Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel‐ or texture‐based mapping approaches do not fully exploit the information content provided by the high spatial resolution. Here, to fully harness this spatial detail, we apply deep learning techniques, that is, Convolutional Neural Networks (CNNs), on regular tiles of UAV‐orthoimagery (here 2–5 m) to identify the cover of target plant species and plant communities. The approach was tested with UAV‐based orthomosaics and photogrammetric 3D information in three case studies, that is, (1) mapping tree species cover in primary forests, (2) mapping plant invasions by woody species into forests and open land and (3) mapping vegetation succession in a glacier foreland. All three case studies resulted in high predictive accuracies. The accuracy increased with increasing tile size (2–5 m) reflecting the increased spatial context captured by a tile. The inclusion of 3D information derived from the photogrammetric workflow did not significantly improve the models. We conclude that CNN are powerful in harnessing high resolution data acquired from UAV to map vegetation patterns. The study was based on low cost red, green, blue (RGB) sensors making the method accessible to a wide range of users. Combining UAV and CNN will provide tremendous opportunities for ecological applications

Stress and worry in the 2020 coronavirus pandemic: Relationships to trust and compliance with preventive measures across 48 countries in the COVIDiSTRESS global survey

The COVIDiSTRESS global survey collects data on early human responses to the 2020 COVID-19 pandemic from 173 429 respondents in 48 countries. The open science study was co-designed by an international consortium of researchers to investigate how psychological responses differ across countries and cultures, and how this has impacted behaviour, coping and trust in government efforts to slow the spread of the virus. Starting in March 2020, COVIDiSTRESS leveraged the convenience of unpaid online recruitment to generate public data. The objective of the present analysis is to understand relationships between psychological responses in the early months of global coronavirus restrictions and help understand how different government measures succeed or fail in changing public behaviour. There were variations between and within countries. Although Western Europeans registered as more concerned over COVID-19, more stressed, and having slightly more trust in the governments' efforts, there was no clear geographical pattern in compliance with behavioural measures. Detailed plots illustrating between-countries differences are provided. Using both traditional and Bayesian analyses, we found that individuals who worried about getting sick worked harder to protect themselves and others. However, concern about the coronavirus itself did not account for all of the variances in experienced stress during the early months of COVID-19 restrictions. More alarmingly, such stress was associated with less compliance. Further, those most concerned over the coronavirus trusted in government measures primarily where policies were strict. While concern over a disease is a source of mental distress, other factors including strictness of protective measures, social support and personal lockdown conditions must also be taken into consideration to fully appreciate the psychological impact of COVID-19 and to understand why some people fail to follow behavioural guidelines intended to protect themselves and others from infection. The Stage 1 manuscript associated with this submission received in-principle acceptance (IPA) on 18 May 2020. Following IPA, the accepted Stage 1 version of the manuscript was preregistered on the Open Science Framework at https://osf.io/g2t3b. This preregistration was performed prior to data analysis

Heat Loss Measurements on Parabolic Trough Receivers

A measurement set-up to determine the heat losses of single parabolic trough receiver components at steady state conditions has been developed at Schott. This paper describes the functionality of the set-up and a comparative campaign with three Schott receivers of 7.0, 8.9 and 11.1% emittance (400°C) including test stands at German Aerospace Center (DLR) and U.S. National Renewable Energy Laboratory (NREL). This campaign showed generally good agreement with deviations <10%. Additionally, results are compared to heat loss predictions derived from optical measurements of the absorber coating via a one-dimensional simulation. The general trend suggests good accordance with a systematical deviation at lower emissivities. Overall, this non destructive measurement technique provides a good possibility to determine an important receiver specification, its heat loss

Online characterization of operational parameters in an SOFC system with anode‐exhaust gas recirculation by oxygen sensors

In solid oxide fuel cell (SOFC) system with anode-exhaust gas recirculation (AEGR), a part of the depleted anode-exhaust gas is recirculated and mixed with fresh natural gas prior to entering the reformer, resulting in a high potential of direct current-efficiencies of up to 65%. However, for SOFC systems, it is of crucial importance to monitor relevant characteristic parameters and keep them within safe and durable operating limits. Such characteristic parameters are oxygen-to-carbon-ratio and fuel utilization, which must not exceed system-specific thresholds. Monitoring and control of these characteristic parameters is not trivial and a challenging task due to the enhanced system complexity. The authors present an approach and studies for determination and controlling characteristic parameters in an SOFC system with AEGR based on oxygen sensors. Therefore, the oxygen sensors, being a mature and easily available technology, are aimed to be in the fuel gas path at anode inlet and outlet of an SOFC-stack. Analytical correlations of the output value of oxygen sensors to characteristic parameters and simulative studies on the basis of a reference natural gas composition are shown. Additionally, first results of experimental studies of oxygen sensors in representative gas compositions for the anode inlet and outlet of an SOFC system with AEGR are presented

Control of oxygen-to-carbon ratio and fuel utilization with regard to solid oxide fuel cell systems with anode-offgas recirculation: A review

One of the possible SOFC system-configurations providing the highest potential of electrical DC-efficiency of up to 65% is a SOFC-system with anode exhaust gas recirculation (AEGR), where part of the depleted anode exhaust gas is recirculated and mixed with fresh natural gas upstream of the reformer. For safe and durable operation of a SOFC-system, the oxygen-to-carbon-ratio and the fuel utilization as characteristic parameters must not exceed stack- and reformer-specific thresholds. The determination and control of the characteristic parameters are therefore of crucial importance. However, this poses especially for SOFC-systems with AEGR due to enhanced system complexity a challenging task. In this paper, the authors present an overview on representative control strategies as well as different approaches to determine or diagnose characteristic parameters with emphasis on SOFC-systems with AEGR. Some conclusions are discussed based on the provided overview and outlines recommendations for future research work

Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Abstract Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel- or texture-based mapping approaches do not fully exploit the information content provided by the high spatial resolution. Here, to fully harness this spatial detail, we apply deep learning techniques, that is, Convolutional Neural Networks (CNNs), on regular tiles of UAV-orthoimagery (here 2–5 m) to identify the cover of target plant species and plant communities. The approach was tested with UAV-based orthomosaics and photogrammetric 3D information in three case studies, that is, (1) mapping tree species cover in primary forests, (2) mapping plant invasions by woody species into forests and open land and (3) mapping vegetation succession in a glacier foreland. All three case studies resulted in high predictive accuracies. The accuracy increased with increasing tile size (2–5 m) reflecting the increased spatial context captured by a tile. The inclusion of 3D information derived from the photogrammetric workflow did not significantly improve the models. We conclude that CNN are powerful in harnessing high resolution data acquired from UAV to map vegetation patterns. The study was based on low cost red, green, blue (RGB) sensors making the method accessible to a wide range of users. Combining UAV and CNN will provide tremendous opportunities for ecological applications