Crop identification in the irrigation district based on SPOT5 satellite imagery

High resolution satellite imagery is one of important data sources for crop monitoring and assessment, and has important application values in the field of crop planning and yield estimation. Distribution of crops in the Yingke irrigation district of Zhangye City was analyzed by using a combined data of SPOT5 image, obtained in 2008, with four spectral bands (green, red, nearinfrared and shortwave infrared) and 2.5 m pixel size covering. Two images with pixel sizes of 10 m and 30 m were also generated from the original combined image to simulate coarser resolution satellite imagery. Five supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM) and support vector machine (SVM), were applied to identify crop types in the study. The effects of pixel size on classification results were also examined. Kappa analysis showed that the maximum likelihood and SVM, though there were no statistical differences between them, performed better than those from other classified methods and the value of Kappa were 0.871 9 and 0.862 5, respectively. Accuracy assessment showed that the maximum likelihood gave the best result with overall accuracy values of 90.6%. The results also showed that increasing pixel size from 2.5 m to 10 m or 30 m did not significantly affect the classification accuracy for crop identification. Overall results indicate that SPOT5 image in conjunction with maximum likelihood and SVM classification techniques can be used for identifying crop types and estimating crop areas.