Daehyun Kim

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  • Associate Professor of Physical Geography
  • Executive Committee of Earth Surface Systems Joint Graduate Program
  • Geography
853 Patterson Office Tower
(859) 257-6057
Research Interests:

Ph.D., Texas A&M University, 2009
M.A., Seoul National University, 2004
B.A., Seoul National University, 2000


Daehyun’s research focus is bio–geophysical complexity in which spatial and temporal patterns of vegetation and soil are molded in dynamic, complex relationships with landform, climate, and hydrology. His overarching goal is to develop both conceptual and simulation models that articulate how complex systems reciprocal interactions create biogeographic and biogeomorphic patterns.

He has pursued this goal through the use of geographic information systems that allowed him to analyze and visualize dynamics of natural resources observed through field-based studies. Such analysis and visualization involved predictive vegetation/soil mapping, digital terrain modeling, and spatial/multivariate statistics.

One of his ongoing research projects is the investigation of long-term dynamics of salt marsh vegetation responding to changes in environmental conditions in collaboration with David Cairns at Texas A&M and Jesper Bartholdy at the University of Copenhagen. They are in an unusual situation to work with floristic, sedimentological, hydrological, and climatic data acquired from a Danish salt marsh since the 1930s.

Since he joined UK in 2009, Daehyun has initiated research projects concerning

  • forest community structure changes responding to repeated prescribed fire in the Daniel Boone National Forest, eastern Kentucky (with Mary Arthur in the Department of Forestry)
  • soil–landform modeling using digital terrain analysis and spatial statistics (with Yanbing Zheng in the Department of Statistics and Tom Mueller in Deere & Company)
  • long-term vegetation dynamics in the forests of the UK Arboretum and the Raven Run Nature Sanctuary since 1980s (with Julian Campbell in the Bluegrass Woodland Restoration Center)

The following ten items clearly represent Daehyun's recent major interests:

Scale-dependence; Biogeomorphic feedback; Spatial autocorrelation; GIS-based spatial analysis; Soil–landform modeling; Sea-level variation; Disturbance; Complexity; Spatial heterogeneity; Ecological multivariate analysis

Selected Publications: 

Kim, D. & Shin, Y.H. (2016) Spatial autocorrelation potentially indicates the degree of changes in the predictive power of environmental factors for plant diversity. Ecological Indicators 60: 1130–1141. 

Kim, D., DeWitt, T.J., Costa, C.S.B., Kupfer, J.A., McEwan, R.W. & Stallins, J.A. (2015) Beyond bivariate correlations: Three-block partial least squares illustrated with vegetation, soil, and topography. Ecosphere 6: Article #135. 

Kim, D. & Arthur, M.A. (2014) Changes in community structure and species–landform relationship after repeated fire disturbance in an oak-dominated temperate forest. Ecological Research 29: 661–671. 

Kim, D., Cairns, D.M. & Bartholdy, J. (2013) Tidal creek morphology and sediment type influence spatial trends in salt marsh vegetation. The Professional Geographer 65: 544–560 (Winner of 2012 J. Warren Nystrom Award from the Association of American Geographers). 

Kim, D., Cairns, D.M., Bartholdy, J. & Morgan, C.L.S. (2012) Scale-dependent correspondence of floristic and edaphic gradients across salt marsh creeks. Annals of the Association of American Geographers 102: 276–294. 

Kim, D. & Zheng, Y. (2011) Scale-dependent predictability of DEM-based landform attributes for soil spatial variability in a coastal dune system. Geoderma 164: 181–194. 

Kim, D., Cairns, D.M. & Bartholdy, J. (2010) Environmental controls on multiscale spatial pattern of salt marsh vegetation. Physical Geography 31: 58–78. 

Kim, D. & Yu, K.B. (2009) A conceptual model of coastal dune ecology synthesizing spatial gradients of vegetation, soil, and geomorphology. Plant Ecology 202: 135–148. 

Kim, D., Yu, K.B. & Park, S.J. (2008) Identification and visualization of complex spatial pattern of coastal dune soil properties using GIS-based terrain analysis and geostatistics. Journal of Coastal Research 24(4C): 50–60. 

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