Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland

DC FieldValueLanguage
dc.contributor.authorEsch, T.
dc.contributor.authorMetz, A.
dc.contributor.authorMarconcini, M.
dc.contributor.authorKeil, M.
dc.date.accessioned2021-12-23T16:13:47Z-
dc.date.available2021-12-23T16:13:47Z-
dc.date.issued2014
dc.identifier.issn03032434
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/10745-
dc.description.abstractA key factor in the implementation of productive and sustainable cultivation procedures is the frequent and area-wide monitoring of cropland and grassland. In particular, attention is focused on assessing the actual status, identifying basic trends and mitigating major threats with respect to land-use intensity and its changes in agricultural and semi-natural areas. Here, multi-seasonal analyses based on satellite Earth Observation (EO) data can provide area-wide, spatially detailed and up-to-date geo-information on the distribution and intensity of land use in agricultural and grassland areas. This study introduces an operational, application-oriented approach towards the categorization of agricultural cropland and grassland based on a novel scheme combining multi-resolution EO data with ancillary geo-information available from currently existing databases. In this context, multi-seasonal high (HR) and medium resolution (MR) satellite imagery is used for both a land parcel-based determination of crop types as well as a cropland and grassland differentiation, respectively. In our experimental analysis, two HR IRS-P6 LISS3 images are first employed to delineate the field parcels in potential agricultural and grassland areas (determined according to the German Official Topographic Cartographic Information System - ATKIS). Next, a stack of seasonality indices is generated based on 5 image acquisitions (i.e., the two LISS scenes and three additional IRS-P6 LISS - AWiFS scenes). Finally, a C5.0 tree classifier is applied to identify main crop types and grassland based on the input imagery and the derived seasonality indices. The classifier is trained using sample points provided by the European Land Use/Cover Area Frame Survey (LUCAS). Experimental results for a test area in Germany assess the effectiveness of the proposed approach and demonstrate that a multi-scale and multi-temporal analysis of satellite data can provide spatially detailed and thematically accurate geo-information on crop types and the cropland-grassland distribution, respectively. (C) 2013 Elsevier B.V. All rights reserved.
dc.description.sponsorshipGerman Federal Environment Agency; German Federal Agency for Cartography and Geodesy (BKG); The authors thank the European Commission for the funding of the FP7 GMES project of Geoland2-Euroland in which context the basic techniques presented in this publication were further stimulated and developed. The authors would also like to thank the German Federal Environment Agency for funding the initial technical developments, the German Federal Agency for Cartography and Geodesy (BKG) for providing GeoBasis-DE data (ATKIS) for this study and the GAF AG and EUROMAP GmbH for the provision of IRS-P6 AWiFS data in the context of the IRS-P6 Scientific Data Pool.
dc.language.isoen
dc.publisherELSEVIER SCIENCE BV
dc.relation.ispartofINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
dc.subjectCLASSIFICATION
dc.subjectCONSERVATION
dc.subjectCrop types
dc.subjectEUROPE
dc.subjectGrassland
dc.subjectHigh and medium resolution data
dc.subjectLAND-COVER
dc.subjectMulti-seasonal analysis
dc.subjectNDVI DATA
dc.subjectObject-oriented classification
dc.subjectRemote Sensing
dc.subjectSEGMENTATION
dc.subjectTIME-SERIES
dc.titleCombined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland
dc.typejournal article
dc.identifier.doi10.1016/j.jag.2013.12.007
dc.identifier.isiISI:000332429000022
dc.description.volume28
dc.description.startpage230
dc.description.endpage237
dc.publisher.placePO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
dcterms.isPartOf.abbreviationInt. J. Appl. Earth Obs. Geoinf.
dcterms.oaStatusGreen Accepted
Show simple item record

Google ScholarTM

Check

Altmetric