Training and Advising

Supervising and Teaching at FAU

Graduate Student Mentoring

Donna Selch (Ph.D., Graduated in summer 2016): Donna’s dissertation research focuses on water quality monitoring and modeling in the Florida Everglades using remote sensing and GIS techniques. Donna is an Assistant Professor at Coast Guard Academy.

Nicole Gamboa (M.S., graduated in Spring 2016): Now with Sigma Space. Nicole applied RS and GIS to map FAU campus using lidar and aerial photography.

Hannah Cooper (Ph.D., graduated in Summer 2018): Hannah’s research focuses on application of GIS/remote sensing in sea level rise and coastal mapping. Hannah joined East Carolina University as an Assistant Professor after graduation.

Jing Liu (Ph.D., graduated Summer 2020): Jing’s research focuses on modeling sediment accretion in coastal wetlands using SET and remote sensing data.

Sara Denka (Ph.D., graduated Spring 2020): Sara’s research focuses on drone application in coastal wetlands. She is a drone expert working as a scientist at AECOM.

Molly Smith (Ph.D., graduated Summer 2020): Molly’s dissertation focuses on geological and spectroscopic techniques for sand analyses. Molly is with Geosyntec Consultant.

Heather Nicholson (graduated Summer 2022): Heather’s research focuses on application of remote sensing in coastal marshes.

David Brodylo (Graduated Summer 2023): David’s research focuses on permafrost mapping and modeling using remote sensing and machine learning. 

Mizanur Rahman (PhD, Fall 2020): developing deep learning techniques for wetland mapping.

Abdullah Al Fazari (PhD, Fall 2022): wetland flux modeling and mapping.

Fiona Benzi (PhD, Fall  2023): Drone mapping

Sandip Rijal (PhD, Spring 2023): watershed flood mapping

David Ramirez (MS, Spring 2023): watershed flood mapping

Madan Thapa Chhetri (MS, Spring 2023): watershed flood mapping  


Hyperspectral Remote Sensing, Fall 2010 – present (Syllabus).


This course introduces state-of-the-art techniques for the processing and interpretation of hyper- and ultra-spectral data with a focus on thematic information extraction from airborne and spaceborne hyperspectral sensors. The course will cover the full hyperspectral remote sensing processing chain: data acquisition, data processing, and thematic mapping. This course is now conducted fully on-line.

Example projects previous students conducted:

1)       Hilton A. Cordoba: The Effects of Water on Soil Spectra.

2)       D. J. Forbes: A CO2 Sustainability Index Based on Night Time Hyperspectral Remote Sensing.

3)       Christine Mitchell: Comparing Classification and Assessment within ENVI.

4)       Tom Kenny: Differentiating Bermudagrass from Vegetation in an Urban Scene Using Hyperspectral Imagery

5)       John G. Zahina-Ramos: The Potential Application of Hyperspectral Data for mapping hydrologic and Topographic variability: A Test of Concept.

6)       Donna Selch: Spectrum Analysis of Salinity in Clean Water.


Photogrammetry and Aerial Photo Interpretation, Spring 2011- present (Syllabus). 80% is on-line.


This course introduces concepts, theories and applications of photogrammetry. It will cover history, principle, interpretation, geometry, stereoscopy of aerial photography, and fundamentals of analytical photogrammetry. Students will learn state-of-art techniques for digital orthophoto production using Leica Photogrammetry Suite (LPS) for ERDAS IMAGINE, and go through a sequence of hands-on soft-copy photogrammetric procedures and image interpretation labs. Software packages including ERDAS IMAGINE, Stereo Analyst, and ArcGIS will also be used for this class. There is no prerequisite for this class, but students need to have basics for math calculations and high school algebra. This course is mixing/on-line (80% is on-line).


Digital Image Analysis, Offered each Spring and Fall (Syllabus), Fall 2011-present


Students will learn advanced theories and common applications for remote sensing of the earth, and they will go through a sequence of hands-on remote sensing procedures and projects with a variety of common remote sensing data sets.  Preliminary exposure to digital image analysis procedures in Remote Sensing would have already prepared students for this second course, Digital Image Analysis. This course is now conducted fully on-line.


LiDAR Remote Sensing, Fall 2012-present ( Syllabus )


This course introduces principles of LiDAR, LiDAR sensors and platforms, LiDAR data view, processing, and analysis, and LiDAR applications. Students will master basic skills of LiDAR needed to leverage the commercial LiDAR sources and information products in a broad range of applications, including topographic mapping, vegetation characterization, and 3-D modeling of urban infrastructure. Students will learn several software packages (ArcGIS LAS Dataset; FUSION/LDV; PointVue LE; LAStools) for LiDAR data displaying, processing, and analyzing. This course is now fully on-line.


Remote Sensing of Environment, Fall 2018-present

This is the first course in a three-course remote sensing sequence, based on the national model for remote sensing curriculum. It covers the basic principles of remote sensing technology applied to environmental and urban analysis and includes a survey of remote sensing data sources. This course is now fully on-line.