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
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
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.
Hyperspectral Remote Sensing, Fall
2010 – present (Syllabus).
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:
Cordoba: The Effects of Water on Soil Spectra.
D. J. Forbes:
A CO2 Sustainability Index Based on Night Time Hyperspectral Remote
Mitchell: Comparing Classification and Assessment within ENVI.
Tom Kenny: Differentiating
Bermudagrass from Vegetation in an Urban Scene Using Hyperspectral
Zahina-Ramos: The Potential Application of Hyperspectral Data for
mapping hydrologic and Topographic variability: A Test of Concept.
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),
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
LiDAR Remote Sensing, Fall 2012-present (Syllabus)
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,
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.