Analysis of fine-scale spatial pattern of a grassland from remotely-sensedimagery and field collected data

An important practical problem in the analysis of spatial pattern in ecolog ical systems is that requires spatially-intensive data, with both fine reso lution and large extent. Such information is often difficult to obtain from field-measured variables. Digital imagery can offer a valuable, alternativ e source of information in the analysis of ecological pattern. In the prese nt paper, we use remotely-sensed imagery to provide a link between field-ba sed information and spatially-explicit modeling of ecological processes. We analyzed one digitized color infrared aerial photograph of a serpentine gr assland to develop a detailed digital map of land cover categories (31.24 m x 50.04 m of extent and 135 mm of resolution), and an image of vegetation index (proportional to the amount of green biomass cover in the field). We conducted a variogram analysis of the spatial pattern of both field-measure d (microtopography, soil depth) and image-derived (land cover map, vegetati on index, gopher disturbance) landscape variables, and used a statistical s imulation method to produce random realizations of the image of vegetation index based upon our characterization of its spatial structure. The analysi s revealed strong relationships in the spatial distribution of the ecologic al variables (e.g., gopher mounds and perennial grasses are found primarily on deeper soils) and a non-fractal nested spatial pattern in the distribut ion of green biomass as measured by the vegetation index. The spatial patte rn of the vegetation index was composed of three basic components: an expon ential trend from 0 m to 4 m, which is related to local ecological processe s, a linear trend at broader scales, which is related to a general change i n topography across the study site, and a superimposed periodic structure, which is related to the regular spacing of deeper soils within the study si te. Simulations of the image of vegetation index confirmed our interpretati on of the variograms. The simulations also illustrated the limits Of statis tical analysis and interpolations based solely on the semivariogram, becaus e they cannot adequately characterize spatial discontinuities.