Suitability of ground-based SfM-MVS for monitoring glacial and periglacial processes

Photo-based surface reconstruction is rapidly emerging as an alternative survey technique to lidar (light detection and ranging) in many fields of geoscience fostered by the recent development of computer vision algorithms such as structure from motion (SfM) and dense image matching such as multi-view stereo (MVS). The objectives of this work are to test the suitability of the ground-based SfM-MVS approach for calculating the geodetic mass balance of a 2.1km2 glacier and for detecting the surface displacement of a neighbouring active rock glacier located in the eastern Italian Alps. The photos were acquired in 2013 and 2014 using a digital consumer-grade camera during single-day field surveys. Airborne laser scanning (ALS, otherwise known as airborne lidar) data were used as benchmarks to estimate the accuracy of the photogrammetric digital elevation models (DEMs) and the reliability of the method. The SfM-MVS approach enabled the reconstruction of high-quality DEMs, which provided estimates of glacial and periglacial processes similar to those achievable using ALS. In stable bedrock areas outside the glacier, the mean and the standard deviation of the elevation difference between the SfM-MVS DEM and the ALS DEM was-0.42 \ub1 1.72 and 0.03 \ub1 0.74 m in 2013 and 2014, respectively. The overall pattern of elevation loss and gain on the glacier were similar with both methods, ranging between-5.53 and + 3.48 m. In the rock glacier area, the elevation difference between the SfM-MVS DEM and the ALS DEM was 0.02 \ub1 0.17 m. The SfM-MVS was able to reproduce the patterns and the magnitudes of displacement of the rock glacier observed by the ALS, ranging between 0.00 and 0.48 m per year. The use of natural targets as ground control points, the occurrence of shadowed and low-contrast areas, and in particular the suboptimal camera network geometry imposed by the morphology of the study area were the main factors affecting the accuracy of photogrammetric DEMs negatively. Technical improvements such as using an aerial platform and/or placing artificial targets could significantly improve the results but run the risk of being more demanding in terms of costs and logistics

Gazing at the Solar System: Capturing the Evolution of Dunes, Faults, Volcanoes, and Ice from Space

Gazing imaging holds promise for improved understanding of surface characteristics and processes of Earth and solar system bodies. Evolution of earthquake fault zones, migration of sand dunes, and retreat of ice masses can be understood by observing changing features over time. To gaze or stare means to look steadily, intently, and with fixed attention, offering the ability to probe the characteristics of a target deeply, allowing retrieval of 3D structure and changes on fine and coarse scales. Observing surface reflectance and 3D structure from multiple perspectives allows for a more complete view of a surface than conventional remote imaging. A gaze from low Earth orbit (LEO) could last several minutes allowing for video capture of dynamic processes. Repeat passes enable monitoring time scales of days to years. Numerous vantage points are available during a gaze (Figure 1). Features in the scene are projected into each image frame enabling the recovery of dense 3D structure. The recovery is robust to errors in the spacecraft position and attitude knowledge, because features are from different perspectives. The combination of a varying look angle and the solar illumination allows recovering texture and reflectance properties and permits the separation of atmospheric effects. Applications are numerous and diverse, including, for example, glacier and ice sheet flux, sand dune migration, geohazards from earthquakes, volcanoes, landslides, rivers and floods, animal migrations, ecosystem changes, geysers on Enceladus, or ice structure on Europa. The Keck Institute for Space Studies (KISS) hosted a workshop in June of 2014 to explore opportunities and challenges of gazing imaging. The goals of the workshop were to develop and discuss the broad scientific questions that can be addressed using spaceborne gazing, specific types of targets and applications, the resolution and spectral bands needed to achieve the science objectives, and possible instrument configurations for future missions. The workshop participants found that gazing imaging offers the ability to measure morphology, composition, and reflectance simultaneously and to measure their variability over time. Gazing imaging can be applied to better understand the consequences of climate change and natural hazards processes, through the study of continuous and episodic processes in both domains

ICEPY4D: A PYTHON TOOLKIT FOR ADVANCED MULTI-EPOCH GLACIER MONITORING WITH DEEP-LEARNING PHOTOGRAMMETRY

Glacier monitoring plays a crucial role in understanding the impacts of climate change on these dynamic natural systems. One or more time-lapse cameras are often employed to acquire short-term observations of glacier flow dynamics. However, the lack of multi-camera photogrammetric software packages for multi-temporal 3D scene reconstruction, especially in case of wide camera baselines, hinders the application of Structure-from-Motion techniques to these scenarios. To address this, we present ICEpy4D, a novel Python toolkit designed for 4D monitoring of alpine glaciers using low-cost time-lapse cameras and state-of-the-art computer vision techniques. ICEpy4D leverages deep-learning-based matching algorithms to solve 3D reconstruction with wide camera baselines, making it well-suited for challenging scenarios encountered in mountainous regions. The toolkit offers comprehensive functionalities for multi-epoch monitoring, enabling short-term glacier 3D reconstruction and extraction of relevant information from time-series point clouds, such as volume variations and glacier retreat. In a pilot study on the Belvedere Glacier northern snout (Italian Alps), ICEpy4D estimated glacier volume loss of 63 × 103 m3 of ice and ∼17.5m of retreat. Results showcased the toolkit’s potential for analyzing a glacier ice cliff, with prospects for application to other glaciers with varying characteristics. ICEpy4D is actively being developed as an open-source project at github.com/labmgf-polimi/icepy4d/, promoting ease of extension and customization

The glaciers climate change initiative: Methods for creating glacier area, elevation change and velocity products

Glaciers and their changes through time are increasingly obtained from a wide range of satellite sensors. Due to the often remote location of glaciers in inaccessible and high-mountain terrain, satellite observations frequently provide the only available measurements. Furthermore, satellite data provide observations of glacier character- istics that are difficult to monitor using ground-based measurements, thus complementing the latter. In the Glaciers_cci project of the European Space Agency (ESA), three of these characteristics are investigated in detail: glacier area, elevation change and surface velocity. We use (a) data from optical sensors to derive glacier outlines, (b) digital elevation models from at least two points in time, (c) repeat altimetry for determining elevation changes, and (d) data from repeat optical and microwave sensors for calculating surface velocity. For the latter, the two sensor types provide complementary information in terms of spatio-temporal coverage. While (c) and (d) can be generated mostly automatically, (a) and (b) require the intervention of an analyst. Largely based on the results of various round robin experiments (multi-analyst benchmark studies) for each of the products, we suggest and describe the most suitable algorithms for product creation and provide recommendations concerning their practical implementation and the required post-processing. For some of the products (area, velocity) post-processing can influence product quality more than the main-processing algorithm

Quantifying spatial uncertainties in structure from motion snow depth mapping with drones in an alpine environment

Due to the heterogeneous nature of alpine snow distribution, advances in hydrological monitoring and forecasting for water resource management require an increase in the frequency, spatial resolution and coverage of field observations. Such detailed snow information is also needed to foster advances in our understanding of how snowpack affects local ecology and geomorphology. Although recent use of structure-from-motion multi-view stereo (SFM-MVS) 3D reconstruction techniques combined with aerial image collection using drones has shown promising potential to provide higher spatial and temporal resolution snow depth data for snowpack monitoring, there still remain challenges to produce high-quality data with this approach. These challenges, which include differentiating observations from noise and overcoming biases in the elevation data, are inherent in digital elevation model (DEM) differencing. A key issue to address these challenges is our ability to quantify measurement uncertainties in the SFM-MVS snow depths which can vary in space and time. The purpose of this thesis was to develop data-driven approaches for spatially quantifying, characterizing and reducing uncertainties in SFM-MVS snow depth mapping in alpine areas. Overall, this thesis provides a general framework for performing a detailed analysis of the spatial pattern of SFM-MVS snow depth uncertainties, as well as provides an approach for correction of snow depth errors due to changes in the sub-snow topography occurring between survey acquisition dates. It also contributes to the growing support of SFM-MVS combined with imagery acquired from drones as a suitable surveying technique for local scale snow distribution monitoring in alpine areas

A pipeline for automated processing of declassified Corona KH-4 (1962-1972) stereo imagery

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100300) and the Swiss National Science Foundation (200021E 177652/1) within the framework of the DFG Research Unit GlobalCDA (FOR2630).The Corona KH-4 reconnaissance satellite missions acquired panoramic stereo imagery with high spatial resolution of 1.8–7.5m from 1962-1972. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents the Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utilizes deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time-dependent exterior orientation parameters is employed. Using the entire frame of the Corona image, bundle adjustment with well-distributed GCPs results in an average standard deviation or σ0 of less than two pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the 3D reconstruction accuracy. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long-term storage likely cause systematic deviations of up to six pixels. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation of elevation differences of ≈ 4m over an area of approx. 4000km2 after a tile-based fine coregistration of the DEMs. We further assess CoSP on complex scenes involving high relief and glacierized terrain and show that the resulting DEMs can be used to compute long-term glacier elevation changes over large areas.PostprintPeer reviewe