Improving Predictive Capabilities of Environmental Change with GLOBE Data

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dc.contributor.advisorDubayah, Ralphen_US
dc.contributor.authorRobin, Jessicaen_US
dc.contributor.departmentGeographyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2006-09-12T05:48:49Z
dc.date.available2006-09-12T05:48:49Z
dc.date.issued2006-07-25en_US
dc.description.abstractThis dissertation addresses two applications of Normalized Difference Vegetation Index (NDVI) essential for predicting environmental changes. The first study focuses on whether NDVI can improve model simulations of evapotranspiration for temperate Northern (> 35) regions. The second study focuses on whether NDVI can detect phenological changes in start of season (SOS) for high Northern (> 60) environments. The overall objectives of this research were to (1) develop a methodology for utilizing GLOBE data in NDVI research; and (2) provide a critical analysis of NDVI as a long-term monitoring tool for environmental change. GLOBE is an international partnership network of K-12 students, teachers, and scientists working together to study and understand the global environment. The first study utilized data collected by one GLOBE school in Greenville, Pennsylvania and the second utilized phenology observations made by GLOBE students in Alaska. Results from the first study showed NDVI could predict transpiration periods for environments like Greenville, Pennsylvania. In phenological terms, these environments have three distinct periods (QI, QII, and QIII). QI reflects onset of the growing season (mid March - mid May) when vegetation is greening up (NDVI < 0.60) and transpiration is less than 2mm/day. QII reflects end of the growing season (mid September - October) when vegetation is greening down and transpiration is decreasing. QIII reflects height of the growing season (mid May - mid September) when transpiration rates average between 2 and 5 mm per day and NDVI is at its maximum (>0.60). Results from the second study showed that a climate threshold of 153 ± 22 growing degree days was a better predictor of SOS for Fairbanks than a NDVI threshold applied to temporal AVHRR and MODIS datasets. Accumulated growing degree days captured the inter-annual variability of SOS better than the NDVI threshold and most closely resembled actual SOS observations made by GLOBE students. Overall, biweekly composites and effects of clouds, snow, and conifers limit the ability of NDVI to monitor phenological changes in Alaska. Both studies did show that GLOBE data provides an important source of input and validation information for NDVI research.en_US
dc.format.extent771763 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/3811
dc.language.isoen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pqcontrolledRemote Sensingen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pquncontrolledNDVIen_US
dc.subject.pquncontrolledAVHRRen_US
dc.subject.pquncontrolledMODISen_US
dc.subject.pquncontrolledSPOT4en_US
dc.subject.pquncontrolledPhenologyen_US
dc.subject.pquncontrolledEvapotranspirationen_US
dc.titleImproving Predictive Capabilities of Environmental Change with GLOBE Dataen_US
dc.typeDissertationen_US

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