10,230 research outputs found
Introduction to remote sensing of geomorphology
No full textIn this book you will find chapters reviewing and exploring state-of-the-art remote-sensing techniques relevant to geomorphology. We hope that the chapters will serve as both a reference for experienced practitioners and a guide to geomorphologists looking to use remote-sensing techniques to benefit their studies
Charlie May Simon materials
No full textThis collection contains materials relating to Arkansas author Charlie May Simon
Introduction to remote sensing of geomorphology
No full textIn this book you will find chapters reviewing and exploring state-of-the-art remote-sensing techniques relevant to geomorphology. We hope that the chapters will serve as both a reference for experienced practitioners and a guide to geomorphologists looking to use remote-sensing techniques to benefit their studies
Reservoir theory for studying the geochemical evolution of soils
Full text linkLinking mineral weathering rates measured in the laboratory to those measured at the landscape scale is problematic. In laboratory studies, collections of minerals are exposed to the same weathering environment over a fixed amount of time. In natural soils, minerals enter, are mixed within, and leave the soil via erosion and dissolution/leaching over the course of soil formation. The key to correctly comparing mineral weathering studies from laboratory experiments and field soils is to consistently define time. To do so, we have used reservoir theory. Residence time of a mineral, as defined by reservoir theory, describes the time length between the moment that a mineral enters (via soil production) and leaves (via erosion and dissolution/leaching) the soil. Age of a mineral in a soil describes how long the mineral has been present in the soil. Turnover time describes the time needed to deplete a species of minerals in the soil by sediment efflux from the soil. These measures of time are found to be sensitive to not only sediment flux, which controls the mineral fluxes in and out of a soil, but also internal soil mixing that controls the probability that a mineral survives erosion. When these measures of time are combined with published data suggesting that a mineral's dissolution reaction rate decreases during the course of weathering, we find that internal soil mixing, by partially controlling the age distribution of minerals within a soil, might significantly alter the soil's mass loss rate via chemical weathering.</p
Geomorphometric delineation of floodplains and terraces from objectively defined topographic thresholds
Full text linkFloodplain and terrace features can provide information about current and past fluvial processes, including channel response to varying discharge and sediment flux; sediment storage; and the climatic or tectonic history of a catchment. Previous methods of identifying floodplain and terraces from digital elevation models (DEMs) tend to be semi-automated, requiring the input of independent datasets or manual editing by the user. In this study we present a new, fully automated method of identifying floodplain and terrace features based on two thresholds: local gradient, and elevation compared to the nearest channel. These thresholds are calculated statistically from the DEM using quantile-quantile plots and do not need to be set manually for each landscape in question. We test our method against field-mapped floodplain initiation points, published flood hazard maps, and digitised terrace surfaces from seven field sites from the US and one field site from the UK. For each site, we use high-resolution DEMs derived from light detection and ranging (LiDAR) where available, as well as coarser resolution national datasets to test the sensitivity of our method to grid resolution. We find that our method is successful in extracting floodplain and terrace features compared to the field-mapped data from the range of landscapes and grid resolutions tested. The method is most accurate in areas where there is a contrast in slope and elevation between the feature of interest and the surrounding landscape, such as confined valley settings. Our method provides a new tool for rapidly and objectively identifying floodplain and terrace features on a landscape scale, with applications including flood risk mapping, reconstruction of landscape evolution, and quantification of sediment storage routing
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Proceedings of the CHIME Music and Human Computer Interaction Conference 2024 (Music and HCI)
Full text linkReproducible topographic analysis
No full text© 2020 Elsevier B.V. The ability to reproduce the results of an experiment is a fundamental component of the scientific method. However, precisely what is meant by the terms replicable and reproducible often varies between and within disciplines. Here, we present clear definitions of these two terms for geomorphic research and communicate the importance of performing reproducible analysis of remotely sensed topographic data. We argue that the reproducibility of an analysis is not a static, binary state but rather that there is a continuum from irreproducibility to replicability, with reproducibility falling between the two and that the aim of a researcher should be to get as close to reproducibility as possible, favoring a pragmatic rather than dogmatic approach. A brief review of the development of topographic analysis as a discipline is used to highlight the progress made in making topographic analysis more reproducible, and the challenges inherent within common working patterns. The chapter concludes with a series of recommendations on how best to achieve reproducible topographic analysis
How long is a hillslope?
Full text linkHillslope length is a fundamental attribute of landscapes, intrinsically linked to drainage density, landslide hazard, biogeochemical cycling and hillslope sediment transport. Existing methods to estimate catchment average hillslope lengths include inversion of drainage density or identification of a break in slope–area scaling, where the hillslope domain transitions into the fluvial domain. Here we implement a technique which models flow from point sources on hilltops across pixels in a digital elevation model (DEM), based on flow directions calculated using pixel aspect, until reaching the channel network, defined using recently developed channel extraction algorithms. Through comparisons between these measurement techniques, we show that estimating hillslope length from plots of topographic slope versus drainage area, or by inverting measures of drainage density, systematically underestimates hillslope length. In addition, hillslope lengths estimated by slope–area scaling breaks show large variations between catchments of similar morphology and area. We then use hillslope length–relief structure of landscapes to explore nature of sediment flux operating on a landscape. Distinct topographic forms are predicted for end-member sediment flux laws which constrain sediment transport on hillslopes as being linearly or nonlinearly dependent on hillslope gradient. Because our method extracts hillslope profiles originating from every ridgetop pixel in a DEM, we show that the resulting population of hillslope length–relief measurements can be used to differentiate between linear and nonlinear sediment transport laws in soil mantled landscapes. We find that across a broad range of sites across the continental United States, topography is consistent with a sediment flux law in which transport is nonlinearly proportional to topographic gradient
Global junction angle dataset
Full text linkA global dataset of channel junction angles.
File is a zipped csv file.
Data is extracted using LSDTopoTools (https://github.com/LSDtopotools). The DEM underlying this data is NASA’s 30 m resolution void-filled Shuttle Radar Topography Mission Digital Elevation Model Version 3 (SRTM-DEM).
Dataset headers: latitude (decimal degrees), longitude (decimal degrees), donor1_stream_order (Horton-Strahler stream order of first tributary), donor2_stream_order (Horton-Strahler stream order of second tributary), receiver_stream_order (Horton-Strahler stream order of the channel formed at the junction), donor1_drainage_area (m2), donor2_drainage_area (m2), this_junction_drainage_area (m2), donors_junction_angle (°), donor1_receiver_junction_angle (°), donor2_receiver_junction_angle (°), gradient_donor1, gradient_donor2, gradient_receiver, ai (aridity index value from Trabucco et al., (2019)), AR (ratio of tributary drainage areas), AI_class (as according to Trabucco et al., (2019))junction_angles_global.zip: A zipped file that contains a single csv file, junction_angles_global.csv
This file contains the headers:
latitude (decimal degrees), longitude (decimal degrees), donor1_stream_order (Horton-Strahler stream order of first tributary), donor2_stream_order (Horton-Strahler stream order of second tributary), receiver_stream_order (Horton-Strahler stream order of the channel formed at the junction), donor1_drainage_area (m2), donor2_drainage_area (m2), this_junction_drainage_area (m2), donors_junction_angle (°), donor1_receiver_junction_angle (°), donor2_receiver_junction_angle (°), gradient_donor1, gradient_donor2, gradient_receiver, ai (aridity index value from Trabucco et al., (2019)), AR (ratio of tributary drainage areas), AI_class (as according to Trabucco et al., (2019)
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