Instructions

 

  1. Discussion leaders should coordinate the assignment of readings/tasks.
  2. Submit to the instructor a hardcopy or digital copy of each article they will review TWO weeks before the scheduled discussion meeting.
  3. Email the list of discussion questions to the instructor 2 days before the scheduled meeting (e.g., submit on Tuesday if the meeting is on Thursday).
  4. Submit the review (synopsis and critique) and answers to the discussion questions before the discussion.

 

April 14. Remote Sensing Applications, Decision tree

Darrell Fuhriman, Annalisa Romano

 

McCauley, S.; Goetz, S.J. 2004. Mapping residential density patterns using multi-temporal Landsat data and a decision-tree classifier. International Journal of Remote Sensing 25 (6): 1077-1094.

 

Joy, S. M.; Reich, R. M.; Reynolds, R. T. 2003. A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees. International Journal of Remote Sensing 24 (9): 1835 – 1852.

 

Herold M, Scepan J, Clarke KC  2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. ENVIRONMENT AND PLANNING A 34 (8): 1443-1458.

 

 

April 21. Land-use/cover change

Jack Anliker, Annalisa Romano, Dara Zike

 

Lo, C. P. and X. J. Yang. 2002. Drivers of Land-Use/Land-Cover Changes and Dynamic Modeling for The Atlanta, Georgia Metropolitan Area. Photogrammetric Engineering and Remote Sensing 68 (10): 1073-1082.

 

Seto, KC, Woodcock, C.E, Song, C, et al. 2002. Monitering Land Use Change In Pearl River Delta Using Landsat TM. International Journal of Remote Sensing 23 (10): 1985-2004.

 

Prol-Ledesma, R. M.; Uribe-Alcantara, E. M.; Diaz-Molina, O. 2002. Use of Cartographic data and Landsat TM images to determine Land Use change in Vicinity of Mexico City. International Journal of Remote Sensing 23 (9): 1927-1933.

 

Cohen WB, Spies TA, Alig RJ, et al. 2002. Characterizing 23 years (1972-95) of stand replacement disturbance in western Oregon forests with Landsat imagery.   ECOSYSTEMS 5 (2): 122-137.

 

April 28. Fuzzy classification

Belinda Beller, Jack Anliker, Tom Kuhn

 

Metternicht GI, Zinck JA. 2003. Remote sensing of soil salinity: potentials and constraints. REMOTE SENSING OF ENVIRONMENT 85 (1): 1-20.

 

Lein, J.K. 2003. Applying evidential reasoning methods to agricultural land cover classification. International Journal of Remote Sensing  24 (21): 4161 - 4180.

 

Shackelford AK, Davis CH. 2003. A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 41 (9): 1920-1932.

 

May 5. Atmospheric correction

Belinda Beller, Don Brown, Shane Metcalf

 

Yang X; Lo C.P. 2000. Relative Radiometric Normalization Performance for Change Detection from Multi-Date Satellite Images.  PE&RS 66(8): 967-980.

 

Pax-Lenney M, Woodcock CE, Macomber SA, Gopal S, and Song C. 2001. Forest mapping with a generalized classifier and Landsat TM data. REMOTE SENSING OF ENVIRONMENT 77 (3): 241-250.

 

KAUFMAN YJ, REMER LA 1994. DETECTION OF FORESTS USING MID-IR REFLECTANCE - AN APPLICATION FOR AEROSOL STUDIES. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 32 (3): 672-683.

 

Simpson JJ, Stitt JR. 1998. A procedure for the detection and removal of cloud shadow from AVHRR data over land. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 36 (3): 880-897.

 

May 10. Object-based high-resolution image classification

Tom Kuhn, Bill Hines

 

Shackelford, AK, Davis, CH 2003. A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sensing 41(10): 2354-2363.


Kiema JBK 2002. Texture analysis and data fusion in the extraction of topographic objects from satellite imagery. International Journal of Remote Sensing 23 (4): 767-776
.

 

JANSSEN, LLF, MOLENAAR, M. 1995. TERRAIN OBJECTS, THEIR DYNAMICS AND THEIR MONITORING BY THE INTEGRATION OF GIS AND REMOTE-SENSING. IEEE Trans. Geosci. Remote Sensing 33 (3): 749-758.

 

May 12. Change detection

Don Brown, Kurt Hellman

 

Comber AJ, Law ANR, Lishman JR. 2004. Application of knowledge for automated land cover change monitoring. INTERNATIONAL JOURNAL OF REMOTE SENSING 25 (16): 3177-3192.

 

Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E 2004. Digital change detection methods in ecosystem monitoring: a review. INTERNATIONAL JOURNAL OF REMOTE SENSING 25 (9): 1565-1596.

 

Sader SA, Hayes DJ, Hepinstall JA, et al. 2001. Forest change monitoring of a remote biosphere reserve. INTERNATIONAL JOURNAL OF REMOTE SENSING 22 (10): 1937-1950.

 

Guild LS, Cohen WB, Kauffman JB 2004. Detection of deforestation and land conversion in Rondonia, Brazil using change detection techniques. INTERNATIONAL JOURNAL OF REMOTE SENSING 25 (4): 731-750.

 

May 19. Hyperspectral remote sensing

Kurt Hellman, Bill Hines

 

Harris AT, Asner GP, Miller ME 2003. Changes in vegetation structure after long-term grazing in pinyon-juniper ecosystems: Integrating imaging spectroscopy and field studies. ECOSYSTEMS 6 (4): 368-383.

 

Williams DJ, Rybicki NB, Lombana AV, et al. 2003. Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing. ENVIRONMENTAL MONITORING AND ASSESSMENT 81 (1-3): 383-392.

 

May 26. Slope aspect correction

Dara Zike, Shane Metcalf, Darrell Fuhriman

 

Sandmeier S, Itten KI 1997. A physically-based model to correct atmospheric and illumination effects in optical satellite data of rugged terrain. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 35 (3): 708-717.

 

Shepherd JD, Dymond JR 2003. Correcting satellite imagery for the varianace of reflectance and illumination with topography. INTERNATIONAL JOURNAL OF REMOTE SENSING 24 (17): 3503-3514.

 

Sun G, Ranson K.J; Kharuk V.I. 2002. Radiometric Slope Correction for Forest Biomass Estimation from SAR Data in the Western Sayani Mountains, Siberia.  Remote Sensing of Environment 79(2-3): 279-287.

 

Levin, N; Bendor, E; Karnieli, A. 2004. Topographic Information of Sand Dunes as Extracted from Shading Effects Using Landsat Images. Remote Sensing of Environment 90(2): 190-209.