Hypertemporal analysis of remotely sensed sea-ice data for climate change studies

Abstract

Climatologists have speculated that a spatially coherent pattern of high-latitude temperature trends could be an early indicator of climatic change. The sensitivity of sea ice to the temperature of the overlying air suggests the possibility that trends in Arctic ice conditions may be useful proxy indicators of general climatic changes. Aspects of the north-polar ice pack which have been identified as key parameters to be monitored include ice extent, concentration, type, thickness and motion dynamics. In spite of the considerable interannual, regional and seasonal variations exhibited by these data, there may be some evidence of an emerging trend towards decreasing ice extent and concentration. Collecting data in such a remote and harsh environment to support these analyses is only possible through satellite remote sensing. Remote sensing in the microwave portion of the electromagnetic spectrum is particularly relevant for polar applications because microwaves are capable of penetrating the atmosphere under virtually all conditions and are not dependent on the sun as a source of illumination. In particular, analyses of passive microwave imagery can provide us with daily information on sea-ice extent, type, concentration, dynamics and melt onset. A historical record of Arctic imagery from orbiting passive microwave sensors starting from 1973 provides us with an excellent data source for climate change studies. The development of analysis tools to support large area monitoring is integral to advancing global change research. The critical need is to create techniques which highlight the space-time relationships in the data rather than simply displaying voluminous quantities of data. In particular, hypertemporal image analysis techniques are required to help find anticipated trends and to discover unexpected or anomalous temporal relationships. Direct hypertemporal classification, principal components analysis and spatial time-series analysis are identified as three primary techniques for enhancing change in temporal image sequences. There is still a need for the development of new tools for spatial- temporal modelling.