Abstract
Abstract. Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.
Highlights
Sustainable management of the agricultural areas is crucial for local authorities and agricultural agencies since agriculture plays an important role at the economy of many developing countries (Branca, 2011).Crop mapping and identification provide an important basis for many agricultural applications with various purposes such as yield estimation, crop rotation records and soil productivity (Löw et al 2013, Fundamental of Remote Sensing)
The results of this study indicate that vegetation indices derived from original spectral bands of RapidEye imagery could be used for crop classification and show satisfactory results
Single band image classification has been implemented to analyse the individual performance of spectral bands on image classification accuracy
Summary
Sustainable management of the agricultural areas is crucial for local authorities and agricultural agencies since agriculture plays an important role at the economy of many developing countries (Branca, 2011). RapidEye, has been used here for crop type classification, is the first high-resolution multispectral satellite system incorporating the red-edge band which is sensitive to vegetation chlorophyll content (Schuster et al 2012). This satellite imagery has been successfully used for classification of vegetation, forestry and agricultural areas recently (Eitel et al 2011, Schuster et al 2012, Tigges 2013, Löw et al 2013). The potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
