A mobile tram system for systematic sampling of ecosystem optical properties John A. Gamon ⁎ , Yufu Cheng, Helen Claudio, Loren MacKinney, Daniel A. Sims Department of Biological Sciences and Center for Environmental Analysis, California State University, Los Angeles, 5151 State University Drive, Los Angeles, CA 91030, USA Received 18 March 2006; received in revised form 1 April 2006; accepted 2 April 2006 Abstract Reliable and repeatable field sampling methods are needed for monitoring ecosystem optical properties linked to carbon flux. Here we describe a tram system, consisting of a dual-detector spectrometer mounted on a robotic cart for mobile sampling of ecosystem spectral reflectance. To illustrate the application of this system for monitoring dynamic ecosystem activity, we illustrate how the tram can be used for exploring the multiple factors influencing the Normalized Difference Vegetation Index (NDVI), a measure of vegetation greenness and a key optical indicator of vegetation carbon dioxide assimilation. With this system, we collected five years of NDVI data for a chaparral ecosystem in Southern California subject to extreme disturbance. Key factors affecting NDVI at this site included snow cover, sky conditions (clear vs. cloudy), time of day, season, species composition, and environmental perturbations such as rainfall, drought and fire. Applications of this tram system include ecosystem monitoring, satellite validation, and developing surface-atmosphere flux models from remote sensing. © 2006 Elsevier Inc. All rights reserved. Keywords: Robotic tram system; Spectral reflectance; NDVI; Disturbance; Chaparral; FLUXNET; SpecNet 1. Introduction The exchanges of carbon dioxide and water vapor between terrestrial ecosystems and the atmosphere exert large impacts on the global carbon and water cycles, and are critical to the regu- lation of the Earth's climate. We now have a large set of tools for measuring these fluxes, each with a particular set of strengths and limitations. Eddy covariance can measure whole ecosys- tems, but the sampling region is limited to a relatively small “footprint” (typically a few hectares or less) and cannot readily isolate the contribution of component parts (Moncreiff et al., 1996). Other methods, including inversion models utilizing the flask sampling network (Tans et al., 1990) and satellite remote sensing (Running et al., 2004), provide synoptic coverage for large regions of the globe, but cannot directly resolve local or regional fluxes, and are inherently difficult to compare to point sampling methods operating at much finer scales. One reason for the lack of comparability lies in the different sampling domains—space and time. Flux towers provide a direct means of measuring surface-atmosphere fluxes of carbon dioxide and water vapor (Baldocchi et al., 1988). They sample a fluctuating region (footprint) through time from a discrete geographic point, so they cannot directly resolve spatial pat- terns. Additionally, they are costly, limited to relatively flat, uniform terrain, and cannot readily be installed at all sites, leaving much of the world unsampled (Running et al., 1999). This undersampling problem is often tackled by remote sensing, which can provide synoptic coverage of large regions or the whole earth, but can be difficult to compare the point measure- ments provided by flux towers (Cheng et al., 2006-this issue). Typically, remote measurements of spectral reflectance are used to calculate “vegetation indices” (e.g. the Normalized Difference Vegetation Index), which are then used in models to estimate carbon fluxes (e.g. Running et al., 2004). Remote sensing traditionally provides an image for a defined period or discrete point in time, but again with some loss of spatial resolution, depending upon the pixel size. Frequent global satel- lite coverage (e.g. the MODIS sensors on the Aqua and Terra Remote Sensing of Environment 103 (2006) 246 – 254 www.elsevier.com/locate/rse ⁎ Corresponding author. E-mail address: jgamon@gmail.com (J.A. Gamon). 0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2006.04.006