Showing papers by "Regula Frauenfelder published in 2012"

Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery

Abstract: . Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object–oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.

Hydrometeorological threshold conditions for debris flow initiation in Norway

Abstract: . Debris flows, triggered by extreme precipitation events and rapid snow melt, cause considerable damage to the Norwegian infrastructure every year. To define intensity-duration (ID) thresholds for debris flow initiation critical water supply conditions arising from intensive rainfall or snow melt were assessed on the basis of daily hydro-meteorological information for 502 documented debris flow events. Two threshold types were computed: one based on absolute ID relationships and one using ID relationships normalized by the local precipitation day normal (PDN). For each threshold type, minimum, medium and maximum threshold values were defined by fitting power law curves along the 10th, 50th and 90th percentiles of the data population. Depending on the duration of the event, the absolute threshold intensities needed for debris flow initiation vary between 15 and 107 mm day−1. Since the PDN changes locally, the normalized thresholds show spatial variations. Depending on location, duration and threshold level, the normalized threshold intensities vary between 6 and 250 mm day−1. The thresholds obtained were used for a frequency analysis of over-threshold events giving an estimation of the exceedance probability and thus potential for debris flow events in different parts of Norway. The absolute thresholds are most often exceeded along the west coast, while the normalized thresholds are most frequently exceeded on the west-facing slopes of the Norwegian mountain ranges. The minimum thresholds derived in this study are in the range of other thresholds obtained for regions with a climate comparable to Norway. Statistics reveal that the normalized threshold is more reliable than the absolute threshold as the former shows no spatial clustering of debris flows related to water supply events captured by the threshold.

Displacement monitoring at Svartevatn dam with interferometric SAR

Abstract: In Norway, deformation monitoring of dams is regulated by law; traditionally it is done by geodetic measurements [1]. To ensure early recognition of problem areas at the dam and/or at the slopes around the reservoir, frequent deformation measurements are necessary. Here, we investigate the feasibility of applying satellite based SAR interferometry to monitor deformations at dams and reservoir slopes. We present results of an InSAR study at Svartevatn dam in south west Norway, an earth-rockfill dam. Two ERS image stacks have been acquired in ascending and descending mode, respectively. Each image stack has been processed with the Small Baseline Subset (SBAS) to generate deformation maps. The results show that historic SAR data can be used to monitor deformations at the dam with a resolution comparable to geodetic measurements.

Automated Avalanche Deposit Mappping from VHR Optical Imagery

Abstract: Using eCognition we developed an algorithm to automatically detect and map avalanche deposits in Very High Resolution (VHR) optical remote sensing imagery acquired from satellites and airplanes. The algorithm relies on a cluster-based object-oriented image interpretation approach which employs segmentation and classification methodologies to identify avalanche deposits. The algorithm is capable of detecting avalanche deposits of varying size, composition, and texture. A discrete analysis of one data set (airborne imagery collected near Davos, Switzerland) demonstrates the capability of the algorithm. By comparing the automated detection results to the manually mapped results for the same image, 33 of the 35 manually digitized slides were correctly identified by the automated method. The automated mapping approach characterized 201 667 m2, of the image as being representative of a fresh snow avalanche, roughly 8.5% of the image. Through a spatial intersection between the manually mapped avalanches and the automatically mapped avalanches, 184 432 m2, or 89%, of the automatically mapped regions are spatially linked to the manually mapped regions. The rate of false positive was less than 1% of the pixels in the image. The initial results of the algorithm are promising, future development and implementation is currently being evaluated. The ability to automatically identify the location and extent of avalanche deposits using VHR optical imagery can assist in the development of detailed regional maps of zones historically prone to avalanches. This in turn can help to validate issued avalanche warnings.

Remote-sensing derived avalanche inventory data

Abstract: Every year snow avalanches pose a significant threat to transportation infrastructure. The societal demand to minimize closures of the main transport network while maintaining an acceptable level of personal safety at the same time has dramatically increased over the past decade. In Norway, decisions regarding avalanche warning, including pre-emptive road closure, are based on factors such as snow depth, meteorological conditions and expert opinion. The ability to automatically identify snow avalanches using very-high resolution optical imagery would greatly assist in the development of highly accurate, widespread, detailed maps of zones prone to avalanches. This would provide decision makers with better knowledge of previous events and details regarding the size and extent of historical events. We present the results of a 'proof-of-concept' study on the operation of a service providing the Norwegian Public Roads Administration (NPRA) with satellite data derived avalanche inventory data. We have explored the use of imagery from high-resolution and very-high-resolution space-borne satellites by developing and testing automated image segmentation and classification algorithms for the detection and mapping of avalanche deposits.