Close-range sensing including 2D and 3D imaging and laser scan techniques is one of the most dynamically developing methods for data acquisition in the field of Geoscience. It extents the radius of operation, scale, and enhances traditional measurement techniques in terms of radiometric, spectral, spatial and time resolution. New developments in sensor technology and platforms enable for example dynamic i.e. kinematic sensing and autonomous mapping by means of robotics. Further advances in close-range sensing arise by enhancement of field and reference data collection procedures, integration of measurements from ancillary sources such as geo-sensor networks and cooperative sensor systems, and bridging the gap between ground control measurements and airborne and satellite remote sensing. Increasing availability of mobile and portable close-range sensors lead to the phenomenon of crowd-sensing. Recent achievements on algorithms for automation of data capture and processing stimulate new research questions in the field of geo-environmental research. Close-range sensing helps for better understanding of geo-environmental processes, their triggers, spatial extent and changes over time. Submissions are requested for geospherical and biospherical research integrating close-range sensing with processes in the cryosphere, geomorphology, vegetation phenology, vegetation-geomorphology interaction, and biodiversity.

Today several methods exist to capture 3D point clouds for geoscientific analysis at a wide range of spatial scales with high-end but also low-cost tools and devices. Most prominent approaches are image-based point cloud generation and active laser scanning (also referred to as LiDAR) operated on various platforms: e.g. hand-held, static on tripods, or kinematic on cars and airborne manned or unmanned vehicles (UAV). The strongly increasing availability of 3D point clouds demands for new methods and their evaluation for direct usage of point clouds in geosciences.
Contributions focusing on 3D point cloud capturing, georeferencing, processing, management, infrastructures, analysis, and visualization in the geosciences are welcome. This includes studies on low-cost sensing (e.g. Kinect, smartphones), image-based point cloud generation (e.g. new SfM and dense image matching approaches) and innovative LiDAR point cloud studies. Additionally, studies dealing with new point cloud features, classification approaches (machine learning), change detection results, and object recognition, detection and modelling methodologies are asked for. Finally, smart data reduction techniques, quality assessment, fusion of different data sources, and GIS workflows using 3D point clouds are in the focus of this session. Particularly, contributions tackling processing and evaluation of multi-temporal 3D point clouds will be promoted.
We specifically encourage early-stage researchers to present their studies. A special issue in a recognized international journal (SCI listed) will be considered for publication after the presentations and meeting at EGU 2016.