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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 Stony Brook University.

 

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.

 

Pramod Pandy (Ph.D. student since Fall 2015): Pramod’s dissertation focuses on land cover land use dynamics and modeling in Nepal.

 

Jing Liu (Ph.D. student since Spring 2016): Jing’s research focuses on modeling sediment accretion in coastal wetlands using SET and remote sensing data.

 

Sara Denka (Ph.D. student since Fall 2016): Sara’s research focuses on drone application in coastal wetlands. She is a drone expert.

 

Molly Smith (Ph.D. student since Fall 2016): Molly’s dissertation focuses on geological and spectroscopic techniques for sand analyses.

 

Heather Nicholson (Ph.D. student since Fall 2017): Heather’s research focuses on application of remote sensing in coastal marshes.

 

David Brodylo (Ph.D. student since Fall 2018): David’s research focuses on vulnerability of coastal wetlands to sea level rise and hurricanes using Google Earth Engine (GEE), remote sensing and GIS techniques.

 

Teaching

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.

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