Biographical sketch: Robert E. Kennedy Phone: 617.358.7119 / Department of Earth & Environment / Boston University Email: [email protected] Summary I use remote sensing tools to examine natural and anthropogenic processes occurring on large landscapes, exploring how that knowledge can help improve our understanding of the consequence and causes of landscape change. Academic background Department of Biology, University of Biology B.S. 1992 Houston, Houston, TX Department of EPO Biology, University of Biology M.A. 1994 Colorado, Boulder, CO Department of Forest Science, Oregon State Forest Science PhD 2004 University, Corvallis, OR Appointments July 2012 - Assistant Professor, Department of Earth & Environment, Boston University December 2009 to Assistant Professor, Senior Research Department of Forest June 2012 Ecosystems and Society, Oregon State University April 2008 to Nov Research Associate, Department of Forest Science, Oregon State 2008 University April 2004-April Postdoc, USDA Forest Service, Pacific Northwest Research 2008 Station Selected Publications Kennedy, R.E., Yang, Z., Cohen, W.B., Pfaff, E., Braaten, J., & Nelson, P. 2012. Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sensing of Environment 122, 117-133. Pflugmacher, D.L., Cohen, W. B., & Kennedy, R.E. 2012. Using Landsat-derived disturbance history (1972-2010) to predict current forest structure. Remote Sensing of Environment Doi: 10.1016/j.rse.2011.09.025. Ohmann, J.L., Gregory, M. J., Roberts, H.M., Cohen, W.B., Kennedy, R.E., and Yang, Z. 2012. Mapping change of older forest with nearest-neighbor imputation and Landsat time series. Forest Ecology and Management 272:13-25. Kennedy, R.E. (2012). New views on changing arctic vegetation. Environmental Research Letters, 7, 011001. Griffiths, P., Kuemmerle, T., Kennedy, R.E., Abrudan, I.V., Knorn, J., & Hostert, P. (2012). Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania. Remote Sensing of Environment, 118:199-214 Meigs, G.W., Kennedy, R.E., & Cohen, W.B. 2011. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sensing of Environment. 115:3707-3718. Kennedy, R. E., Yang, Z., Braaten, J., Nelson, P., & Cohen, W.B. 2011. Monitoring landscape dynamics of national parks in the western United States. Chapter 3 In Remote sensing of protected lands. Editor: Y.Q. Wang. CRC Press. Turner, D.P., Ritts, W.D., Yang, Z.Q., Kennedy, R.E., Cohen, W.B., Duane, M.V., Thornton, P.E., & Law, B.E. 2011. Decadal trends in net ecosystem production and net ecosystem carbon balance for a regional socioecological system. Forest Ecology and Management, 262: 1318-1325 Kennedy, R.E., Yang, Z., & Cohen, W.B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. Remote Sensing of Environment 114: 2897-2910. Cohen, W.B., Yang, Z., & Kennedy, R.E. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. Remote Sensing of Environment 114: 2911-2924. Powell, S.L., Cohen, W.B., Healey, S.P., Kennedy, R.E., Moisen, G.G., Pierce, K.B., & Ohman, J.L. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment, 114:1053-1068 Kennedy, R. E., Townsend, P.A., Gross, J., Cohen, W. B., Bolstad, P., Wang, Y.Q., & Adams, P. 2009. Remote Sensing Change Detection Tools for Natural Resource Managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment 113:1382-1396. Kennedy, R. E., Cohen, W.B., Kirschbaum, A.A., and Haunreiter, E. 2007. “Protocol for Landsat-based monitoring of landscape dynamics at North Coast and Cascades Network Parks.” US Geological Survey Techniques and Methods 2-G1, Biological Resources Division. http://pubs.usgs.gov/tm/2007/tm2g1/ Kennedy, R. E., Cohen, W. B. and Schroeder, T. A. 2007. “Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment 110:370-386. Science team memberships Landsat science team – a USGS & NASA sponsored needs and applications team to improve use of the Landsat archive and the upcoming Landsat Datat Continuity Mission (LDCM). Land-cover monitoring system (LCMS) science team – a USDA Forest Service sponsored effort to develop consistent methods for yearly, all-lands land cover monitoring across the conterminous United States. Carbon Monitoring System (CMS) science team – a NASA sponsored effort to improve national carbon monitoring using remote observation platforms and Earth system models. Current Projects “Integrated, observation-based carbon monitoring for wooded ecosystems in Washington, Oregon, and California.” Principal investigator. A three-year project funded by the USDA National Institute for Food and Agriculture ($770,843). Link LandTrendr with Forest Inventory and Analysis (FIA) field plot data using nearest-neighbor statistical methods; map forest biomass for Washington, Oregon, and California for the years 1985-2010; map methodological uncertainty at pixel scale; link with 3-d lidar-based biomass maps. “Integrating and expanding a regional carbon monitoring system in the NASA CMS.” Principal investigator. An 18-month project funded by NASA ($413,507). Utilize yearly biomass maps from WA, OR, and CA to evaluate national-scale biomass maps developed under NASA CMS and other projects; test biomass-mapping strategy in eastern forests. “Using time-series approaches to improve Landsat’s characterization of land surface dynamics.” ($924,214). Principal investigator. Test and adapt methods for yearly land cover mapping in all major global ecosystems and regions.

Also: Several smaller projects with the National Park Service and with other PIs to utilize LandTrendr-based mapping for habitat monitoring and for integration into biogeochemical models.