A Field Guide for Monitoring Riverine Macroplastic Entrapment in Water Hyacinths

River plastic pollution is an environmental challenge of growing concern. However, there are still many unknowns related to the principal drivers of river plastic transport. Floating aquatic vegetation, such as water hyacinths, have been found to aggregate and carry large amounts of plastic debris in tropical river systems. Monitoring the entrapment of plastics in hyacinths is therefore crucial to answer the relevant scientific and societal questions. Long-term monitoring efforts are yet to be designed and implemented at large scale and various field measuring techniques can be applied. Here, we present a field guide on available methods that can be upscaled in space and time, to characterize macroplastic entrapment within floating vegetation. Five measurement techniques commonly used in plastic and vegetation monitoring were applied to the Saigon river, Vietnam. These included physical sampling, Unmanned Aerial Vehicle imagery, bridge imagery, visual counting, and satellite imagery. We compare these techniques based on their suitability to derive metrics of interest, their relevancy at different spatiotemporal scales and their benefits and drawbacks. This field guide can be used by practitioners and researchers to design future monitoring campaigns and to assess the suitability of each method to investigate specific aspects of macroplastic and floating vegetation interactions.

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Flight Path Planning of Robot for Environmental Measurement Considering Accuracy Assurance

Impact ◽

10.21820/23987073.2019.10.90 ◽

2019 ◽

Vol 2019 (10) ◽

pp. 90-92

Author(s):

Kae Doki ◽

Yuki Funabora ◽

Shinji Doki

Keyword(s):

Large Scale ◽

Highway Bridge ◽

Climate Research ◽

Small Uav ◽

Inspection Process ◽

Aerial View ◽

Aerial Vehicle ◽

Infrastructure Inspection ◽

Institute Of Technology ◽

Small Team

Every day we are seeing an increasing number of robots being employed in our day-to-day lives. They are working in factories, cleaning our houses and may soon be chauffeuring us around in vehicles. The affordability of drones too has come down and now it is conceivable for most anyone to own a sophisticated unmanned aerial vehicle (UAV). While fun to fly, these devices also represent powerful new tools for several industries. Anytime an aerial view is needed for a planning, surveillance or surveying, for example, a UAV can be deployed. Further still, equipping these vehicles with an array of sensors, for climate research or mapping, increases their capability even more. This gives companies, governments or researchers a cheap and safe way to collect vast amounts of data and complete tasks in remote or dangerous areas that were once impossible to reach. One area UAVs are proving to be particularly useful is infrastructure inspection. In countries all over the world large scale infrastructure projects like dams and bridges are ageing and in need of upkeep. Identifying which ones and exactly where they are in need of patching is a huge undertaking. Not only can this work be dangerous, requiring trained inspectors to climb these megaprojects, it is incredibly time consuming and costly. Enter the UAVs. With a fleet of specially equipped UAVs and a small team piloting them and interpreting the data they bring back the speed and safety of this work increases exponentially. The promise of UAVs to overturn the infrastructure inspection process is enticing, but there remain several obstacles to overcome. One is achieving the fine level of control and positioning required to navigate the robots around 3D structures for inspection. One can imagine that piloting a small UAV underneath a huge highway bridge without missing a single small crack is quite difficult, especially when the operators are safely on the ground hundreds of meters away. To do this knowing exactly where the vehicle is in space becomes a critical variable. The job can be made even easier if a flight plan based on set waypoints can be pre-programmed and followed autonomously by the UAV. It is exactly this problem that Dr Kae Doki from the Department of Electrical Engineering at Aichi Institute of Technology, and collaborators are focused on solving.

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Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

Integrated Environmental Assessment and Management ◽

10.1002/ieam.4493 ◽

2021 ◽

Author(s):

Silvia Huber ◽

Lars B. Hansen ◽

Lisbeth T. Nielsen ◽

Mikkel L. Rasmussen ◽

Jonas Sølvsteen ◽

...

Keyword(s):

Large Scale ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation ◽

Novel Approach

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Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

Remote Sensing ◽

10.3390/rs13071238 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1238

Author(s):

Jere Kaivosoja ◽

Juho Hautsalo ◽

Jaakko Heikkinen ◽

Lea Hiltunen ◽

Pentti Ruuttunen ◽

...

Keyword(s):

Reference Data ◽

Measurement Techniques ◽

Synthetic Data ◽

Ground Truth ◽

Precision Farming ◽

Aerial Imaging ◽

Aerial Vehicle ◽

Uav Images ◽

Reference Measurements

The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Microbial Cell Factories ◽

10.1186/s12934-021-01614-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jingru Zhou ◽

Yingping Zhuang ◽

Jianye Xia

Keyword(s):

Aspergillus Niger ◽

Large Scale ◽

Measurement Techniques ◽

Metabolic Model ◽

System Level ◽

Metabolic Phenotype ◽

Omics Data ◽

Prediction Ability ◽

Phenotype Prediction ◽

Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

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Large-scale motion in the intermittent region of a turbulent boundary layer

Journal of Fluid Mechanics ◽

10.1017/s0022112070000629 ◽

1970 ◽

Vol 41 (2) ◽

pp. 283-325 ◽

Cited By ~ 374

Author(s):

Leslie S. G. Kovasznay ◽

Valdis Kibens ◽

Ron F. Blackwelder

Keyword(s):

Boundary Layer ◽

Turbulent Boundary Layer ◽

Large Scale ◽

Cross Correlation ◽

Turbulent Regime ◽

Conditional Sampling ◽

Correlation Data ◽

Measuring Techniques ◽

Conditional Averaging ◽

Scale Motion

The outer intermittent region of a fully developed turbulent boundary layer with zero pressure gradient was extensively explored in the hope of shedding some light on the shape and motion of the interface separating the turbulent and non-turbulent regions as well as on the nature of the related large-scale eddies within the turbulent regime. Novel measuring techniques were devised, such as conditional sampling and conditional averaging, and others were turned to new uses, such as reorganizing in map form the space-time auto- and cross-correlation data involving both the U and V velocity components as well as I, the intermittency function. On the basis of the new experimental results, a conceptual model for the development of the interface and for the entrainment of new fluid is proposed.

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Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forests ◽

10.3390/f12040397 ◽

2021 ◽

Vol 12 (4) ◽

pp. 397

Author(s):

Riccardo Dainelli ◽

Piero Toscano ◽

Salvatore Filippo Di Gennaro ◽

Alessandro Matese

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Sustainable Management ◽

Large Scale ◽

Spectral Signature ◽

Accurate Information ◽

Data Interoperability ◽

Critical Issues ◽

Management Context ◽

Aerial Vehicle

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV”+“forest”. This result is even more surprising when compared with similar research for “UAV”+“agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely,(1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

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Low-Power FPGA Architecture Based Monitoring Applications in Precision Agriculture

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea11040039 ◽

2021 ◽

Vol 11 (4) ◽

pp. 39

Author(s):

Amine Saddik ◽

Rachid Latif ◽

Abdelhafid El Ouardi

Keyword(s):

Precision Agriculture ◽

Agricultural Fields ◽

Vegetation Monitoring ◽

Computing Power ◽

Monitoring Applications ◽

Aerial Vehicle ◽

On Chip ◽

The Right ◽

Power And Energy ◽

Monitoring Algorithm

Today’s on-chip systems technology has grounded impressive advances in computing power and energy consumption. The choice of the right architecture depends on the application. In our case, we were studying vegetation monitoring algorithms in precision agriculture. This study presents a system based on a monitoring algorithm for agricultural fields, an electronic architecture based on a CPU-FPGA SoC system and the OpenCL parallel programming paradigm. We focused our study on our own dataset of agricultural fields to validate the results. The fields studied in our case are in the Guelmin-Oued noun region in the south of Morocco. These fields are divided into two areas, with a total surface of 3.44 Ha2 for the first field and 3.73 Ha2 for the second. The images were collected using a DJI-type unmanned aerial vehicle and an RGB camera. Performance evaluation showed that the system could process up to 86 fps versus 12 fps or 20 fps in C/C++ and OpenMP implementations, respectively. Software optimizations have increased the performance to 107 fps, which meets real-time constraints.

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Influence of Propeller Overlap on Large-Scale Tandem UAV Performance

Unmanned Systems ◽

10.1142/s2301385019500092 ◽

2019 ◽

Vol 07 (04) ◽

pp. 245-260

Author(s):

Adrian B. Weishäupl ◽

Stephen D. Prior

Keyword(s):

Carbon Fiber ◽

Unmanned Aerial Vehicle ◽

Large Scale ◽

Vertical Separation ◽

Hovering Flight ◽

Wide Range ◽

Aerial Vehicle

This paper investigates the interference that arises from overlapping Unmanned Aerial Vehicle (UAV) propellers during hovering flight. The tests have been conducted on [Formula: see text] ultralight carbon fiber propellers using a bespoke mount and the RCBenchmark Series 1780 dynamometer at various degrees of overlap [Formula: see text] and vertical separation [Formula: see text]. A great deal of confusion regarding the losses that are associated with mounting propellers in a co-axial configuration is reported in the literature, with a summary of historical tandem helicopters having been conducted. The results highlight a region of beneficial overlap (0–20%), which has the potential to be advantageous to a wide range of UAVs.

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Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2005990117 ◽

2020 ◽

Vol 117 (46) ◽

pp. 28784-28794

Author(s):

Sisi Chen ◽

Paul Rivaud ◽

Jong H. Park ◽

Tiffany Tsou ◽

Emeric Charles ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Large Scale ◽

Probabilistic Models ◽

Single Cells ◽

Measurement Techniques ◽

Patient Specific ◽

Cell Populations ◽

Cell Measurement ◽

The Impact

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

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Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

Remote Sensing ◽

10.3390/rs12142188 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2188 ◽

Cited By ~ 2

Author(s):

Simona Niculescu ◽

Jean-Baptiste Boissonnat ◽

Cédric Lardeux ◽

Dar Roberts ◽

Jenica Hanganu ◽

...

Keyword(s):

Time Series ◽

Aquatic Macrophytes ◽

Aquatic Vegetation ◽

Myriophyllum Spicatum ◽

Danube Delta ◽

Backscatter Coefficient ◽

Macrophyte Communities ◽

Optical Images ◽

Detailed Classification ◽

Floating Vegetation

In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.

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