Rate My Professor Jan van Aardt

Rochester Institute of Technology

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5.04/30/2025

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About Jan van

Dr. Jan van Aardt serves as Professor and Director of the Chester F. Carlson Center for Imaging Science in the Dean’s Office of the College of Science at Rochester Institute of Technology. He holds a BSc in Forestry specializing in biometry and silviculture, and an Honours degree in Forestry with a focus on remote sensing and Geographical Information Systems from the University of Stellenbosch, South Africa. He earned his MS and PhD in Forestry from Virginia Polytechnic Institute and State University in Blacksburg, Virginia, with research centered on imaging spectroscopy (hyperspectral) and light detection and ranging (LiDAR) applications in forestry.

Before joining RIT, Dr. van Aardt was a postdoctoral researcher at Katholieke Universiteit Leuven in Belgium and a research group leader at the Council for Scientific and Industrial Research in South Africa. His research expertise encompasses hyperspectral sensing, LiDAR, and multi-temporal remote sensing for ecosystem and forestry applications. These include assessments of land quality and global change through multi-temporal analysis, structural evaluation of forests and savannas using discrete and waveform LiDAR systems, and estimation of foliar chemistry and vegetation state via hyperspectral methods. Additional interests involve imaging algorithms, vegetation traits and structure, vegetation physiology, precision agriculture, forestry, and unmanned aerial systems.

Dr. van Aardt has authored numerous publications, including “Assessment of image fusion procedures using entropy, image quality, and multispectral classification” (Journal of Applied Remote Sensing, 2008), “Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system” (Remote Sensing of Environment, 2012), “Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications” (European Journal of Agronomy, 2007), “Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery” (Remote Sensing, 2023), and “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, 2022). He received the 2021-2022 Eisenhart Award for Outstanding Teaching at RIT. He serves as Associate Editor for the LiDAR section of IEEE Transactions on Geoscience and Remote Sensing (2019-2020) and contributed to the National Ecological Observatory Network (NEON) Technical Working Group for Airborne Sampling Design and LiDAR (2017-2021).