Agricultural modern land transformations are usually undertaken with minimal landscape planning, and soil conservation practices are largely ignored during the construction phase. As a consequence, changes to terrain morphology induce dramatic geomorphological effects that can endure well beyond any single farming cycle and over many human generations. Thus, agricultural activities that may not be obvious to the observers of contemporary landscapes can continue to influence surface processes and landforms strongly; these effects are even more critical in the current situation of climate change and rainfall event intensification. An understanding and successful prediction of pathways of runoff and associated soil erosion due to terracing are, therefore, of considerable societal relevance. Such predictions would allow improvements to the protection of the environment by the reduction of impacts of the agricultural activities or by a correct restoration of abandoned agricultural land (especially terraces). Using two examples from northern Spain and northern Italy, this chapter shows how new remote sensing technologies (i.e., airborne lidar), available to the public, can provide a better understanding of the interaction between anthropogenic elements, potential erosion, and the associated sediment delivery. During the planning and management phase, the farmers and authorities in charge of monitoring both the subsidy process and land management should consider an approach similar to that presented in this research, together with a holistic view of the processes in the areas, specifically related to climate and soil. It is currently possible to forecast how agricultural practices could affect sediment fluxes in the future, and thus to have a better understanding of the human-landscape interactions. This type of integrated analysis is a cost-effective strategy for the management, restoration, or development of terraced lands, especially in areas with high economic value for agricultural productions and tourism.
The ability to reproduce the results of an experiment is a fundamental component of the scientific method. However, precisely what is meant by the terms replicable and reproducible often varies between and within disciplines. Here, we present clear definitions of these two terms for geomorphic research and communicate the importance of performing reproducible analysis of remotely sensed topographic data. We argue that the reproducibility of an analysis is not a static, binary state but rather that there is a continuum from irreproducibility to replicability, with reproducibility falling between the two and that the aim of a researcher should be to get as close to reproducibility as possible, favoring a pragmatic rather than dogmatic approach. A brief review of the development of topographic analysis as a discipline is used to highlight the progress made in making topographic analysis more reproducible, and the challenges inherent within common working patterns. The chapter concludes with a series of recommendations on how best to achieve reproducible topographic analysis.
Laser scanning or light detection and ranging (LiDAR) has been revolutionary for landslide research because it provides high-resolution three-dimensional (3D) topography, which allows for characterizing all types of landslides in greater detail. The major advances in various techniques and a short history of its development are presented. The basics of LiDAR performance are reviewed and an overview of the advantages and limitations of this 3D data acquisition technique are presented. LiDAR data analysis has improved landslide mapping as well as the characterization of rock discontinuities. The analyses of the changes between two point clouds (PC) or digital terrain models (DTM) provide, for instance, volumes leading to sediment budgets or inventories of rockfalls, and can be used to monitor slope movements. Various LiDAR configurations are available, from handheld LiDAR to LiDAR using drones that integrate positioning and/or inertial systems. The evolution of this technique will continue to improve landslide research.
In this book you will find chapters reviewing and exploring state-of-the-art remote-sensing techniques relevant to geomorphology. We hope that the chapters will serve as both a reference for experienced practitioners and a guide to geomorphologists looking to use remote-sensing techniques to benefit their studies.
High-resolution topography (HRT) is a powerful observational tool for studying the Earth's surface, vegetation, and urban landscapes, with broad scientific, engineering, and education-based applications. Submeter resolution imaging is possible when collected with laser and photogrammetric techniques using the ground, air, and space-based platforms. Open access to these data and a cyberinfrastructure platform that enables users to discover, manage, share, and process then increases the impact of investments in data collection and catalyzes scientific discovery. Furthermore, open and online access to data enables broad interdisciplinary use of HRT across academia and in communities such as education, public agencies, and the commercial sector. OpenTopography, supported by the US National Science Foundation, aims to democratize access to Earth science-oriented, HRT data and processing tools. We utilize cyberinfrastructure, including large-scale data management, high-performance computing, and service-oriented architectures to provide efficient web-based visualization and access to large, HRT datasets. OT colocates data with processing tools to enable users to quickly access custom data and derived products for their application, with the ultimate goal of making these powerful data easier to use. OT's rapidly growing data holdings currently include 283 lidar and photogrammetric, point cloud datasets (> 1.2 trillion points) covering 236,364 km2. As a testament to OT's success, more than 86,000 users have processed over 5 trillion lidar points. This use has resulted in more than 290 peer-reviewed publications across numerous academic domains including Earth science, geography, computer science, and ecology.
Terrestrial laser scanner (TLS) offers an unprecedented combination of sub-cm resolution, mm precision and survey extent that uniquely captures the geometry of individual pebbles and allows the spatial variability of channel evolution to be quantified precisely. Data processing can, however, be challenging, and the full scientific potential of fluvial 3D datasets remains arguably untapped. This chapter is an introduction to using TLS to solve fluvial geomorphology problems, synthesizing data acquisition, processing methods, and application examples. It covers practical aspects of field acquisition and addresses the respective benefits of TLS and structure from motion (SfM). Three-dimensional (3D) point cloud processing methods involved in data registration, vegetation classification, and spatial analysis are presented. Processing of repeat surveys and change detection methods are synthesized and the choice of raster-based or 3D point cloud differencing methods discussed in the context of fluvial processes and dynamics.
Noninvasive methods for the characterization of shallow subsurface have been used routinely for some 20–30 years. The growth in these methods has been driven by a variety of breakthroughs in the use of electrical, electromagnetic, and seismic methods, to mention only the most common techniques. Increasing field capabilities and computational power have yet to yield all their potential fruits. In this chapter, we introduce readers to the basic concepts of shallow subsurface methods. We guide them through some of the physical details and present a number of application examples all derived from our own experience, concerning both structural characterization and (fluid)-dynamic understanding of the shallow subsurface. Finally, we propose ideas concerning the future development of this wide and exciting discipline.
