scholarly journals Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
José P. Granadeiro ◽  
João Belo ◽  
Mohamed Henriques ◽  
João Catalão ◽  
Teresa Catry
Keyword(s):  
Large Scale ◽  
Exposure Period ◽  
Intertidal Area ◽  
Digital Elevation ◽  
Geographical Bias ◽  
Novel Method ◽  
Tidal Stage ◽  
Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

scholarly journals Using Scuba for In Situ Determination of Chlorophyll Distributions in Corals by Underwater Near Infrared Fluorescence Imaging

2020 ◽  
Vol 8 (1) ◽  
pp. 53
Author(s):  
Thomas Oh ◽  
Jittiwat Sermsripong ◽  
Barry W. Hicks
Keyword(s):  
Fluorescence Imaging ◽  
Large Scale ◽  
Near Infrared ◽  
Fluorescent Proteins ◽  
Fluorescence Emission ◽  
Florida Keys ◽  
Nir Fluorescence ◽  
Novel Method ◽  
Near Infrared Fluorescence Imaging

Studies reporting quantitation and imaging of chlorophyll in corals using visible fluorescent emission in the red near 680 nm can suffer from competing emission from other red-emitting pigments. Here, we report a novel method of selectively imaging chlorophyll distributions in coral in situ using only the near infrared (NIR) fluorescence emission from chlorophyll. Commercially available equipment was assembled that allowed the sequential imaging of visible, visible-fluorescent, and NIR-fluorescent pigments on the same corals. The relative distributions of chlorophyll and fluorescent proteins (GFPs) were examined in numerous corals in the Caribbean Sea, the Egyptian Red Sea, the Indonesian Dampier Strait, and the Florida Keys. Below 2 m depth, solar induced NIR chlorophyll fluorescence can be imaged in daylight without external lighting, thus, it is much easier to do than visible fluorescence imaging done at night. The distributions of chlorophyll and GFPs are unique in every species examined, and while there are some tissues where both fluorophores are co-resident, often tissues are selectively enriched in only one of these fluorescent pigments. Although laboratory studies have clearly shown that GFPs can be photo-protective, their inability to prevent large scale bleaching events in situ may be due to their limited tissue distribution.

scholarly journals Use of optical absorption indices to assess seasonal variability of dissolved organic matter in Amazon floodplain lakes

2020 ◽  
Vol 17 (21) ◽  
pp. 5355-5364
Author(s):  
Maria Paula da Silva ◽  
Lino A. Sander de Carvalho ◽  
Evlyn Novo ◽  
Daniel S. F. Jorge ◽  
Claudio C. F. Barbosa
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Optical Absorption ◽  
Dissolved Organic Matter ◽  
Seasonal Variability ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Floodplain Lakes ◽  
Sentinel 2

Abstract. Given the importance of dissolved organic matter (DOM) in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. In this study we assess the use of optical absorption indices available in the literature based on in situ data to both characterize the seasonal variability of DOM in a highly complex environment and for application in large-scale studies using remote sensing data. The study area comprises four lakes located in the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of colored dissolved organic matter (CDOM) and measurements of remote sensing reflectance (Rrs) were acquired in situ. The Rrs was used to simulate the response of the visible bands of the Sentinel-2 MultiSpectral Instrument (MSI), which was used in the proposed models. Differences between lakes were tested using the CDOM indices. The results highlight the role of the flood pulse in the DOM dynamics at the floodplain lakes. The validation results show that the use of the absorption coefficient of CDOM (aCDOM) as a proxy of the spectral slope between 275 and 295 nm (S275–295) during rising water is worthwhile, demonstrating its potential application to Sentinel-2 MSI imagery data for studying DOM dynamics on the large scale.

scholarly journals Computerised grinding procedure for large scale crankshaft machining

2019 ◽  
Vol 252 ◽  
pp. 01002
Author(s):  
Zbigniew Siemiątkowski
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Computer Monitoring ◽  
The One ◽  
Form Deviation ◽  
Machining Operations ◽  
Product Specifications

The paper describes sequence of machining operations that leads to the desired quality of the produced crankshaft, as well as in-situ inspection, correction and compensation procedures performed and controlled by computer. The form deviation values after correction are being compared with those obtained before. In case of crank pins, values of form deviations, and hence those of corrections, are much larger than for main journals. During the measurement, the probe collects data from 3600 points per revolution, and then averaging procedure reduces data down to 360 points. There are several algorithms for data processing available, so the operator may choose the one most appropriate. Substantial difference between out-of-roundness values of main journals and crank pivot was registered. Before form compensation, the former was between 0.01 and 0.02 mm, while the latter were in range 0.07-0.09 mm. Program of grinding is parametric, i.e. at each stage of the process all values responsible for the tool movement undergo correction. The applied computer monitoring enabled to achieve the demanded quality of grinded surface, as well as dimensions and form deviations in the tolerances set by the product specifications. Form compensation procedure enabled to reduce peak-to-peak deviation from 30.37 μm down to 8.14 μm.

scholarly journals ON THE DEVELOPMENT OF A NOVEL APPROACH FOR IDENTIFYING PERENNIAL DRAINAGE IN SOUTHERN BRAZIL: A STUDY CASE INTEGRATING SENTINEL-2 AND HIGH-RESOLUTION DIGITAL ELEVATION MODELS WITH MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 161-168
Author(s):  
A. Montibeller ◽  
M. Vilela ◽  
F. Hino ◽  
P. Mallmann ◽  
M. Nadas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
High Resolution ◽  
Digital Elevation Models ◽  
Machine Learning Techniques ◽  
Water Runoff ◽  
Learning Techniques ◽  
Digital Elevation ◽  
Sentinel 2

Abstract. Riparian vegetation plays a key role in maintaining water quality and preserving the ecosystems along riverine systems, as they prevent soil erosion, retain water by increased infiltration, and act as a buffer zone between rivers and their surroundings. Within urban spaces, these areas have also an important role in preventing illegal occupation in areas of hydrologic risk, such as in floodplains. The goal of this research is to propose a framework for identifying areas of permanent protection associated with perennial drainage, utilizing satellite imagery and digital elevation models (DEM) in association with machine learning techniques. The specific objectives include the development of a decision tree to retrieve perennial drainage over high resolution, 1-meter DEM’s, and the development of digital image processing workflow to retrieve surface water bodies from Sentinel-2 imagery. In-situ information on perennial and ephemeral conditions of streams and rivers were obtained to validate our results, that happened in the first trimester of 2020. We propose a minimum of 7 days without precipitation prior to in-situ validation, for more accurate assessment of streamflow conditions, in order to minimize impacts of surface water runoff in flow regime. The proposed method will benefit decision makers by providing them with reliable information on drainage network and their buffer zones, as well as yield detailed mapping of the areas of permanent protection that are key to urban planning and management.

scholarly journals Surface Water Pollution Risk From Vietnam Water Quality Index (VN-WQI) in the Ca Mau City, Mekong Delta

2021 ◽  
Vol 20 (4) ◽  
Author(s):  
Nguyen Ngan Ha ◽  
Tran Thi Thu Huong ◽  
Pham The Vinh ◽  
Tran Thi Van
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Sentinel 2

This paper presents the study of integrating the remote sensing technology with in-situ ground observation for assessing the status of water quality in Ca Mau city through the Vietnam Water Quality Index (VN-WQI). The Sentinel-2 image and in-situ surface water samples were collected on 20 February 2020 for this study. The sample results were then specified by samples’ coordination. Besides, Sentinel-2 imaging was processed by radiometric and atmospheric correction, geometric registration, and extracted pixel spectral values from the sample locations. The multiple linear regressions of seven water quality parameters including BOD5, COD, NH4, PO4, TSS, pH, Coliform with surface water’s pixel spectral values from the satellite images were calculated and used to simulate water quality parameters on the satellite image. They were integrated into the VN-WQI to estimate, classify, and evaluate the general surface water quality of the Ca Mau city. The results show that there is a regressive correlation between measured data and image spectral values, and the simulation also well fits with the data with an acceptable error. The surface water quality of Ca Mau city is heavily polluted with almost all water quality parameters recognized at B1 to above B2 level according to the QCVN08-MT:2015/BTNMT. In terms of VN-WQI, the results also illustrate the low quality of surface water and heavy pollution only used for water transportation, not for domestic use. This approach can be a powerful method in spatially monitoring water quality and supporting environment management.

scholarly journals DeepSatData: Building large scale datasets of satellite images for training machine learning models

2021 ◽  
Author(s):  
Michael Tarasiou
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Point Of View ◽  
Learning Models ◽  
Ground Truth Data ◽  
Machine Learning Models ◽  
Sentinel 2

This paper presents DeepSatData a pipeline for automatically generating satellite imagery datasets for training machine learning models. We also discuss design considerations with emphasis on dense classification tasks, e.g. semantic segmentation. The implementation presented makes use of freely available Sentinel-2 data which allows the generation of large scale datasets required for training deep neural networks (DNN). We discuss issues faced from the point of view of DNN training and evaluation such as checking the quality of ground truth data and comment on the scalability of the approach.

scholarly journals The movement and forces of spinning solution in the nozzle during high-speed centrifugal spinning

2019 ◽  
Vol 14 ◽  
pp. 155892501982820 ◽  
Author(s):  
Yaoshuai Duan ◽  
Zhiming Zhang ◽  
Binbin Lu ◽  
Boya Chen ◽  
Zilong Lai
Keyword(s):  
Rotational Speed ◽  
High Speed ◽  
Large Scale ◽  
Production Efficiency ◽  
Critical Value ◽  
Parametric Model ◽  
Rotating Speed ◽  
Centrifugal Spinning ◽  
Novel Method

High-speed centrifugal spinning is a novel method to fabricate nanofiber. It has the potential to fabricate nanofiber on a large scale because its production efficiency is much greater than traditional methods. Nozzle is an important part of high-speed centrifugal spinning equipment because its length, shape, and diameter all will affect the morphology and quality of nanofiber. It is useful to study the movement and forces of spinning solution in the nozzle. In this article, the principle and equipment structure of high-speed centrifugal spinning are briefly introduced at first. Then the movement and forces of spinning solution are analyzed by establishing parametric model at nozzle. It can be found that the spinning solution is ejected from nozzle when the rotating speed reaches a critical value. The critical rotating speed is inversely proportional to the radius of nozzle and directly proportional to the viscosity of spinning solution. There are several nozzle structures proposed and compared for nozzle optimization. Finally, the effects of nozzle parameters, concentration of spinning solution, and rotational speed on the morphology of nanofiber are verified by high-speed centrifugal spinning experiments. It lays the foundation for optimizing spinning equipment.

Length‐Scale‐Dependent Relationships between VS30 and Topographic Slopes in Southern California

2019 ◽  
Vol 109 (6) ◽  
pp. 2614-2625
Author(s):  
Jessica Lin ◽  
Seulgi Moon ◽  
Alan Yong ◽  
Lingsen Meng ◽  
Paul Davis
Keyword(s):  
Large Scale ◽  
Southern California ◽  
Length Scales ◽  
Motion Models ◽  
Digital Elevation ◽  
Seismic Site Effects ◽  
Topographic Attributes ◽  
Elevation Model ◽  
Ground Motion Models

Abstract In engineering seismology, the time‐averaged shear‐wave velocity (VS) of the upper 30 m of the crust (VS30) is the primary parameter used in ground‐motion models to predict seismic site effects. VS30 is typically derived from in situ recordings of VS, although proxy‐based approaches (using geologic and/or geomorphometric classifications) are provisionally adopted when measurement‐based VS30 are sparse or not readily available. Despite the acceptance of proxy approaches, there are limited studies that examine the empirical relationships between VS30 and topographic attributes measured from various length scales and different resolutions of the digital elevation model. In this study, we examine the relationships between compiled VS30 measurements from 218 sites in southern California and topographic metrics of slope and relief measured over various length scales. We find that the correlations between topographic metrics and VS30 are weak but statistically significant. The correlations are improved when topographic slopes and relief are measured over length scales longer than typical hillslopes and VS30 sites are separated by different geologic groups. This is likely because VS30, especially on the rock sites, is better reflected in topographic metrics that capture large‐scale topographic relief, as well as landscape positions such as hilltops and valley bottoms.

scholarly journals Ultra-parallel ChIP-seq by barcoding of intact nuclei

2018 ◽  
Author(s):  
L. Arrigoni ◽  
H. Al-Hasani ◽  
F. Ramírez ◽  
I. Panzeri ◽  
D.P Ryan ◽  
...  
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Large Scale ◽  
Time Points ◽  
Invaluable Tool ◽  
Associated Proteins ◽  
Novel Method ◽  
Workload Reduction ◽  
User Friendly ◽  
Different Sources

AbstractChromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is an invaluable tool for mapping chromatin-associated proteins. However, sample preparation is still a largely individual and labor-intensive process that hinders assay throughput and comparability. Here, we present a novel method for ultra-parallelized high-throughput ChIP-seq that addresses the aforementioned problems. The method, called RELACS (Restriction Enzyme-based Labeling of Chromatin in Situ), employs barcoding of chromatin within intact nuclei extracted from different sources (e.g. tissues, treatments, time points). Barcoded nuclei are pooled and processed within the same ChIP, for maximal comparability and significant workload reduction. The choice of user-friendly, straightforward, enzymatic steps for chromatin fragmentation and barcoding makes RELACS particularly suitable for implementation large-scale clinical studies and scarce samples. RELACS can generate ChIP-seq libraries from hundreds of samples within three days and with less than 1000 cells per sample.

scholarly journals DeepSatData: Building large scale datasets of satellite images for training machine learning models

2021 ◽  
Author(s):  
Michael Tarasiou
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Point Of View ◽  
Learning Models ◽  
Ground Truth Data ◽  
Machine Learning Models ◽  
Sentinel 2

This paper presents DeepSatData a pipeline for automatically generating satellite imagery datasets for training machine learning models. We also discuss design considerations with emphasis on dense classification tasks, e.g. semantic segmentation. The implementation presented makes use of freely available Sentinel-2 data which allows the generation of large scale datasets required for training deep neural networks (DNN). We discuss issues faced from the point of view of DNN training and evaluation such as checking the quality of ground truth data and comment on the scalability of the approach.

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