Monitoring Glacier Changes Using Multitemporal Multipolarization SAR Images

Abstract

This paper presents a processing chain for change detection of Arctic glaciers from multitemporal multipolarization synthetic aperture radar images. We produce terrain corrected multilook complex (MLC) covariance data by including the effects of topography on both geolocation and SAR radiometry as well as azimuth slope variations on polarization signature. An unsupervised contextual non-Gaussian clustering algorithm is employed for segmentation of each terrain corrected polarimetric SAR image and subsequently labeled with the aid of ground truth data into glacier facies. We demonstrate the consistency of the segmentation algorithm by characterizing the expected random error level for different SAR acquisition conditions. This allows us to determine whether an observed variation is statistically significant and therefore can be used for post-classification change detection of Arctic glaciers. Subsequently, the average classified images of succeeding years are compared, and changes are identified as the detected differences in the location of boundaries between glacier facies. In the current analysis, a series of dual polarization Cband ENVISAT ASAR images over the Kongsvegen glacier, Svalbard, is used for demonstration.

A Pattern-Based Method For Handling Confidence Measures While Mining Satellite Displacement Field Time Series: Application to Greenland Ice Sheet and Alpine Glaciers

“…The highlighted part in the middle is the Nordenskjöld glacier (1), for which a slowdown along one longitudinal transect was exhibited in [25]. The map shows that this deceleration pattern also occurs in the northern part, close to the Sarqardliup sermia (3), and in the South in the area of the Polonia glacier (2). Notice that according to the color scale the end of the pattern is observed earlier in the middle-left part (mostly blue) than in the northern and southern parts (magenta).…”

“…Studying glacier dynamics is of primary interest to assess the local impact of climate change and derive their contribution to the sea level rise. One effective way to characterize glacier evolutions consists in analyzing Displacement Field Time Series (DFTS) obtained from Satellite Image Time Series (SITS) (e.g., [2,19,5,9]). When it comes to automatically gain insights into a DFTS, data mining techniques are to be considered.…”

A Pattern-Based Mining System for Exploring Displacement Field Time Series

“…Displacement Field Time Series (DFTS) analysis is an emerging research field fueled by the development of photogrammetry and remote sensing techniques, and by application needs of primary importance, such as landslide monitoring (e.g., [1,2]) or glacier dynamics analysis (e.g., [3][4][5][6]). Basically, a DFTS covers a geographical zone and gives the surface displacements observed between acquisitions performed at different dates.…”

Polarimetric SAR Change Detection With the Complex Hotelling–Lawley Trace Statistic

“…The use of synthetic aperture radar (SAR) sensors is attractive in temporal studies, because SAR sensors do not suffer from the limitations of cloud cover and solar incidence, contrary to optical sensors. Many studies have demonstrated the potential of SAR images in change detection and time series analysis, e.g., [1]- [6]. This study is devoted to the multichannel polarimetric SAR (Pol-SAR) sensor, which potentially provides increased detection capability, as compared to single-polarization SAR [7].…”