Recommended reading: Mundia, C.N. and Aniya, M., Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS, Int. J. of Remote Sensing Vol. 26, No. 13, 10 July 2005, 2831–2849.
Hyperion data sets have 242 bands of information and have been shown to be highly useful for distinguishing soil types. However, little work has been done to explore their utility for distinguishing vegetation types. This study uses the National Wetland Inventory to guide spectral signature analysis of an April 2004 Hyperion scene in Central Connecticut.
Recommended reading: Hirano, A.; Madden, M. and Welch, R., Hyperspectral Image Data for Mapping Wetland Vegetation, Wetlands, Vol. 23, No. 2, June 2003, pp. 436–448.
Recommended reading: Krankina, O.N.; Harmon, M.; Cohen, W.B.; Oetter, D.R.; Zyrina, O. and Duane, M.V. Carbon Stores, Sinks, and Sources in Forests of Northwestern Russia: Can We Reconcile Forest Invetories with Remote Sensing Results?, Climatic Change 67: 257–272, 2004.
Recommended reading: Turner, D.P. et al, Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring, Global Change Biology (2005) 11, 666–684.
This project is part of a larger study looking at change in peat swamp forest cover, biomass, and flammability risk over the past two decades across the Indonesian island of Borneo. Over the 2005 summer field season, data was taken regarding the size class, height, density, and floristic characteristics of a large peat swamp forest in Western Kalimantan. During the advanced seminar in remote sensing, I will use this data to pursue two primary goals – exploring different change detection techniques to categorize the direction and magnitude of change in the area over time, and working on methodologies to link ground data regarding biomass to remotely sensed forest signals.
Recommended reading: Langmann, B. and Heil, A., Release and dispersion of vegetation and peat fire emissions in the atmosphere over Indonesia 1997/1998, Atmos. Chem. Phys. Discuss., 4, 2117–2159, 2004.
Traditional forest management decisions are based in part on the accurate collection and description of forest stand attributes such as forest cover type and wood volume. Forest cover type is both a qualitative description of the stand structure and development stage as well as a quantitative description of the percentages of unit areas by dominant species type. Hyperspectral analysis offers the potential to use a much higher spectral resolution to differentiate amongst vegetative types and their abundance. This project will explore the abilities and limitations of several current and developing methods in hyperspectral analyses to determine physical properties such as forest cover type and wood volume. Spectral endmember mapping using single feature identification and using un-mixing methods will be compared for overall accuracy in the classification of several Connecticut woodlots.
Recommended reading: Williams, A.P. and Hunt, E.R., Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering, Remote Sensing of Environment 82: (2002) 446–456.
Recommended reading: Hall, D.K.; Chang, A.T.C., and Siddalingaiah, H., Reflectances of Glaciers as Calculated Using Landsat-5 Thematic Mapper Data, Remote Sensing of Environment 25: (1988) 311-321.
25 October 2005