The Generic Mapping Tools Version 6 Wessel, P.; Luis, J. F.; Uieda, L. ...
Geochemistry, geophysics, geosystems : G3,
November 2019, 2019-11-00, 20191101, Letnik:
20, Številka:
11
Journal Article
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The Generic Mapping Tools (GMT) software is ubiquitous in the Earth and ocean sciences. As a cross‐platform tool producing high‐quality maps and figures, it is used by tens of thousands of scientists ...around the world. The basic syntax of GMT scripts has evolved very slowly since the 1990s, despite the fact that GMT is generally perceived to have a steep learning curve with many pitfalls for beginners and experienced users alike. Reducing these pitfalls means changing the interface, which would break compatibility with thousands of existing scripts. With the latest GMT version 6, we solve this conundrum by introducing a new “modern mode” to complement the interface used in previous versions, which GMT 6 now calls “classic mode.” GMT 6 defaults to classic mode and thus is a recommended upgrade for all GMT 5 users. Nonetheless, new users should take advantage of modern mode to make shorter scripts, quickly access commonly used global data sets, and take full advantage of the new tools to draw subplots, place insets, and create animations.
Plain Language Summary
The Generic Mapping Tools software is widely used in Earth and ocean sciences to process data and make maps and illustrations. This new version simplifies usage, adds quick access to key data sets, and provides a tool for making scientific animations.
Key Points
A new version of the Generic Mapping Tools (GMT) is released
A new modern mode, complementing the existing classic mode, greatly simplifies GMT scripting
Easy access to remote data sets and advanced animation building facilitate science communication
The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Within the past two decades, soil scientists have applied ML ...to a wide range of scenarios, by mapping soil properties or classes with various ML algorithms, on spatial scale from the local to the global, and with depth. The wide adoption of ML for soil mapping was made possible by the increase in data availability, the ease of accessing environmental spatial data, and the development of software solutions aided by computational tools to analyse them. In this article, we review the current use of ML in DSM, identify the key challenges and suggest solutions from the existing literature. There is a growing interest in the use of ML in DSM. Most studies emphasize prediction and accuracy of the predicted maps for applications, such as baseline production of quantitative soil information. Few studies account for existing soil knowledge in the modelling process or quantify the uncertainty of the predicted maps. Further, we discuss the challenges related to the application of ML for soil mapping and suggest solutions from existing studies in the natural sciences. The challenges are: sampling, resampling, accounting for the spatial information, multivariate mapping, uncertainty analysis, validation, integration of pedological knowledge and interpretation of the models. Overall, the current literature shows few attempts in understanding the underlying soil structure or process using the predicted maps and the ML model, for example by generating hypotheses on mechanistic relationships among variables. In this regard, several additional challenging aspects need to be considered, such as the inclusion of pedological knowledge in the ML algorithm or the interpretability of the calibrated ML model. Tackling these challenges is critical for ML to gain credibility and scientific consistency in soil science. We conclude that for future developments, ML could incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to couple model prediction with pedological explanation and understanding of the underlying soil processes.
This article reviews the current research and applications of various digital soil mapping (DSM) techniques used to map Soil Organic Carbon (SOC) concentration and stocks following a systematic ...mapping approach from 2013 until present (18 February 2019). It is intended that this review of relevant literature will assist prospective researchers by identifying knowledge clusters and gaps in relation to the digital mapping of SOC. Of 120 studies, most were clustered in some specific countries such as China, Australia and the USA. The highest number publications were in 2016 and 2017. Regarding the predictive models, there was a progression from Linear Models towards Machine Learning (ML) techniques, and hybrid models in Regression Kriging (RK) framework performed better than individual models. Multiple Linear Regression (MLR) was the most frequently used method for predicting SOC, although it was outperformed by other ML techniques in most studies. Random Forest (RF) was found to perform better than MLR and other ML techniques in most comparative studies. Other common and competitive techniques were Cubist, Neural Network (NN), Boosted Regression Tree (BRT), Support Vector Machine (SVM) and Geographically Weighted Regression (GWR). Due to the inconsistency in various comparative studies, it would be advisable to calibrate the competitive algorithms using specific experimental datasets. This review also reveals the environmental covariates that have been identified as the most important by RF technique in recent years in regard to digital mapping of SOC, which may assist in selecting optimum sets of environmental covariates for mapping SOC. Covariates representing organism/organic activities were among the most frequent among top five covariates, followed by the variables representing climate and topography. Climate was reported to be influential in determining the variation in SOC level at regional scales, followed by parent materials, topography and land use. However, for mapping at a resolution that represents smaller areas such as a farm- or plot-scale, land use and vegetation indices were stated to be more influential in predicting SOC. Furthermore, unlike a previous review work, all recent studies in this review incorporated validation and 41% of them estimated spatially explicit prediction of uncertainty. Only 9.16% studies performed external validation, whereas most studies used data-splitting and cross-validation techniques which may not be the best options for datasets obtained through non-probability sampling.
GPlates: Building a Virtual Earth Through Deep Time Müller, R. Dietmar; Cannon, John; Qin, Xiaodong ...
Geochemistry, geophysics, geosystems,
July 2018, 2018-07-00, 20180701, Letnik:
19, Številka:
7
Journal Article
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GPlates is an open‐source, cross‐platform plate tectonic geographic information system, enabling the interactive manipulation of plate‐tectonic reconstructions and the visualization of geodata ...through geological time. GPlates allows the building of topological plate models representing the mosaic of evolving plate boundary networks through time, useful for computing plate velocity fields as surface boundary conditions for mantle convection models and for investigating physical and chemical exchanges of material between the surface and the deep Earth along tectonic plate boundaries. The ability of GPlates to visualize subsurface 3‐D scalar fields together with traditional geological surface data enables researchers to analyze their relationships through geological time in a common plate tectonic reference frame. To achieve this, a hierarchical cube map framework is used for rendering reconstructed surface raster data to support the rendering of subsurface 3‐D scalar fields using graphics‐hardware‐accelerated ray‐tracing techniques. GPlates enables the construction of plate deformation zones—regions combining extension, compression, and shearing that accommodate the relative motion between rigid blocks. Users can explore how strain rates, stretching/shortening factors, and crustal thickness evolve through space and time and interactively update the kinematics associated with deformation. Where data sets described by geometries (points, lines, or polygons) fall within deformation regions, the deformation can be applied to these geometries. Together, these tools allow users to build virtual Earth models that quantitatively describe continental assembly, fragmentation and dispersal and are interoperable with many other mapping and modeling tools, enabling applications in tectonics, geodynamics, basin evolution, orogenesis, deep Earth resource exploration, paleobiology, paleoceanography, and paleoclimate.
Plain Language Summary
The GPlates virtual globe software provides the capability to reconstruct geodata attached to tectonic plates to develop and modify models that describe how the plates and their boundaries have evolved through time. It allows users to deform plates and to visualize surface tectonics in the context of convecting mantle structure and evolution by importing seismic tomography models or outputs from geodynamic models. GPlates applications include tectonics, geodynamics, basin evolution, orogenesis, deep Earth resource exploration, paleobiology, paleoceanography, and paleoclimate. The software is enabling end‐users in universities, government organizations, industry, and schools to explore the evolution of planet Earth on their desktop.
Key Points
GPlates is an open‐source plate tectonic geographic information system, enabling the interactive manipulation of tectonic reconstructions
GPlates enables the building of topological plate models, including plate deformation, and allows the visualization of subsurface volumes
GPlates applications include tectonics, geodynamics, basin evolution, orogenesis, resource exploration, paleobiology, and paleoclimate
Digital soil mapping (DSM) is a successful sub discipline of soil science with an active research output. The success of digital soil mapping is a confluence of several factors in the beginning of ...2000 including the increased availability of spatial data (digital elevation model, satellite imagery), the availability of computing power for processing data, the development of data-mining tools and GIS, and numerous applications beyond geostatistics. In addition, there was an increased global demand for spatial data including uncertainty assessments, and a rejuvenation of many soil survey and university centres which helped in the spreading of digital soil mapping technologies and knowledge. The theoretical framework for digital soil mapping was formalised in a 2003 paper in Geoderma. In this paper, we define what constitutes digital soil mapping, sketch a brief history of it, and discuss some lessons. Digital soil mapping requires three components: the input in the form of field and laboratory observational methods, the process used in terms of spatial and non-spatial soil inference systems, and the output in the form of spatial soil information systems, which includes outputs in the form of rasters of prediction along with the uncertainty of prediction. We also illustrate the history with a number of sleeping beauty papers that seem too precocious and consequently the ideas were not taken up by contemporaries and largely forgotten. It took another 30 to 40years before the ideas were rediscovered and then flourished. Examples include proximal soil sensing that was developed in the 1920s, soil spectroscopy in 1970s, and soil mapping based on similarity of environmental factors in 1979. In summary, the coming together of emerging topics and timeliness greatly assists in the development of paradigm. We learned that research and ideas that are too precocious are largely ignored — such work warrants (re)discovery.
•Digital soil mapping is defined in terms of input, process, and output.•Digital soil mapping has shifted from a research phase into practical use.•Confluence of emerging topics and timeliness warrants the success of a new paradigm.•Research that is too precocious is left unrecognised and unknown.
Digital Soil Mapping (DSM) products are simplified representations of more complex and partially unknown patterns of soil variations. Therefore, any prediction of a soil property that can be derived ...from these products has an irreducible uncertainty that needs to be mapped. The objective of this study was to compare the most current DSM method – Regression Kriging (RK) – with a new approach derived from RandomForest – Quantile Regression Forest (QRF) – in regard to their ability of predicting the uncertainties of GlobalSoilMap soil property grids. The comparison was performed for three soil properties, pH, organic carbon and clay content at 5–15cm depth in a 27,236km2 Mediterranean French region with sparse sets of measured soil profiles (1/13.5km2) and for a set of environmental covariates characterizing the relief, climate, geology and land use of the region. Apart from classical performance indicators, comparisons involved accuracy plots and the visual examinations of the uncertainty maps provided by the two methods.
The results obtained for the three soil properties showed that QRF provided more accurate and more interpretable predicted patterns of uncertainty than RK did, while having similar performances in predicting soil properties. The use of QRF in operational DSM is therefore recommended, especially when spatial sampling of soil observations are too sparse for applying RK.
•Two methods of uncertainty estimation of 3 GSM products were tested in South France.•100 validations sets were built by iterative sampling of 25% of the sites.•Accuracy plots were proposed for validating uncertainty predictions.•Quantile Regression Forests outperformed Regression Kriging in mapping uncertainty.•Quantile Regression Forests is recommended in situations of sparse soil data.
Geomorphology of the oceans Harris, P.T.; Macmillan-Lawler, M.; Rupp, J. ...
Marine geology,
06/2014, Letnik:
352
Journal Article
Recenzirano
We present the first digital seafloor geomorphic features map (GSFM) of the global ocean. The GSFM includes 131,192 separate polygons in 29 geomorphic feature categories, used here to assess ...differences between passive and active continental margins as well as between 8 major ocean regions (the Arctic, Indian, North Atlantic, North Pacific, South Atlantic, South Pacific and the Southern Oceans and the Mediterranean and Black Seas). The GSFM provides quantitative assessments of differences between passive and active margins: continental shelf width of passive margins (88km) is nearly three times that of active margins (31km); the average width of active slopes (36km) is less than the average width of passive margin slopes (46km); active margin slopes contain an area of 3.4millionkm2 where the gradient exceeds 5°, compared with 1.3millionkm2 on passive margin slopes; the continental rise covers 27millionkm2 adjacent to passive margins and less than 2.3millionkm2 adjacent to active margins. Examples of specific applications of the GSFM are presented to show that: 1) larger rift valley segments are generally associated with slow-spreading rates and smaller rift valley segments are associated with fast spreading; 2) polar submarine canyons are twice the average size of non-polar canyons and abyssal polar regions exhibit lower seafloor roughness than non-polar regions, expressed as spatially extensive fan, rise and abyssal plain sediment deposits — all of which are attributed here to the effects of continental glaciations; and 3) recognition of seamounts as a separate category of feature from ridges results in a lower estimate of seamount number compared with estimates of previous workers.
•We present the first digital, global, seafloor geomorphic features map (GSFM).•Methods used to produce the GSFM are documented.•We provide quantitative geomorphic differences between passive and active margins.•Geomorphic differences between polar and non-polar margins are defined.•The GSFM provides data on rift valley size related to seafloor spreading rate.
We adapted the automated, open source NASA Ames Stereo Pipeline (ASP) to generate digital elevation models (DEMs) and orthoimages from very-high-resolution (VHR) commercial imagery of the Earth. ...These modifications include support for rigorous and rational polynomial coefficient (RPC) sensor models, sensor geometry correction, bundle adjustment, point cloud co-registration, and significant improvements to the ASP code base. We outline a processing workflow for ∼0.5m ground sample distance (GSD) DigitalGlobe WorldView-1 and WorldView-2 along-track stereo image data, with an overview of ASP capabilities, an evaluation of ASP correlator options, benchmark test results, and two case studies of DEM accuracy. Output DEM products are posted at ∼2m with direct geolocation accuracy of <5.0m CE90/LE90. An automated iterative closest-point (ICP) co-registration tool reduces absolute vertical and horizontal error to <0.5m where appropriate ground-control data are available, with observed standard deviation of ∼0.1–0.5m for overlapping, co-registered DEMs (n=14,17). While ASP can be used to process individual stereo pairs on a local workstation, the methods presented here were developed for large-scale batch processing in a high-performance computing environment. We are leveraging these resources to produce dense time series and regional mosaics for the Earth’s polar regions.
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