Winter activity of a population of greater horseshoe bats (Rhinolophus ferrumequinum)

Activity patterns of a greater horseshoe bats Rhinolophus ferrumequinum were investigated at caves in Cheddar (south-west England) during the hibernation season. An ultrasound detector and datalogger were used to monitor and record the number of echolocation calls in a single cave. Activity of R. ferrumequinum remained largely nocturnal throughout winter, and the mean time of activity over 24 hours was 88 to 369 minutes (1.47 to 6.15 hours) after sunset. There was an increase in diurnal activity from late May to early June, probably because bats remained active after foraging at dawn towards the end of the hibernation season. Visits to the cave did not increase bat activity. Cave air temperature reflected external climatic temperature, although there was variation in cave temperature and its range within and across caves. Individual R. ferrumequinum are usually dispersed in caves in regions where temperature fluctuations correlate with climatic variations in temperature. There was a positive correlation between the number of daily bat passes monitored by the bat detector and datalogger (= daily activity) and cave temperature. Nocturnal activity may sometimes be associated with winter feeding. Neither date nor barometric pressure had a significant effect on daily activity. Activity patterns largely reflected the findings from individual R. ferrumequinum studied by telemetry (Park, 1998), in that bat activity increased with cave and climatic temperatures, and the temporal pattern of activity remained consistently nocturnal throughout winter, starting at dusk

Frequent Arousal from Hibernation Linked to Severity of Infection and Mortality in Bats with White-Nose Syndrome

White-nose syndrome (WNS), an emerging infectious disease that has killed over 5.5 million hibernating bats, is named for the causative agent, a white fungus (Geomyces destructans (Gd)) that invades the skin of torpid bats. During hibernation, arousals to warm (euthermic) body temperatures are normal but deplete fat stores. Temperature-sensitive dataloggers were attached to the backs of 504 free-ranging little brown bats (Myotis lucifugus) in hibernacula located throughout the northeastern USA. Dataloggers were retrieved at the end of the hibernation season and complete profiles of skin temperature data were available from 83 bats, which were categorized as: (1) unaffected, (2) WNS-affected but alive at time of datalogger removal, or (3) WNS-affected but found dead at time of datalogger removal. Histological confirmation of WNS severity (as indexed by degree of fungal infection) as well as confirmation of presence/absence of DNA from Gd by PCR was determined for 26 animals. We demonstrated that WNS-affected bats aroused to euthermic body temperatures more frequently than unaffected bats, likely contributing to subsequent mortality. Within the subset of WNS-affected bats that were found dead at the time of datalogger removal, the number of arousal bouts since datalogger attachment significantly predicted date of death. Additionally, the severity of cutaneous Gd infection correlated with the number of arousal episodes from torpor during hibernation. Thus, increased frequency of arousal from torpor likely contributes to WNS-associated mortality, but the question of how Gd infection induces increased arousals remains unanswered

Wing pathology of white-nose syndrome in bats suggests life-threatening disruption of physiology

White-nose syndrome (WNS) is causing unprecedented declines in several species of North American bats. The characteristic lesions of WNS are caused by the fungus Geomyces destructans, which erodes and replaces the living skin of bats while they hibernate. It is unknown how this infection kills the bats. We review here the unique physiological importance of wings to hibernating bats in relation to the damage caused by G. destructans and propose that mortality is caused by catastrophic disruption of wing-dependent physiological functions. Mechanisms of disease associated with G. destructans seem specific to hibernating bats and are most analogous to disease caused by chytrid fungus in amphibians

Microdrone-based Indoor Mapping with Graph SLAM

Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating indoor exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with laser range finders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph op-timization. It performs loop-closure detection and correction to recognize previously visited places and correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multi-layer LI-DAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicated that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling of the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31 m long ac-quisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multi-layer LIDAR-based macrodrone given the low deviation between the point clouds built by both drones. About 85 % of the cloud-to-cloud distances were less than 10 cm

LISS panel - Resilience towards robotization: The willingness, opportunity and ability of individuals to prepare for automation at the workplace

This research aims to examine whether there are differences in the extent to which people in the Netherlands are interested in reskilling or upskilling to prepare for automation, have access to relevant types of education, and have the ability to engage successfully in reskilling or upskilling.Suggestions for data usage: The data files are accessible via CentERdata. For more information, please use the link under Relations or www.lissdata.nl

Multi-scale assessment of interactions between surface water and groundwater fluxes in hard rock, water limited environment

This is a PhD research dataset by S M Tanvir Hassan, Water Resources Department of the Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, The Netherlands. The abstract of the research outcome is as follows: The multi-scale assessment of surface-groundwater interactions in hard rock (granite), water limited environments was carried out first in the ~80 km2 Sardon catchment and finally in a small, 7.6 ha Trabadillo study area (Western Spain). Three different thematic studies (Chapters 2-4) had been carried out applying various techniques including: field data acquisition, automated monitoring, remote sensing and integrated hydrological modelling using GSFLOW code. Chapter 2 addresses the assessment of dynamics of surface-groundwater interactions at the Sardon catchment scale; the main findings were: (1) intense groundwater exfiltration; (2) groundwater flow characterized by short groundwater flow-path and short residence time; (3) declining trend of catchment groundwater outflow; and (4) large variability of infiltration influenced by land cover type. That impact of land cover upon dynamics of surface-groundwater interactions triggered expansion of further research in Chapters 3 and 4, but also underpinned some hardcoded deficiencies of GSFLOW, related to formulation of driving forces. That initiated the study in Chapter 3, addressing spatiotemporal tree rainfall interception loss (Ei). Experimental measurements of Ei on selected evergreen Quercus ilex and deciduous Quercus pyrenaica oaks were carried out during two years and spatiotemporally upscaled into two homogeneous (1 ha) plots and into the entire Sardon catchment; the main findings were: (1) yearly Ei was larger in wet than in dry hydrological years but if reflected as percent of rainfall, then it was opposite; (2) Ei of Quercus ilex trees were larger than of Quercus pyrenaica but for the catchment scale Ei, it was opposite, because of much larger catchment population of the latter; (3) catchment Ei was primarily dependent on tree density and species type. The Chapter 4 focusses on simulating surface-groundwater interactions at very fine grid (5x5 m) and at temporal daily resolution, using 20-year time-series observation to assess the net recharge dependence upon land cover type expressed through hydrological terrain units (hydrotopes). The findings of this study confirmed all findings of the Chapter 2, adding the following: (1) hydrotope-dependent variability of water fluxes; (2) non-negligible grass interception; and (3) much lower net recharge under tree than grass hydrotopes, meaning more trees, less water resources

Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel- 2 data

Crop lodging assessment is essential for evaluating yield damage and informing crop management decisions for sustainable agricultural production. While a few studies have demonstrated the potential of optical and SAR data for crop lodging assessment, large-scale crop lodging assessment has been hampered by the unavailability of dense satellite time series data. The unprecedented availability of free Sentinel-1 and Sentinel-2 data may provide a basis for operational detection and monitoring of crop lodging. In this context, this study aims to understand the effect of lodging on backscatter/coherence and spectral reflectance derived from Sentinel-1 and Sentinel-2 data and to detect lodging incidence in wheat using time-series analysis. Crop biophysical parameters were measured in the field for both healthy and lodged plots from March to June 2018 in a study site in Ferrara, Italy, and the corresponding Sentinel images were downloaded and processed. The lodged plots were further categorised into different lodging severity classes (moderate, severe and very severe). Temporal profiles of backscatter, coherence, reflectance and continuum removed spectra were studied for healthy and lodging severity classes throughout the stem elongation to ripening growth stages. The Kruskal Wallis and posthoc Tukey tests were used to test for significant differences between different classes. Our results for Sentinel-2 showed that red edge (740 nm) and NIR (865 nm) bands could best distinguish healthy from lodged wheat (particularly healthy and very severe). For Sentinel-1, the analysis revealed the potential of VH backscatter and the complementarity of VV and VH/VV backscatter in distinguishing a maximum number of classes. Our findings demonstrate the potential of Sentinel data for near real-time detection of the incidence and severity of lodging in wheat. To the best of our knowledge, there is no study that has contributed to this application

Agressie-incidenten in de asielopvang

In 2015, the Netherlands were confronted with a strong increase in asylum applications. This high number of asylum seekers arriving in the country increased the pressure on the reception centres of The Central Agency for the Reception of Asylum Seekers (COA). At the same time, an increasing number of incidents of violence and aggression were reported in the reception centres for asylum seekers (for example in the regular asylum seekers’ centres (azc), in the reception locations for unaccompanied minor asylum seekers (amv), and in the emergency accommodation). The aim of this research report was to answer the following questions:What is the nature of incidents of violence and aggression in asylum seekers’ centres?;To what extent do COA_employees feel fully equipped to prevent or handle these incidents of violence and aggression in COA reception locations, and;How could (new) insights be implemented to help prevent or effectively resolve incidents of violence and aggression in the future

Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data

Crop lodging - the bending of crop stems from their upright position or the failure of root-soil anchorage systems - is a major yield-reducing factor in wheat and causes deterioration of grain quality. The severity of lodging can be measured by a lodging score (LS)- an index calculated from the crop angle of inclination (CAI) and crop lodged area (LA). LS is difficult and time consuming to measure manually meaning that information on lodging occurrence and severity is limited and sparse. Remote sensing-based estimates of LS can provide more timely, synoptic and reliable information on crop lodging across vast areas. This information could improve estimates of crop yield losses, inform insurance loss adjusters and influence management decisions for subsequent seasons. This research - conducted in the 600 ha wheat sown area in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy - evaluated the performance of RADARSAT-2 and Sentinel-1 data to discriminate and classify lodging severity based on field measured LS. We measured temporal crop status characteristics related to lodging (e.g. lodged area, CAI, crop height) and collected relevant meteorological data (wind speed and rainfall) throughout May-June 2018. These field measurements were used to distinguish healthy (He) wheat from lodged wheat with different degrees of lodging severity (moderate, severe and very severe). We acquired multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 and Sentinel-1 (40°) images and derived multiple metrics from them to discriminate and classify lodging severity. As a part of our data exploration, we performed a correlation analysis between the image-based metrics and LS. Next, a multi-temporal discriminant analysis approach, including a partial least squares (PLS-DA) method, was developed to classify lodging severities. We used the area under the curve-receiver operating characteristics (AUC-ROC) and confusion matrices to evaluate the accuracy of the PLS-DA classification models. Results show that (1) volume scattering components were highly correlated with LS at low incidence angles while double and surface scattering was more prevalent at high incidence angles; (2) lodging severity was best classified using low incidence angle R-FQ8 data (overall accuracy 72%) and (3) the Sentinel-1 data-based classification model was able to correctly identify 60% of the lodging severity cases in the study site. The results from this first study on classifying lodging severity using satellite-based SAR platforms suggests that SAR-based metrics can capture a substantial proportion of the observed variation in lodging severity, which is important in the context of operational crop lodging assessment in particular, and sustainable agriculture in general

Developing a SLAM-based backpack mobile mapping system for indoor mapping

These files are to support the published journal and thesis about the IMU and LIDAR SLAM for indoor mapping. They include datasets and functions used for point clouds generation