Linearized vegetation indices based on a formal statistical framework

• Published in: IEEE transactions on geoscience and remote sensing Vol. 42; no. 7; pp. 1575 - 1585
• Main Authors: Unsalan, C., Boyer, K.L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied geophysics; Crops; Earth sciences; Earth, ocean, space; Electromagnetic wave absorption; Exact sciences and technology; Image converters; Image resolution; Internal geophysics; Multispectral imaging; Principal component analysis; Remote sensing; Satellites; Sun; Vegetation mapping; IKONOS data; density; vegetation indices; multispectral remote sensing; Space remote sensing; imagery; vegetation; multispectral images; principal components analysis (PCA); principal components analysis
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2004.826787

Vegetation indices have been used extensively to estimate the vegetation density from satellite and airborne images for many years. In this paper, we focus on one of the most popular of such indices, the normalized difference vegetation index (NDVI), and we introduce a statistical framework to analyze it. As the degree of vegetation increases, the corresponding NDVI values begin to saturate and cannot represent highly vegetated regions reliably. By adopting the statistical viewpoint, we show how to obtain a linearized and more reliable measure. While the NDVI uses only red and near-infrared bands, we use the statistical framework to introduce new indices using the blue and green bands as well. We compare these indices with that obtained by linearizing the NDVI with extensive experimental results on real IKONOS multispectral images.