@article{CalsamigliaGarciaComendadorFortesaetal.2018, author = {Calsamiglia, Aleix and Garcia-Comendador, Julian and Fortesa, Josep and Lopez-Tarazon, Jos{\´e} Andr{\´e}s and Crema, S. and Cavalli, M. and Calvo-Cases, A. and Estrany, Joan}, title = {Effects of agricultural drainage systems on sediment connectivity in a small Mediterranean lowland catchment}, series = {Geomorphology : an international journal on pure and applied geomorphology}, volume = {318}, journal = {Geomorphology : an international journal on pure and applied geomorphology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0169-555X}, doi = {10.1016/j.geomorph.2018.06.011}, pages = {162 -- 171}, year = {2018}, abstract = {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.}, language = {en} } @article{KorzeniowskaPfeiferLandtwing2018, author = {Korzeniowska, Karolina and Pfeifer, Norbert and Landtwing, Stephan}, title = {Mapping gullies, dunes, lava fields, and landslides via surface roughness}, series = {Geomorphology : an international journal on pure and applied geomorphology}, volume = {301}, journal = {Geomorphology : an international journal on pure and applied geomorphology}, publisher = {Elsevier Science}, address = {Amsterdam}, issn = {0169-555X}, doi = {10.1016/j.geomorph.2017.10.011}, pages = {53 -- 67}, year = {2018}, abstract = {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.}, language = {en} } @phdthesis{Richter2018, author = {Richter, Rico}, title = {Concepts and techniques for processing and rendering of massive 3D point clouds}, doi = {10.25932/publishup-42330}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423304}, school = {Universit{\"a}t Potsdam}, pages = {v, 131}, year = {2018}, abstract = {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.}, language = {en} }