Automotive Radar in a UAV to Assess Earth Surface Processes and Land Responses.

The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to handle. Therefore, the use of low-cost radar sensors is becoming more relevant. In this article, to make the usage of radar simpler and more efficient, we developed with automotive radar technology. We introduce basic radar techniques and present two radar sensors with their specifications. To record the radar data, we developed a system with an integrated camera and sensors. The weight of the whole system is about 315 g for the small radar and 450 g for the large one. The whole system was integrated into a UAV and test flights were performed. After that, several flights were carried out, to verify the system with both radar sensors. Thereby, the records provide an insight into the radar data. We demonstrated that the recording system works and the radar sensors are suitable for the usage in a UAV and future earth science research because of its autonomy, precision, and lightweight.

A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing.

This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1-5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2-12.5 µm (instrument NEDT 0.05 K-0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0-10.25 µm and 10.25-12.5 µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1-3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.

Unveiling patterns in human dominated landscapes through mapping the mass of US built structures.

Built structures increasingly dominate the Earth's landscapes; their surging mass is currently overtaking global biomass. We here assess built structures in the conterminous US by quantifying the mass of 14 stock-building materials in eight building types and nine types of mobility infrastructures. Our high-resolution maps reveal that built structures have become 2.6 times heavier than all plant biomass across the country and that most inhabited areas are mass-dominated by buildings or infrastructure. We analyze determinants of the material intensity and show that densely built settlements have substantially lower per-capita material stocks, while highest intensities are found in sparsely populated regions due to ubiquitous infrastructures. Out-migration aggravates already high intensities in rural areas as people leave while built structures remain - highlighting that quantifying the distribution of built-up mass at high resolution is an essential contribution to understanding the biophysical basis of societies, and to inform strategies to design more resource-efficient settlements and a sustainable circular economy.

NanoSIMS50 - a powerful tool to elucidate cellular localization of halogenated organic compounds.

Persistent organic pollutants are widely distributed in the environment and lots of toxicological data are available. However, little is known on the intracellular fate of such compounds. Here a method applying secondary ion mass spectrometry is described that can be used to visualize cellular localization of halogenated compounds and to semi-quantitatively calculate concentrations of such compounds. Of the model compounds tested, TBBPA was homogenously distributed in the cell membrane of the H295R cells while PFOS accumulated in very distinct locations in the cell membrane. Relative intracellular concentrations of 4-OH-BDE69 and 4-OH-BDE121 in GH3.TRE were 61 % and 18 %, respectively, compared to the parent compounds. These differences may partly explain that observed effect concentrations for 4-OH-BDEs in in vitro experiments are usually lower than what would be expected based on receptor binding studies. NanoSIMS50 proved to be a powerful tool to describe the cellular distribution of halogenated compounds. The semi-quantitative data that can be obtained may help to further explain results from in vitro or in vivo experiments.

Environmental DNA from archived leaves reveals widespread temporal turnover and biotic homogenization in forest arthropod communities.

A major limitation of current reports on insect declines is the lack of standardized, long-term, and taxonomically broad time series. Here, we demonstrate the utility of environmental DNA from archived leaf material to characterize plant-associated arthropod communities. We base our work on several multi-decadal leaf time series from tree canopies in four land use types, which were sampled as part of a long-term environmental monitoring program across Germany. Using these highly standardized and well-preserved samples, we analyze temporal changes in communities of several thousand arthropod species belonging to 23 orders using metabarcoding and quantitative PCR. Our data do not support widespread declines of α-diversity or genetic variation within sites. Instead, we find a gradual community turnover, which results in temporal and spatial biotic homogenization, across all land use types and all arthropod orders. Our results suggest that insect decline is more complex than mere α-diversity loss, but can be driven by ß-diversity decay across space and time.

Insects are a barometer of environmental health. Ecosystems around the world are being subjected to unprecedented man-made stresses, ranging from climate change to pollution and intensive land use. These stresses have been associated with several recent, dramatic declines in insect populations, particularly in areas with heavily industrialised farming practices. Despite this, the links between insect decline, environmental stress, and ecosystem health are still poorly-understood. A decline in one area might look catastrophic, but could simply be part of normal, longer-term variations. Often, we do not know whether insect decline is a local phenomenon or reflects wider environmental trends. Additionally, most studies do not go far back enough in time or cover a wide enough geographical range to make these distinctions. To understand and combat insect decline, we therefore need reliable methods to monitor insect populations over long periods of time. To solve this problem, Krehenwinkel, Weber et al. gathered data on insect communities from a new source: tree leaves. Originally, these samples were collected to study air pollution, but they also happen to contain the DNA of insects that interacted with them before they were collected ­ for example, DNA deposited in chew marks where the insects had nibbled on the leaves. This is called environmental DNA, or eDNA for short. To survey the insect communities that lived in these trees, Krehenwinkel, Weber et al. first extracted eDNA from the leaves and sequenced it. Analysis of the different DNA sequences from the leaf samples revealed not only the number of insect species, but also the abundance (or rarity) of each species within each community. Importantly, the leaves had been collected and stored in stable conditions over several decades, allowing changes in these insect populations to be tracked over time. eDNA analysis revealed subtle changes in the make-up of forest insect communities. In the forests where the leaves were collected, the total number of insect species remained much the same over time. However, many individual species still declined, only to be replaced by newcomer species. These 'colonisers' are also widespread, which will likely lead to an overall pattern of fewer species that are more widely distributed ­ in other words, more homogeneity. The approach of Krehenwinkel, Weber et al. provides a reliable method to study insect populations in detail, over multiple decades, using archived samples from environmental studies. The information gained from this has real-world significance for environmental issues with enormous social impact, ranging from conservation, to agriculture and even public health.

Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging.

Hyperspectral cameras onboard unmanned aerial vehicles (UAVs) have recently emerged for monitoring crop traits at the sub-field scale. Different physical, statistical, and hybrid methods for crop trait retrieval have been developed. However, spectra collected from UAVs can be confounded by various issues, including illumination variation throughout the crop growing season, the effect of which on the retrieval performance is not well understood at present. In this study, four retrieval methods are compared, in terms of retrieving the leaf area index (LAI), fractional vegetation cover (fCover), and canopy chlorophyll content (CCC) of potato plants over an agricultural field for six dates during the growing season. We analyzed: (1) The standard look-up table method (LUTstd), (2) an improved (regularized) LUT method that involves variable correlation (LUTreg), (3) hybrid methods, and (4) random forest regression without (RF) and with (RFexp) the exposure time as an additional explanatory variable. The Soil-Leaf-Canopy (SLC) model was used in association with the LUT-based inversion and hybrid methods, while the statistical modelling methods (RF and RFexp) relied entirely on in situ data. The results revealed that RFexp was the best-performing method, yielding the highest accuracies, in terms of the normalized root mean square error (NRMSE), for LAI (5.36%), fCover (5.87%), and CCC (15.01%). RFexp was able to reduce the effects of illumination variability and cloud shadows. LUTreg outperformed the other two retrieval methods (hybrid methods and LUTstd), with an NRMSE of 9.18% for LAI, 10.46% for fCover, and 12.16% for CCC. Conversely, LUTreg led to lower accuracies than those derived from RF for LAI (5.51%) and for fCover (6.23%), but not for CCC (16.21%). Therefore, the machine learning approaches-in particular, RF-appear to be the most promising retrieval methods for application to UAV-based hyperspectral data.

Microbial community dynamics in replicate anaerobic digesters exposed sequentially to increasing organic loading rate, acidosis, and process recovery.

BACKGROUND: Volatile fatty acid intoxication (acidosis), a common process failure recorded in anaerobic reactors, leads to drastic losses in methane production. Unfortunately, little is known about the microbial mechanisms underlining acidosis and the potential to recover the process. In this study, triplicate mesophilic anaerobic reactors of 100 L were exposed to acidosis resulting from an excessive feeding with sugar beet pulp and were compared to a steady-state reactor. RESULTS: Stable operational conditions at the beginning of the experiment initially led to similar microbial populations in the four reactors, as revealed by 16S rRNA gene T-RFLP and high-throughput amplicon sequencing. Bacteroidetes and Firmicutes were the two dominant phyla, and although they were represented by a high number of operational taxonomic units, only a few were dominant. Once the environment became deterministic (selective pressure from an increased substrate feeding), microbial populations started to diverge between the overfed reactors. Interestingly, most of bacteria and archaea showed redundant functional adaptation to the changing environmental conditions. However, the dominant Bacteroidales were resistant to high volatile fatty acids content and low pH. The severe acidosis did not eradicate archaea and a clear shift in archaeal populations from acetotrophic to hydrogenotrophic methanogenesis occurred in the overfed reactors. After 11 days of severe acidosis (pH 5.2 ± 0.4), the process was quickly recovered (restoration of the biogas production with methane content above 50 %) in the overfed reactors, by adjusting the pH to around 7 using NaOH and NaHCO3. CONCLUSIONS: In this study we show that once the replicate reactors are confronted with sub-optimal conditions, their microbial populations start to evolve differentially. Furthermore the alterations of commonly used microbial parameters to monitor the process, such as richness, evenness and diversity indices were unsuccessful to predict the process failure. At the same time, we tentatively propose the replacement of the dominant Methanosaeta sp. in this case by Methanoculleus sp., to be a potential warning indicator of acidosis.

Determination of oral uptake and biodistribution of platinum and chromium by the garden snail (Helix aspersa) employing nano-secondary ion mass-spectrometry.

Environmental heavy metal contamination is a case of concern for both animal and human health. Studying the fate of metals in plant or animal tissues may provide information on pollution. In the present study, we investigated the possibility to follow the biological fate of chromium and platinum uptake in common garden snails (Helix aspersa), typically accumulating high concentrations of metals from their environment. Chromium and platinum were administered orally to snails in 5 groups (n=25/group): control, food contaminated by ca. 2.5 µg g(-1) and 19 µg g(-1) chromium and 2.5 µg g(-1) and 25 µg g(-1) platinum, for 8 weeks. Following exposure, surviving snails were sacrificed, shell and remaining tissue investigated by ICP-MS, and shell, midgut gland and mantle by nano-secondary ion mass-spectrometry (Nano-SIMS). (12)C(14)N-normalized platinum and (40)Ca-normalized chromium measurements indicated highest enrichments in cellular vesicles of the midgut gland, and lower concentrations in mantle and shell, with significantly higher platinum and chromium concentrations in the 2 exposure groups vs. control (P<0.05), with somewhat differing distribution patterns for chromium and platinum. Comparable results were obtained by ICP-MS, with both chromium and platinum fed snails showing drastically elevated concentrations of metals in shell (up to 78 and 122 µg g(-1) dw platinum and chromium, respectively) and in other tissues (up to 200 and 1125 µg g(-1) dw platinum and chromium, respectively). Nano-SIMS allowed for semi-quantitative comparison of metal fate in snail tissues, making this an interesting technique for future studies in the area of environmental pollution.

Estimating deoxynivalenol contents of wheat samples containing different levels of Fusarium-damaged kernels by diffuse reflectance spectrometry and partial least square regression.

Fusarium head blight is a fungal disease causing yield losses and mycotoxin contamination in wheat and other cereals. Wheat kernels (cultivar Ritmo) were sampled in 2001, 2002, 2003, and 2006 and Fusarium-damaged kernels were separated from sound grain based on visual assessment. Subsequently, grain lots containing 0, 20, 40, 60, 80, and 100% of damaged kernels were compiled. Each lot was split and the spectrometric reflectance (wavelengths 350-2500nm) was measured using subgroup one, while the concentration of the mycotoxin deoxynivalenol (DON) was determined by high-performance liquid chromatography in subgroup two. DON concentrations in batches classified as sound were not significantly different from 0. Estimating DON contents from the percentage of Fusarium-damaged kernels was impeded by vast variability, resulting in a coefficient of determination of 0.49. Using spectrometric data subjected to partial least square regression allowed estimating DON contents with higher accuracy, in particular at elevated percentages of damaged kernels. The coefficient of determination was 0.84 for the relationship between DON contents estimated based on spectrometric data and the DON contents measured. The intercept of a regression line fitted through a plot of estimated versus measured DON contents was 0.89+/-3.61mg/kg. Since intercept+standard error was larger than the actual legal limit (1.25mg DON per kg dry grain in the European Union), the spectrometric procedure was still not precise enough to allow a reliable separation of grain samples with DON contents below 1.25mg/kg from samples with DON contents above the limit. However, spectrometric data also allowed estimating the DON content of the average damaged kernel within a given lot composed of sound and damaged kernels, which is probably the reason for the reduction of the fraction of unexplained variance by 35% compared to the visual approach and illustrates that spectrometric approaches can make a contribution to reducing DON contents of wheat grain.

Identification of scrapie infection from blood serum by Fourier transform infrared spectroscopy.

We describe a new Fourier transform infrared (FT-IR) spectroscopy-based diagnostic approach, which may provide for the first time a rapid, reliable, and inexpensive blood test for scrapie and related transmissible spongiform encephalopathies (TSE). Blood serum from 146 terminally ill Syrian hamsters infected with 263K scrapie via different routes of inoculation and from 166 healthy control animals was analyzed by FT-IR spectroscopy and artificial neural networks (ANN). This revealed characteristic molecular alterations in the serum of infected donors. Different ANN models were constructed and challenged with previously unknown samples in test runs in order to establish whether the new method is able to discriminate between normal and infected animals. Optimized ANNs consistently yielded test sensitivities and specificities of 97% and 100%, respectively. The predictive value of a positive (negative) test was 100% (98%). The proposed serum test circumvents substantial drawbacks of conventional TSE diagnostics and can be fully automated.

Antemortem identification of bovine spongiform encephalopathy from serum using infrared spectroscopy.

Since 1986, more than 180 000 clinical cases of bovine spongiform encephalopathy (BSE) have been observed in the U.K. alone. Most of these cases were confirmed by postmortem examination of brain tissue. However, BSE-related risk assessment and risk management would greatly benefit from antemortem testing on living animals. A serum-based test could allow for screening of the cattle population; thus, even a BSE eradication program would be conceivable. Here we report on a novel method for antemortem BSE testing, which combines infrared spectroscopy of serum samples with multivariate pattern recognition analysis. A classification algorithm was trained using infrared spectra of bovine sera from more than 800 animals (including BSE-positive, healthy controls and animals suffering from classical viral or bacterial infections). In two validation studies, sensitivities of 85 and 84% and specificities of 86 and 91% were achieved, respectively. The combination of classification algorithms increased the sensitivity and specificity of BSE detection to 96 and 92%, respectively.