DOI: 10.1016/j.jag.2013.12.007
Scopus记录号: 2-s2.0-84897386677
论文题名: Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland
作者: Esch T ; , Metz A ; , Marconcini M ; , Keil M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 28, 期: 1 起始页码: 230
结束页码: 237
语种: 英语
英文关键词: Crop types
; Grassland
; High and medium resolution data
; Multi-seasonal analysis
; Object-oriented classification
Scopus关键词: agricultural land
; grassland
; image classification
; image resolution
; mapping
; satellite data
; satellite imagery
; Germany
英文摘要: A 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 LISS- 3 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 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. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79680
Appears in Collections: 气候变化事实与影响
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作者单位: German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, Germany; University of Osnabrueck, Institute for Geoinformatics and Remote Sensing, Germany
Recommended Citation:
Esch T,, Metz A,, Marconcini M,et al. Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,28(1)