Abstract
Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. <br><br> As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. <br><br> The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.
Highlights
1.1 Motivation archive has been made freely available to the public which further calls for and facilitates improved and systematic exploitation of these stereo data over large regions.Glaciers and their changes over time are key elements of the Earth’s water cycle influencing global sea level and, in many mountain regions, local water availability for household, agricultural and hydropower use
Even if the existence of such raw data over such a long time span is a great starting point, DEMs generated by NASA with SilcAst (SILC, 2015) (ASTER DMO products) do not provide a sufficient geometric quality for glacier volume change estimation over short periods, the expected change being significantly smaller than the accuracy of the product
In (Girod et al, 2015), we presented a method to extract a DEM from the VNIR images of an ASTER L1A dataset with a correction on the cross-track jitter
Summary
1.1 Motivation archive has been made freely available to the public which further calls for and facilitates improved and systematic exploitation of these stereo data over large regions. Glaciers and their changes over time are key elements of the Earth’s water cycle influencing global sea level and, in many mountain regions, local water availability for household, agricultural and hydropower use. Long time series are crucial for glacier change detection in order to separate short-term variations in surface mass balance from the more long-term changes driven by variations in climate and glacier dynamics (Wang and Kääb, 2015).
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More From: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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