Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396885
Title: Vegetation, topography and snow melt at the Forest-Tundra Ecotone in arctic Europe : a study using synthetic aperture radar
Author: Dean, Andrew Mark
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 2003
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
This research was conducted as part of DART (Dynamic Response of the Forest-Tundra Ecotone to Environmental Change), a four year (1998-2002) European Commission funded international programme of research addressing the potential dynamic response of the (mountain birch) forest-tundra ecotone to environmental change. Satellite remote sensing was used to map landscape scale (lO(^1)-lO(^3) m) patterns of vegetation and spatial dynamics of snow melt at the forest-tundra ecotone at three sites along ca. an 8º latitudinal gradient in the Fermoscandian mountain range. Vegetation at the forest-tundra ecotone was mapped using visible -near infrared (VIR) satellite imagery, with class definition dependent on the timing of the acquisition of imagery (related to highly dynamic vegetation phenology) and spatial variation in the FTE. Multi-temporal spacebome ERS-2 synthetic aperture radar (SAR) was used for mapping snow melt. Comprehensive field measurements of snow properties and meteorological data combined with a physically based snow backscatter model indicated potential for mapping wet snow cover at each site. Significant temporal backscatter signatures enabled a classification algorithm to be developed to map the pattern of snow melt across the forest- tundra ecotone. However, diurnal and seasonal melt-freeze effects relative to the timing of ERS-2 SAR image acquisition effectively reduce the temporal resolution of data. Further, the study sites with large topographic variation and complex vegetative cover, provided a challenging operating environment and problems were identified with the robustness of classification during the later stages of snow melt because of the effects of vegetation. Significant associations were identified between vegetation, topography, and snow melt despite limitations in the snow mapping.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.396885  DOI: Not available
Share: