dc.contributor.author | Cheruiyot, E. K. | |
dc.contributor.author | Mito, C.O | |
dc.contributor.author | Kaduki, K. A | |
dc.date.accessioned | 2013-05-08T11:13:39Z | |
dc.date.available | 2013-05-08T11:13:39Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | African Spectral Imaging Network (AFSIN), International Workshop on Spectral Imaging in Remote Sensing, Nairobi, Kenya, 24-28 September 2012 | en |
dc.identifier.uri | http://hdl.handle.net/11295/20284 | |
dc.description.abstract | Following the great potential of optical remote sensing
and its increased application in quality assessment of
inland waters, we have developed time dependent
vegetation abundance prediction models based on its
statistical relationship with the concentrations of total
suspended matter (TSM) and phytoplankton
chlorophyll (Chl-a) water quality (WQ) parameters in
the lake as well as the amount of rainfall in its drainage
basin.
We start by retrieving the selected WQ parameters from
MERIS (Medium Resolution Imaging Spectrometer)
multispectral satellite imagery of Lake Victoria based on
their optical properties, and obtain their seasonal
variations over the period 2003 to 2010. We then carry
out regression analysis to establish the time dependent
statistical correlation between estimated vegetation
abundance and the retrieved WQ constituents as well as
rainfall after various response periods, and identify an
optimal response period for each precursor | en |
dc.language.iso | en | en |
dc.title | Application of Multi-spectral Satellite Imagery in Monitoring of Aquatic Vegetation and Water Quality Parameters in Large Inland Waters | en |
dc.type | Presentation | en |
local.publisher | Department of Physics, University of Nairobi, Kenya | en |