| Title | Snow and vegetation monitoring in the Scandinavian Mountains |
| Author | Amlien, J.; Eikvil, L.; Koren, H.; Solberg, R.; Aurdal, L.; Huseby, R.; Pulver, K.A. |
| Author Affil | Amlien, J., Norwegian Computing Center, Oslo, Norway. Other: Karl-Franzens University of Graz, Austria; University of Oslo, Norway |
| Source | Grazer Schriften der Geographie und Raumforschung, Vol.43, p.109-111, ; 9th international symposium on High mountain remote sensing cartography, Graz, Austria, Sept. 14-15, 2006, edited by V. Kaufmann and W. Sulzer. Publisher: Institute of Geography and Regional Science, University of Graz, Graz, Austria. ISBN: 9-78935- 0227635 |
| Publication Date | 2007 |
| Notes | In English. 8 refs. GeoRef Acc. No: 287581 |
| Index Terms | Alpine landscapes; ecology; mapping; remote sensing; snow; snow cover; temperature; vegetation; Norway; alpine environment; Europe; monitoring; satellite methods; Scandinavia; Scandinavian Mountains; spatial distribution; terrestrial environment; Western Europe |
| Abstract | The monitoring of snow and vegetation in mountainous areas in Scandinavia are motivated from needs within climatic monitoring, hydrology, nature protection/management and resource management. We present our long time activities within satellite image based snow monitoring in Scandinavia, as well as more recent work in the field of vegetation mapping and monitoring of mountainous vegetation in Norway. A set of daily products that are related to the metamorphosis and the melting of the snow cover has been developed. It comprises SCA (Snow Covered Area), SGS: Snow Grain Size, STS: (Snow Temperature at Surface) SSW (Snow Surface Wetness) and SDP (Snow distribution pattern) The SCA product are improved by combining cloud free parts of several acquisitions Vegetation mapping and monitoring involves requires multi-temporal sets of imagery to cope with and take advantage of the phenological variations during the growth season. This requires methods for time-alignment between scenes from different years. A contextual classification method that considers the phenological stages is presented. The application of the methods are the mapping of domestic animal feeding capacity of mountainous vegetation and the monitoring long-term vegetation changes in the mountains. One significant and common problem for efficient monitoring and mapping from space is the cloud cover issue. The strategies we have developed to overcome this problem involves combination of several dataset. The approaches for the snow distribution pattern, as well as phenology alignment involves combinations of different spatial resolutions. |
| Publication Type | conference paper or compendium article |
| Record ID | 63000352 |