Quality Assessment of Photogrammetric Methods—A Workflow for Reproducible UAS Orthomosaics

Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.This research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0—Sensing Biodiversity. The grassland study was funded by the Spanish Science Foundation FECYT-MINECO through the BIOGEI (GL2013- 49142-C2-1-R) and IMAGINE (CGL2017-85490-R) projects, and by the University of Lleida; and supported by a FI Fellowship to C.M.R. (2019 FI_B 01167) by the Catalan Government

Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro

The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results

Semi-Automatic Generation of Training Samples for Detecting Renewable Energy Plants in High-Resolution Aerial Images

Deep learning (DL)—in particular convolutional neural networks (CNN)—methods are widely spread in object detection and recognition of remote sensing images. In the domain of DL, there is a need for large numbers of training samples. These samples are mostly generated based on manual identification. Identifying and labelling these objects is very time-consuming. The developed approach proposes a partially automated procedure for the sample creation and avoids manual labelling of rooftop photovoltaic (PV) systems. By combining address data of existing rooftop PV systems from the German Plant Register, the Georeferenced Address Data and the Official House Surroundings Germany, a partially automated generation of training samples is achieved. Using a selection of 100,000 automatically generated samples, a network using a RetinaNet-based architecture combining ResNet101, a feature pyramid network, a classification and a regression network is trained, applied on a large area and post-filtered by intersection with additional automatically identified locations of existing rooftop PV systems. Based on a proof-of-concept application, a second network is trained with the filtered selection of approximately 51,000 training samples. In two independent test applications using high-resolution aerial images of Saarland in Germany, buildings with PV systems are detected with a precision of at least 92.77 and a recall of 84.47

Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation

Importance of target-oriented validation strategies for spatio-temporal prediction models is illustrated using two case studies: (1) modelling of air temperature () in Antarctica, and (2) modelling of volumetric water content (VW) for the R.J. Cook Agronomy Farm, USA. Performance of a random k-fold cross-validation (CV) was compared to three target-oriented strategies: Leave-Location-Out (LLO), Leave-Time-Out (LTO), and Leave-Location-and-Time-Out (LLTO) CV. Results indicate that considerable differences between random k-fold ( = 0.9 for and 0.92 for VW) and target-oriented CV (LLO = 0.24 for and 0.49 for VW) exist, highlighting the need for target-oriented validation to avoid an overoptimistic view on models. Differences between random k-fold and target-oriented CV indicate spatial over-fitting caused by misleading variables. To decrease over-fitting, a forward feature selection in conjunction with target-oriented CV is proposed. It decreased over-fitting and simultaneously improved target-oriented performances (LLO CV = 0.47 for and 0.55 for VW)

Data_files_-_ECOG03137_Pinkert_et_al_2017

Here we provide the environmental, distributional and trait data used in Pinkert et al. 2017 (ECOG-03137), at both the species-level (for habitat preferences, i.e. lentic/lotic) and aggregated at the assemblage-level. We also provide the breakpoints from segmented regressions of the relationship between latitude and four different measures of the diversity of European dragonfly assemblages (species richness, corrected weighted endemism, the residuals of total taxonomic distinctiveness against species richness and standardized effect sizes of mean pairwise distances). For further information and a more detailed description see the manuscript or contact the first author via email ([email protected])

Data from: Evolutionary processes, dispersal limitation and climatic history shape current diversity patterns of European dragonflies

We investigated the effects of contemporary and historical factors on the spatial variation of European dragonfly diversity. Specifically, we tested to what extent patterns of endemism and phylogenetic diversity of European dragonfly assemblages are structured by (i) phylogenetic conservatism of thermal adaptations and (ii) differences in the ability of post-glacial recolonization by species adapted to running waters (lotic) and still waters (lentic). We investigated patterns of dragonfly diversity using digital distribution maps and a phylogeny of 122 European dragonfly species, which we constructed by combining taxonomic and molecular data. We calculated total taxonomic distinctiveness and mean pairwise distances across 4,192 50 km × 50 km equal-area grid cells as measures of phylogenetic diversity. We compared species richness with corrected weighted endemism and standardized effect sizes of mean pairwise distances or residuals of total taxonomic distinctiveness to identify areas with higher or lower phylogenetic diversity than expected by chance. Broken-line regression was used to detect breakpoints in diversity–latitude relationships. Dragonfly species richness peaked in central Europe, whereas endemism and phylogenetic diversity decreased from warm areas in the south-west to cold areas in the north-east and with an increasing proportion of lentic species. Except for species richness, all measures of diversity were consistently higher in formerly unglaciated areas south of the 0°C isotherm during the Last Glacial Maximum than in formerly glaciated areas. These results indicate that the distributions of dragonfly species in Europe were shaped by both phylogenetic conservatism of thermal adaptations and differences between lentic and lotic species in the ability of post-glacial recolonization/dispersal in concert with the climatic history of the continent. The complex diversity patterns of European dragonflies provide an example of how integrating climatic and evolutionary history with contemporary ecological data can improve our understanding of the processes driving the geographical variation of biological diversity

Introducing Nature 4.0: A sensor network for environmental monitoring in the Marburg Open Forest

Successful conservation strategies require frequent observations and assessments of the landscape. Although expert surveys provide a great level of detail, the trade-off is the limited spatial coverage and repetition with which they are executed. Remote sensing technology can partially resolve these issues; nevertheless, it still requires experts’ experience to create conservation planning and reaction options. Nature 4.0 seeks to address these shortcomings by developing a prototype of a modular environmental monitoring system for high-resolution observation of species, habitats, and processes. The project combines expert surveys by nature conservationists, remote sensing, and a network of environmental sensors, which are integrated into stationary units as well as attached to unmanned aerial vehicles, rovers, or animals. By utilizing powerful data integration and analysis methods, Nature 4.0 will enable researchers to effectively observe landscapes through a set of diverse lenses. Time series data from the project will also inform the development of early warning indicators. Following the open-source principle, as much of the project as possible will be made publicly available, including, for instance, schematics for sensor units, algorithms for data integration or information on species occurrence. In summary, Nature 4.0 will establish new methods and protocols in the field of comprehensive environmental monitoring by combining traditional sampling, remote sensing, and automated measurement stations. The prototype system is being developed in the Marburg Open Forest, an open research, education, and development platform for environmental monitoring methods. The Marburg Open Forest brings a cross-disciplinary group of scientists together with nature conservation experts from the private sector and the state government, as well as local schools and private citizens to collaborate and bridge the gap between basic and applied environmental research. After one year, we will present the results of the initial phase and share our experience with developing Nature 4.0