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Anthony Vodacek is a professor of imaging science at the Rochester Institute of Technology. He is affiliated with the Digital Imaging and Remote Sensing Laboratory (DIRS). As a remote sensing scientist he has expertise in a varierty of environmental remote sensing topics such as land cover/land cover change mapping and water quality monitoring with medium resolution scale imagery. The environmental applications of this work are primarily through integrated sensing and modeling systems. Much of his recent research has been situated in Rwanda. Other research areas are fluorescence lidar for water quality assessment, and near infrared and thermal infrared sensing for wildland fire detection, fine resolution urban sensing, and education in spatial thinking skills. He is an Associate Editor for the Journal of Great Lakes Research and from 2016 through 2017 held the Paul and Francena Miller Chair in International Education for the RIT College of Science. He is currently Leader Facutly for Study Abroad and International Education in the College of Science. Dr. Vodacek is on the Fulbright Specialist roster from 17 August 2018 to 17 August 2021.
PhD, Environmental Engineering, Cornell University, 1990
MS, Environmental Engineering, Cornell University, 1985
BS, Chemistry, University of Wisconsin - Madison, 1981
News Links (2017)
Recent Invited Presentations
January 2018. Remote Sensing of Lake Kivu whiting events to trace surface circulation. University of Rwanda, Kigali, Rwanda
July 2017. Plenary: Remote sensing of vegetation variables from drones: spectral, spatial, and temporal approaches. University of Kibungo International Scientific Conference. Rwamagana, Rwanda
June 2017. Keynote: Remote sensing data for biodiversity conservation and natural resource management: spectral, spatial, and temporal approaches. University of Rwanda Scientific Conference Week. Kigali, Rwanda
October 2016. Keynote: Overview of imaging spectrometry activities at RIT. HSI2016. Coventry, England
1. Monitoring Harmful Algal Blooms with Landsat 8, Funding source: USGS. John Schott was a PI on the Landsat Science Team. Under that project I supervised a graduate student who is continuing to investigate 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 and employing the Random Forest classifier. 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.
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:
Basnet, B., and A. Vodacek. 2015. Tracking land use/land cover dynamics in cloud prone areas using moderate resolution satellite data: A case study in Central Africa. Remote Sensing. 2015, 7(6), 6683-6709; doi:10.3390/rs70606683
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. 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 and employing the Random Forest classifier. 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. 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
Peace Bamurigire, (PhD, University of Rwanda), IoT based water and moisture level monitoring system in smart rice farming
Thadee Gatera, (PhD, University of Rwanda), Improved fish farming in Rwanda using fusioned-intelligent sensors
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. Monsanto
Donath Uwanyirigira, MS 2016, Assessment of the Rwanda Rural Road Network Development Project Using Pan-Sharpened Landsat-8 Data. University of Rwanda
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. Lockheed Martin
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, Exelis
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, Vencore
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 Goddard Space Flight Center
Former postdoctoral advisee, research area, and present position
Ambrose Ononye, Feature extraction of wildland fire parameters, Rock-Pone Technologies
IEEE - Senior Member (2013)
Fulbright Specialist (17 August 2018 - 17 August 2021)