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Title: Professor, Paul and Francena Miller Chair in International Education
Phone: (585) 475-7816
PhD, Environmental Engineering, Cornell University, 1990
MS, Environmental Engineering, Cornell University, 1985
BS, Chemistry, University of Wisconsin - Madison, 1981
Recent Invited Presentations
October 2016. Overview of imaging spectrometry activities at RIT. HSI2016. Coventry, England.
December 2015. Remote Sensing: Background and Applications. Izmir Katip Celebi University, Department of Geomatics Engineering. Izmir, Turkey.
June 2014. Innovative Methods for Agricultural Data Collection: A Remote Sensing Tutorial. The World Bank Development Impact Evaluation Workshop: Innovations for Agriculture, Kigali, Rwanda.
1. Monitoring Harmful Algal Blooms with Landsat 8, Funding source: USGS. John Schott is the PI on the Landsat Science Team. Under that project I am supervising a graduate student who is investigating the application of Landsat 8 to monitoring cyanobacteria blooms. We are using the Look Up Table and spectrum matching approach to assess accuracy for cyanobacteria bloom identification and quantification. We are also testing for band combinations that may be added to Landsat 10 to enhance the detection and monitoring of cyanobacteria blooms in inland and coastal waters.
2. DDDAS for Vehicle Tracking, Funding source: AFOSR. We are combining image analysis, vehicle motion models, crowdsourced data, and adaptive sensing in a dynamic data-driven application system (DDDAS) for object tracking. Our test data is high resolution video generated by DIRSIG. This project is ongoing, now with Matt Hoffman as PI. The following are some papers from this project:
Uzkent, B.; Hoffman, M.J.; Vodacek, A., 2016. Integrating Hyperspectral Likelihoods in a Multi-dimensional Assignment Algorithm for Aerial Vehicle Tracking. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9:4325-4333. doi:10.1109/JSTARS.2016.2560220.
Uzkent, B.; Hoffman, M.J.; Vodacek, A.; Chen, B., 2015. Feature Matching with an Adaptive Optical Sensor in a Ground Target Tracking System. IEEE Sensors Journal. 15:510-519. doi:10.1109/JSEN.2014.2346152.
Chen, B., A. Vodacek, and N. D. Cahill. 2013. A Novel Adaptive Scheme for Evaluating Spectral Similarity in High-resolution Urban Scenes. IEEE J. Selected Topics Appl. Earth Obs. Remote Sens. (JSTARS). 6:1376-1385. doi:10.1109/JSTARS.2013.2254702
Uzkent, B.; Hoffman, M.J.; Vodacek, A.; Kerekes, J.P.; Chen, B., 2013. Feature Matching and Adaptive Prediction Models in an Object Tracking DDDAS, Procedia Computer Science, 18, pp. 1939-1948. doi:10.1016/j.procs.2013.05.363
Chen, B., A. Vodacek, and N.D. Cahill. 2012. Novel spectral similarity measure for high resolution urban scenes. Proc. IEEE 2012 Geoscience and Remote Sensing Symposium (IGARSS). pp. 6637-6640. doi: 10.1109/IGARSS.2012.6352077
Vodacek, A., J.P. Kerekes, M.J. Hoffman. 2012. Adaptive Optical Sensing in an Object Tracking DDDAS, Procedia Computer Science, 9, pp. 1159-1166. doi:10.1016/j.procs.2012.04.125
Some Past Projects
1. Promoting spatial thinking in natural resource management through community mapping: the case of urban and rural secondary schools of Rwanda, Funding source: Innovation for Education (MINEDUC and UKAID). We are introducing spatial thinking into Rwandan high school curriculum in two target schools by teacher training and by providing mobile technology mapping applications to the students and encouraging them to explore and map their own communitites. Through rigorous assessment of student performance on spatial thinking assessments, we will quantify improvements in spatial thinking ability while also monitoring innovation by students and teachers alike. The following is our initial contribution from this project:
Tomaszewski, B.; Vodacek, A.; Parody, R.; Holt, N. 2015. Spatial Thinking Ability Assessment in Rwandan Secondary Schools: Baseline Results, Journal of Geography. 114(2):39-48, doi:10.1080/00221341.2014.918165.
A specific data product initiated by the Lake Kivu project funded by the MacArthur Foundation (see below) and continued with support by this project is a set of land cover classifications for the Lake Kivu Region. These classification maps were produced from the Landsat archive using a method for compositing a time series of images as a means to minimize cloud cover. The results are classification maps for 1988, 2001, and 2011 and can be downloaded at the links below. When unzipped these files contain a folder with a set of files suitable for ingestion and display of the classification map with GIS software. There is also a pdf version of the classification map. NOTE: These files were updated 11 May 2015 with the results from an improved classification, the addition of the pdf file, and minor changes to the legend. Please replace files downloaded prior to 11 May 2015 with these files.
2. Dynamics of the Lake Kivu System: Geological, Biological and Hydrographic Impacts on Biodiversity and Human Wellbeing, Funding source: John D. and Catherine T. MacArthur Foundation. A multidisciplinary of the past history and current status of the Lake Kivu System. The work combines baseline surveys including sediment cores, seismic profiling, earthquake monitoring, and terrestrial remote sensing to understand past change at a variety of spatial and temporal scales. From this new understanding of the dynamics of the system we gain a better understanding of system component interactions and potential scenarios for future change. The following are some published papers from this project:
Zhang, X., C.A. Scholz, R.E. Hecky, D.A. Wood, H.J. Zal, and C.J. Ebinger. 2014. Climatic control of the late Quaternary turbidite sedimentology of Lake Kivu, East Africa: Implications for deep mixing and geologic hazards. Geology, G35818.1, First published on July 25, 2014. doi:10.1130/G35818.1
Basnet, B., and A. Vodacek. 2014. Anthony, Monitoring the dynamics of land cover in the Lake Kivu region using multi-temporal Landsat imagery. 2014 International Geoscience and Remote Sensing Symposium, pp. 4250-4253, Quebec, Quebec, Canada. doi:10.1109/IGARSS.2014.6947427 [Note: This paper describes the methods used and classification error assessment for the 1988, 2001, and 2011 data sets].
Basnet, B., and A. Vodacek. 2012. Multitemporal Landsat Imagery Analysis to Study the Dynamics of Land Cover over Lake Kivu Region. Proc. EARSeL 1st Int. Workshop on Temporal Analysis of Satellite Images. http://www.earsel.org/SIG/timeseries/proceedings.php
A specific data product produced by this project and continued by the Innovation for Education project (see above) is a set of land cover classifications for the Lake Kivu Region. These classification maps were produced from the Landsat archive using a method for compositing a time series of images as a means to minimize cloud cover. The results are classification maps for 1988, 2001, and 2011 and can be downloaded at the links below. When unzipped these files contain a folder with a set of files suitable for ingestion and display of the classification map with GIS software. There is also a pdf version of the classification map. NOTE: These files were updated 11 May 2015 with the results from an improved classification, the addition of the pdf file, and minor changes to the legend. Please replace files downloaded prior to 11 May 2015 with these files. These are the same data sets provided as links under the Innovation for Education project section above.
3. Collaborative Research: ITR: DDDAS: Data Dynamic Simulation for Disaster Management, Funding source: NSF
The following are some papers produced from this project:
Wang, Z., A. Vodacek, and J. Coen. 2009. Generation of synthetic infrared remote-sensing scenes of wildland fire. International Journal of Wildland Fire. 18:302-309. doi:10.1071/WF08089
Ononye, A., A. Vodacek, and E. Saber. 2007. Automated Extraction of Fire Line Parameters from Multispectral Infrared Images. Remote Sensing of Environment. 108:179-188. doi:10.1016/j.rse.2006.09.029
Li, Y., A. Vodacek, and Y. Zhu. 2007. An automatic statistical segmentation algorithm for extraction of fire and smoke regions. Remote Sensing of Environment. 108:171-178. doi:10.1016/j.rse.2006.10.023
4. Forest Fires Imaging Experimental System (FIRES)
Some project papers (see below for my full list of publications for others):
Li, Y., A. Vodacek, R.L. Kremens, A.E. Ononye, and C. Tang. 2005. A hybrid contextual approach to wildland fire detection using multispectral imagery., IEEE Trans. Geosci. Remote Sens. 43:2115-2126.
Vodacek, A., R.L. Kremens, A.J. Fordham, S.C. VanGorden, D. Luisi, J.R. Schott, and D.J. Latham. 2002. Remote optical detection of biomass burning using a potassium emission signature. International Journal of Remote Sensing, 23:2721-2726.
Current graduate students and research topic
Ryan Ford, (PhD), Assessing the capabilities of Landsat-8 for monitoring harmful algal blooms.
Former graduate students, thesis/project titles, and present position
Bikash Basnet, PhD 2016, Monitoring Cloud-prone Complex Landscapes At Multiple Spatial Scales Using Medium And High Resolution Optical Data: A Case Study In Central Africa
Donath Uwanyirigira, MS 2016, Assessment of the Rwanda Rural Road Network Development Project Using Pan-Sharpened Landsat-8 Data.
Bin Chen, PhD 2015, Multispectral Image Road Extraction Based Upon Automated Map Conflation. OmniVision Technologies.
Alvin Spivey, PhD 2011, Multiple Scale Landscape Pattern Index Interpretation for the Persistent Monitoring of Land-Cover and Land-Use. Exelis.
Geoff Franz, MS 2009, Calibration of a Picosecond Camera for Use in a Lawer-Based Spectrofluorometer, DRS Technologies
Shari McNamara, MS 2007, Using Multispectral Sensor WASP-Lite to Analyze Harmful Algal Blooms, ITT
Zhen Wang, PhD 2007, Modeling Wildland Fire Radiance in Synthetic Remote Sensing Scenes, Consultant
Yan Li, PhD 2007, An Integrated Water Quality Modeling System with Dynamic Remote Sensing Feedback, Rapiscan Systems.
Ying Li, PhD 2006, Remote Sensing Algorithm Development for Wildland Fire Detection and Mapping, GE Healthcare.
Gretchen Sprehe, MS 2005, Application of Phenology to Assist in Hyperspectral Species Classification of a Northern Hardwood Forest, NGA.
Andrew Fordham, MS 2002, Band Selection and Algorithm Development for Remote Sensing of Wildfires, The SI.
Nikole Wilson, MS 2000, Hyperspectral Imaging for Bottom Type Classification and Water Depth Determination, Dr. of Veterinary Medicine
Kirk Knobelspiesse, MS 2000, Atmospheric Compensation for SeaWiFS Images of Lake Superior Utilizing Spatial Information, NASA-ORAU Postdoctoral Program
Former postdoctoral advisee, research area, and present position
Ambrose Ononye, Feature extraction of wildland fire parameters, Rock-Pone Technologies
- IEEE - Senior Member (2013)