Jan van Aardt Headshot

Jan van Aardt

Director of Carlson Center for Imaging Science

Chester F. Carlson Center for Imaging Science
College of Science
jvacis@rit.edu
585-475-4229
Office Location
CAR-3124
Office Mailing Address
Chester F. Carlson Center for Imaging Science Building 8-3124 (CAR-3124)

Jan van Aardt

Director of Carlson Center for Imaging Science

Chester F. Carlson Center for Imaging Science
College of Science

Education

BSc, University of Stellenbosch (South Africa); MS, Ph.D., Virginia Polytechnic Institute and State University

Bio

Dr. Jan van Aardt obtained a BSc Forestry degree (biometry and silviculture specialization) from the University of Stellenbosch, South Africa. This was followed by a Hons. Forestry degree with a remote sensing and Geographical Information Systems (GIS) specialization, also from the University of Stellenbosch. Jan then completed MS and PhD Forestry degrees at Virginia Polytechnic Institute and State University, Blacksburg, Virginia - these degrees respectively focused on imaging spectroscopy (hyperspectral) and light detection and ranging (lidar) applications in forestry. Hyperspectral, lidar, and multi-temporal sensing form the core of his efforts, with various ecosystem and forestry projects, e.g., land quality and global change (multi-temporal), forest and savanna structural assessment using discrete and waveform lidar systems, and estimation of foliar chemistry and vegetation state (hyperspectral). He is a professor in the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology, following stints at the Katholieke Universiteit Leuven as post-doc and the Council for Scientific and Industrial Research, South Africa, as research group leader.

Areas of Expertise
Remote Sensing
Imaging Algorithms
Imaging Spectroscopy
Hyperspectral Sensing
Lidar
Vegetation Traits
Vegetation Structure
Vegetation Physiology
Precision Agriculture
Forestry
Unmanned Aerial Systems

Select Scholarship

Journal Paper
Saif, Mohammed, et al. "Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery." Remote Sensing 15. (2023): 21. Web.
Zhang, Fei, et al. "Evaluation of Leaf Area Index (LAI) of Broadacre Crops Using UAS-Based LiDAR Point Clouds and Multispectral Imagery." IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15. (2022): 18. Web.
Hassanzadeh, Amirhossein, et al. "Toward Crop Maturity Assessment via UAS-Based Imaging Spectroscopy—A Snap Bean Pod Size Classification Field Study." IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60. (2021): 17. Web.
Published Conference Proceedings
Zhang, Fei, et al. "WHITE MOLD AND WEED DETECTION IN SNAP BEANS USING UAS-BASED LIDAR." Proceedings of the IEEE IGARSS. Ed. N/A. Kuala Lumpur, Malaysia: n.p., Web.
Zhang, Fei, et al. "Toward a Structural Description of Row Crops Using UAS-based Lidar Point Clouds." Proceedings of the Proceedings of the International Geoscience and Remote Sensing Symposium; 4p.; September 29, 2020 (online/virtual; due to COVID-19). Ed. N/A. Online, Online: n.p., Web.
Hassanzadeh, Amirhossein, et al. "Toward Maturity Assessment of Snap Bbean Crops: A Best-case Greenhouse Scenario." Proceedings of the Proceedings of the International Geoscience and Remote Sensing Symposium; 4p.; September 29, 2020 (online/virtual; due to COVID-19). Ed. N/A. Online, Online: n.p., Web.
Journal Editor
Aardt, Jan van, ed. IEEE Transcations on Geoscience and Remote Sensing ( (Associate. Editor - LiDAR). New York: IEEE, 2020. Web.
Aardt, Jan A. van, ed. IEEE Transactions on Geoscience and Remote Sensing. New York: IEEE, 2019. Web.
External Scholarly Fellowships/National Review Committee
6/1/2017 -12/31/2021
     Technical Working Group, Airborne Sampling Design; National Ecological Observatory Network (NEON)
     Amount: 0
6/1/2017 -12/31/2021
     oTechnical Working Group, Lidar; National Ecological Observatory Network (NEON)
     Amount: 0
1/1/2017 -12/31/2020
     National Ecological Observation Network (NEON)
     Amount: 0
Invited Keynote/Presentation
Aardt, Jan van, et al. "Transforming Disease Management Through the use of Unmanned Aerial Systems." International Congress of Plant Pathology (ICPP) 2018: Plant Health in A Global Economy. ICCP. Boston, MA. 29 Jul. 2018. Conference Presentation.
Book Chapter
Aardt, Jan A. van, et al. "Localisation of Biomass Potentials." Bioenergy from Wood: Sustainable Production in the Tropics, Managing Forest Ecosystems 26. Ed. T. Seifert. Dordrecht, The Netherlands: Springer, 2014. Print.
Aardt, Jan A. van, et al. "Assessing Degradation Across a Land Use Gradient in the Kruger National Park Area Using Advanced Remote Sensing Modalities." Observations on Environmental Change in South Africa. Stellenbosch, Western Cape, South Africa: Sun Press, 2011. 97-103. Print.
Aardt, Jan A. van, et al. "Ecosystems: Case Studies in Capital Intensive Crops Towards System Modeling of Ecosystems Using Integrated Hyperspectral Remote Sensing and In Situ Inputs." Observations on Environmental Change in South Africa. Stellenbosch, Western Cape, South Africa: Sun Press, 2011. 116-123. Print.
Published Article
Cho, M.A., P. Debba,R. Mathieu, L. Naidoo, J.A. van Aardt, and G.P. Asner. “Improving discrimination of savanna tree species through a multiple endmember spectral angle mapperapproach: Canopy-level analysis.” IEEE Transactions on Geoscience and Remote Sensing, 48.11 (2010): 4133-4142. Print. £
Somers, B., S. Delalieux, W. Verstraeten, J.A. van Aardt, G. Albrigo, and P. Coppin. “An automated waveband selection technique for optimizedhyperspectral mixture analysis.” International Journal of Remote Sensing, 31.20 (2010): 5549-5568. Print. £
Cho, M.A., J.A. van Aardt, R. Main, and B. Majeke. “Evaluating variations of physiology-based hyperspectral features along a soil water gradient ina Eucalyptus grandis plantation.” International Journal of Remote Sensing, 31.12 (2010): 3143-3159. Print. £
Formal Presentation
Van Aardt, J.A., J. Wu, J. Fisher, B.F. Erasmus, K. Wessels, R. Mathieu, G.P Asner, T. Kennedy-Bowdoin, and D. Knapp. “Comparing discrete return-to waveform lidar data for vegetation structural assessment: A contemporaneous, small-footprint study ina savanna ecosystem.” American Society for Photogrammetric Engineering and Remote Sensing (ASPRS), ASPRS 2010 Annual Conference. San Diego, CA. April 2010. Presentation. " 
Van Aardt, J.A., D. McKeown, A. Vodacek, S. Duvvuri, A. Pillai, C. Renschler, J.W. Faulring, H. Bischoff, H. Collins, and D. Boyd. “Information Products Laboratory for Emergency Response: Rapid Turnaround Geospatial Disaster Management Products with Fire and Earthquake Response as Case Studies.” American Society for Photogrammetric Engineering and Remote Sensing (ASPRS), ASPRS 2010 Annual Conference. San Diego, CA. April 2010. Presentation. " 
Van Aardt, J.A., M. Arthur, G. Carmean, R.L. Kremens, J.W. Faulring, and D. McKeown. “A Sampling Approach to Forest Fuel Load Assessment Across Different Fire Regimes in Eastern Deciduous Forests using Small-footprint Discrete Return Lidar.” American Society for PhotogrammetricEngineering and Remote Sensing (ASPRS),ASPRS 2010 Annual Conference. San Diego, CA. April 2010. Presentation.

Currently Teaching

ENVS-795
Environmental Science Graduate Research
1 - 4 Credits
This course is a graduate level, faculty-directed, student project or research involving laboratory or field work, computer modeling, or theoretical calculations that could be considered of an original nature. The level of study is appropriate for students in Environmental Science graduate program.
ENVS-798
Advanced Environmental Science Independent Study
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in the Environmental Science graduate program.
IMGS-532
Advanced Environmental Applications of Remote Sensing
3 Credits
This course will focus on a broader selection of analytical techniques with an application-centric presentation. These techniques include narrow-band indices, filtering in the spatial and frequency domains, principal component analysis, textural analysis, hybrid and object-oriented classifiers, change detection methods, and structural analysis. All of these techniques are applied to assessment of natural resources. Sensing modalities include imaging spectroscopy (hyperspectral), multispectral, and light detection and ranging (lidar) sensors. Applications such as vegetation stress assessment, foliar biochemistry, advanced image classification for land use purposes, detecting change between image scenes, and assessing topography and structure in forestry and grassland ecosystems (volume, biomass, biodiversity) and built environments will be examined. Real-world remote sensing and field data from international, US, and local sources are used throughout this course.
IMGS-632
Advanced Environmental Applications of Remote Sensing
3 Credits
This course will focus on a broader selection of analytical techniques with an application-centric presentation. These techniques include narrow-band indices, filtering in the spatial and frequency domains, principal component analysis, textural analysis, hybrid and object-oriented classifiers, change detection methods, and structural analysis. All of these techniques are applied to assessment of natural resources. Sensing modalities include imaging spectroscopy (hyperspectral), multispectral, and light detection and ranging (lidar) sensors. Applications such as vegetation stress assessment, foliar biochemistry, advanced image classification for land use purposes, detecting change between image scenes, and assessing topography and structure in forestry and grassland ecosystems (volume, biomass, biodiversity) and built environments will be examined. Real-world remote sensing and field data from international, US, and local sources are used throughout this course. Students will be expected to perform a more comprehensive final project and homework assignments, including literature review and discussion and interpretation of results.
IMGS-699
Imaging Science Graduate Co-op
0 Credits
This course is a cooperative education experience for graduate imaging science students.
IMGS-790
Research & Thesis
1 - 6 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-791
Continuation of Thesis
0 Credits
Continuation of Thesis
IMGS-799
Imaging Science Independent Study
1 - 4 Credits
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their graduate studies.
IMGS-890
Research & Thesis
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-891
Continuation of Thesis
0 Credits
Continuation of Thesis

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