Journal Articles

(Pdf files are available in I:\Students\Instructors\Geoffrey_Duh\GEOG4582\Readings)

 

Aquatic remote sensing:

  • Wynne, Timothy T., Stumpf, Richard P., Tomlinson, Michelle C, Ransibrahmanakul, Varis, Villareal, Tracy A. 2005.  Detecting Karenia brevis blooms and algal resuspension in the Western Gulf of Mexico with satellite ocean color imagery. [MC: I found this article using the keyterm "algae blooms and remote sensing" and thought it was an interesting and different use than I have encountered in detecting vegetation in water as opposed to land.] (Wynne_etal_2005.pdf)

 

Change Detection Preprocessing:

  • TOWNSHEND, J (1992). "THE IMPACT OF MISREGISTRATION ON CHANGE DETECTION". IEEE transactions on geoscience and remote sensing (0196-2892), 30 (5), p. 1054. [SD: I think this article would be interesting because in my field of study, I will likely be using land cover change detection to determine changes in habitat (i.e. canopy cover) for fish. The article explains the importance of accurately registering images for change detection studies and how to better accomplish the task of determining registration accuracy.] (Townshend_etal_1992.pdf)

 

Change Detection:

  • Ahmed, M.H., et al. 2009. Application of remote sensing to site characterization and environmental change analysis of North African coastal lagoons. Hydrobiologia, 622: 147 – 171. [NJ] (Ahmed_etal_2009.pdf)
  • Dewan, Ashraf M. and Yamaguchi, Yasushi. 2008. Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960 – 2005. Environmental Monitoring and Assessment, 150: 237 – 249. [NJ, SP: This paper describes the results of land use/ land cover classification in Dhaka, Bangladesh using topographic maps and multi-temporal remotely sensed data.  I am interested in this article because it focuses on detecting change over time in an urban area that has been experiencing rapid urbanization.  I am interested in learning how to utilize remotely sensed images and data to conduct analyses of change over time in urban areas, so that I can ascertain how rapid urbanization affects the local-scale hydrologic budget and how negative impacts to water resources can potentially be mitigated through sustainable urban design.  The use of space-borne remotely sensed data is also important in the context of this article because it can be very difficult to obtain consistent and high quality maps and aerial photos of urban areas in developing countries and thus using remotely sensed images creates a framework for conducting this type of research.] (Dewan&Yamaguchi_2008.pdf)
  • Hakan A., Hakan D., Yuksel I.U., Springer 2009.  Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: the case of Kahramanmaras, Turkey. Environ Monit Assess, 151:327-336. [DW: The Hakan article was interesting to me mostly because it was familiar ISODATA techniques of land classification we had done in class before.  I understood what the authors were trying to do, which wasn’t the case in many articles.] (Hakan_etal_2009.pdf)
  • Paul, F (2002). "Changes in glacier area in Tyrol, Austria, between 1969 and 1992 derived from Landsat 5 thematic mapper and Austrian Glacier Inventory data". International journal of remote sensing (0143-1161), 23 (4), p. 787. [SD: I am interested in this article because I would like to focus my project for this class on the change in glacial snow and ice on Mt. Hood, Oregon. I would like to employ the techniques used in this article to complete some of the technical tasks that will be required in my project, such as how to determine snow and/or ice covered area of the mountain and how to deal with debris-covered glacial areas.] (Paul_2002.pdf)
  • Sanli, F. B., F. B. Balcik, and C. Goksel. 2008. Defining temporal spatial patterns of  mega city Istanbul to see the impacts of increasing population. Environmental Monitoring and Assessment 146: 267-275. [LH: The objective of the research reported in this article is to assess and detect land use/land cover change that occurred between 1992 and 2005.  The authors used the method of post classification change detection and Landsat TM images to analyze the heavy population growth and uncontrolled urban development in the city of Istanbul, Turkey.  I am interested in this article because it focuses on detecting change over time in an urban area experiencing rapid growth, which is causing harm to natural resources.  I am interested in using remote sensing techniques to detect temporal change and to use the data to better inform future urban design plans so that urbanization can be more sustainable.] (Sanli_etal_2008.pdf)

 

Decision Tree Classification:

  • Rogan, J., J. Miller, D. Stow, J. Franklin, L. Levien, and C. Fischer. 2003. Land-cover change monitoring with classification trees using Landsat TM and ancillary data. PE&RS, 69(7): 793-804. [SP: This study uses a decision-tree structure to classify land cover change in San Diego County from 1990 to 2006 at three hierarchical levels of detail.  In addition to Landsat imagery, the classification is performed with the use of ancillary data, such as elevation, fire history, and slope, for the more detailed classification levels.  I am interested in reviewing this article because classification trees and ancillary data are both tools that I have not used or seen discussed in detail, and I would like to know more about how they can increase the accuracy of classifications.] (Rogan_etal_2003.pdf)
  • Wang, T., Skidmore, A.K., Toxopeus, A. G., and Liu, X.  Understory Bamboo discrimination using a winter image. PE & RS, 75 (1): 37-47. [EC: The authors of this article attempted to classify and quantify bamboo coverage in the Foping Nature Reserve, an area heavily populated by Giant Pandas. I’m interested in this article because it compares a decision tree classification to conventional multispectral classifiers.  In addition, the article also includes information regarding a variety of vegetation indices, seasonal considerations when classifying vegetation, and accuracy assessment.  I subscribe to this journal and found the article while skimming the issue.] (Wang_etal_2009.pdf)

 

Environmental Modeling:

  • Owen, T.W., Carlson, T.N., and Gillies, R.R. 1998. An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. International Journal of Remote Sensing 19(9):1663-1681. [BB: The satellite remote sensing of the Earth is the most reliable means of monitoring land cover change associated with urbanization over a temporal scale.  I picked this early piece on remote sensing for greater ease in understanding the basic concepts.] (Owen_etal_1998.pdf)
  • Yue, W., Xu, J., Tan, W., and Xu, L. 2007. The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing 28(15):3205-3226. [BB: The urban heat island (UHI) is becoming an important issue with the global trend of populations moving to cities.  Some data indicates that the UHI induced changes to local microclimates may be more significant than global climate changes through this century.] (Yue_etal_2007.pdf)

 

High-resolution Image Analysis:

  • Mesev, V. Fusion of point-based postal data with Ikonos imagery. Information Fusion, 8 (2): 157-167. [EC: This article provides an example of how GIS data at the point level (postal addresses) can assist in classification of high spatial resolution images.  I am interested in this article because the researcher used the e-Cognition object-based classification software.  In addition, the article illustrates how ancillary data can assist the classification process.  Mesev used nearest neighbor indices to identify spacing patterns between residential and commercial buildings.  Search terms were e-Cognition and classification.] ()

 

Fire Applications:

  • Baffetta, F; Corona, P; Fattorini, L; Franceschi, S.  Mar. 2009. Design-based approach to k-nearest neighbors technique for coupling field and remotely sensed data in forest surveys.  Remote Sensing of Environment, Vol. 113,  no. 3: 463-475. [NB: The above comes from the same journal as the first.  It's of interest to me again as an employee of a federal land management agency but also because it discusses combining remotely sensed digital imagery with data from probabilistic sampling schemes.  My knowledge is basic on the topic and I would like to learn more.] ()
  • Miller, J.D., Creasy, RM; Isbell,CJ; Key, CH; Knapp, EE; Sherlock, JW; Skinner, CN. Mar. 2009. Calibration and validation of the relative differenced Normalized Burn Ration (RdNBR) to three measures of fire severity in the Sierra Nevada and Kalamath Mountains, California, USA. Remote Sensing of Environment, Vol. 113, no. 3: 645-656. [NB: The above article pertains to the remote sensing method of choice utilized by federal land agencies to map fire severity due to wild fires.  The authors claim a bias exists within the analysis method and their paper hopes to correct for it.  I work in fire for the USFS so it is of professional interest to me.  A copy must be obtained through inter library loan.] (Miller_etal_2009_RemoteSensingofEnvironment.pdf)
  • Garcia-Haro, F.J., Gilabert, M.A. and Melia, J. 2001.  Monitoring fire-affected areas using Thematic Mapper data.  International Journal of Remote Sensing, 2001, Vol. 22, No. 4, p.533-549. [MC: I found this article using the keyterm "change vector analysis and found the use of NDVI especially interesting in monitoring fire activity.] (Garcia-Haro_etal_2001.pdf)

 

Multi-temporal Classification:

  • Odenweller, J. B. and Johnson, K. I. 1984. Crop Identification Using Landsat Temporal-Spectral Profiles.  RSofE, 14: 39-54. [SF: I’m interested in plant species identification through remote sensing and crop identification seems like a good place to start.  The article is from 1984 (kind of old) so I’m hoping the techniques are similar to techniques I would be able to replicate during this course.] (Odenweller&Johnson_1984.pdf)
  • Thomas, S. J., Deschamps, A., Landry, R., van der Sanden, J. J., and Hall, R. J. 2007.  Mapping Insect Defoliation Using Multi-Temporal Landsat Data.  Our Common Borders – Safety, Security, and the Environment Through Remote Sensing Conference, Oct 28 -  Nov 1, 2007, Ottawa, Ontario, Canada. [SF: I’ve visited forests that were dying due to insect infestation and the ability to detect such areas remotely seemed a worthwhile and interesting task.] (Thomas_etal_2007.pdf)

 

Pattern Recognition:

  • Fleuret F., Elsevier 2008.  Multi-layer boosting for pattern recognition.  Pattern Recognition Letters, 30 (3): 237-241. [DW: This article interested me because it is a bit beyond my current knowledge and its central theme is boosting the ability of a two layer classifier.  This got me interested as classification is integral to my project.] (Fleuret_2008.pdf)

 

Spectral Unmixing:

  • Yang, L., G. Xian, J.M. Klaver, and B. Deal. 2003. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. PE&RS, 69(9): 1003-1010. [SP: This study uses a regression algorithm to estimate change in impervious surface area at the sub-pixel scale between 1993 and 2001 in Georgia.  Because my research is concerned with urban hydrology, I am interested in learning about different methods of estimating impervious surface area.  The sub-pixel approach used in this study is new to me, and I would like to understand how it works.] (Yang_etal_2003.pdf)