Rising global temperatures over the past decades is directly affecting glacier dynamics. To understand glacier fluctuations and document regional glacier-state trends, glacier-boundary detection is necessary. Debris-covered glacier (DCG) mapping, however, is notoriously difficult using conventional geospatial technology methods. Therefore, in this research for automated DCG mapping, we evaluate the utility of a convolutional neural network (CNN), which is a deep learning feed-forward neural network. The CNN inputs include Landsat satellite images, an Advanced Land Observation Satellite (ALOS) digital elevation model (DEM) and DEM-derived land-surface parameters. Our CNN based deep-learning approach named GlacierNet was designed by appropriately choosing the type, number and size of layers and filters, and encoder depth based on the properties of the input data, CNN segmentation process and empirical results. The GlacierNet was then trained using input data and corresponding glacier boundaries from the Global Land Ice Measurements from Space (GLIMS) database, and tested on glaciers in the Karakoram and Nepal Himalaya. Our results show proof-of-concept that GlacierNet reasonably identifies the boundaries of DCGs with a relatively high degree of accuracy, and that morphometric parameters improves boundary detection. INDEX TERMS Debris-covered glacier (DCG), Himalaya, Karakoram, convolutional neural network (CNN), deep-learning, image segmentation.
Well‐exposed gypsum veins in the Triassic Moenkopi formation in southern Utah, USA, are similar to veins at Endeavour and Gale Craters on Mars. Both Moenkopi and Mars veins are hydrated calcium sulfate, have fibrous textures, and crosscut other diagenetic features. Moenkopi veins are stratigraphically localized with strontium and sulfur isotope ratios similar to primary Moenkopi sulfate beds and are thus interpreted to be sourced from within the unit. Endeavour veins seem to be distributed by lithology and may have a local source. Gale veins cut across multiple lithologies and appear to be sourced from another stratigraphic interval. Evaluation of vein network geometries indicates that horizontal Moenkopi veins are longer and thicker than vertical veins. Moenkopi veins are also generally oriented with the modern stress field, so are interpreted to have formed in the latest stages of exhumation. Endeavour veins appear to be generally vertical and oriented parallel to the margins of Cape York and are interpreted to have formed in response to topographic collapse of the crater rim. Gale horizontal veins appear to be slightly more continuous than vertical veins and may have formed during exhumation. Abrupt changes in orientation, complex crosscutting relationships, and fibrous (antitaxial) texture in Moenkopi and Mars veins suggest emplacement via hydraulic fracture at low temperatures. Moenkopi and Mars veins are interpreted as late‐stage diagenetic features that have experienced little alteration since emplacement. Moenkopi veins are useful terrestrial analogs for Mars veins because vein geometry, texture, and chemistry record information about crustal deformation and vein emplacement.
Research involving anisotropic-reflectance correction (ARC) of multispectral imagery to account for topographic effects has been ongoing for approximately 40 years. A large body of research has focused on evaluating empirical ARC methods, resulting in inconsistent results. Consequently, our research objective was to evaluate commonly used ARC methods using first-order radiation-transfer modeling to simulate ASTER multispectral imagery over Nanga Parbat, Himalaya. Specifically, we accounted for orbital dynamics, atmospheric absorption and scattering, direct- and diffuse-skylight irradiance, land cover structure, and surface biophysical variations to evaluate their effectiveness in reducing multi-scale topographic effects. Our results clearly reveal that the empirical methods we evaluated could not reasonably account for multi-scale topographic effects at Nanga Parbat. The magnitude of reflectance and the correlation structure of biophysical properties were not preserved in the topographically-corrected multispectral imagery. The CCOR and SCS+C methods were able to remove topographic effects, given the Lambertian assumption, although atmospheric correction was required, and we did not account for other primary and secondary topographic effects that are thought to significantly influence spectral variation in imagery acquired over mountains. Evaluation of structural-similarity index images revealed spatially variable results that are wavelength dependent. Collectively, our simulation and evaluation procedures strongly suggest that empirical ARC methods have significant limitations for addressing anisotropic reflectance caused by multi-scale topographic effects. Results indicate that atmospheric correction is essential, and most methods failed to adequately produce the appropriate magnitude and spatial variation of surface reflectance in corrected imagery. Results were also wavelength dependent, as topographic effects influence radiation-transfer components differently in different regions of the electromagnetic spectrum. Our results explain inconsistencies described in the literature, and indicate that numerical modeling efforts are required to better account for multi-scale topographic effects in various radiation-transfer components.
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