People / Profile

Anthony Vodacek

Contact Information

Title: Professor
Phone: (585) 475-7816
Office: CAR-3258


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 an Academic Editor for PLOS ONE. From 2016 through 2017 held the Paul and Francena Miller Chair in International Education for the RIT College of Science. He is currently Leader Faculty 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 2022.


PhD, Environmental Engineering, Cornell University, 1990
MS, Environmental Engineering, Cornell University, 1985
BS, Chemistry, University of Wisconsin - Madison, 1981

Other details

News Links (2017)

Collaboration strengthens Rwanda through education

Drone might help Nazareth, teens ID algae entry points into waterways


Recent Invited Presentations

July 2020. Invited: Multi-Modal Sensing in Ranomafana National Park, Madagascar: Acoustic Soundscape and Lidar Structure. Delivered online, Center of Excellence in Biodiversity and Natural Resource Management, University of 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


Current Projects

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.

1988 data (zip file, 16.0MB)

2001 data (zip file, 13.6MB)

2011 data (zip file,  10.9MB)


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.

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.

1988 data (zip file, 16.0MB)

2001 data (zip file, 13.6MB)

2011 data (zip file,  10.9MB)


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

Zach Mulhollan, (PhD), Methods for rapid scene simulation and updates in DIRSIG using Dynamic Data Driven Applications Systems concepts

Peace Bamurigire, (PhD, University of Rwanda), IoT based water and moisture level monitoring system in smart rice farming

Christine Musanase, (PhD, University of Rwanda), IoT for soil characterizatio in Rwanda

Fidele Maniraguha, (PhD, University of Rwanda), Weather radar detection of flying insect agricultural pests

Former graduate students, thesis/project titles, and present position

Ryan Ford, PhD 2019, Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms

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


Professional activities

IEEE - Senior Member (2013)

Fulbright Specialist (On the roster 17 August 2018 - 17 August 2022). Served at Universiti Abdul Tunku Rahman Sungai Long Campus in Kuala Lumpur, Malaysia May/June 2019. Project title: Novel Intelligent Video Analytics To Improve CCTV System. Project host: Prof. Tee Yee Kai.