nm, as well as the shortwave infrared bands of both sensors and related vegetation indices contributed the most to the respective models. Linear regression with lasso penalty and Sentinel-2 data performed consistently better, compared to the other models. The results (CP (10.1 - 23.1%): max R2 0.53, RMSE 1.78%; oADF (22.7 - 39.5%): max R2 0.72, RMSE 2.3%) demonstrate the potential of remote sensing as an information tool in supporting the conservation management of grassland areas with limited access."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57015"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.publisher","EGF"],["dc.publisher.place","Ireland"],["dc.relation.conference","27th General Meeting of the European Grassland Federation"],["dc.relation.crisseries","Grassland Science in Europe"],["dc.relation.eventend","2018-06-21"],["dc.relation.eventlocation","Cork, Ireland"],["dc.relation.eventstart","2018-06-17"],["dc.relation.isbn","978-1-84170-643-6"],["dc.relation.isbn","978-1-84170-644-3"],["dc.relation.ispartof","Sustainable meat and milk production from grasslands"],["dc.relation.ispartofseries","Grassland Science in Europe;23"],["dc.relation.orgunit","Abteilung Wildtierwissenschaften"],["dc.title","Methods for spatially explicit estimation of NATURA 2000 grassland forage quality using satellites"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["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"]]

[["dc.bibliographiccitation.firstpage","253"],["dc.bibliographiccitation.lastpage","257"],["dc.bibliographiccitation.seriesnr","62"],["dc.contributor.author","Tonn, B."],["dc.contributor.author","Hüppe, C."],["dc.contributor.author","Kunze, N."],["dc.contributor.author","Raab, C."],["dc.contributor.author","Isselstein, J."],["dc.contributor.editor","Christian-Albrechts-Universität zu Kiel, Institut für Pflanzenbau und Pflanzenzüchtung, Abteilung Grünland und Futterbau/Ökologischer Landbau"],["dc.date.accessioned","2019-06-13T11:11:29Z"],["dc.date.available","2019-06-13T11:11:29Z"],["dc.date.issued","2018"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57963"],["dc.language.iso","de"],["dc.notes.status","fcwi"],["dc.publisher","Arbeitsgemeinschaft Grünland und Futterbau der Gesellschaft für Pflanzenbauwissenschaften e. V."],["dc.relation.conference","62. Jahrestagung der AGGF"],["dc.relation.crisseries","Mitteilungen der Arbeitsgemeinschaft Grünland und Futterbau"],["dc.relation.eventend","2018-09-01"],["dc.relation.eventlocation","Kiel"],["dc.relation.eventstart","2018-08-30"],["dc.relation.isbn","978-3-00-060516-1"],["dc.relation.ispartof","Leistungen von Gras und Klee-Gras auf Acker und Grünland. Tagungsband. Vorträge und Posterbeiträge der 62. Jahrestagung der AGGF in Kiel. 30. August – 01. September 2018"],["dc.relation.ispartofseries","Mitteilungen der Arbeitsgemeinschaft Grünland und Futterbau;62"],["dc.title","Räumliche Präferenzen weidender Rinder verursachen Nährstoffumverteilung auf mehreren Maßstabsebenen"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Barrett, B."],["dc.contributor.author","Bika, K."],["dc.contributor.author","Ali, I."],["dc.contributor.author","Raab, C."],["dc.date.accessioned","2019-07-31T07:18:18Z"],["dc.date.available","2019-07-31T07:18:18Z"],["dc.date.issued","2016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62223"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.relation.conference","X/TanDEM-X Science Team Meeting, German Aerospace Center (DLR)"],["dc.relation.eventend","2013-10-20"],["dc.relation.eventlocation","Oberpfaffenhofen, Germany"],["dc.relation.eventstart","2016-10-17"],["dc.relation.iserratumof","yes"],["dc.title","Grassland management and biomass retrieval in an intensive dairy farm in Ireland using TerraSAR-X Staring Spotlight mode data"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Raab, C."],["dc.contributor.author","Tonn, B."],["dc.contributor.author","Meißner, M."],["dc.contributor.author","Isselstein, J."],["dc.date.accessioned","2019-07-31T07:21:49Z"],["dc.date.available","2019-07-31T07:21:49Z"],["dc.date.issued","2016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62224"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.relation.conference","8. Rotwildsymposium - Der Hirsch als Naturschützer"],["dc.relation.eventend","2016-07-09"],["dc.relation.eventlocation","Kurhaus Casino, Baden-Baden, Germany"],["dc.relation.eventstart","2016-07-07"],["dc.relation.iserratumof","yes"],["dc.title","Erhalt von Offenlandschaften – wildlebende Rothirsche als Landschaftpfleger – Vegetation und Fernerkundung"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Meißner, M."],["dc.contributor.author","Raab, C."],["dc.contributor.author","Richter, L."],["dc.contributor.author","Riesch, F."],["dc.date.accessioned","2019-07-31T07:25:18Z"],["dc.date.available","2019-07-31T07:25:18Z"],["dc.date.issued","2016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62225"],["dc.language.iso","de"],["dc.relation.conference","8. Rotwildsymposium - Der Hirsch als Naturschützer"],["dc.relation.date","2016-07"],["dc.relation.eventlocation","Kurhaus Casino, Baden-Baden, Germany"],["dc.title","Erhalt von Offenlandschaften durch zielgerichtetes Flächen- und Wildtiermanagement - wildlebende Rothirsche als Landschaftspfleger"],["dc.type","lecture"],["dc.type.internalPublication","yes"],["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"]]