Semi-automated classification of river ice types on the Peace River using RADARSAT-1 synthetic aperture radar (SAR) imagery

Abstract

The winter regime of the Peace River in northern British Columbia and Alberta is a determining factor for the operation of the British Columbia Hydro and Power Authority Williston Reservoir. Therefore, fine beam RADARSAT-1 synthetic aperture radar (SAR) satellite images were acquired in winter and spring 2000 and 2001 and analyzed for ice cover types. Video footage of the ice conditions on the Peace River was obtained from aerial ice observations that were conducted simultaneously with the image acquisitions. The analysis of the images was done (i) visually and (ii) using an unsupervised Fuzzy K-means classification. To streamline the process in an operational environment, the unsupervised ice classification was semi-automated. The unsupervised classification broke the data into seven classes, which represent the major ice cover types observed on the Peace River. The spatial distribution of ice cover types as generated by the ice classification generally coincides well with air-truth information and backscatter signatures from SAR images. The location of the boundaries between the ice types appears to be accurate, but it may not be precise. The ice maps can be used for ice monitoring, decision-making purposes, or the verification of ice models.Key words: river ice, remote sensing, RADARSAT-1, Peace River, semi-automated classification.

Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

“…The strength of the backscattered signal from the target is highly dependent on the electrical properties, the structure of the surface and subsurface layers (e.g., surface roughness, thickness of layers) and incident angle. Detailed descriptions on how microwaves interact with freshwater ice and SAR characteristics can be found in Weber et al [10], Nghiem and Leshkevich [22], and Unterschultz et al [13]. Regarding the electrical properties of water-ice layers, the parameter quantifying the electrical properties of any given layer, relative to those of free space, is known as the relative dielectric constant (Ɛr).…”

“…An ice layer with higher dielectric constant and higher water and moisture content, produces higher signal return compared with that of the layers with low dielectric constant, such as clear and dry snow/ice layers. If the absorption loss is high, the penetration depth of the microwave signal is low and, thus, the recorded backscatter largely reflects the properties of surface layers (e.g., snow) rather than the underlying ice [10,13]. For example, an ice layer comprised of wet snow with as little as 5% free water, significantly reduces the penetration of microwaves to 10 cm in depth at 5 GHz frequency [23].…”

“…In such ice layers, the microwave signal with the frequency from 1 to 10 GHz can penetrate to depths ranging from 100 m to 10 m, respectively. As a result, the appearance of a given ice layer in radar images differs significantly in wet conditions, as compared to dry conditions [10].…”

“…In particular, to the best of our knowledge, there are no such studies using remote sensing CV analysis to better identity spatio-temporal changes in river ice covers. Regarding river ice types during the freeze-up period, studies have been very well conducted for many Canadian rivers, e.g., the Peace River [7,[10][11][12] and the Athabasca River [13] in Alberta, the Mackenzie River in the Northwest Territories [14,15], the Koksoak River and Saint-Francois River in Quebec [12,16,17], and the Red River and Dauphin River in Manitoba [2,8,18,19]. Synthetic aperture radar (SAR) data (e.g., RADARSAT-1 and RADARSAT-2) were extensively used to characterize ice types along these river systems, using both visual interpretation and automatic classification of ice covers.…”

“…Synthetic aperture radar (SAR) data (e.g., RADARSAT-1 and RADARSAT-2) were extensively used to characterize ice types along these river systems, using both visual interpretation and automatic classification of ice covers. For example, Weber et al [10] used filtering approaches such as Kuan and median filters to reduce speckle and then applied unsupervised fuzzy k-means classification to identify ice types on the Peace River, using RADARSAT-1 imagery and its fine-beam mode (8 m resolution). In addition to filtering approaches, Gauthier et al [16] included a texture analysis of SAR imagery to classify river ice types of the Koksoak River, relying on the unsupervised pixel-based fuzzy k-mean classification algorithm.…”