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In this paper three different techniques for detecting and quantifying mountain permafrost creep are compared: (1) rock glacier inventory and characterization from in situ indicators, (2) space-borne radar interferometry, and (3) digital processing of repeated airborne imagery. The specific characteristics of the three methods and their complementarity are investigated for the Fletschhorn mountain range in the Simplon/Saas valley region, Swiss Alps. We found that radar interferometry is suitable to quantify the degree of activity and the order of surface velocity of rock glaciers over large areas in one process, with the possibility to also detect very small movements of inactive and relict rock glaciers. On the other hand, aero-photogrammetry represents a valuable base for additional interpretation of the three-dimensional surface flow field (including speed, direction and change in thickness) of the most active rock glaciers. Results from radar interferometry can also form the basis for further detailed in situ investigations. [ABSTRACT FROM AUTHOR]

Considering recent progress in the development of techniques and methods to achieve biomass estimates and full carbon accounting, remote sensing research of forested ecosystems needs to be aimed towards the retrieval of information at global scales. In this paper, an algorithm for the estimation of growing stock volume, an important parameter for the commercial forest community and a proxy for woody biomass density, from ERS and JERS synthetic aperture radar (SAR) data is described. The algorithm is based on the information content of both ERS tandem coherence and JERS backscatter images and was developed using ground data, made available by the Russian Forestry Services. It is tested on SAR datasets of boreal forests in Siberia, a managed, temperate forest plantation in the United Kingdom and a semi-natural boreal forest at Siggefora in Sweden. Comparisons of the classified products, comprising three growing stock interval classes and one non-forest class are made with ground data. The results of this accuracy assessment exercise show that the algorithm yields, in all cases, overall classification accuracies of greater than 70%. A visual comparison is made of the algorithm performance over a tropical forest region of Brazil. The results indicate that the algorithm has the potential to retrieve growing stock volume estimates in forest ecosystems throughout the globe. [ABSTRACT FROM AUTHOR]