Multivariate Spatio-Temporal Clustering: A Framework for Integrating Disparate Data to Understand Network Representativeness and Scaling Up Sparse Ecosystem Measurements

Disparate in situ and remote sensing time series data are being collected to understand the structure and function of ecosystems and how they may be affected by climate change. However, resource and logistical constraints limit the frequency and extent of observations, particularly in the harsh environments of the arctic and the tropics, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent variability at desired scales. These regions host large areas of potentially vulnerable ecosystems that are poorly represented in Earth system models (ESMs), motivating two new field campaigns, called Next Generation Ecosystem Experiments (NGEE) for the Arctic and Tropics, funded by the U.S. Department of Energy. Multivariate Spatio-Temporal Clustering (MSTC) provides a quantitative methodology for stratifying sampling domains, informing site selection, and determining the representativeness of measurement sites and networks. We applied MSTC to down-scaled general circulation model results and data for the State of Alaska at a 4 km2 resolution to define maps of ecoregions for the present (2000-2009) and future (2090-2099), showing how combinations of 37 bioclimatic characteristics are distributed and how they may shift in the future. Optimal representative sampling locations were identified on present and future ecoregion maps, and representativeness maps for candidate sampling locations were produced. We also applied MSTC to remotely sensed LiDAR measurements and multi-spectral imagery from the WorldView-2 satellite at a resolution of about 5 m2 within the Barrow Environmental Observatory (BEO) in Alaska. At this resolution, polygonal ground features—such as centers, edges, rims, and troughs—can be distinguished. Using these remote sensing data, we up-scaled vegetation distribution data collected on these polygonal ground features to a large area of the BEO to provide distributions of plant functional types that can be used to parameterize ESMs. In addition, we applied MSTC to 4 km2 global bioclimate data to define global ecoregions and understand the representativeness of CTFS-ForestGEO, Fluxnet, and RAINFOR sampling networks. These maps identify tropical forests underrepresented in existing observations of individual and combined networks.