Dr. Anita Simic


Position: Assistant Professor
Phone: 419-372-4035
Email: asimic@bgsu.edu
Address: 190 Overman Hall

Education & Experience:

PhD  University of Toronto, 2005 - 2009
MS    University of Toronto, 2000 - 2002
HBS. University of Toronto, 1994 – 1998

• Bowling Green State University - School of Earth, Environment and Society – Assistant Professor, 2013 - present
• University of Victoria, Canada - Limited Term Senior Instructor, 2012
• Institut National de la Recherche Agronomique (INRA), France - Postdoctoral position, 2010 – 2012
• University of Toronto, Canada - Research Associate, 2005; Sessional Instructor, 2005 - 2010
• Ryerson University, Canada -- Sessional Instructor, 2005-2016
• Wuhan University, China -- Sessional Instructor 2009
• Canada Centre for Remote Sensing, Canada (CCRS) -- Research Scientist 2002-2005
• O’Connor Associates Environmental Inc., Canada-- Geoscience Project Manager, 1997 - 2000

Specialty Areas of Interest:

Remote sensing and GIS applications
Vegetation and soil science
Hydrology and water resources
Bioenergy sustainability
Environmental resources mapping and modeling

Current BGSU Courses:

Remote Sensing of Environment - Applied Remote Sensing - Quantitative Methods in Geology - GIS - Environmental Studies

Recent and Current Research or Grants:

NOAA / ODNR / Univeristy of Toledo USA

America View / USGS USA

Bowling Green State University Ohio

World Bank (WB) and Ministry of Science and Education Croatia

European Space Agency / Canada Space Agency

Recent Peer Reviewed Publications and Submissions:

·      BALENOVIC I, SIMIC MILAS A, MARJANOVIC H. 2017. A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions. Remote Sensing, http://www.mdpi.com/2072-4292/9/3/205.

·      JIAO S, YU J, SIMIC MILAS A, LI X, LIU L. 2017. Assessing the impact of building volume on land subsidence in the central business district of Beijing with SAR tomography. Canadian Journal of Remote Sensing, http://dx.doi.org/10.1080/07038992.2017.1291335.

·      SIMIC MILAS A, K AREND, C MAYER, MA SIMONSON, S MACKEY. 2017. Different Colors of Shadows: Classification of UAV images. International Journal of Remote Sensing, in press, http://dx.doi.org/10.1080/01431161.2016.1274449.

·      Lekki et al. 2017. Airborne Hyperspectral Sensing of Monitoring Harmful Algal Blooms in the Great Lakes Region: System Calibration and Validation, NASA Technical Report: https://ntrs.nasa.gov/search.jsp?R=20170002298.

·      SIMIC MILAS A, RK VINCENT. 2016. Monitoring Landsat vegetation indices for different crop treatments and soil chemistry. International Journal of Remote Sensing, 38(1): 141-160. DOI: 10.1080/01431161.2016.1259680.

·      ILANGAKOON NT, GORSEVSKI PV, SIMIC MILAS, A. 2015. Estimating Leaf Area Index by Bayesian Linear Regression Using Terrestrial LiDAR, LAI-2200 Plant Canopy Analyzer, and Landsat TM Spectral Indices. Canadian Journal of Remote Sensing, 41, 315–333.

·      SIMIC MILAS A. 2015. Variability in the fraction of intercepted photosynthetically active radiation: Effects of irradiance conditions and temporal scales. Journal of Global Ecology and Environment, 2, (3), 140-154.

·      GORSEVSKI PV, BROWN KM, PANTER K, ONASCH CM, SIMIC A, SNYDER J. 2015. Landslide Detection and Susceptibility Mapping using LiDAR and Artificial Neural Network Approach: a Case Study in the Cuyahoga Valley National Park, Ohio. Landslides, DOI: 10.1007/s10346-015-0587-0 (Print ISSN 1612-510X; Online ISSN 1612-5118).

·      SIMIC MILAS A, RUPASINGHE P, BALENOVIC I, GORSEVSKI P. 2015. Assessment of Forest Damage in the mountainous area of Croatia using Landsat-8. SEEFOR, 6(2), e1-e11. DOI: http://dx.doi.org/10.15177/seefor.15-14.

·      CROFT H, CHEN MJ, ZHANG Y, SIMIC A, NOLAND T, NESBITT N. 2015. Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework. ISPRS Journal of Photogrammetry and Remote Sensing, 102, 85-95.

·      SIMIC A, FERNANDES R, WANG S. 2014. Assessing the impact of leaf area index on evapotranspiration and groundwater recharge across a shallow water region for diverse land cover and soil properties. Journal of Water Resources and Hydraulic Engineering, 3, (4), 60-73.

SIMIC, A., CHEN, J., LEBLANC, G. S., DYK, A., CROFT, H. and HAN, T. (2013) Testing the top-down model inversion method of estimating leaf reflectance used to retrieve vegetation biochemical content within the empirical approach. IEEE JSTARS (DOI: 10.1109/JSTARS.2013.2271583).

CROFT, H., CHEN, J.M., ZHANG, Y. and SIMIC, A. (2013). Modelling leaf chlorophyll content in broadleaf and needle canopies from ground, Landsat TM 5 and MERIS reflectance data. Remote Sensing of Environment, 133, 128-140.

SIMIC, A., CHEN, J.M. and NOLAND, T.L. (2011). Retrieving leaf chlorophyll content with improved structural parameters: an analysis of the refined concept of combining hyperspectral and multi-angle data, International Journal of Remote Sensing, 32 (20), 5621-5644.

PISEK, J., CHEN, J.M., MILLER, J.R., FREEMANTLE, J.R., PELTONIEMI, J.I. and SIMIC, A. (2010). Mapping forest background reflectance in a boreal region using multi-angle Compact Airborne Spectrographic Imager (CASI) data, IEEE Transactions on Geosciences and Remote Sensing, 48 (1, 2), 499-510.

SIMIC, A., CHEN, J.M., FREEMANTLE, J., MILLER, J.R. and PISEK, J. (2010). Improving clumping and LAI algorithms based on multi-angle airborne and ground measurements, IEEE Transactions on Geosciences and Remote Sensing, 48 (4, 1), 1742-1759.

SIMIC, A. and CHEN, J.M. (2008). Refining a Hyperspectral and Multi-angle concept for vegetation parameters assessment, Canadian Journal for Remote Sensing, 34-3, 174-191.

SIMIC, A., CHEN, J.M., LIU, J. and CSILLAG, F. (2004). Spatial scaling of net primary productivity using sub-pixel information, Remote Sensing of Environment, 93-1, 246-258.

SIMIC, A., FERNANDES, R., BROWN, R., ROMANOV, P. and PARK, W. (2004). Validation of Vegetation, Modis and GOES+SSM/I snow cover products over Canada based on surface snow depth observations, Hydrological Processes, 18, 1089-1104.