Fine-scale, sub-annual satellite stereo observations of snow cover and snow depth can help improve quantification of snow water equivalent at critical times during the accumulation and ablation season. We are refining very-high-resolution (VHR) spaceborne optical stereo methods to generate spatially-continuous digital surface models (DSMs) and maps of snow depth and snow water equivalent (SWE) over mountain sites in the Western U.S. In this work, we leverage the open-source software of NASA’s Ames Stereo Pipeline for extensive and iterative testing of stereogrammetric processing parameters to produce snow-free and snow-covered DSMs. Using open-source tools, we customize and improve automated surface co-registration using snow-free DSMs generated from spaceborne stereogrammetry and airborne lidar. High-resolution land cover classification maps derived from the input stereo images using machine learning methods improve the co-registration results and snow depth product quality. We assess our stereo-derived DSM and snow depth mapping methods across multiple sites in Colorado using USGS 3D Elevation Program (3DEP) and the Airborne Snow Observatory (ASO) airborne lidar DSMs and snow depth products. We present initial evaluations of our surface elevation reconstructions across variable terrain and land cover. Finally, we use a bulk density approach and empirical density models to convert snow depth maps into maps of snow water equivalent. We are developing a user-friendly notebook for the full workflow with default processing parameters tuned for mountain terrain. We hope that these tools will enable new users with limited photogrammetry experience to produce maps of snow depth and snow water equivalent from VHR satellite imagery.

Image feature tracking with medium-resolution optical satellite imagery (e.g., Landsat-8) offers measurements of glacier surface velocity on a global scale. However, for slow-moving glaciers (<0.1 m/day), the larger pixel sizes (~15-30 m) and longer repeat intervals (minimum of 16 days, assuming no cloud cover) limit temporal sampling, often precluding analysis of sub-annual velocity variability. As a result, detailed records of short-term glacier velocity variations are limited to a subset of glaciers, often from dedicated SAR image tasking and/or field observations. To address these issues, we are leveraging large archives of very-high-resolution (~0.3-0.5 m) DigitalGlobe WorldView/GeoEye imagery with ~monthly repeat interval and high-resolution (~3-5 m) Planet PlanetScope imagery with ~daily-weekly repeat interval for the period from 2014 to 2019. We are using automated, open-source tools to develop corrections for sensor geometry and image geolocation, and integrating new, high resolution DEMs for improved orthorectification, reducing the uncertainty of short-term (monthly to seasonal) velocity measurements. These temporally dense records will be integrated with other velocity products (e.g., NASA ITS_LIVE), which will allow us to study the evolution of glacier dynamics, and its relationships with local climatology, geomorphology, and hydrology on a regional scale. In this study, we present initial results for surface velocity mapping for glaciers in Khumbu Himalaya, Nepal and Mt. Rainier, USA. We are using high-performance computing environments to scale this analysis to larger glacierized regions in High Mountain Asia and Continental U.S.

We present interannual to decadal glacier and geomorphic change measurements at multiple sites across Western North America from the 1950s until present. Glacierized study sites differ in terms of glacial geometry and climatology, from continental mountains (e.g., Glacier National Park) to maritime stratovolcanoes (e.g., Mt. Rainier). Quantitative measurements of glacier and land surface change are obtained using the Historical Structure from Motion (HSfM) package. The automated HSfM processing pipeline can derive high-resolution (0.5-2.0 m) Digital Elevation Models (DEMs) and orthomosaics from historical aerial photography, without manual ground control point selection. All DEMs are co-registered to modern airborne lidar and commercial satellite stereo reference DEMs to accurately measure geodetic surface elevation change and uncertainty. We use scanned historical images from the USGS North American Glacier Aerial Photography (NAGAP) archive and other aerial photography campaigns from the USGS EROS Aerial Photo Single Frames archive. We examine the impact of regional climate forcing on glacier volume change and dynamics using downscaled climate reanalysis products. By augmenting the record of quantitative glacier change measurements and better understanding the relationship between climate forcing and heterogeneous glacier response patterns, we aim to improve our understanding of regional glacier mass change, as well as inform management decisions impacting downstream water resources, ecosystem management, and geohazard risks.

Glacier velocity reflects the dynamics of ice flow, and its change over time serves a key role in predicting the future sea-level rise. Glacier feature tracking (also known as offset tracking or pixel tracking) is one of the most widely-used approaches for mapping glacier velocity using remote sensing data. However, running this workflow relies on multiple empirical parameter choices such as correlation kernel selection, image filter, and template size. As each target glacier area has different data availability, surface feature density, and ice flow width, there is no one-size-fits-all parameter set for glacier feature tracking. Finding an ideal parameter set for a given glacier requires quantitative and objective metrics to determine the quality of resulting velocity maps. The objective of our Glacier feature tracking test (gftt) project is both to devise a set of widely applicable metrics and to build a Python-based tool for calculating them. These metrics can be thus used for comparing the performance of different tracking parameters. We use Kaskawulsh glacier, Canada, as a test case to compare the velocity mapping results using Landsat 8 and Sentinel-2 images, various software packages (including Auto-RIFT, CARST, GIV, and vmap), and a range of input parameters. To begin with, we calculate random error over stable terrain, a metric that has been used for evaluating the uncertainty of the velocity products. We develop two other workflows for exploring new metrics and validating existing metrics, including the test with synthetic pixel offsets and the comparison with GNSS records. These existing and new metrics, calculated through the gftt software, will help determine optimal parameter sets for feature tracking of Kaskawulsh glacier and any other glacier around the world. This work is supported by the NSF Earth Cube Program under awards 1928406, 1928374.