Land-Surface Parameters for Spatial Predictive Mapping and Modeling

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks

Physical geomorphometry for elementary land surface segmentation and digital geomorphological mapping

By interpretations related to energy, elementary land surface segmentation can be treated as a physical problem. Many pieces of such a view found in the literature can be combined into a synthetic comprehensive physical approach. The segmentation has to be preceded by defining the character and size of searched units to result from the segmentation. A high-resolution digital elevation model (DEM) is the key input for this task; it should be generalized to the resolution best expressing information about the searched units. Elementary land surface units can be characterized by various parts of potential gravitational energy associated with a set of basic geomorphometric variables. Elevation above sea level (z) represents Global Geomorphic Energy (GGE). Regional and Local Geomorphic Energy (RGE and LGE) are parts of GGE, represented respectively by relative elevation above the local base level (zrel) and local relief (elevation differential in a moving window Δz). Derivation (change) of elevation defines the slope inclination (S), determining the local Potential Energy of Surface (PES) applicable to mass flow. Normal slope line (profile) curvature (kn)s and normal contour (tangential) curvature (kn)c express change in the PES value (ΔPES(kn )s, ΔPES(kn )c), responsible for acceleration/deceleration and convergence/ divergence of flow. Mean curvature (kmean) determines the Potential Energy of Surface applicable to Diffusion (PESD). Energetic interpretation of basic geomorphometric variables enables their direct comparison and systematic evaluation. Consequently, the homogeneity of basic geomorphometric variables defines a hierarchy of states of local geomorphic equilibria: static equilibrium, steady state, and non-steady state dynamic equilibrium. They are local attractors of landform development reflected in the geomorphometric tendency to symmetry (horizontality, various types of linearity, and curvature isotropy, together expressed by gravity concordance). Nonequilibrium and transitional states can be characterized by the PES excess (PESe) determined by difference curvature (kd), by gravity discordant change of the PES characterized by twisting curvature (τg)c, and by Integral Potential Energy of Surface Curvature (IPESC) expressed by Casorati curvature (kC) (general curvedness). Excluding zrel and Δz, all these energy-related geomorphometric variables are local point-based. Local area-based and regional variables such as Glock’s Available Relief, Melton Ruggedness Number, Stream Power Index, Openness, Topographic Position Index, Topographic Wetness Index, and Index of Connectivity also have energetic interpretations although their definition is more complex. Therefore we suggest exclusive use of the local point-based variables in designs of elementary land surface segmentation. The segmentation should take notice of natural interconnections, the hierarchy of geomorphometric variables, elements of Local Geomorphic Energy, and (dis)equilibria states, so that elementary segments are clearly interpretable geomorphologically. This is exemplified by Geographic Object-Based Image Analysis (GEOBIA) segmentation of Sandberg, a territory on the boundary of the Carpathians and Vienna Basin with a complex geomorphic history and marked morphodynamics. Compared with expert-driven field geomorphological mapping, the automatic physically-based segmentation resulted in a more specific delineation and composition of landforms. Physical-geomorphometric characteristics of the elementary forms enabled the formulation of their system and subsequent improvement of the expert-based qualitative genetic analysis, with interpretation leading to a deeper understanding of the development and recent dynamics of the landscape

Characterising the ocean frontier : a review of marine geomorphometry

Geomorphometry, the science that quantitatively describes terrains, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using Geographic Information Systems (GIS) has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade, a suite of geomorphometric techniques have been applied (e.g. terrain attributes, feature extraction, automated classification) to investigate the characterisation of seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is nevertheless much common ground between terrestrial and marine geomorphology applications and it is important that, in developing the science and application of marine geomorphometry, we build on the lessons learned from terrestrial studies. We note, however, that not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four- dimensional nature of the marine environment causes its own issues, boosting the need for a dedicated scientific effort in marine geomorphometry. This contribution offers the first comprehensive review of marine geomorphometry to date. It addresses all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry.peer-reviewe

Geomorphometry 2020. Conference Proceedings

Geomorphometry is the science of quantitative land surface analysis. It gathers various mathematical, statistical and image processing techniques to quantify morphological, hydrological, ecological and other aspects of a land surface. Common synonyms for geomorphometry are geomorphological analysis, terrain morphometry or terrain analysis and land surface analysis. The typical input to geomorphometric analysis is a square-grid representation of the land surface: a digital elevation (or land surface) model. The first Geomorphometry conference dates back to 2009 and it took place in Zürich, Switzerland. Subsequent events were in Redlands (California), Nánjīng (China), Poznan (Poland) and Boulder (Colorado), at about two years intervals. The International Society for Geomorphometry (ISG) and the Organizing Committee scheduled the sixth Geomorphometry conference in Perugia, Italy, June 2020. Worldwide safety measures dictated the event could not be held in presence, and we excluded the possibility to hold the conference remotely. Thus, we postponed the event by one year - it will be organized in June 2021, in Perugia, hosted by the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR IRPI) and the Department of Physics and Geology of the University of Perugia. One of the reasons why we postponed the conference, instead of canceling, was the encouraging number of submitted abstracts. Abstracts are actually short papers consisting of four pages, including figures and references, and they were peer-reviewed by the Scientific Committee of the conference. This book is a collection of the contributions revised by the authors after peer review. We grouped them in seven classes, as follows: • Data and methods (13 abstracts) • Geoheritage (6 abstracts) • Glacial processes (4 abstracts) • LIDAR and high resolution data (8 abstracts) • Morphotectonics (8 abstracts) • Natural hazards (12 abstracts) • Soil erosion and fluvial processes (16 abstracts) The 67 abstracts represent 80% of the initial contributions. The remaining ones were either not accepted after peer review or withdrawn by their Authors. Most of the contributions contain original material, and an extended version of a subset of them will be included in a special issue of a regular journal publication