No abstract

Snowmelt can be a significant contributor to major floods, and hence updated snow information is very important to flood forecasting services. This study assesses whether operational runoff simulations could be improved by applying satellite-derived snow covered area (SCA) from both optical and radar sensors. Currently the HBV model is used for runoff forecasting in Norway, and satellite-observed SCA is used qualitatively but not directly in the model. Three catchments in southern Norway are studied using data from 1995 to 2002. The results show that satellite-observed SCA can be used to detect when the models do not simulate the snow reservoir correctly. Detecting errors early in the snowmelt season will help the forecasting services to update and correct the models before possible damaging floods. The method requires model calibration against SCA as well as runoff. Time-series from the satellite sensors NOAA AVHRR and ERS SAR are used. Of these, AVHRR shows good correlation with the simulated SCA, and SAR less so. Comparison of simultaneous data from AVHRR, SAR and Landsat ETM+ for May 2000 shows good inter-correlation. Of a total satellite-observed area of 1,088 km2, AVHRR observed a SCA of 823 km2 and SAR 720 km2, as compared to 889 km2 using ETM+.

The process of converting an analog map into structured digitized information requires several di erent operations, which are all time-consuming when performed manually. Strictly automatic processing is not always a possible solution, and an interactive approach can then be an alternative. This paper describes a tool for map conversion, focusing on the functionality for extraction of line structures. An interactive approach is used a s i t g i v e s t h e user an opportunity to survey the process, and utilize human knowledge. The methods are b ased o n c ontour following, extracting centre p oints needed for accurate vector representation of the line during tracing.

Very high resolution satellite images allow automated monitoring of road traffic conditions. Satellite surveillance has several obvious advantages over current methods, which consist of expensive single-point measurements made from pressure sensors, video surveillance, etc., in/or close to the road. The main limitation of using satellite surveillance is the time resolution; the continuously changing traffic situation must be deduced from a snapshot image. In cooperation with the Norwegian Road Authorities, we have developed an approach for detection of vehicles in Quick-Bird images. The algorithm consists of a segmentation step followed by object-based maximum likelihood classification. Additionally, we propose a new approach for prediction of vehicle shadows. The shadow information is used as a contextual feature in order to improve classification. The correct classification rate was 89 percent, excluding noise samples. The proposed method tends to underestimate the number of vehicles when compared to manual counts and in-road equipment counts.

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.