Donna Selch (Graduated in summer 2016): Donna
finished her M.A. at FAU in 2012. Her dissertation research focuses
on water quality monitoring and modeling in the Florida Everglades
using remote sensing and GIS techniques. Donna is now a visiting
assistant professor at Stony Brook University.
Nicole Gamboa (M.S., graduated in Spring 2016):
Now with Sigma Space.
Hannah Cooper (Ph.D. student since Fall 2013):
Hannah finished her M.S. from University of Hawaii. Her research
focuses on application of GIS/remote sensing in sea level rise and
coastal mapping. http://student.fau.edu/hcooper2013/web/
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. student since Fall
2017): Heather’s research focuses on application of remote sensing in
Hyperspectral Remote Sensing, Fall
2010, 2011, 2013, 2014, 2016 (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.
Differentiating Bermudagrass from Vegetation in an Urban Scene Using
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, 2012, 2014, 2015, 2016 (Syllabus)
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).
Example projects previous students did:
Change Detection of Pompano Beach Air Park, 1940- Present
Ferran: Mastering Techniques for the Utilization of GIS & Remote
Sensing as Tools in Environmental Assessment.
The Evolution of Tourist Lodging in the Walt Disney World Areas: A
change Detection Analysis Through the Use of Aerial Photograph.
Gammack-Clark: An Investigation of “Grass Roots” Aerial Photography.
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 fully on-line.
LiDAR Remote Sensing, Fall 2012,
2014, 2015 (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.