Structural analysis of forests by UAV is currently growing in popularity. Given the reduction in platform costs, and the number of algorithms available to analyze data output, the number of applications has grown rapidly. Forest structures are not only linked to economic value in forestry, but also to biodiversity and vulnerability issues. LiDAR remains the most promising technique for forest structural assessment, but small LiDAR sensors suitable for UAV applications are expensive and are limited to a few manufactures. The estimation of 3D-structures from two-dimensional image sequences called 'Structure from motion' (SfM) overcomes this limitation by photogrammetrically reconstructing point clouds similar to those rendered from LiDAR sensors. The result of these techniques in highly structured terrain strongly depends on the methods employed during image acquisition, therefore structural indices might be vulnerable to misspecifications in flight campaigns. In this paper, we outline how image overlap and ground sampling distances affect image reconstruction completeness in 2D and 3D. Higher image overlaps and coarser GSDs have a clearly positive influence on reconstruction quality. Therefore, higher accuracy requirements in the GSD must be compensated by a higher image overlap. The best results are achieved with an image overlap of > 95% and a resolution of > 5 cm. The most important environmental factors have been found to be wind and terrain elevation, which could be an indicator of vegetation density.

Due to the relatively high average age of the rail infrastructure in Germany and the thus often historic plans, as-built documentation has a very high priority at Deutsche Bahn AG. The inventory and updating of existing plans represent an enormous challenge for the operator, DB Netz AG. More than 4.6 million inventory plans must be continuously checked to ensure that they are up to date,correct,adjusted and supplemented as necessary. The most fragile structures are railroad bridges. These are the focus of this paper. For now, all information of bridges such as planning documents, statics, status reports of bridge examination, etc. are collected in decentral locations of the owner or operator. The existing information is available in a wide variety of formats, e.g. pdf files, plans on paper, scanned paper plans, digitally created plans, SAPdata and photos. We tackled this problem of non-uniform and decentralized data management within the mdfBIM project. Within the scope of this project, a process model was developed that describes the merging of the various data sources in the planning process and attempts to identify the primary data source in each case. The validation and adaptation of this model was carried out continuously after it had been set up based on a railway bridge in Hannover, Germany. We used machine learning algorithms to enable an automated object classification for the most common objects to derive the highest possible degree of automation. Another important step towards automation was the consolidation of the numerous data sources. This existing, inhomogeneous data was homogenized in a defined process. During this homogenization, the data sets -ranging from existing as-built plans, photo documentation, maintenance and conversion reports, SAP extracts, construction books and construction plans to the newly recorded laser point cloud -was evaluated. In this paper; the complete process chain and the first results are presented. Furthermore, an outlook is given on further research tasks and the further development of the elaborated process chain.

The pressure on vegetation, whether forests, meadows or cultivated areas, is becoming increasingly greater. Climate change, extreme weather and ever higher yields taking place at the same time are creating enormous challenges for areas under cultivation. Drought stress, heavy rains and cultivation of monocultures stress both, the soil and the crops themselves. Regular monitoring of the crops or trees as well as soil condition is essential for a sustainable land use. The use of unmanned aerial vehicles (UAVs) for aerial structural surveys, the recording of soil parameters such as soil temperature, soil moisture and gas exchange have so far mostly been carried out independently of each other. Combining these measurement techniques, a holistic picture of the state of these ecosystems becomes possible.The Fraunhofer-Institute for Physical Measurement Techniques IPM presents a coherent process chain for the fully comprehensive recording of ecosystems. A recording by means of LiDAR systems from the ground, multispectral aerial images, terrestrial laser scans and the recording of nitrous oxide emission.Thus, we obtain a full structural image of the ecosystem enriched with metadata on plant condition and soil parameters. This forms the basis of an analysis of the overall condition of the full ecosystem. We present the results of the different sensors and the fused data of a first measurement campaign.

For decades, satellite and aerial imagery have been the central element of mapping the earth's surface. The need for fast and frequent imaging, especially of smaller areas with unmanned aerial vehicles (UAVs) is gaining importance in various industries. Regular monitoring of agricultural areas, forests, coastal regions and urban areas are of utmost interest. Using UAVs to examine the worlds surface allows high resolution imagery captured in short time intervals without high costs as with the use of airplanes. This time series monitoring allows a quick detection of changes in the study area. The Fraunhofer-Institute for Physical Measurement Techniques IPM presents a compact and lightweight multispectral camera system. A 12 MP RGB-sensor and two 5.5 MP grayscale sensors are simultaneously capturing images. High frame rates, ultralightweight design, and the ability to easily adjust the aperture angle and wavelengths to be recorded open up many new applications. This ensures the use on a wide range of UAVs. Even with fast flying fixed wing UAVs, images can be acquired with high overlap. Further, we present results of a first measurement campaign.