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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Using Reflectance Spectroscopy to Characterize Surface Landforms and Volcanic Deposits on Deception Island (Antarctica)
摘要： Deception Island is an active volcano in the Antarctic Peninsula region, affected by different geomorphological processes interacting to form a complex mosaic of landforms and deposits. The use of visible near infrared reflectance is an ideal tool for characterizing and monitoring surface covers and substrates. The objective of this work was to use reflectance spectroscopy to identify spectral characteristics of surface covers related to different volcanic deposits in ice-free areas of Deception Island, South-Shetland Islands. A site specific spectral library containing 220 reference spectra was compiled. Image-derived spectra from multispectral satellite data were easily labeled using the reference spectra. A preliminary distribution has distinguished five different deposit types over the entire area of Deception Island.
关键词： Reflectance spectra,spectral library,volcanic deposits,multispectral,geomorphology,South Shetland Islands
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Lunar Geomorphology Three-Dimensional Visualization within WorldWide Telescope Based on GPU
摘要： The three-dimensional visualization of lunar geomorphology was a significant research topic. There exist some problems during the three-dimensional visualization of lunar geomorphology image such as massive dataset rendering, parallelized visualization algorithms on GPU, and lunar geomorphology image projection transformation. So this paper utilized the WorldWide Telescope platform as the image rendering architecture and adopted the OSWorkflow engine and OpenPBS scheduler to enhance parallel efficiency. Beyond above, one map transformation called tessellated octahedral adaptive subdivision transform was also designed and applied into the three-dimensional rendering of lunar geomorphology image.
关键词： lunar geomorphology image,three-dimensional visualization,map projection transformation
更新于2025-09-23 15:22:29
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[Developments in Earth Surface Processes] Remote Sensing of Geomorphology Volume 23 || Landslide analysis using laser scanners
摘要： The advent of light detection and ranging (LiDAR) (see Table 1 for acronyms) has revolutionized the study of landslides and geomorphology because it provides an extraordinarily fine resolution topography (Carter et al., 2001). Electronic components and computers at affordable prices have made this technique widely available since the beginning of the 21st century. The number of geoscientific publications is increasing at a nearly exponential rate since around 1990 demonstrating its impact on geosciences (Abellan et al., 2016).
关键词： LiDAR,landslides,geomorphology,topography,laser scanning
更新于2025-09-23 15:19:57
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[Developments in Earth Surface Processes] Remote Sensing of Geomorphology Volume 23 || Terrestrial laser scanner applied to fluvial geomorphology
摘要： Measuring river geometry and its evolution through time has always been a cornerstone of fluvial geomorphology. While experimental and numerical modeling of fluvial dynamics has been central in understanding long-term dynamics and testing ideas, they remain simplified versions of complex natural systems and cannot necessarily include all relevant processes. Field measurements are thus central to our understanding of elementary processes such as sediment entrainment and deposition, bank erosion, bedrock incision as well as the macroscopic dynamics of river reaches such as channel bed accretion/erosion, bedforms mobility, and river meandering. It is therefore not surprising that fluvial geomorphologists have quickly embraced the use of terrestrial laser scanner (TLS) to study rivers (e.g., Heritage and Hetherington, 2007; Hodge et al., 2009a). TLS allows 3D digitization of fluvial environment in a dense (sub-cm), accurate (mm precision), and nearly exhaustive way (Fig. 1). The very large range of spatial scales covered is particularly impressive, from individual pebbles to km long river reaches (e.g., Brasington et al., 2012). Sub-cm accuracy also offers the possibility of detecting very subtle changes (Lague et al., 2013), a key attribute to measure slow processes such as bedrock abrasion (Beer et al., 2017). Given the recent emphasis on the role of riparian processes on fluvial processes, the ability to digitize vegetation in 3D in relation to channel morphology offers a unique perspective in biogeomorphology. However, many of the promises of TLS have not really been fulfilled, and the scientific potential of the TLS dataset remains often untapped. This is largely due to the challenging aspects surrounding the processing of TLS data which, to a large extent, also apply to structure from motion (SfM) surveys (Passalacqua et al., 2015). Three challenges, akin to typical Big Data issues can be identified as follows: 1. Data Complexity: TLS data are 3D data and nearly exhaustive. This makes for very rich data but also extremely complex to process as the relevant information (e.g., ground, grains, riverbanks, vegetation) must be detected prior to scientific analysis (Fig. 1). TLS data is also natively non-regularly sampled, with strong spatial variations in point density and requires processing methods that are more complex than for 2D raster-based data such as satellite imagery. 2. Data Volume: the latest generation of TLS instruments generates billions of points in a day. Manual processing cannot realistically be applied, and automatic processing methods are paramount. This requires good programing skills as well as a culture of machine learning and computer vision approaches that are not necessarily part of the training of geomorphologists and requires bridging the gap with computer sciences. 3. Data Incompleteness: despite the very large field of view of TLS sensors, the resulting 3D data do not sample the entire surface (Fig. 1). The ground-based viewpoint imparts missing data behind obstacles (grains of any size and vegetation) and the laser is generally fully absorbed by water resulting in the lack of bathymetric data, a strong limitation in river environments. Processing methods must account for this lack of information.
关键词： Terrestrial laser scanner,sediment transport,vegetation classification,bank erosion,3D digitization,point cloud processing,bedrock incision,fluvial geomorphology
更新于2025-09-23 15:19:57
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Machine learning-based mapping of micro-topographic earthquake-induced paleo Pulju moraines and liquefaction spreads from a digital elevation model acquired through laser scanning
摘要： The advent of public open source airborne laser scanning-produced digital elevation models (ALS DEM) has provided new perspectives on glacial geomorphology in the Nordic countries. Seismically-induced micro-topographic paleo-landforms can now be identified and mapped throughout the former Fennoscandian Ice Sheet, allowing spatial safety assessment for nuclear waste disposal. Automated machine learning techniques enable recognition of these fine-scale geomorphological features efficiently and in a consistent way nationwide. The current study focuses on automated recognition of paleo liquefaction spreads and Pulju moraines in northern Finland. Geomorphometric variables in different cell sizes were first derived from the 2 m ALS DEM by Gabor and principal curvature filtering to emphasize the elevational multi-scale texture of these paleo-seismic landforms. The Gabor textural variables were considered as a baseline method and the principal curvature features, including maximum and minimum curvature, were used because they have previously been proven critical in recognition of concave and convex elongated features. Both sets of raster variables were then turned into histogram-based features and input into a non-linear supervised multilayer perceptron early-stop committee which is a neural network classifier. The leave-one-out cross-validation performance results indicated principal curvature features to be highly successful with 94% accuracy. Principal curvatures provided a clear improvement to Gabor based features which provided significantly lower accuracies between 83?85%. The study demonstrates the high success of supervised neural network-based classification of ALS DEM data and derived textural features capturing the multi-scale nature of the micro-topographic liquefaction spreads and Pulju moraines. The approach could be utilized for time-efficient mapping of these paleo-seismic geomorphologies to complete paleo-seismic databases in formerly glaciated regions.
关键词： rotation invariant,histogram-based features,leave-one-out cross-validation,principal curvature,area invariant,multilayer perceptron,landforms,paleo-seismology,geomorphology,Gabor filter,Pulju moraine,liquefaction spreads
更新于2025-09-23 15:19:57
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A synthetic review of remote sensing applications to detect nearshore bars
摘要： Nearshore bars are important morphologic features associated with intermediate and dissipative natural beaches. Bars impact the direction, magnitude, and patterns of sediment transport in the nearshore. They serve as a buffer against extreme and meso-scale events. In this review article, we investigate remotely-based observations, specifically near-Earth and satellite imagery, which have been used to investigate nearshore bars. Several recent advances in technology and techniques allow the remote measurement of bar width and height, beach slope, shoreline orientation, and bar count. Video monitoring imagery is presently the most popular method to derive these data. However, spatial prediction models using satellite imagery can also provide reliable bar morphodynamic information.
关键词： Bar monitoring,Coastal morphodynamics,Remote sensing,Geomorphology,Bar storm response
更新于2025-09-04 15:30:14