% -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % Second edition book and chapters @book{Reuter2026geomorphometry2ndEd, title = {Geomorphometry - concepts, software, applications}, author = {Reuter, Hannes I and Grohmann, Carlos Henrique and Lecours, Vincent}, abstract = {Geomorphometry: Concepts, Software, Applications, Second Edition aims to inform, educate, and assist users in the field by expanding the reach of the Geomorphometry community's knowledge. Geomorphometry, the science of quantitative land-surface analysis, evolved from geomorphology and quantitative terrain analysis, rooted in 19th-century geometry and physical geography. The modern discipline addresses the processing of elevation data, topography visualization, and numerous numerical analyses. Focusing on continuous land-surface and discrete features like watersheds, geomorphometry's operational goal is the extraction of measures and spatial features from digital topography.Since the book's first edition in 2009, there have been significant advancements in data production methods, the data itself, and software applications. The editors have proposed a second edition, maintaining the original structure while incorporating these improvements. The book also covers the refinement and analysis of digital elevation models (DEMs), the use of GIS tools, and the application of geomorphometric techniques in various fields such as hydrology, ecology, and urban planning. - Provides a unique consolidation of theoretical background, software applications, and applications in the field - Presents researchers with the unique opportunity to gain a complete overview within a single book not limited to a single software or single perspective - Shows content that is written in close collaboration with members of the International Society of Geomorphometry.}, url = {https://doi.org/10.1016/c2023-0-52503-0}, DOI = {10.1016/c2023-0-52503-0}, publisher = {Elsevier}, ISBN = {978-0-443-33376-7}, ISSN = {0166-2481}, year = 2026, address = {Amsterdam, Netherlands}, } @inbook{Reuter2026, title = {{Geomorphometry 2.0: a brief guide}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00011-2}, DOI = {10.1016/b978-0-44-333376-7.00011-2}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Reuter, Hannes I. and Grohmann, Carlos H. and Lecours, Vincent}, year = {2026}, pages = {3–45}, abstract ={Geomorphometry, the science of quantitative land-surface analysis, evolved from geomorphology and quantitative terrain analysis. It is rooted in 19th-century geometry and physical geography. Since this book's first edition in 2009, there have been significant advancements in data used for geomorphometry, their production methods, the software to process and analyze them, and their applications. This chapter begins by defining geomorphometry and its scope, then outlines key concepts, including data structures, the definition of the land surface, and other terrain-related terminology. Algorithms for geomorphometry are also presented. The chapter then places geomorphometry in its historical context before outlining the various disciplines that use it. The chapter finally introduces the three different sections of the book: I) the concepts, II) the software, and III) the applications, before introducing the common dataset that was provided to all contributors for use as a case study.} } @inbook{Evans2026, title = {{Conceptual, mathematical, & digital models of the land surface}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00012-4}, DOI = {10.1016/b978-0-44-333376-7.00012-4}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Evans, Ian S. and Minár, Jozef and Feciskanin, Richard}, year = {2026}, pages = {47–81}, abstract = {The single-valued land surface, is systematically examined through three interlocking layers of representation: conceptual, mathematical, and digital models. Conceptually, it is scale-dependent and dynamic, with a skeleton of ridge lines and valley lines. Bare-earth Digital Terrain Models are distinguished from uppermost Digital Surface Models. Geodetic considerations include datums and coordinate projections. Digitally, surfaces are represented via triangulated irregular networks, contour-slopeline models, or the most dominant regular grids, with resolution guided by the Nyquist–Shannon sampling theorem. Crucially, the chapter provides a detailed derivation and comparison of algorithms for computing first-, second-, and third-order partial derivatives from gridded datasets. This comparative analysis covers central differences, the Horn weighted-difference approach, and polynomial fitting methods including Evans–Young, Shary, and Zevenbergen–Thorne. Ultimately, the trade-off between method error and data error depends on grid resolution and terrain character, meaning algorithm selection must be tailored to specific dataset properties and analytical goals.} } @inbook{Viana2026, title = {{DEM production methods}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00013-6}, DOI = {10.1016/b978-0-44-333376-7.00013-6}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Viana, Camila D. and Lecours, Vincent and Rizzoli, Paola and Reuter, Hannes I.}, year = {2026}, pages = {83–121}, abstract = {Encompassing a wide range of ground survey and remote sensing techniques, DEM production methods have evolved substantially over the past decades. This chapter reviews the main approaches to height sampling, gridding, and interpolation, discussing the strengths and limitations of methods ranging from linear interpolation and kriging to splines and the ANUDEM algorithm. It further describes the primary DEM data sources — ground surveys, topographic map digitization, photogrammetry and Structure from Motion (SfM-MVS), airborne and spaceborne lidar, and SAR interferometry — alongside bathymetric techniques including satellite-derived bathymetry and acoustic remote sensing. The chapter closes with an updated comparative summary of resolution, accuracy, coverage, and cost across key DEM products.} } @inbook{Grohmann2026, title = {{DEM sources—from local to global}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00014-8}, DOI = {10.1016/b978-0-44-333376-7.00014-8}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Grohmann, Carlos H. and Reuter, Hannes I. and Lecours, Vincent and Guth, Peter L.}, year = {2026}, pages = {123–172}, abstract = {This chapter provides an overview of sources of digital elevation data available for global and regional coverage of Earth's terrestrial and oceanic areas, as well as other planetary bodies of the Solar System. Links to official data portals and documentation are provided to help readers locate and use these resources effectively. When the first edition of this book was published, the availability of digital elevation data was far more limited than it is today. In many regions, topographic information was still derived primarily from digitized contour lines on paper maps, and SRTM represented a relatively new dataset. Choices were constrained, spatial resolution was modest by today's standards, and access to high-quality elevation data was often restricted by cost, availability, or institutional capacity. Today, more and more datasets are freely available, and users have access to multiple global and regional DEMs, as well as very-high-resolution datasets. Nevertheless, users must always remember that the quality of any analysis depends on understanding the data being used, in terms of its resolution, accuracy, acquisition method, and known limitations.} } @inbook{Hawker2026, title = {{Preparation of DEMs for geomorphometric analysis}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00015-X}, DOI = {10.1016/b978-0-44-333376-7.00015-x}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Hawker, Laurence and Lindsay, John B. and Papasodoro, Charles and Huber, Martin}, year = {2026}, pages = {173–211}, abstract = {Digital Elevation Models (DEMs) underpin geomorphological, hydrological, and environmental analyses, but invariably contain errors that propagate into derived land-surface parameters. This chapter reviews preprocessing algorithms for detecting, reducing, and removing DEM errors, organised into four groups. Empirical methods rely on expert knowledge of terrain features and, while effective, are time-consuming at fine scales. Filtering methods include geostatistical approaches that use variogram models and thresholds to suppress outliers. Simulation methods are fully data-driven, averaging multiple equiprobable DEM realisations to produce a more natural representation without specifying filter weights or window sizes. Machine learning methods have proliferated rapidly, both in dedicated algorithms and in correcting global DEM products. We identify three emerging trends: the continued expansion of machine and deep learning supported by multi-sensor integration; the growing importance of the temporal dimension as multi-temporal lidar becomes available; and the automation of DEM processing through cloud-based workflows.} } @inbook{Guth2026, title = {{Basic land-surface parameters}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00016-1}, DOI = {10.1016/b978-0-44-333376-7.00016-1}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Guth, Peter L. and Trevisani, Sebastiano and Minár, Jozef and Jenčo, Marián}, year = {2026}, pages = {213–255}, abstract = {Land-surface parameters (LSPs) are geomorphometric characteristics derived from a digital elevation model (DEM) without additional information. The most common include slope and aspect, hillshades, openness, roughness indices and a large family of curvatures and changes of curvature. Results of the LSP computation depend on the resolution of the DEM, the selected algorithm, and the window chosen. Additional computations include line-of-sight or topographic profiles, viewsheds, and computation of the horizon, which also depend on the starting point location. The ease of computing LSPs depends on the complexity of the algorithms and the number of choices required from the user. Further complexity arises when computing multi-scale LSPs. For many applications LSPs provide more useful information than the original DEM.} } @inbook{Lindsay2026, title = {{Land-surface parameters and objects in hydrology}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00017-3}, DOI = {10.1016/b978-0-44-333376-7.00017-3}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Lindsay, John B.}, year = {2026}, pages = {257–283}, abstract = {This chapter explores the application of geomorphometry in hydrological modelling, focusing on deriving parameters and objects from Digital Elevation Models (DEMs). Key concepts covered include flow lines, flow direction, and contributing area, which are fundamental to understanding water movement across landscapes. The text details the preparation of DEMs for hydrological analysis, addressing issues like depressions and flat areas that require hydrological enforcement. It then delves into various flow algorithms, differentiating between nondispersive and dispersive methods, highlighting their respective strengths and weaknesses in simulating water flow and their impact on derived parameters. The chapter also examines flow accumulation algorithms, crucial for calculating upslope areas, and discusses land-surface parameters derived from catchment areas and flow lines, such as the Topographic Wetness Index (TWI), Stream Power Index (SPI), and Length-Slope (LS) factor. Finally, it covers the extraction of land-surface objects like river networks and watershed boundaries based on flow variables, emphasizing the importance of selecting appropriate algorithms and considering data quality for accurate hydrological analysis and modelling.} } @inbook{Bhner2026, title = {{Land-surface parameters in climatology and meteorology}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00018-5}, DOI = {10.1016/b978-0-44-333376-7.00018-5}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {B\"{o}hner, J\"{u}rgen and Antonić, Oleg and Hasson, Shabeh ul}, year = {2026}, pages = {285–331}, abstract = {This chapter addresses the significant influence of the Earth's land surface on climate and meteorological processes at different spatial and temporal scales. Altitude and topography strongly affect key climatic variables such as surface radiation, temperature, moisture, wind patterns, and precipitation distribution. We describe dynamic and thermodynamic topographic effects, which modulate near-surface atmospheric processes including radiation fluxes, differential heating, airflow deflection and topographic precipitation. Various scales of topographically induced phenomena are discussed with a particular focus on climatic features at topo-climatic scales. The chapter outlines methods for parameterizing these effects using geomorphometric parameters derived from Digital Elevation Models (DEMs), facilitating improved spatial climate modelling and supporting statistical and dynamical climate downscaling in complex terrain.} } @inbook{Jasiewicz2026, title = {{Landform—a key concept of geomorphology and geomorphometry}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00019-7}, DOI = {10.1016/b978-0-44-333376-7.00019-7}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Jasiewicz, Jarosław}, year = {2026}, pages = {333–368}, abstract = {This chapter discusses the evolution of the landform concept as a central component in geomorphology and modern geomorphometry. It focuses on the dual perception of the Earth's surface: as a continuous phenomenon and as a set of discrete units referred to as landforms. The analysis contrasts the traditional genetic-semantic classification approach with numerical methods that primarily utilize digital elevation models. The chapter provides a detailed discussion of the multilevel hierarchy of geomorphological objects, including elementary forms, complex forms, and extensive landscape systems that operate across various spatial and temporal scales. The second part systematically reviews methods for the automatic classification of elementary forms using digital elevation models. It analyzes systems based on profiles and local curvatures, the index of relative topographic position (such as the TPI), and classification techniques derived from computer vision, with particular attention to the geomorphon algorithm. The discussion also covers methods for studying landscape types based on geomorphometric signatures. Additionally, the chapter addresses the influence of data resolution and the choice of analytical window size on the accuracy of relief identification. The conclusion highlights that traditional numerical procedures are increasingly being replaced by advanced artificial intelligence and deep learning methods, which are shaping the future direction of the discipline.} } @inbook{Grohmann2026, title = {{Overview of software packages used in geomorphometry}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00021-5}, DOI = {10.1016/b978-0-44-333376-7.00021-5}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Grohmann, Carlos H. and Lecours, Vincent and Reuter, Hannes I.}, year = {2026}, pages = {371–383}, abstract = {This section considers some of the software that have been developed to implement the theory and techniques for measurement of the shape of the landscape. What is considered here is not an exhaustive list of all geomorphometry software, but rather a selection of packages that represent different approaches to analysing surface data. They range from generic Geographic Information Systems (GIS) to specific domain-focused application software; from proprietary commercial software to free open-source (FOSS) solutions; from graphical user interfaces to scripting. Eight desktop packages are discussed in this section: ArcGIS, GRASS, MICRODEM, PCRaster (in QGIS), SAGA, RiverTools, Whitebox Geospatial, and TopoToolbox. Additionally, there is one chapter dedicated to geomorphometry using FOSS programming languages, with example analyses in R, Python, and Julia.} } @inbook{Arosio2026, title = {{Geomorphometry in ArcGIS}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00022-7}, DOI = {10.1016/b978-0-44-333376-7.00022-7}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Arosio, Riccardo and Kopp, Steve}, year = {2026}, pages = {385–415}, abstract = {In the past two decades ArcGIS has rapidly evolved, working closely with the scientific community to provide a wide range of tools and utilities for the specific purpose of geomorphometric analysis. Simultaneously, the community has been incredibly prolific in building and sharing toolboxes and procedures. In this chapter an introduction to the use of Esri ArcGIS for geomorphometric analysis is given. Firstly, the reader is provided with basic information on the ArcGIS Pro interface, access, creation and preparation of digital elevation models. Secondly, the chapter focuses on the production of land surface parameters, hydrologic analysis, and visualization. Lastly, some guidance on how processing workflows and new analytical methods can be implemented in Python is given. The chapter also presents a list of relevant external applications and toolboxes, which can be integrated with ArcGIS through its package manager to extend its capabilities.} } @inbook{White2026, title = {{Geomorphometry in GRASS}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00023-9}, DOI = {10.1016/b978-0-44-333376-7.00023-9}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {White, Corey T. and Mitasova, Helena and Neteler, Markus and Petrasova, Anna and Hofierka, Jaroslav}, year = {2026}, pages = {417–442}, abstract = {Geomorphometry quantifies geometric properties of the land surface for use in hydrological, ecological, and geomorphological modelling. Modern workflows are increasingly computationally demanding as LiDAR and global DEM products push analyses to finer resolutions and larger extents. GRASS is an open-source geospatial processing engine with an ecosystem of geomorphometric tools, scaling from desktop to cloud and HPC environments and offering over 800 modules for raster, vector, voxel, and point cloud analysis, alongside Python, R, and C/C++ interfaces. This chapter highlights GRASS capabilities most relevant to geomorphometry, including signature tools for hydrological analysis (r.watershed, r.stream toolset), solar radiation (r.sun), visibility (r.viewshed, r.horizon), and overland flow and sediment transport simulation (r.sim.water, r.sim.sediment). We also cover scripting and automation through the Python API, Jupyter visualizations, and R, along with new support for temporal datasets and approaches for reproducible and batch workflows at scale.} } @inbook{Guth2026, title = {{Geomorphometry in MICRODEM}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00024-0}, DOI = {10.1016/b978-0-44-333376-7.00024-0}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Guth, Peter L.}, year = {2026}, pages = {443–456}, abstract = {MICRODEM is a Windows graphical user interface (GUI) geographical information system (GIS) program with a heavy emphasis on geomorphometry. The open source Delphi (object Pascal) code is on github; a compiled Windows executable is also available. MICRODEM performed the data manipulation and analysis for the DEMIX work, which compared and ranked one arc second global digital elevation models and recommends the Copernicus DEM. The program also compares algorithms and the output from other programs, especially slope and aspect. MICRODEM automates calls to command line programs like GDAL, GRASS, SAGA, and Whitebox within the GUI environment, sparing users the challenges of writing calls to directly use the command line.} } @inbook{vanderKwast2026, title = {{Geomorphometry in QGIS/PCRaster}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00025-2}, DOI = {10.1016/b978-0-44-333376-7.00025-2}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {van der Kwast, Johannes}, year = {2026}, pages = {457–472}, abstract = {This chapter introduces QGIS as a powerful open-source GIS platform and highlights its plugin ecosystem for terrain-focused workflows. We present plugins for acquiring elevation data from global DEM sources, and we discuss the expanding set of point-cloud processing capabilities available within QGIS. A central component of the chapter is the PCRaster Tools plugin, a dedicated processing provider that brings around one hundred map algebra operations into the QGIS Processing framework. These operations support a wide range of geomorphometric and process-based analyses, including local, focal, and zonal terrain calculations, as well as specialised functions for modelling water flow, erosion and deposition, and the transport of materials across landscapes. The tools can be used interactively, incorporated into graphical models, or scripted through PyQGIS, enabling flexible and reproducible spatial workflows. The chapter also explores QGIS's 2D, 3D, and temporal visualisation features.} } @inbook{Conrad2026, title = {{Geomorphometry in SAGA}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00026-4}, DOI = {10.1016/b978-0-44-333376-7.00026-4}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Conrad, Olaf and Wichmann, Volker}, year = {2026}, pages = {473–492}, abstract = {This chapter presents SAGA as an open-source, cross-platform GIS for high-performance geoscientific analysis with a strong emphasis on terrain analysis and geomorphometry. It outlines SAGA's modular architecture - an extensible geospatial API, an interactive GUI for exploration and visualization, and command-line and Python interfaces for automation - enabling scalable workflows from desktops to HPC systems. The chapter highlights straightforward installation and integration with common scientific and GIS ecosystems, supporting scripted, reproducible analyses. It provides a general workflow for preparing elevation data, including importing, projecting, and deriving DEMs from sources such as lidar. An overview of terrain analysis tools is given, including morphometry, hydrology, climate, illumination and visibility, landform classification, and selected process simulations. Compound tools encapsulate best practices to rapidly generate multi-parameter outputs. Accompanying scripts demonstrate the complete workflow used to process the Ponui Island data set. Scripting and automation support transparent data processing and reproducible results, positioning SAGA as a versatile platform for education, research, and professional applications.} } @inbook{Schwanghart2026, title = {{Geomorphometry in TopoToolbox}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00027-6}, DOI = {10.1016/b978-0-44-333376-7.00027-6}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Schwanghart, Wolfgang and Kearney, William S. and Scherler, Dirk}, year = {2026}, pages = {493–507}, abstract = {TopoToolbox is a software for digital terrain analysis written in MATLAB. Its design fosters creative research approaches in digital elevation model (DEM) analysis, leveraging the flexible MATLAB programming ecosystem for rapid prototyping, visualization, automation, and interactive application development. TopoToolbox adopts techniques from object-oriented programming and offers fast algorithms based on graph theory, image processing and mathematical morphology, and has provided the backbone of other simulation and analysis software focusing on digital terrain data. Here we report on the design of TopoToolbox and provide examples on DEM visualization, DEM preprocessing, river-profile and χ-analysis. Further developments will facilitate engagement of the research community and will make the software available in other languages such as R and Python.} } @inbook{Lindsay2026, title = {{Geomorphometry in Whitebox}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00028-8}, DOI = {10.1016/b978-0-44-333376-7.00028-8}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Lindsay, John B.}, year = {2026}, pages = {509–530}, abstract = {The chapter describes Whitebox, a comprehensive suite of geospatial analysis software with a strong focus on geomorphometric and hydrologic applications. The chapter describes how to get started and interact with Whitebox, allowing the readers to follow workflows to prepare data and deriving a variety of DEM products include mapping land-surface objects and landforms including multiscale geomorphometric analysis.} } @inbook{Peckham2026, title = {{Geomorphometry in RiverTools}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00029-X}, DOI = {10.1016/b978-0-44-333376-7.00029-x}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Peckham, Scott D.}, year = {2026}, pages = {531–551}, abstract = {RiverTools is a point-and-click application that makes it easy to extract all kinds of digital products and measurements from a DEM—with an emphasis on hydrology, flow paths, and river networks—and then to analyze and visualize those products. Available since 1998, it has evolved significantly since its first release. Versions 1 through 4 were desktop applications that supported the Windows, MacOS, and Linux operating systems. The performance, capabilities, and ease-of-use of RiverTools have seen continual improvements over the years, with feedback from a large and international community of users. Version 5.0 represents a major leap forward that builds on this long development history. This new version is a web app that runs in your browser and performs computations in the cloud.} } @inbook{Ilich2026, title = {{Geomorphometry using open-source programming languages}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00030-6}, DOI = {10.1016/b978-0-44-333376-7.00030-6}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Ilich, Alexander R. and Nowosad, Jakub and Hugonnet, Romain and Pronk, Maarten}, year = {2026}, pages = {553–581}, abstract = {This chapter provides an introduction to the use of the open-source programming languages R, Python, and Julia for geomorphometric analysis. We discuss the installation and setup of each language, introduce commonly used spatial-analysis packages, and demonstrate comparable workflows for reading, visualizing, and processing topographic data. Using lidar-derived elevation data as an example, we demonstrate comparable workflows for constructing digital terrain models (DTMs), deriving land-surface parameters, and visualizing terrain data across the three programming environments. We also highlight differences in ecosystem design, performance, and available functionality among the languages and their associated packages. Finally, we discuss the broader advantages of open-source software in geomorphometry, including reproducibility, transparency, extensibility, and integration with modern data-science and high-performance computing workflows.} } @inbook{Lecours2026, title = {{Geomorphometry—a key to surface mapping and modelling}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00032-X}, DOI = {10.1016/b978-0-44-333376-7.00032-x}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Lecours, Vincent and Grohmann, Carlos H. and Reuter, Hannes I.}, year = {2026}, pages = {585–596}, abstract = {Topography is one of the key factors in controlling many of the most significant natural processes of interest to humans and other living beings. This chapter examines how digital elevation models can act as sources of information to support the mapping and modeling of landscapes and phenomena associated with them. The most common groups of applications of geomorphometry are presented, including Earth and planetary sciences, computer science, engineering, and environmental sciences. A summary of each chapter included in the “Applications“ section of the book is provided. The topics of these chapters are: soil mapping, geomorphology, mass movements, machine learning and computer vision, ecology, hydrology, archaeology, climate change, and underwater and extraterrestrial applications. A brief context for each application is provided, and, when relevant, a contrast is drawn with the first version of this book. The last chapter introduced also presents perspectives on what the community believes the future of geomorphometry to be.} } @inbook{SamuelRosa2026, title = {{Soil-mapping applications}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00033-1}, DOI = {10.1016/b978-0-44-333376-7.00033-1}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Samuel-Rosa, Alessandro and Nussbaum, Madlene and Qin, Cheng-Zhi and Vasconcelos, Vinicius}, year = {2026}, pages = {597–626}, abstract = {This chapter provides a comprehensive overview of soil mapping, tracing its evolution from conventional field-based surveys to modern, data-driven approaches. It explores the fundamental concept of soil-landscape relationships, emphasizing the influence of key soil-forming factors, particularly topography and its associated land-surface parameters like elevation, slope, aspect, and curvature. The discussion details how land-surface parameters are integrated into both conventional and digital soil mapping workflows, including their utility in delineating mapping units, designing efficient sampling schemes, and predicting soil properties. Within digital soil mapping, the chapter discusses the crucial application of quantitative models and machine learning algorithms, often utilizing multi-scale terrain analysis, to generate high-resolution soil maps. A key takeaway is the central role of land-surface parameters as predictors and proxies for other environmental factors in nearly all modern soil mapping applications. Finally, a case study demonstrates the practical selection of sampling locations and the mapping of soil organic carbon content.} } @inbook{Evans2026, title = {{Applications in geomorphology}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00034-3}, DOI = {10.1016/b978-0-44-333376-7.00034-3}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Evans, Ian S. and Minár, Jozef}, year = {2026}, pages = {627–660}, abstract = {Each process-based subfield of geomorphology has specific morphometric techniques, but there are commonalities. Use of DEMs opened the newer field of general geomorphometry and revolutionised specific geomorphometry, with digitization and automated extraction of characteristics following manual delineation of landforms. Techniques for automated delineation of landforms are being tested and improved. Geomorphometry assesses the clarity of landform classifications as well as landform scaling, scale-specificity and allometry. Valuable insights come from relating slope, size and shape to age, process, geology and position. Faster processing of increasingly large data sets is permitting regional comparisons and generalisations. DEMs repeated over time are vital for many studies of geomorphological processes. Flow regimes are interpreted from the shape, orientation and direction of bedforms. Some forms give evidence of wind directions, present or former. Digital geomorphological mapping is testing the consistency of older, subjective approaches: using physically meaningful variables, it is morphogenetically based.} } @inbook{Alvioli2026, title = {{Modelling mass movements and landslide susceptibility}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00035-5}, DOI = {10.1016/b978-0-44-333376-7.00035-5}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Alvioli, Massimiliano and Mergili, Martin and Brenning, Alexander}, year = {2026}, pages = {661–680}, abstract = {Landslide susceptibility and runout models use geomorphometric information to support disaster risk management. This chapter outlines state-of-the-art workflows in data-driven and process-based modelling, focusing on open-source implementations of the generalized additive models, the 3D rockfall model r.stone, and the r.avaflow simulation framework. Case studies from La Réunion illustrate the application of statistical susceptibility models operating on slope units and grid cells; specifically, we show the difference in susceptibility maps obtained within a generalized linear model and a generalized additive model, both in a grid-based and a slope unit-based approach. Next, we show applications of process-based, or physically-based models, featuring dynamic runout simulations incorporating interactions with runoff. Current research directions are discussed, including hybrid models, transfer learning, and the incorporation of climate and land-use scenarios into landslide hazard assessments.} } @inbook{Misiuk2026, title = {{Landform-element recognition through machine learning and computer vision}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00036-7}, DOI = {10.1016/b978-0-44-333376-7.00036-7}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Misiuk, Benjamin and Jasiewicz, Jarosław and Arosio, Riccardo}, year = {2026}, pages = {681–709}, abstract = {Geomorphologists increasingly turn to automated machine learning approaches in order to characterize the land surface. This chapter reviews the concepts underpinning such approaches. We describe the ways in which machine learning is applied to geomorphometry problems, including methods of supervised, unsupervised, and self-supervised learning, pixel- and object-based analyses, and deep learning. We additionally explore the strengths, limitations, and opportunities presented by machine learning geomorphometry approaches. Unsupervised and self-supervised case studies are presented demonstrating the classification of topographic landforms without ground truth data.} } @inbook{Lecours2026, title = {{Ecological applications of geomorphometry}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00037-9}, DOI = {10.1016/b978-0-44-333376-7.00037-9}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Lecours, Vincent and LeClerc, Emma and Moudrý, Vítězslav and Vancine, Maurício Humberto}, year = {2026}, pages = {711–731}, abstract = {The shape of the Earth's surface can affect biodiversity and ecological patterns and processes at multiple spatial scales, either directly or indirectly. Geomorphometry is thus another tool in ecologists' toolboxes that can inform various types of classification or modelling. This chapter provides an overview of general mapping approaches used in ecology that can integrate geomorphometric elements. Two broad types of approaches are presented: ecological classifications and distribution models. Expert-based manual classifications, unsupervised and supervised classifications, pixel-based and object-based classifications, and fuzzy classifications are introduced, in addition to distribution modelling (e.g., species distribution modelling, habitat suitability modelling, niche modelling). Then, a selection of case studies (unsupervised pixel-based classification, supervised pixel-based classification, supervised object-based classification, species distribution model) focused on areas of mānuka or kānuka and based on the common dataset for this book, which combines spectral and topographic information, is presented.} } @inbook{Qin2026, title = {{Geomorphometry in spatial hydrological modelling}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00038-0}, DOI = {10.1016/b978-0-44-333376-7.00038-0}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Qin, Cheng-Zhi and Zhu, Liang-Jun}, year = {2026}, pages = {733–750}, abstract = {Geomorphometry is fundamental for spatial hydrological modelling as well as the following applications based on spatial hydrological models. Starting with a brief description on spatial hydrological modelling, this chapter presents the key roles of using both terrain features and topographic attributes in spatial hydrological modelling, as well as the advances of terrain units and their spatial gradation information supporting the beneficial management practices (BMP) scenario analysis and optimization based on spatial hydrological modelling. Different types of geomorphometric tools for spatial hydrological modelling are also described, in a perspective towards automated, flexible, till intelligent functionality for better adapting to complex application context.} } @inbook{Ullah2026, title = {{Geomorphometry for archaeology}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00039-2}, DOI = {10.1016/b978-0-44-333376-7.00039-2}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Ullah, Isaac I. and Barton, C. Michael}, year = {2026}, pages = {751–796}, abstract = {This chapter examines the role of geomorphometry in the analysis of archaeological landscapes, emphasizing how terrain, landforms, and surface processes mediate relationships between past human societies and the Earth's surface. It situates archaeological geomorphometry within landscape archaeology, GIS techniques, affordance theory, and anthropogenic landscape research, then reviews core analytical workflows for integrating terrain parameters with archaeological evidence. Topics include overlay statistics, proximity analysis, least-cost movement modelling, site catchments, territoriality, visibility, visual prominence, predictive modelling, machine learning, Bayesian approaches, paleolandscape reconstruction, and assessment of long-term human impacts on landscape change. The chapter argues that geomorphometry provides archaeology with a flexible framework for modelling both how people perceived, used, and organized landscapes and how human activity contributed to geomorphological transformation over generational and historical timescales.} } @inbook{Gesch2026, title = {{Geomorphometry in a time of climate change}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00040-9}, DOI = {10.1016/b978-0-44-333376-7.00040-9}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Gesch, Dean and Hawker, Laurence}, year = {2026}, pages = {797–812}, abstract = {Digital Elevation Models (DEMs) are essential tools for understanding and assessing the impacts of climate change on Earth's surface processes. This chapter examines the role of geomorphometry in climate-related applications, with emphasis on coastal environments, hydrology, and cryosphere monitoring. Topics include coastal inundation exposure, storm surge modelling, intertidal mapping, permafrost thaw, flood-risk assessment, glacier monitoring, and water-resource analysis. The chapter highlights the importance of high-resolution and high-accuracy DEMs, particularly lidar-derived and topographic-bathymetric models, for reliable climate impact assessments. Advances in remote sensing technologies, including satellite imagery, lidar, radar, and Structure-from-Motion methods, have improved the spatial and temporal quality of elevation data. Special attention is given to DEM uncertainty, subsidence, and the need for “fit-for-purpose” elevation data. The chapter concludes that DEMs will remain indispensable for climate adaptation, monitoring, and environmental decision-making.} } @inbook{Lecours2026, title = {{Underwater geomorphometry}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00041-0}, DOI = {10.1016/b978-0-44-333376-7.00041-0}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Lecours, Vincent and Arosio, Riccardo and Misiuk, Benjamin and Barrett, Rachel and Dolan, Margaret}, year = {2026}, pages = {813–853}, abstract = {Understanding the shape of Earth's seafloor is critical for many applications, yet very little is known about it. The dynamic factors that shape the seafloor operate at multiple spatial and temporal scales and are complex, ever-changing, and relatively poorly understood. This chapter contrasts underwater applications of geomorphometry with the terrestrial applications presented in the other chapters of this book. The characteristics of digital bathymetric models, along with the seafloor-surface variables and objects that can be extracted from them, are presented. Applications of underwater geomorphometry that are introduced include substrate mapping, sediment transport modelling, characterization of submarine geomorphology, the mapping and modelling of mass movements and submarine landslides, underwater archaeology, and marine ecology, habitat mapping, and species distribution modelling. For each section, we highlight the differences in geomorphometric approaches when the DEM on which they are applied represents the seafloor, riverbed, or lake bottom rather than the land.} } @inbook{Mitusov2026, title = {{Extraterrestrial geomorphometry}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00042-2}, DOI = {10.1016/b978-0-44-333376-7.00042-2}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Mitusov, Andrey V.}, year = {2026}, pages = {855–871}, abstract = {Geomorphometry is becoming essential for studying the surface of celestial bodies. The growing availability of high-resolution topographic data makes this especially timely. Using the polar regions of the Moon, Mercury, and the Shackleton impact crater as test areas, the article demonstrates how morphometric variables reveal features that remain invisible in optical imagery, including within permanently shadowed regions. Maximal Catchment Area highlights the smoothing of lunar slopes. Closed-depression mapping helps identify regions with a shared formation history. Curvature maps (kmax, kmin, kh, kv) provide additional insights. The kh map shows directions of mass movement. The kv map outlines landslide boundaries. Extreme values of kmax allow automatic detection of crater rims. These results demonstrate that geomorphometric tools, originally developed for Earth, gain new meaning under fundamentally different conditions on extraterrestrial landscapes. The broader application of geomorphometry is likely to reveal many previously unrecognized features beyond Earth.} } @inbook{Reuter2026, title = {{The future of geomorphometry}}, ISBN = {9780443333767}, ISSN = {0166-2481}, url = {http://dx.doi.org/10.1016/B978-0-44-333376-7.00043-4}, DOI = {10.1016/b978-0-44-333376-7.00043-4}, booktitle = {Geomorphometry - Concepts, Software, Applications}, edition = {2nd}, publisher = {Elsevier}, author = {Reuter, Hannes I. and Lecours, Vincent and Grohmann, Carlos H.}, year = {2026}, pages = {873–886}, abstract = {This chapter discusses the past, present, and future developments and trends in the field of geomorphometry and contrasts them with those from the first edition, published in 2009. The authors take stock of the current situation in various fields. Several predictions in data and software capabilities have manifested—such as global and local DEM availability, lidar data processing, and autonomous technologies—while others, like knowledge libraries and semantic models, remain limited. The authors identify current needs of the geomorphometry community that they wish to be addressed in the future, for example, standardized algorithm benchmarking, a unified library for geomorphometric methods, as well as comprehensive space-time analytical tools integrating multiple elevation sources with uncertainty estimates as a major goal.} } % Ponui Island Dataset @dataset{ponui_dataset, author = {Reuter, Hannes I and Grohmann, Carlos Henrique and Lecours, Vincent}, title = {{Ponui Island geomorphometry.org dataset}}, month = jan, year = 2026, publisher = {Zenodo}, doi = {10.5281/zenodo.18314107}, url = {https://doi.org/10.5281/zenodo.18314107}, } % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % First edition book and chapters @book{Hengl_Reuter_2009_geomorphometry, author = {Hengl, T. and Reuter, H. I.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, organization = {Elsevier}, publisher = {Elsevier}, date = {2008}, issn = {9780123743459}, pages = {772}, series = {Developments in Soil Science}, title = {{Geomorphometry: Concepts, Software, Applications}}, volume = {33}, } @incollection{Pike2009_gmph_ch01, author = {Pike, R. J. and Evans, I. S. and Hengl, T.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00001-9}, pages = {3--30}, series = {Developments in Soil Science}, title = {{Chapter 1. Geomorphometry: a brief guide}}, volume = {33}, } @incollection{Hengl2009_gmph_ch02, author = {Hengl, T. and Evans, I. S.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00002-0}, pages = {31--63}, series = {Developments in Soil Science}, title = {{Chapter 2. Mathematical and Digital Models of the Land Surface}}, volume = {33}, } @incollection{Nelson2009_gmph_ch03, author = {Nelson, A. and Reuter, H. I. and Gessler, P.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00003-2}, pages = {65--85}, series = {Developments in Soil Science}, title = {{Chapter 3. DEM Production Methods and Sources}}, volume = {33}, } @incollection{Reuter2009_gmph_ch04, author = {Reuter, H. I. and Hengl, T. and Gessler, P. and Soille, P.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/s0166-2481(08)00004-4}, pages = {87--120}, series = {Developments in Soil Science}, title = {{Chapter 4. Preparation of DEMs for Geomorphometric Analysis}}, volume = {33}, } @incollection{Temme2009_gmph_ch05, author = {Temme, A. J. A. M. and Heuvelink, G. B. M. and Schoorl, J. M. and Claessens, L.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00005-6}, pages = {121--140}, series = {Developments in Soil Science}, title = {{Chapter 5. Geostatistical Simulation and Error Propagation in Geomorphometry}}, volume = {33}, } @incollection{Olaya2009_gmph_ch06, author = {Olaya, V.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00006-8}, pages = {141--169}, series = {Developments in Soil Science}, title = {{Chapter 6. Basic Land-Surface Parameters}}, volume = {33}, } @incollection{Gruber2009_gmph_ch07, author = {Gruber, S. and Peckham, S.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00007-X}, pages = {171--194}, series = {Developments in Soil Science}, title = {{Chapter 7. Land-Surface Parameters and Objects in Hydrology}}, volume = {33}, } @incollection{Bohner2009_gmph_ch08, author = {B\"{o}hner, J. and Antoni\'{c}, O.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00008-1}, pages = {195--226}, series = {Developments in Soil Science}, title = {{Chapter 8. Land-Surface Parameters Specific to Topo-Climatology}}, volume = {33}, } @incollection{MacMillan2009_gmph_ch09, author = {MacMillan, R. A. and Shary, P. A.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00009-3}, pages = {227--254}, series = {Developments in Soil Science}, title = {{Chapter 9. Landforms and Landform Elements in Geomorphometry}}, volume = {33}, } @incollection{Wood2009_gmph_ch10, author = {Wood, J.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00010-X}, pages = {257--267}, series = {Developments in Soil Science}, title = {{Chapter 10. Overview of Software Packages Used in Geomorphometry}}, volume = {33}, } @incollection{Reuter2009_gmph_ch11, author = {Reuter, H. I. and Nelson, A.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00011-1}, pages = {269--291}, series = {Developments in Soil Science}, title = {{Chapter 11. Geomorphometry in ESRI Packages}}, volume = {33}, } @incollection{Olaya2009_gmph_ch12, author = {Olaya, V. and Conrad, O.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00012-3}, pages = {293--308}, series = {Developments in Soil Science}, title = {{Chapter 12. Geomorphometry in SAGA}}, volume = {33}, } @incollection{Hengl2009_gmph_ch13, author = {Hengl, T. and Maathuis, B. H. P. and Wang, L.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00013-5}, pages = {309--331}, series = {Developments in Soil Science}, title = {{Chapter 13. Geomorphometry in ILWIS}}, volume = {33}, } @incollection{Wood2009_gmph_ch14, author = {Wood, J.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00014-7}, pages = {333--349}, series = {Developments in Soil Science}, title = {{Chapter 14. Geomorphometry in LandSerf}}, volume = {33}, } @incollection{Guth2009_gmph_ch15, author = {Guth, P.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00015-9}, pages = {351--366}, series = {Developments in Soil Science}, title = {{Chapter 15. Geomorphometry in MicroDEM}}, volume = {33}, } @incollection{Lindsay2009_gmph_ch16, author = {Lindsay, J. B.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00016-0}, pages = {367--386}, series = {Developments in Soil Science}, title = {{Chapter 16. Geomorphometry in TAS GIS}}, volume = {33}, } @incollection{Hofierka2009_gmph_ch17, author = {Hofierka, J. and Mitasova, H. and Neteler, M.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00017-2}, pages = {387--410}, series = {Developments in Soil Science}, title = {{Chapter 17. Geomorphometry in GRASS GIS}}, volume = {33}, } @incollection{Peckham2009_gmph_ch18, author = {Peckham, S. D.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00018-4}, pages = {422--430}, series = {Developments in Soil Science}, title = {{Chapter 18. Geomorphometry in RiverTools}}, volume = {33}, } @incollection{Hengl2009_gmph_ch19, author = {Hengl, T. and MacMillan, R. A.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00019-6}, pages = {433--460}, series = {Developments in Soil Science}, title = {{Chapter 19. Geomorphometry -- A Key to Landscape Mapping and Modelling}}, volume = {33}, } @incollection{Dobos2009_gmph_ch20, author = {Dobos, E. and Hengl, T.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00020-2}, pages = {461--479}, series = {Developments in Soil Science}, title = {{Chapter 20. Soil Mapping Applications}}, volume = {33}, } @incollection{Jelaska2009_gmph_ch21, author = {Jelaska, S. D.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00021-4}, pages = {481--496}, series = {Developments in Soil Science}, title = {{Chapter 21. Vegetation Mapping Applications}}, volume = {33}, } @incollection{Evans2009_gmph_ch22, author = {Evans, I. S. and Hengl, T. and Gorsevski, P.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00022-6}, pages = {497--525}, series = {Developments in Soil Science}, title = {{Chapter 22. Applications in Geomorphology}}, volume = {33}, } @incollection{Gruber2009_gmph_ch23, author = {Gruber, S. and Huggel, C. and Pike, R.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00023-8}, pages = {527--550}, series = {Developments in Soil Science}, title = {{Chapter 23. Modelling Mass Movements and Landslide Susceptibility}}, volume = {33}, } @incollection{MacMillan2009_gmph_ch24, author = {MacMillan, R. A. and Torregrosa, A. and Moon, D. and Coup\'{e}, R. and Philips, N.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00024-X}, pages = {551--578}, series = {Developments in Soil Science}, title = {{Chapter 24. Automated Predictive Mapping of Ecological Entities}}, volume = {33}, } @incollection{Peckham2009_gmph_ch25, author = {Peckham, S. D.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00025-1}, pages = {579--602}, series = {Developments in Soil Science}, title = {{Chapter 25. Geomorphometry and Spatial Hydrologic Modelling}}, volume = {33}, } @incollection{Emeis2009_gmph_ch26, author = {Emeis, S. and Knoche, H. R.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00026-3}, pages = {603--622}, series = {Developments in Soil Science}, title = {{Chapter 26. Applications in Meteorology}}, volume = {33}, } @incollection{Reuter2009_gmph_ch27, author = {Reuter, H. I. and Kersebaum, K. C.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00027-5}, pages = {623--636}, series = {Developments in Soil Science}, title = {{Chapter 27. Applications in Precision Agriculture}}, volume = {33}, } @incollection{Gessler2009_gmph_ch28, author = {Gessler, P. and Pike, R. and MacMillan, R. A. and Hengl, T. and Reuter, H. I.}, editor = {Hengl, T. and Reuter, H. I.}, location = {Amsterdam}, publisher = {Elsevier}, booktitle = {Geomorphometry: Concepts, Software, Applications}, date = {2009}, doi = {10.1016/S0166-2481(08)00028-7}, pages = {637--652}, series = {Developments in Soil Science}, title = {{Chapter 28. The Future of Geomorphometry}}, volume = {33}, } % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % -------------------------------------------------------------- % all other references @article{Aalstad2018, author = {Aalstad, K. and Westermann, S. and Schuler, T. 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D.}, % institution = {University of Colorado at Boulder}, % date = {1995}, % title = {{Self-similarity in the three-dimensional geometry and dynamics of large river basins}}, % type = {Ph.D. thesis}, % } @inproceedings{Peckham_2009c, author = {Peckham, Scott D.}, location = {Zurich, Switzerland}, url = {https://www.geomorphometry.org/uploads/pdf/pdf2009/peckham2009geomorphometry.pdf}, booktitle = {{Proceedings of Geomorphometry 2009}}, date = {2009}, pages = {34--37}, title = {{A new algorithm for creating DEMs with smooth elevation profiles}}, } @incollection{Peckham_2009a, author = {Peckham, Scott D.}, publisher = {Elsevier}, booktitle = {{Geomorphometry: Concepts, Software and Applications, Developments in Soil Science, vol. 33}}, date = {2009}, doi = {10.1016/S0166-2481(08)00025-1}, note = {Chapter 25}, pages = {570--602}, title = {{Geomorphometry and spatial hydrologic modeling}}, } @incollection{Peckham_2009b, author = {Peckham, Scott D.}, publisher = {Elsevier}, booktitle = {{Geomorphometry: Concepts, Software and Applications, Developments in Soil Science, vol. 33}}, date = {2009}, doi = {10.1016/S0166-2481(08)00018-4}, note = {Chapter 18}, pages = {411--430}, title = {{Geomorphometry in RiverTools}}, } @inproceedings{Peckham_2011b, author = {Peckham, Scott D.}, location = {Redlands, California}, url = {http://www.geomorphometry.org/uploads/pdf/pdf2011/Peckham2011bgeomorphometry.pdf}, booktitle = {{Proceedings of Geomorphometry 2011}}, date = {2011}, pages = {31--34}, title = {{Monkey, starfish and octopus saddles}}, } @inproceedings{Peckham_2011a, author = {Peckham, Scott D.}, location = {Redlands, California}, url = {http://www.geomorphometry.org/uploads/pdf/pdf2011/Peckham2011ageomorphometry.pdf}, booktitle = {{Proceedings of Geomorphometry 2011}}, date = {2011}, pages = {27--30}, title = {{Profile, plan and streamline curvature: A simple derivation and applications}}, } @inproceedings{Peckham_2013, author = {Peckham, Scott D.}, location = {Nanjing, China}, url = {http://www.geomorphometry.org/uploads/pdf/pdf2013/Peckham2013geomorphometry.pdf}, booktitle = {{Proceedings of Geomorphometry 2013}}, date = {2013}, pages = {O-11-1 to O-11--4}, title = {{Mathematical surfaces for which specific and total contributing area can be computed: Testing contributing area algorithms}}, } @inproceedings{Peckham_2014b, author = {Peckham, Scott D.}, location = {New York, New York}, organization = {CUNY Academic Works}, url = {http://academicworks.cuny.edu/cc_conf_hic/464/}, booktitle = {{Proceedings of HIC 2014, 11th International Conference on Hydroinformatics}}, date = {2014}, title = {{EMELI 1.0: An experimental smart modeling framework for automatic coupling of self-describing models}}, } @inproceedings{Peckham_2014a, author = {Peckham, Scott D.}, location = {San Diego, California}, organization = {International Environmental Modelling and Software Society (iEMSs)}, url = {http://scholarsarchive.byu.edu/iemssconference/2014/Stream-A/12/}, booktitle = {{Proceedings of the 7th International Congress on Environmental Modelling and Software}}, date = {2014}, pages = {O-11-1 to O-11--4}, title = {{The CSDMS Standard Names: Cross-domain naming conventions for describing process models, data sets and their associated variables}}, } @inproceedings{Peckham_2015, author = {Peckham, Scott D.}, location = {Bogucki Wydawnictwo Naukowe, Adam Mickiewicz University in Poznań, Institute of Geoecology and Geoinformation, Poznań, Poland}, url = {http://www.geomorphometry.org/uploads/pdf/pdf2015/Peckham2015geomorphometry.pdf}, booktitle = {{Geomorphometry for Geosciences}}, date = {2015}, isbn = {978-83-7986-059-3}, pages = {137--140}, title = {{Longitudinal elevation profiles of rivers: Curve fitting with functions predicted by theory}}, } @inproceedings{Peckham_2018, author = {Peckham, Scott D.}, location = {Boulder, Colorado}, url = {http://www.geomorphometry.org/uploads/pdf/pdf2018/Peckham_2018_geomorphometry.pdf}, booktitle = {{Proceedings of Geomorphometry 2018}}, date = {2018}, pages = {O-11-1 to O-11--4}, title = {{Using the open-source TopoFlow Python package for extracting D8 grids from DEMs and for fluvial landscape evolution modeling}}, } @misc{TopoFlow_Ngen_Utils, author = {Peckham, Scott D.}, url = {https://github.com/peckhams/topoflow36/tree/master/topoflow/utils/ngen}, date = {2024}, note = {accessed: 2024-10-10}, title = {{TopoFlow version 3.6 GitHub Repository, NextGen utilities}}, } @article{Peckham_et_al_2013, author = {Peckham, Scott D. and Hutton, E. 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