Skip to main content
ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #198416
Title: AN OBJECT-BASED IMAGE ANALYSIS APPROACH FOR DETERMINING FRACTIONAL COVER OF SENESCENT AND GREEN VEGETATION WITH DIGITAL PLOT PHOTOGRAPHY

Author
item LALIBERTE, ANDREA - NEW MEXICO STATE UNIV
item Rango, Albert
item Herrick, Jeffrey - Jeff
item Frederickson, Eddie
item Burkett, Laura

Submitted to: Journal of Arid Environments
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/26/2006
Publication Date: 11/3/2006
Citation: Laliberte, A., Rango, A., Herrick, J.E., Fredrickson, E.L., Burkett, L.M. 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments. 69:1-14.

Interpretive Summary: While ground-based plot methods for measuring vegetation cover and bare soil are commonly used in rangeland monitoring, image analysis of digital ground photos is becoming more widespread because the image-based methods are less labor and time intensive. However, in most studies only overall vegetation cover has been estimated. In arid regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material and it is important that both types of vegetation can be quantified. We developed an image analysis approach for estimating fractional cover of green and senescent vegetation using very high-resolution ground photography, and compared image- and ground-based estimates. We analyzed fifty digital ground photos with an object-based image analysis approach and classified the images into soil, shadow, green vegetation, and senescent vegetation. Shadow and soil were effectively masked out by using the intensity and saturation bands of the imagery, and in a subsequent step, green and senescent vegetation was successfully classified. The correlation coefficients between ground- and image-based estimates for green and senescent vegetation were high (0.88 and 0.95 respectively). The image-based method is less labor and time intensive than and a viable alternative to the ground-based plot method, and it offers a non-biased approach for estimation of ground cover. Due to the need for assessing vast areas of rangelands, labor- and time-savings are crucial, and this image analysis approach has the potential to be incorporated into rangeland monitoring protocols.

Technical Abstract: 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- and ground-based estimates. We acquired ground photography for fifty 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 ground- 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 ground-based plot method, is a viable alternative to these methods, and has the potential to be incorporated into rangeland monitoring protocols.