Lab of Environmental Modeling and Spatial Analysis at The Ohio State University
My research focuses on mapping, monitoring, modeling, and managing terrestrial environments across scales, especially in the context of global environmental changes. My research employs a combined toolset (e.g., geotechnology, spatial analysis, machine learning, biophysical & climate modeling, ecological modeling, Bayesian statistics, and eddy-covariance) to characterize the status and change in ecosystems, and examine biophysical and ecological responses of terrestrial ecosystems to disturbances and climate change. Example of my research questions include how much carbon forests store, where terrestrial ecosystems have been disturbed and why, how compositions of vegetation communities can be mapped over extensive areas from the air, how long-term vegetation activities have been driven by climate change, where and how forestry and land-use activities generate best climate regulation services, and how crop productivity may change in a warming or dry world? One of my particular emphases is to develop geospatial applications, using tools such as hyper spectral imaging, high-resolution imagery, hyper-temporal remote sensing, and lidar, to characterize ecosystem structure and functioning.
Prospective graduate students
I am looking for self-motivated individuals who want to pursue research in areas that generally align well with mine, especially those with quantitative and computational backgrounds who would like to apply such skills to address environmental issues related to either natural or human-dominated ecosystems. Although my current research is biased toward remote sensing, students without a remote sensing background are equally considered. Inquires for potential GRA opportunities are encouraged, together with a CV/resume, a list of previous coursework (or transcripts), and a brief research plan describing what research topics you like to pursue and how your background will fit my research program.
Ph.D. in Forestry, Texas A&M University, 2008
M.S. in Geography, Beijing Normal University, 2004
B.S. in Physics, Beijing Normal University, 2001
K. Zhao, & R. Jackson. 2014. Biophysical forcings of land-use changes from potential forestry activities in North America. Ecological Monographs, 84 (2), 329-353.
Y. Zhou, S. Smith, C. Elvidge, K. Zhao, A. Thomson, & M Imhoff. 2014. A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment 147, 173-185
R. Jackson, A. Down, N. Phillips, R. Ackley, C. Cook, D. Plata, & K. Zhao. 2014. Natural gas pipeline leaks across Washington, DC. Environmental Science & Technology, 48(3), 2051–2058 (Most-Read Papers).
X. Zhang, R. Sahajpal, D. Manowitz, K. Zhao, S. LeDuc, M. Xu, W. Xiong, A. Zhang, R. Izaurralde, A. Thomson, T. West, & W. Post. 2014. Geospatial agroecosystem modeling: a case study on the influence of soil data resolution on carbon flux estimate. Science of the Total Environment, 479:138-150.
K. Zhao, D. Valle, S. Popescu, X. Zhang, & B. Mallick. 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian Model averaging with variable and band aelection. Remote Sensing of Environment, 132:102-119 (BMA codes available upon request).
R. Jackson, A. Vengosh, S. Osborn, N. Warner, T. Darrah, A. Down, K. Zhao, & J. Karr. 2013. Increased stray-gas abundance in drinking water accompanying Marcellus Shale-gas extraction. Proceedings of the National Academy of Sciences, 110(28), 11250-11255 (Most Read Papers).
M. Gloor, L. Gatti, R. Brienen, T. R. Feldpausch, O. L. Phillips, J. Miller, J. P. Ometto, H. Rocha, T. Baker, Ben de Jong, S. Houghton, Y. Malhi, L. Aragao, J.-L. Guyot, K. Zhao, R. Jackson, P. Peylin, S. Sitch, B. Poulter, M. Lomas, S. Zaehle, C. Huntingford, J. Lloyd. 2012. The carbon balance of South America: status, decadal trends and main determinants. Biogeosciences, 9, 627-671.
M. García, S. Popescu, D. Riaño, K. Zhao, A. Neuenschwander, M. Mutlu, & E. Chuvieco. 2012. Characterization of canopy fuels using ICESat/GLAS data. Remote Sensing of Environment, 123, 81-89.
N. Warner, R. Jackson, T. Darrah, S. Osborn, A. Down, K. Zhao, A. White, & A. Vengosh. 2012. Geochemical evidence for possible natural migration of Marcellus formation brine to shallow aquifers in Pennsylvania. Proceedings of the National Academy of Sciences, 109(30):11961-11966 (Most-read article).
S. Popescu, K. Zhao, A. Neuenschwander, & C. Lin. 2011. Satellite Lidar vs. Small Footprint Airborne Lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sensing of Environment, 115:2786-2797 (Top 25 Hottest Articles).
K. Zhao, S. Popescu, X. Meng, Y. Pang, & M. Agca. 2011. Characterizing forest canopy structure with Lidar composite metrics and machine learning. Remote Sensing of Environment, 115:1979-1996 (Top 25 Hottest Articles).
D. Valle, J. Clark, & K. Zhao. 2011. Enhanced understanding of infectious diseases by fusing multiple datasets: A case study on Malaria in the western Brazilian Amazon region, PLoS ONE 6(11).
Y. Xie, K. Zhao, Y. Sun & D. Chen. 2010. Gaussian processes for short-term traffic volume forecasting, Transportation Research Record, 2165:69-78 (2010 TRB best statistical paper award).
X. Meng, N. Currit & K. Zhao. 2010. Ground filtering algorithms for airborne lidar data: A review of critical issues, Remote Sensing, 2(3):833-860 (Best 2014 Remote Sensing Paper Award).
K. Zhao, S. Popescu & R. Nelson. 2009. Lidar remote sensing of forest bomass: A scale-invariant approach using airborne lasers, Remote Sensing of Environment, 112:182-196 (Top 25 Hottest Articles, Most Cited RSE Papers since 2009).
K. Zhao & S. Popescu. 2009. Lidar-based mapping of leaf area index and its comparison with satellite GLOBCARBON LAI products, Remote Sensing of Environment, 113:1628-1645.
S. Popescu & K. Zhao. 2008. A voxel-based lidar method for estimating crown base height for deciduous and pine trees, Remote Sensing of Environment, 112(3): 767-781 (Most Cited RSE Papers since 2008).
M. Mutlu, S.Popescu, & K. Zhao. 2008. Sensitivity analysis of fire behavior modeling with lidar-derived surface fuel maps, Forest Ecology and Management, 256(2): 289-294.