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dc.contributor.advisorGuo, Xulinen_US
dc.creatorLi, Zhaoqinen_US
dc.date.accessioned2010-08-17T12:18:49Zen_US
dc.date.accessioned2013-01-04T04:53:20Z
dc.date.available2011-08-23T08:00:00Zen_US
dc.date.available2013-01-04T04:53:20Z
dc.date.created2010-08en_US
dc.date.issued2010-08en_US
dc.date.submittedAugust 2010en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-08172010-121849en_US
dc.description.abstractVariations in Leaf Area Index (LAI) can greatly alter output values and patterns of various models that deal with energy flux exchange between the land surface and the atmosphere. Customarily, such models are initiated by LAI estimated from satellite-level Vegetation Indices (VIs) including routinely produced Normalized Difference Vegetation Index (NDVI) products. However, the accuracy from LAI-VI relationships greatly varies due to many factors, including temporal and spatial variations in LAI and a selected VI. In addition, NDVI products derived from various sensors have demonstrated variations in a certain degree on describing temporal and spatial variations in LAI, especially in semi-arid areas. This thesis therefore has three objectives: 1) determine a suitable VI for quantifying LAI temporal variation; 2) improve LAI estimation by considering both temporal and spatial variations in LAI; and 3) evaluate routinely produced NDVI products on monitoring temporal and spatial variations in LAI. The study site was set up in conserved semi-arid mixed grassland in St. Denis, Saskatchewan, Canada. One 600 m - long sampling transect was set up across the rolling typography, and six plots with a size of 40 × 40 m each were randomly designed and each was in a relatively homogenous area. Plant Area Index (PAI, which was validated to obtain LAI), ground hyperspectral reflectance, ground covers (grasses, forbs, standing dead, litter, and bare soil), and soil moisture data were collected over the sampling transect and plots from May through September, 2008. Satellite data used are SPOT 4/5 images and 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) 250m, 1km as well as 10-day SPOT-vegetation (SPOT-VGT) NDVI products from May to October, 2007 and 2008. The results show that NDVI is the most suitable VI for quantifying temporal variation of LAI. LAI estimation is much improved by considering both temporal and spatial variations. Based on the ground reflectance data, the r² value is increased by 0.05, 0.31, and 0.23 and an averaged relative error is decreased by 1.57, 1.62, and 0.67 in the early, maximum, and late growing season, respectively. MODIS 250m NDVI products are the most useful datasets and MODIS 1km NDVI products are superior to SPOT-VGT 1km composites for monitoring intra-annual spatiotemporal variations in LAI. The proposed LAI estimation approach can be used in other studies to obtain more accurate LAI, and thus this research will be beneficial for grassland modeling.en_US
dc.language.isoen_USen_US
dc.subjectNDVI productsen_US
dc.subjectLeaf Area Index estimationen_US
dc.subjectTemporal and spatial variationsen_US
dc.subjectSemi-arid mixed grasslanden_US
dc.titleImproved leaf area index estimation by considering both temporal and spatial variationsen_US
thesis.degree.departmentGeographyen_US
thesis.degree.disciplineGeographyen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US


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