GEO 6839: Sect #7626
Advanced Environmental Remote Sensing:
Course description |
Objectives: The goal of this course is twofold: to introduce students with a basic knowledge of remote sensing to advanced topics in digital remote sensing applications and to instill enthusiasm in this subject area to encourage future specialists. The course emphasizes a hands-on learning environment, with in depth insights into theoretical and conceptual underpinnings in satellite remote sensing. Primary focus will be placed on advanced passive sensors characteristics, digital image analysis, and processing for a broad range of sensors and applications. Ultimately, the course will empower students to delve more deeply into advanced issues in remote sensing and to customize and develop image processing tools for their particular area of interest.
Labs
Students developed labs based on their interests and research goals. Each lab is listed below along with a powerpoint presentation on the topic where one was given, and suggested readings which were used to lead a weekly online discussion. We hope this information will be useful to others who are interested in a wider variety of remote sensing topics than can be offered in a single semester course. If these labs are used please be sure to the cite the source, in terms of the student who created it. We are offering the labs as open source and available to all who wish to use them in the hopes of increasing such resources online, especially for students. If you have follow up data or labs you wish to share in return please feel free to email me at jsouthwo@geog.ufl.edu, and we will be glad to extend this website to include this additional information.
This section is currently under construction and will be updated throughout the Fall 2007. Enjoy.
Lab Topics
Pre-Processing
Topic 1: DeStriping, Author: Matt Marsik.
Recommeded readings: Jensen_fftandpca.pdf; schowengerdt_rs_ch7; Singh_standardizedprincipalcomponents.
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 2: Mosaicking, Authors: Karla Rocha and Narcisa Pricope.
Recommeded readings: automatic registration and mosacing; Hinsamouth_Mosaicking; Inampudi_Image_Mosaicing; Jasen_mosiac; Mosaic_aligning_perspectives; wt_landcover_report.
Powerpoint presentation and Online Weekly discussions based on the readings.
Mosaicking Lab A and Lab B (2 different authors)
Topic 3: Topographic Normalization, Author: Natalia Hoyos.
Recommeded readings: gitas_devereux_2006; riano_et_al_2003; rocchini_2004.
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 4: Understanding Differences due to Precipitation Differences in Time Series Analyses, Author: Andrea Gaughan.
Recommeded readings: Barbosa_et_al_2006; Kalnay_et_al_NCEP_1996; richard_and_poccard_1998; Wilmott_Johnson_2005.
Powerpoint presentation and Online Weekly discussions based on the readings.
Analysis and Techniques
Topic 1: Fragmentation Analysis, Author: Andrea Gaughan
Recommeded readings: forman_and_godron_1981; imberon_and_branthomme_IJRS; lanford_2006_Ecosyst
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 2: Thermal Analysis Across Platforms, Author: Cerian Gibbes.
Recommeded readings: goetz.multisens.ndvi; liu.aster.modis.surfacetemp; pu.multisens.surfacetemp; stefanov.aster.modis.ndvi
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 3: Biomass Analyses, Author: Brian Becker.
Recommeded readings: Hall et al 2006; Labrecque et al 2006; Lu 2006; Patenaude et al 2005; Rahman et al 2005
Powerpoint presentation and Online Weekly discussions based on the readings.
Biomass Lab
Topic 4: Scaling, Author: Forrest Stevens.
Recommeded readings: Pohl and Van Genderson; 1998_Review of multis; Wehrmann
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 5: Decision Based Classification, Author: Hector Castenada.
Recommeded readings: 2006 Daniels; Sader; Stefanov
Powerpoint presentation and Online Weekly discussions based on the readings.
Decision Based Classification Lab
Topic 6: Neural Networks, Author: Christa Zweig.
Recommeded readings: Atkinson and Tatnall 1997; Foody and Cutler 2006; VegaGarcia and Chuvieco 2006
Powerpoint presentation and Online Weekly discussions based on the readings.
Neural Network Lab
Topic 7: Sub-pixel Classification, Author: Daniel Godwin.
Recommeded readings: 19511149; Elmore et al; Foody-Untrained; PetrouFoschi
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 8: Fuzzy Classifiers, Author: Sanchayeeta (Sanchi) Adhikari
Recommeded readings: fuzzy classification; Jensen 2006; fuzzy supervised; methods for fuzzy
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 9: Ground to Pixel Relationships, Author: Pinki Mondal.
Recommeded readings: Carvallno et al 2006; Console et al 1997; Lu 2005; Reese et al 2003; Reinke Jones 2006
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 10: Scale and Moving Windows, Author: Lin Cassidy
Recommeded readings: Daley et al 2006; uerschman et al 2003; Purkis et al 2006; Southworth et al 2006. Additional readings: Hagen-Zanker 2006; Hill et al 2006; Im and Jensen 2005; Keramitosglou 2006; Shaick et al
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 11: Hybrid Classifiers, Author: Christa Zweig
Recommeded readings: Hudson 2006; Messina Walsh 2001; Sader 1995
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 12: ERDAS Modeler and Scripting, Author: Forrest Stevens.
Recommeded readings: ERDAS Macro Language; ERDAS Spatial Modeler
Powerpoint presentation and Online Weekly discussions based on the readings.
- simple ERDAS batch htm and swf files
Topic 13: Cost Surface Analysis, Author: Matt Marsik.
Recommeded readings: Arima et al 2005; Cowen et al 2000; Dillabaugh et al 2002; Feldman 1995
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 14: Logistic Modelling, Author: Hector Castenada
Recommeded readings: Allen and Lu; Geoghegan et al; Mace et al; Etter et al; Osborne et al.
Powerpoint presentation and Online Weekly discussions based on the readings.
Logistic Modelling Lab
Topic 15: Cellular Automata, Author: Daniel Godwin.
Recommeded readings: ABM_Report; Douglas Stuart Monitoring; Jema02Weng; Parker et al.
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 16: MODIS, Author: Narcisa Pricope.
Recommeded readings: MODIS drought aplication; MODIS epidemiological; MODIS semiarid env; TVDI 2002 AVHRR
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 17: Fire Analysis, Author: Karla Rocha
Recommeded readings: Brown EOS final; Estimating burned area; Events of high particulate matter; Wilfrid Schrosder
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 18: Object Oriented Classifier, Author: Sanchayeeta (Sanchi) Adhikari
Recommeded readings: Definiens paper elsevier; method for object oriented; region based classification
Powerpoint presentation and Online Weekly discussions based on the readings.
Object Oriented Classifier Lab
Topic 19: Image Segmentation, Author: Pinki Mondal
Recommeded readings: Chen et al; Hay et al
Powerpoint presentation and Online Weekly discussions based on the readings.
Topic 20: Hyperspectral Analysis, Author: Brian Becker
Recommeded readings: Belluro et al 2006; Flippi and Jensen 2006; Underwood et al 2007
Powerpoint presentation and Online Weekly discussions based on the readings.
Hyperspectral Analysis Lab
Topic 21: Accuracy Assessment Analysis for Classification, Author: Cerian Gibbes
Recommeded readings: Pontius 2003; Pontius 2006; Pontius 2007
Powerpoint presentation and Online Weekly discussions based on the readings.
Accuracy Assessment Analysis Lab
Topic 22: Statistical Analysis of Satellite Imagery, Author: Lin Cassidy
Recommeded readings: Bucini Lambin 2002; Iqbal et al 2005; Moleele et al 2001; Rutherford et al 2007; Goovaerts et al 2005
Powerpoint presentation and Online Weekly discussions based on the readings.
No Lab
