An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography

Research into automatic image processing of digital plot photography has increased in recent years. However, in most studies only overall vegetation cover is estimated. In arid regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material and it is important to be able to quantify both types of vegetation. Our objectives were to develop an image analysis approach for estimating fractional cover of green and senescent vegetation using very high-resolution ground photography, and to compare image-based estimates with line-point-intercept (LPI) measures. We acquired ground photography for 50 plots using an eight megapixel digital camera. 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 effectively 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 LPI- and image-based estimates for green and senescent vegetation were 0.88 and 0.95 respectively. Image analysis underestimated total and senescent vegetation by approximately 5%. The object-based image-processing approach is less labor and time intensive than the LPI method, is a viable alternative to ground-based methods, and has the potential to be incorporated into rangeland monitoring protocols.