1 January 2011 Vegetation extraction from IKONOS imagery using high spatial resolution index
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Abstract
In vegetation change monitoring and urban planning, the measurement and mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular measure to generate vegetation maps which resolution depends on that of the input images. High-resolution imagery can lead to better vegetation classification accuracy. Various methods are proposed to provide high spatial resolution vegetation indices based on a fusion concept. IKONOS produces high spatial resolution panchromatic (Pan) images and moderate spatial resolution multispectral (MS) images. Generally, for an image fusion purpose, the conventional bi-cubic interpolation scheme is used to resize MS images. Nevertheless, this scheme fails around edges and consequently produces blurred edges and annoying artifacts in interpolated MS images. To avoid this problem, an artifact-free image interpolation method is proposed. This study presents a modified NDVI that provides high spatial resolution maps which differentiate vegetated surfaces from other surfaces when using IKONOS imagery. This vegetation index (HRNDVI: high resolution NDVI) is based on a newly derived formula including high spatial resolution information from IKONOS. The HRNDVI is computed based on the resampled MS images and the Pan images. The proposed vegetation index takes advantage of both the high spatial resolution information of Pan images and the robustness of the interpolation technique. Visual and quantitative analysis demonstrates that this index appears promising and performs well in vegetation extraction and visualization.
© (2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Miloud Chikr El-Mezouar, Nasreddine Taleb, Kidiyo Kpalma, Joseph Ronsin, "Vegetation extraction from IKONOS imagery using high spatial resolution index," Journal of Applied Remote Sensing 5(1), 053543 (1 January 2011). https://doi.org/10.1117/1.3624518 . Submission:
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