ABSTRACT. A field of vectors showing the average velocity of the surgin g glacier Osbornebreen, Svalbard, was determined by comparing sequential SPOT (Systeme pour l'Obser vation de la Terre) and Landsat thematic m apper images. Crevasses which developed during the initial phase of the surge in the winter of 1986-87 were tracked using a fast Fourier chip cross-correlation technique. A digital elevation model (DEM ) was developed using digital photogrammetry on aerial photographs from 1990. This new DEM was compared with a map drawn in 1966. The velocity field co uld be almost entirely determined with 1 month separation of the images, but only partly determined with images I year apart, due to changes of the crevasse pattern. The velocity fi eld is similar to that found for Kronebreen, a continuously fast-moving tidewater glacier. No distinct zones of compressive flow were present and the data gave no evidence of a compression zone/surge front traveling downstream . The velocity fi eld, the rapid advance of the terminus and the development of transverse crevasses in the upper acc umulation area within a 6 month p eriod m ay indicate that the surge d eveloped as a zone of exte nsion starting near the terminus a nd propagating quickl y upstream. The crevasse pattern in the images is therefore interpreted to be the result of th e extensi on zone traveling upst ream, and, as the whole glacier starts to slide, the crevasse pattern a lters according to the bedrock topography.
A field of vectors showing the average velocity of the surging glacier Osbornebreen, Svalbard, was determined by comparing sequential SPOT (Système pour l’Observation de la Terre) and Landsat thematic mapper images. Crevasses which developed during the initial phase of the surge in the winter of 1986–87 were tracked using a fast Fourier chip cross-correlation technique. A digital elevation model (DEM) was developed using digital photogrammetry on aerial photographs from 1990. This new DEM was compared with a map drawn in 1966. The velocity field could be almost entirely determined with 1 month separation of the images, but only partly determined with images 1 year apart, due to changes of the crevasse pattern. The velocity field is similar to that found for Kronebreen, a continuously fast-moving tidewater glacier. No distinct zones of compressive flow were present and the data gave no evidence of a compression zone/surge front traveling downstream. The velocity field, the rapid advance of the terminus and the development of transverse crevasses in the upper accumulation area within a 6 month period may indicate that the surge developed as a zone of extension starting near the terminus and propagating quickly upstream. The crevasse pattern in the images is therefore interpreted to be the result of the extension zone traveling upstream, and, as the whole glacier starts to slide, the crevasse pattern alters according to the bedrock topography.
The catchment of Øvre Heimdalsvatn and the surrounding area was established as a site for snow remote sensing algorithm development, calibration and validation in 1997. Information on snow cover and snowmelt are important for understanding the timing and scale of many lake ecosystem processes. Field campaigns combined with data from airborne sensors and spaceborne high-resolution sensors have been used as reference data in experiments over many years. Several satellite sensors have been utilised in the development of new algorithms, including Terra MODIS and Envisat ASAR. The experiments have been motivated by operational prospects for snow hydrology, meteorology and climate monitoring by satellite-based remote sensing techniques. This has resulted in new time-series multi-sensor approaches for monitoring of snow cover area (SCA) and snow surface wetness (SSW). The idea was to analyse, on a daily basis, a time series of optical and radar satellite data in multi-sensor models. The SCA algorithm analyses each optical and synthetic aperture radar (SAR) image individually and combines them into a day product based on a set of confidence functions. The SSW algorithm combines information about the development of the snow surface temperature and the snow grain size (SGS) in a time-series analysis. The snow cover algorithm is being evaluated for application in a global climate monitoring system for snow variables. The successful development of these algorithms has led to operational applications of snow monitoring in Norway and Sweden, as well as enabling the prediction of the spring snowmelt flood and thus the initiation of many lake production processes.
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