Alex Koltunov (Ph.D) is a Project Scientist with R&D expertise in multispectral, hyperspectral, and multitemporal remote sensing applied to a broad range of geosciences, ecological, and environmental problems that fall into three major categories:
- Analyzing dynamic surface phenomena,
- Anomaly/change detection, and
- Thematic classification and mapping.
Ph.D in Remote Sensing, 2006. Tel-Aviv University, Israel.
M.Sc in Computer Science, 1999. Weizmann Institute of Science, Rehovot, Israel.
B.Sc-M.Sc in Applied Math, 1993. Kharkov National University, Kharkov, Ukraine.
My research embraces two aspects of remote sensing science: remote sensing of the environment and algorithm development. These aspects are tightly linked: applications motivate, stimulate, and reasonably constrain the methodological work; while new methodologies often broaden the list of tasks ever considered solvable by remote sensing. This deep inter-connection makes remote sensing an unprecedentedly and inevitably collaborative field, wherein exact and environmental sciences form diverse alliances aimed at specific thematic goals. Throughout my career, I have enjoyed participating in these multidisciplinary alliances in different roles: by jointly defining the research questions to be answered by remote sensing; by leading development of advanced information extraction methodologies; and by collaboratively linking the data analysis results to environmental inferences.
My most current research focuses on developing satellite & airborne image analysis systems and data products to support decision-making of operational agencies, including:
- Wildfire detection and monitoring from space;
- Satellite monitoring of forest disturbances, and their ecological, carbon, and hydrological impacts;
- Mapping vegetation types and biophysical and structural properties from remote sensing data;
- Algorithm development for remote sensing to support a science-driven approach to ecosystem management
- GOES Early Fire Detection system (GOES-EFD), using images from NOAA Geostationary Operational Environmental Satellites (GOES)
- Ecosystem Disturbance and Recovery Tracker (eDaRT) system utilizing all available Landsat images for mapping ecosystem disturbance with up to 16 day frequency
- Canopy chemistry retrieval using automated advanced canopy radiative transfer models
Recent Past Projects
- Multi-sensor remote sensing of California's 2013 Rim fire.
- Fuel Moisture Content algorithm for MODIS Direct Readout System.
- Monitoring vegetation and volcanoes from space (2011-2012).
- Coastal Grassland Mapping in NW California using Landsat image time series (2009-2011)
- Phenological Impact of Selective Logging in Amazonian Tropical Forests (2006-2008).
- Thermal anomaly detection in dynamic environment: theory and application (2004-2006).
Recent Grants and Awards
9/2013-9/2016, Principal Investigator, “Multi-sensor remote sensing study of California's Rim fire to inform post-fire ecosystem restoration and effective prevention of future catastrophic wildfires” - Stage 1, sponsor: USFS; Co-PI: S. Ustin (UC Davis). (...read more...)
9/2013-9/2017, Principal Investigator, “Evaluating Operational Potential of Geostationary Early Fire Detection Capabilities at Regional Level”-Stage 2, sponsor: USFS; Co-I’s: S. Ustin (UC Davis), B. Schwind, B. Quayle (USFS RSAC).
2/2013 -1/2016. NASA-ROSES-2011, Co-Investigator; Solicitation: NNH11ZDA001N-HyspIRI “Identification of Plant Functional Types By Characterization of Canopy Chemistry Using an Automated Advanced Canopy Radiative Transfer Model”. PI: S. Ustin (UC Davis).
10/2011-6/2013. Institutional PI; “Evaluation / Enhancement of the GOES Early Fire Detection (GOES-EFD) System Supporting First Responders”. sponsor: Dept. Homeland Security, Sci & Technology Directorate. PI: V. Ambrosia (CSUMB), Co-I’s: S. Ustin.
5/2011-4/2014 Co-Investigator, “Near Real Time Science Processing Algorithm for Live Fuel Moisture Content for the MODIS Direct Readout System” S.Ustin-PI, sponsor: NASA Terrestrial Ecology Program.
2/2011-2/2012, Principal Investigator, “Toward monitoring the relationship between vegetation conditions and volcanic activity with HyspIRI”, sponsor: NASA; Co-I’s: S.Ustin (UC Davis), S. Businger, P. Mouginis-Mark (Univ. of Hawaii).
9/2010-9/2014, Principal Investigator, “Evaluating Operational Potential of Geostationary Early Fire Detection Capabilities at Regional Level”-Stage 1, sponsor: USFS; Co-I’s: S. Ustin (UC Davis), B. Schwind, B. Quayle (USFS RSAC).
Center for Spatial Technologies and Remote Sensing (CSTARS)
Dept. Land, Air, and Water Resources,
University of California
121 Veihmeyer Hall, One Shields Avenue
Davis, CA 95616
akoltunov ([at]) ucdavis dot edu
Khanna, S., M.J. Santos, A. Koltunov, K.D. Shapiro, M. Lay, and S.L. Ustin (2016), "Marsh loss due to cumulative impacts of hurricane Isaac and the Deepwater Horizon oil spill in Louisiana", Remote Sensing, in review.
Garcia, M., Saatchi, S., Casas, A., Koltunov, A., Ustin S. L., & Ramirez, C. (2016), Quantifying biomass consumption and carbon release by the California Rim fire integrating airborne LiDAR and Landsat-OLI data”, Remote Sensing of Environment, in review.
Tempel, D. J., Keane, J. J., Gutiérrez, R. J., Wolfe, J. D., Jones, G. M., Koltunov, A., Ramirez, C. M., Berigan, W. J., Gallagher, C. V., Munton, T. E., Shaklee, P. A., Whitmore, S. A., Peery, M. Z., (2016) "Meta-analysis of California Spotted Owl (Strix occidentalis occidentalis) territory occupancy in the Sierra Nevada: habitat associations and their implications for forest management", The Condor: Ornithological Applications 118: pp.747-765.
Koltunov, A., Ustin S. L., Quayle B., Schwind, B., Ambrosia, V. G., & Li, W. (2016) “The development and ﬁrst validation of the GOES Early Fire Detection (GOES- EFD) algorithm”, Remote Sensing of Environment, v. 184, pp. 436-453.
Casas, A., Garcia, M., Siegel R. B., Koltunov, A., Ramirez, C., & Ustin S. L. (2016), "Burned forest characterization at single-tree level with Airborne Laser Scanning for assessing wildlife habitat", Remote Sensing of Environment, v. 175, pp. 231-241
Gangodagamage, C., Foufoula-Georgiou, E., Brumby, S. P., Chartrand, R, Koltunov, A., Liu, D.,Cai, M., & Ustin S. L. (2016) “Wavelet-compressed representation of landscapes for hydrologic and geomorphologic applications”, IEEE Geoscience and Remote Sensing Letters, 13(4), 480-484.
Khanna, S., Santos, M. D., Ustin, S. L., Koltunov, A., Kokaly, R. F., Hestir, E. L, & Roberts, D. A. (2013) “Detection of Salt Marsh Vegetation Stress after the Deepwater Horizon BP Oil Spill along the Shoreline of Gulf of Mexico using AVIRIS Data”. PLoS One, 8(11): e78989.
Cheng, T., Riaño, D., Koltunov, A., Whiting, M. L., Ustin, S. L. & Rodriguez, J. (2013) "Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data", Remote Sensing of Environment, v.132, pp.1-12.
Koltunov A., Ustin, S. L., Prins, E (2012) “On timeliness and accuracy of wildfire detection by the GOES WF-ABBA algorithm over California during the 2006 fire season”, Remote Sensing of Environment, v. 127, December 2012, pp. 194–209.
Ustin, S.L., Riaño, D., Koltunov A., Roberts, D.A., Dennison P.E. (2009) “Mapping fire risk in Mediterranean ecosystems of California: vegetation type, density, invasive species, and fire frequency”. In Earth Observation of Wildland Fires in Mediterranean Ecosystems, E. Chuvieco (Ed.), Springer-Verlag Berlin Heidelberg, pp. 41-53.
Koltunov, A., Ustin S.L., Asner, G.P., Fung, I., (2009) “Selective logging changes forest phenology in the Brazilian Amazon: Evidence from MODIS images time series analysis”, Remote Sensing of Environment, doi:10.1016/j.rse.2009.07.005.
Koltunov, A., Ben-Dor, E., Ustin S.L. (2009) “Image construction using multitemporal observations and Dynamic Detection Models”, International Journal of Remote Sensing,v.30 (1) pp.57-83.
Koltunov, A., Ustin S.L. (2007) “Early fire detection using non-linear multitemporal prediction of thermal imagery”, Remote Sensing of Environment, v.110(1) pp.18-28.
Koltunov, A., Crouvi, O., Ben-Dor, E., (2006) “Geomorphologic mapping from hyperspectral data, using Gaussian mixtures and lower confidence bounds”, International Journal of Remote Sensing, v.27(20), pp.4545-4566.
Koltunov, A., Ben-Dor, E., (2004) “Mixture density separation as a tool for high-quality interpretation of multi-source remote sensing data and related issues”, International Journal of Remote Sensing, v.25(16), pp.3275-3299.
Koltunov, A., Ben-Dor, E., (2001) “A new approach for spectral feature extraction and for unsupervised classification of hyperspectral data based on the Gaussian mixture model”, Remote Sensing Reviews, v.20(2), pp. 123-167.