Abstract
Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost. While the significance of rock glacier contribution to streamflow remains uncertain, the contribution is likely to be important for certain parts of the world. High-resolution remote sensing data has permitted the creation of rock glacier inventories for large regions. However, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation, which is both time consuming and subjective. Here, we present a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 optical imagery (10 m spatial resolution), Sentinel-1 interferometric coherence data, and a digital elevation model (DEM). CNNs identify recurring patterns and textures and produce a prediction raster, or heatmap where each pixel indicates the probability that it belongs to a certain class (i.e. rock glacier) or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment in the central Himalaya. In total, our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28%. Individual rock glacier polygons howevercontained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user's accuracy to be moderate (63.9–68.9%) even if the producer's accuracy was higher (75.0–75.4%). We repeated our method on very-high-resolution Pléiades satellite imagery and a corresponding DEM (at 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference image resolution makes. We found that working at a higher spatial resolution has little influence on the producer's accuracy (an increase of 1.0%), however the rock glaciers delineated were mapped with a greater user's accuracy (increase by 9.1% to 72.0%). By running all the processing within an object-based environment it was possible to both generate the deep learning heatmap and perform post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories.
Highlights
The cryosphere is in a state of rapid change, for example mountain glaciers have lost 335 ± 144 Gt of mass per year between 2006 and 2016 (Zemp et al, 2019)
It can be difficult to reliably determine from the satellite imagery if these landforms are periglacial, glacial or fluvial in nature When evaluating the influence of running the classification on Pléiades imagery instead of Sentinel-2 imagery, we found that the producer accuracy remains approximately the same (87.4% with Sentinel-2, 88.4% with Pléiades), the user accuracy increases with the use of higher-resolution imagery (62.9% with Sentinel-2, 72.0% for Pléiades).When comparing the rock glaciers found in the validation data for both classifications, CNN_OBIA_Ple outperformed CNN_OBIA on 3 polygons, was equivalent on 1 polygon, and performed worse on 2 polygons
Studies of rock glaciers over catchment to regional scales are hampered by inventories of variable quality that are based on subjective criteria and prone to inter-user inconsistencies
Summary
The cryosphere is in a state of rapid change, for example mountain glaciers have lost 335 ± 144 Gt of mass per year between 2006 and 2016 (Zemp et al, 2019). Rock glaciers are more resilient to climate change than other components of the cryosphere as both the rocky material and the active layer insulate the ice contained within the landform. As such they may become an important future water source, especially for arid and semi-arid regions, where in some cases the Remote Sensing of Environment 250 (2020) 112033 demand for water has been increasing while future climate scenarios predict a decrease in precipitation (Bolch and Marchenko, 2009; Azocar and Brenning, 2010; Rangecroft et al, 2015; Schaffer et al, 2019). It is important to create and maintain up-to-date and accurate inventories of rock glaciers
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