Separating Green and Senescent Vegetation in Very High Resolution Photography Using an Intensity-Hue-Saturation Transformation and Object Based Classification

TitleSeparating Green and Senescent Vegetation in Very High Resolution Photography Using an Intensity-Hue-Saturation Transformation and Object Based Classification
Publication TypeConference Proceedings
Year of Publication2006
AuthorsLaliberte, Andrea S., Rango A., Fredrickson E.L.
Conference NameProceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference
PaginationCDROM
Date PublishedMay 1-5, 2006
Conference LocationReno, NV
ARIS Log Number183019
Keywordsintensity-hue-saturation, senescent, vegetation
Abstract
 
In arid regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material. It is important that both types of vegetation can be quantified for land management purposes and for assessing the nutritional value of grasses. Traditional ground sampling procedures are commonly used but are time consuming. Our goal was to develop an image analysis approach for separating and quantifying green and senescent grasses in the same plot using very high resolution ground photography. The study was conducted in New Mexico at the Jornada Experimental Range (JER), operated by the USDA Agricultural Research Service. We used an eight megapixel digital camera to acquire ground photography from a height of 2.8 m above ground for fifty plots. The images were transformed from the RGB (red, green, blue) color space to the IHS (intensity, hue, saturation) color space. We used an object-based image analysis approach to classify the images into soil, shadow, green vegetation, and senescent vegetation. Shadow and soil were masked out by using the intensity and saturation bands, and a nearest neighbor classification was used to separate green and senescent vegetation using intensity, hue and saturation as well as visible bands. Correlation coefficients between ground-and image-based estimates for green and senescent vegetation were 0.88 and 0.95 respectively, and image analysis underestimated total and senescent vegetation by approximately 5%. The image-based approach is a viable alternative to and less labor and time intensive than ground based plot measures. Research into further automation of the image analysis procedures is ongoing.
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