TY - JOUR A1 - Korzeniowska, Karolina A1 - Pfeifer, Norbert A1 - Landtwing, Stephan T1 - Mapping gullies, dunes, lava fields, and landslides via surface roughness JF - Geomorphology : an international journal on pure and applied geomorphology N2 - Gully erosion is a widespread and significant process involved in soil and land degradation. Mapping gullies helps to quantify past, and anticipate future, soil losses. Digital terrain models offer promising data for automatically detecting and mapping gullies especially in vegetated areas, although methods vary widely measures of local terrain roughness are the most varied and debated among these methods. Rarely do studies test the performance of roughness metrics for mapping gullies, limiting their applicability to small training areas. To this end, we systematically explored how local terrain roughness derived from high-resolution Light Detection And Ranging (LiDAR) data can aid in the unsupervised detection of gullies over a large area. We also tested expanding this method for other landforms diagnostic of similarly abrupt land-surface changes, including lava fields, dunes, and landslides, as well as investigating the influence of different roughness thresholds, resolutions of kernels, and input data resolution, and comparing our method with previously published roughness algorithms. Our results show that total curvature is a suitable metric for recognising analysed gullies and lava fields from LiDAR data, with comparable success to that of more sophisticated roughness metrics. Tested dunes or landslides remain difficult to distinguish from the surrounding landscape, partly because they are not easily defined in terms of their topographic signature. KW - Gullies KW - Surface roughness KW - Curvature KW - Digital terrain model (DTM) KW - LiDAR KW - Geomorphometry Y1 - 2017 U6 - https://doi.org/10.1016/j.geomorph.2017.10.011 SN - 0169-555X SN - 1872-695X VL - 301 SP - 53 EP - 67 PB - Elsevier Science CY - Amsterdam ER - TY - JOUR A1 - Calsamiglia, Aleix A1 - Garcia-Comendador, Julian A1 - Fortesa, Josep A1 - Lopez-Tarazon, José Andrés A1 - Crema, S. A1 - Cavalli, M. A1 - Calvo-Cases, A. A1 - Estrany, Joan T1 - Effects of agricultural drainage systems on sediment connectivity in a small Mediterranean lowland catchment JF - Geomorphology : an international journal on pure and applied geomorphology N2 - Traditional drainage systems combining man-made channels and subsurface tile drains have been used since Roman times to control water excess in Mediterranean lowland regions, favouring adequate soil water regime for agriculture purposes. However, mechanization of agriculture, abandonment or land use changes lead to a progressive deterioration of these drains in the last decades. The effects of these structures on hydrological and sediment dynamics have been previously analyzed in a small Mediterranean lowland catchment (Can Revull, Mallorca, Spain, 1.4 km2) by establishing an integrated sediment budget with a multi-technique approach. Moreover, the recent advances in morphometric techniques enable the completion of this analysis by the accurate identification of active areas (i.e. sources, pathway links, and sinks) and improve the understanding of (de-)coupling mechanisms of water and sediment linkages. In this study, the Borselli's index of connectivity (IC; Cavalli et al. (2013)'s version) derived from a LiDAR-based high resolution DEM (>1 pt m−2; RMSE < 0.2 m) was used to evaluate the spatial patterns of sediment connectivity of the catchment under two different scenarios: (1) the current scenario, including an accurate representation of the 3800 m of artificial channels and levees (CS - Channelled Scenario), and (2) a hypothetical scenario in which these anthropogenic features were removed (US - Unchannelled Scenario). Design and configuration of the drainage system in Can Revull generated changes favouring lateral decoupling between different compartments, with hillslopes-floodplain and floodplain-channels relationships, showing a general decrease of IC values, and high longitudinal connectivity along the artificial channel network. Field observations corroborated these results: structures enabled rapid drainage of the water excess also promoting low surface runoff within the field crops, proving to be an effective management practice for erosion control in agricultural Mediterranean lowland catchments. By contrast, US demonstrated that the abandonment of the current agricultural practices and the subsequent destruction of the drainage system could lead the higher soil loss rates owning to more intense/effective processes of sediment connectivity. KW - Sediment connectivity KW - Traditional drainage systems KW - Catchment management KW - Soil erosion KW - LiDAR Y1 - 2018 U6 - https://doi.org/10.1016/j.geomorph.2018.06.011 SN - 0169-555X SN - 1872-695X VL - 318 SP - 162 EP - 171 PB - Elsevier CY - Amsterdam ER - TY - THES A1 - Richter, Rico T1 - Concepts and techniques for processing and rendering of massive 3D point clouds T1 - Konzepte und Techniken für die Verarbeitung und das Rendering von Massiven 3D-Punktwolken N2 - Remote sensing technology, such as airborne, mobile, or terrestrial laser scanning, and photogrammetric techniques, are fundamental approaches for efficient, automatic creation of digital representations of spatial environments. For example, they allow us to generate 3D point clouds of landscapes, cities, infrastructure networks, and sites. As essential and universal category of geodata, 3D point clouds are used and processed by a growing number of applications, services, and systems such as in the domains of urban planning, landscape architecture, environmental monitoring, disaster management, virtual geographic environments as well as for spatial analysis and simulation. While the acquisition processes for 3D point clouds become more and more reliable and widely-used, applications and systems are faced with more and more 3D point cloud data. In addition, 3D point clouds, by their very nature, are raw data, i.e., they do not contain any structural or semantics information. Many processing strategies common to GIS such as deriving polygon-based 3D models generally do not scale for billions of points. GIS typically reduce data density and precision of 3D point clouds to cope with the sheer amount of data, but that results in a significant loss of valuable information at the same time. This thesis proposes concepts and techniques designed to efficiently store and process massive 3D point clouds. To this end, object-class segmentation approaches are presented to attribute semantics to 3D point clouds, used, for example, to identify building, vegetation, and ground structures and, thus, to enable processing, analyzing, and visualizing 3D point clouds in a more effective and efficient way. Similarly, change detection and updating strategies for 3D point clouds are introduced that allow for reducing storage requirements and incrementally updating 3D point cloud databases. In addition, this thesis presents out-of-core, real-time rendering techniques used to interactively explore 3D point clouds and related analysis results. All techniques have been implemented based on specialized spatial data structures, out-of-core algorithms, and GPU-based processing schemas to cope with massive 3D point clouds having billions of points. All proposed techniques have been evaluated and demonstrated their applicability to the field of geospatial applications and systems, in particular for tasks such as classification, processing, and visualization. Case studies for 3D point clouds of entire cities with up to 80 billion points show that the presented approaches open up new ways to manage and apply large-scale, dense, and time-variant 3D point clouds as required by a rapidly growing number of applications and systems. N2 - Fernerkundungstechnologien wie luftgestütztes, mobiles oder terrestrisches Laserscanning und photogrammetrische Techniken sind grundlegende Ansätze für die effiziente, automatische Erstellung von digitalen Repräsentationen räumlicher Umgebungen. Sie ermöglichen uns zum Beispiel die Erzeugung von 3D-Punktwolken für Landschaften, Städte, Infrastrukturnetze und Standorte. 3D-Punktwolken werden als wesentliche und universelle Kategorie von Geodaten von einer wachsenden Anzahl an Anwendungen, Diensten und Systemen genutzt und verarbeitet, zum Beispiel in den Bereichen Stadtplanung, Landschaftsarchitektur, Umweltüberwachung, Katastrophenmanagement, virtuelle geographische Umgebungen sowie zur räumlichen Analyse und Simulation. Da die Erfassungsprozesse für 3D-Punktwolken immer zuverlässiger und verbreiteter werden, sehen sich Anwendungen und Systeme mit immer größeren 3D-Punktwolken-Daten konfrontiert. Darüber hinaus enthalten 3D-Punktwolken als Rohdaten von ihrer Art her keine strukturellen oder semantischen Informationen. Viele GIS-übliche Verarbeitungsstrategien, wie die Ableitung polygonaler 3D-Modelle, skalieren in der Regel nicht für Milliarden von Punkten. GIS reduzieren typischerweise die Datendichte und Genauigkeit von 3D-Punktwolken, um mit der immensen Datenmenge umgehen zu können, was aber zugleich zu einem signifikanten Verlust wertvoller Informationen führt. Diese Arbeit präsentiert Konzepte und Techniken, die entwickelt wurden, um massive 3D-Punktwolken effizient zu speichern und zu verarbeiten. Hierzu werden Ansätze für die Objektklassen-Segmentierung vorgestellt, um 3D-Punktwolken mit Semantik anzureichern; so lassen sich beispielsweise Gebäude-, Vegetations- und Bodenstrukturen identifizieren, wodurch die Verarbeitung, Analyse und Visualisierung von 3D-Punktwolken effektiver und effizienter durchführbar werden. Ebenso werden Änderungserkennungs- und Aktualisierungsstrategien für 3D-Punktwolken vorgestellt, mit denen Speicheranforderungen reduziert und Datenbanken für 3D-Punktwolken inkrementell aktualisiert werden können. Des Weiteren beschreibt diese Arbeit Out-of-Core Echtzeit-Rendering-Techniken zur interaktiven Exploration von 3D-Punktwolken und zugehöriger Analyseergebnisse. Alle Techniken wurden mit Hilfe spezialisierter räumlicher Datenstrukturen, Out-of-Core-Algorithmen und GPU-basierter Verarbeitungs-schemata implementiert, um massiven 3D-Punktwolken mit Milliarden von Punkten gerecht werden zu können. Alle vorgestellten Techniken wurden evaluiert und die Anwendbarkeit für Anwendungen und Systeme, die mit raumbezogenen Daten arbeiten, wurde insbesondere für Aufgaben wie Klassifizierung, Verarbeitung und Visualisierung demonstriert. Fallstudien für 3D-Punktwolken von ganzen Städten mit bis zu 80 Milliarden Punkten zeigen, dass die vorgestellten Ansätze neue Wege zur Verwaltung und Verwendung von großflächigen, dichten und zeitvarianten 3D-Punktwolken eröffnen, die von einer wachsenden Anzahl an Anwendungen und Systemen benötigt werden. KW - 3D point clouds KW - 3D-Punktwolken KW - real-time rendering KW - Echtzeit-Rendering KW - 3D visualization KW - 3D-Visualisierung KW - classification KW - Klassifizierung KW - change detection KW - Veränderungsanalyse KW - LiDAR KW - LiDAR KW - remote sensing KW - Fernerkundung KW - mobile mapping KW - Mobile-Mapping KW - Big Data KW - Big Data KW - GPU KW - GPU KW - laserscanning KW - Laserscanning Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-423304 ER -