Analysis of RapidEye imagery for agricultural land cover and land use mapping

Abstract

The objective of this paper is to investigate the potential of RapidEye imagery in mapping agricultural land cover and land use types in Songnen Plain, northeast China. A new object-oriented decision tree classifier is applied to analyze the classification results of spectral feature inputs from RapidEye imagery. The incorporation of a red edge band in multi-spectral RapidEye sensor has great potential for improving agricultural land cover and land use classification. The highest positive effects are observed for vegetation classes in the study area.