EDS Seminar Series
Remote Sensing
Data Collection
Data Science

The RMBL Spatial Data Platform: Integrative Remote Sensing to Drive Environmental Discovery

 Dr. Ian Breckheimer explains the RMBL Spatial Data Platform 

Title: The RMBL Spatial Data Platform: Integrative Remote Sensing to Drive Environmental Discovery


Speaker: Ian Breckheimer, Rocky Mountain Biological Laboratory and Western Colorado University


Speaker Bio: Dr. Ian Breckheimer is a Staff Research Scientist at Rocky Mountain Biological Laboratory. Dr. Breckheimer is a landscape ecologist who has been working at the intersection of ecology conservation, data science and remote sensing for over 10 years, most recently as an NSF postdoctoral fellow at Harvard University. In 2020, he joined the staff at RMBL, where he helps field scientists use remote sensing and GIS.


Abstract: The rapid proliferation of remote-sensing platforms over the past decade has provided observations of landscapes at an increasingly wide range of sensing modalities and spatiotemporal scales. These new observations provide important opportunities for understanding the seasonal and long-term dynamics of ecosystems in the face of global change, especially in environments like mountains with high spatial heterogeneity in climate, vegetation, and ecosystem processes. Taking advantage of these new observations, however, requires novel strategies for integrating measurements taken with eld-based instruments, drones, airplanes and satellites. Here we present work that integrates diverse remote observations into a uni1ed multi-scale atlas of environmental and ecological patterns in the vicinity of the Rocky Mountain Biological Laboratory (RMBL) located in the Upper Colorado River Basin: the RMBL Spatial Data Platform. The Platform provides detailed, dynamic maps of microclimate, vegetation patterns, and land surface dynamics at ne spatial and temporal scales (0.1 – 10m in space, daily – weekly in time) derived from fusing eld data and multi-scale remote sensing observations using geostatistical and machine learning methods. We illustrate the numerous diverse applications of this work using case studies from watershed hydrology, plant ecophysiology, ecohydrology, population ecology, and forest dynamics. Finally, we describe our ongoing efforts to leverage contemporary observations to back-cast many important components of the Platform over the past several decades, thus enabling researchers to associate environmental drivers with historical data in order to better understand the patterns and drivers of long-term environmental change. Our integrative approach and its applications collectively demonstrate the value of closely coupling eld data with multi-scale remote observations to understand complex ecological responses to accelerating global change.