• inference;
  • pattern;
  • unscented Kalman filter;
  • vegetation

[1] Spatial organization of vegetation into periodic, coherent patterns arises from the interaction of positive and negative ecological feedbacks. Naturally, the patterns reflect the characteristics of the ecological processes that underlie their formation. Direct inference of the parameters describing these ecological processes from observations of vegetation spatial patterns has not been attempted. If successful, such inference can facilitate the parameterization and predictive use of vegetation pattern models. An inference technique based on nonlinear filtering is proposed here and applied to estimate the parameters of a single-equation phenomenological model of vegetation biomass patterning. Results derived from modeled biomass data indicate that for sufficiently accurate biomass observations (signal-to-noise ratios >4), and spatial resolution of better than 10% of the pattern wavelength, nonlinear filtering techniques recovered model parameters with high fidelity. When applied to real-world imagery, reasonable parameters within the pattern-forming regime were inferred. The study demonstrates, for the first time, the feasibility of inferring quantitative ecological information from spatial observations of vegetation distributions.