Applying image transformation and classification techniques to airborne hyperspectral imagery for mapping Ashe juniper infestations

• Published in: International journal of remote sensing 2009-06, Vol.30 (11), p.2741-2758
• Main Authors: Yang, Chenghai, Everitt, J. H., Johnson, H. B.
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Teledetection and vegetation maps
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160802555812

Ashe juniper (Juniperus ashei Buchholz) in excessive coverage reduces forage production, interferes with livestock management, and degrades watersheds and wildlife habitat on infested rangelands. The objective of this study was to apply minimum noise fraction (MNF) transformation and different classification techniques to airborne hyperspectral imagery for mapping Ashe juniper infestations. Hyperspectral imagery with 98 usable bands covering a spectral range of 475-845 nm was acquired from two Ashe juniper infested sites in central Texas. MNF transformation was applied to the hyperspectral imagery and the transformed imagery with the first 10 and 20 MNF bands was classified using four hard classifiers: minimum distance, Mahalanobis distance, maximum likelihood and spectral angle mapper (SAM). For comparison, the 10- and 20-band MNF imagery was inversely transformed to noise-reduced 98-band imagery in the original data space, which was also classified using the four classifiers. Accuracy assessment showed that the first 10 MNF bands were sufficient for distinguishing Ashe juniper from associated plant species (mixed woody species and mixed herbaceous species) and other cover types (bare soil and water). Although the 20-band MNF imagery provided better results for some classifications, the increase in overall accuracy was not statistically significant. Overall accuracy on the 10-band MNF imagery varied from 88% for SAM to 93% for minimum distance for site 1 and from 84% for SAM to 94% for maximum likelihood for site 2. The 98-band imagery derived from the 10-band MNF imagery resulted in overall accuracy ranging from 91% for both SAM and Mahalanobis distance to 97% for maximum likelihood for site 1 and from 87% for SAM to 93% for minimum distance for site 2. Although both approaches produced comparable classification results, the MNF imagery required smaller storage space and less computing time. These results indicate that airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping Ashe juniper infestations.