Using land surface phenology and information theory to assess and map complex landscape dynamics

Abstract

Context Characterizing landscape ecological complexity and change requires integrated description of spatial and temporal landscape organization and dynamics, as suggested by the shifting mosaic concept. Remotely sensed land surface phenology allows the detection of even small differences among landscape patches and through time, allowing for the analysis of landscapes as shifting mosaics. Objectives We sought to quantify aspects of the complex landscape behaviors that are implied by spatiotemporal variation in land surface phenology. We adapted an information-theoretic (IT) framework from ecosystem ecology to capture landscape-level spatiotemporal complexity and organization and map these properties across large areas. Methods Phenology data were derived from remotely sensed, pixel-level time series of a vegetation greenness index, across a large portion of North America. We summarized multi-year, multi-pixel dynamics in transition matrices, calculated IT metrics from the matrices, and used matrix projection to quantify disequilibrium dynamics and long-term trajectories of the metrics. Results Mapping the IT metrics and their disequilibria revealed gradients in the spatiotemporal complexity and organization of multi-year land surface phenology dynamics at continental to local scales. These gradients suggest influences of biophysical and biogeographic setting, ecological development and disturbances, land use, and other drivers of landscape ecological dynamics. The spatiotemporal IT metrics were influenced by both year-to-year dynamics and spatial landscape heterogeneity, but correlations with spatial and temporal complexity measures varied among the IT metrics. Landscapes showing the strongest disequilibrium dynamics were mostly in the western part of the continent and appeared to be associated with large-scale disturbances including severe fire, forest pathogens, climate variability, and land use change—important subjects for further study. Conclusions This approach reveals novel features of the shifting landscape mosaic, with implications for understanding landscape resilience and sustainability. Resulting spatial data products describing long-term landscape dynamics have potential applications in broad-scale ecological modeling, monitoring, assessment, and prediction.