Vegetation classification method with biochemical composition estimated from remote sensing data

Abstract In this article, a vegetation classification hypothesis based on plant biochemical composition is presented. The basic idea of this hypothesis is that the vegetation species/crops have their own biochemical composition characteristics, which are separable from each other for those co-existing species at a specific region. Therefore, vegetation species can be classified based on the biochemical composition characteristics, which can be retrieved from hyperspectral remote-sensing data. In order to test this hypothesis, an experiment was conducted in north-western China. Field data on the biochemical compositions and spectral responses of different plants and an Earth-observing 1 (EO-1) Hyperion image were simultaneously collected. After analysing the relationship between biochemical composition and spectral data collected from Hyperion, the vegetation biochemical compositions were estimated using sample biochemical data and bands of Hyperion data. The vegetation classification was completed using the biochemical content classifier (BCC) and maximum-likelihood classifier (MLC) with all Hyperion bands (MLCA) and selected bands (MLCS), which were used for estimating considered biochemical contents (cellulose and carotenoid). The overall classification accuracy of the BCC (95. 2%) was as good as MLCS (95. 2%) and better than MLCA (91. 1%), as was the kappa value (BCC 92. 849%, MLCS 92. 845%, MLCA 86. 637%), suggesting that the BCC was a feasible classification method. The biochemical-based classification method has higher vegetation classification accuracy and execution speed, reduces data dimension and redundancy and needs only a few spectral bands to retrieve biochemical contents instead of using all of the spectral bands. It is an effective method to classify vegetation based on plant biochemical composition characteristics. Acknowledgements This work was supported by projects in the National Science & Technology Pillar Programme of China (No. 2006BAK09B01) and the Knowledge Innovation Programmes of the Chinese Academy of Sciences (No. KSCX1-YW-09-01). We are grateful for the comments and suggestions of two anonymous referees and for the help of the editor, Dr S. Paloscia, all of which have led to improvements in the presentation of this article.