Ecosystem sensing


Large-Scale Wireless Sensor Networks for Observation of Ecosystem Processes

Funding: NSF IDEA-0308498

Progress in an array of technologies, including microelectronic sensing and computation, wireless communication, and the self-assembly of autonomous devices into cooperative networks has inspired the vision of wireless sensor networks. While networks of intelligent agents transparently embedded into our physical environment could advance human welfare in a number of domains, research indicates that any successful wireless sensor network must be carefully optimized for its application. One of the most compelling of these applications is dense spatio-temporal sensing of environments to enable better understanding of environmental and ecosystem processes across multiple scales. Our goals are to (i) test this hypothesis in three rigorous field studies, (ii) bootstrap the application of wireless sensor network technology in many applications, and (iii) build awareness of the benefits of the technology to society, and improve collaboration between engineering and the sciences.

The instrumentation development component of this project builds on a successful seed effort in which we have constructed a small proof-of-concept wireless environmental sensing network. We will build a “distributed instrument”—a prototype network comprising hundreds of palm-sized wireless sensors. In anticipation of this prototype development, we have paid careful attention to ensuring that our networking technology would successfully scale up to hundreds and thousands of sensors at up to landscape geographic scales.

The prototype network technology will be deployed to enable a new degree of data quality in three diverse field studies. First, will probe the role of the role of fine-scale environmental phenomena in the maintenance of ecosystem diversity in two Eastern US forests. The second experiment maps the complexity of microclimates in the crowns of the coastal redwoods of California. And in the third field study, we will determine the effects of scale on eddy covariance measurements of ecosystem energy balance in Northern Arizona.

Collaborators: Pankaj Agarwal, Carla Ellis, Paul Flikkema, Alan Gelfand, Kamesh Munagala, Jun Yang

Recent publications:

  • Ghosh, S., D. M. Bell, J.S. Clark, A.E. Gelfand, and P. Flikkema.  2013. Process modeling for soil moisture using sensor network data. Statistical Science, in press.
  • Wu, W., Clark, J.S., and J. Vose. 2013. Response of hydrology to climate change in the southern Appalachian Mountains using Bayesian inference. Hydrologic Processes, in press.
  • Ward, E.J., D.M. Bell, J.S. Clark and R. Oren. 2012. Hydraulic time constants for transpiration of loblolly pine at Duke FACE. Tree Physiology, 33, 123-134.Ward, E.J., R. Oren, D.M. Bell, J.S. Clark, H.R. McCarthy, H. Seok-Kim and J.-C. Domec. 2012. The effects of long-term elevated CO2 and nitrogen fertilization on stomatal conductance estimated from scaled sap flux measurements at Duke FACE.  Tree Physiology, 33, 135-151.
  • Hersh, M.H., J.S. Clark, and R. Vilgalys. 2012. Evaluating the impacts of fungal seedling pathogens on temperate forest seedling survival. Ecology, 93: 511-520.
  • Wu, W., Clark, J.S., and J.M. Vose. 2012. Application of a full hierarchical Bayesian model in assessing streamflow response to a climate change scenario at the Coweeta Basin, NC, USA.  Journal of Resources and Ecology, 3, 118-128.
  • Agarwal, P., T. Mohave, H. Yu, and J. S. Clark. 2011. Exploiting temporal coherence in forest dynamics simulation. SCG ’11 Proceedings of the 27th Annual Symposium on Computational Geometry, Paris, France.
  • Clark, J.S., P. Agarwal, D.M. Bell , P. Flikkema , A. E. Gelfand, X. Nguyen , E. Ward, and J. Yang. 2011. Inferential ecosystem models, from network data to prediction. Ecological Applications, 21,1523–1536.
  • Wu, W., J.S. Clark, and J. Vose. 2010. Assimilating multi-source uncertainties of a parsimonious conceptual hydrological model using hierarchical Bayesian modeling, Journal of Hydrology, 394, 436-446.
  • Flikkema, P.G., P.J. K. Agarwal, J. S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang. 2007. From data reverence to data relevance:  Model-mediated wireless sensing of the physical environment. Pages 988–994 in Y. Shi et al. (Eds.): ICCS 2007, Part I, LNCS 4487.
  • Flikkema, P.G., P.K. Agarwal, J.S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang.  2006. Model-driven dynamic control of embedded wireless sensor networks.  Proc. 6th International Conference on Computational Science, Workshop on Dynamic Data Driven Application Systems, Reading, UK