scholarly journals Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

2021 ◽  
Vol 13 (7) ◽  
pp. 1283
Author(s):  
Rory Gordon Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack ◽  
Eleanor O’Rourke ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Surface Waters ◽  
Extreme Temperature ◽  
Surface Characteristics ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Future Research ◽  
Temporal Heterogeneity ◽  
Surface Stability ◽  
Spatio Temporal

Mapping heterogeneity of the ocean’s surface waters is important for understanding biogeographical distributions, ocean surface habitat mapping, and ocean surface stability. This article describes the Ocean-surface Heterogeneity MApping (OHMA) algorithm—an objective, replicable approach that uses hypertemporal, satellite-derived datasets to map the spatio-temporal heterogeneity of ocean surface waters. The OHMA produces a suite of complementary datasets—a surface spatio-temporal heterogeneity dataset, and an optimised spatio-temporal classification of the ocean surface. It was demonstrated here using a hypertemporal Sea Surface Temperature image dataset of the North Atlantic. Validation with Underway-derived temperature data showed higher heterogeneity areas were associated with stronger surface temperature gradients, or an increased presence of locally extreme temperature values. Using four exploratory case studies, spatio-temporal heterogeneity values were related to a range of region-specific surface and sub-surface characteristics including fronts, currents and bathymetry. The values conveyed the interactions between these parameters as a single metric. Such over-arching heterogeneity information is virtually impossible to map from in-situ instruments, or less temporally dense satellite datasets. This study demonstrated the OHMA approach is a useful and robust tool to explore, examine, and describe the ocean’s surface. It advances our capability to map biologically relevant measures of ocean surface heterogeneity. It can support ongoing efforts in Ocean Surface Partitioning, and attempts to understand marine species distributions. The study highlighted the need to establish dedicated spatio-temporal ocean validation sites, specifically measured using surface transits, to support advances in hypertemporal ocean data use, and exploitation. A number of future research avenues are also highlighted.

The limitations of in situ data for validating satellite-derived spatio-temporal ocean products.

2021 ◽  
Author(s):  
Rory Scarrott ◽  
Fiona Cawkwell ◽  
Mark Jessopp ◽  
Caroline Cusack
Keyword(s):  
Surface Temperature ◽  
Surface Heterogeneity ◽  
Ocean Surface ◽  
Bias Reduction ◽  
Data Validation ◽  
Near Surface ◽  
In Situ Data ◽  
Spatio Temporal ◽  
Image Pixels

<p>The Ocean-surface Heterogeneity MApping (OHMA) algorithm is an objective, replicable approach to map the spatio-temporal heterogeneity of ocean surface waters. It is used to processes hypertemporal, satellite-derived data and produces a single-image surface heterogeneity (SH) dataset for the selected parameter of interest. The product separates regions of dissimilar temporal characteristics. Data validation is challenging because it requires In-situ observations at spatial and temporal resolutions comparable to the hyper-temporal inputs. Validating this spatio-temporal product highlighted the need to optimise existing vessel-based data collection efforts, to maximise exploitation of the rapidly-growing hyper-temporal data resource.</p><p>For this study, the SH was created using hyper-temporal 1km resolution satellite derived Sea Surface Temperature (SST) data acquired in 2011. Underway ship observations of near surface temperature collected on multiple scientific surveys off the Irish coast in 2011 were used to validate the results. The most suitable underway ship SST data were identified in ocean areas sampled multiple times and with representative measurements across all seasons.</p><p>A 3-stage bias reduction approach was applied to identify suitable ocean areas. The first bias reduction addressed temporal bias, i.e., the temporal spread of available In-situ ship transect data across each satellite image pixel. Only pixels for which In-situ data were obtained at least once in each season were selected; resulting in 14 SH image pixels deemed suitable out of a total of 3,677 SH image pixels with available In-situ data. The second bias reduction addressed spatial bias, to reduce the influence of over-sampled areas in an image pixel with a sub-pixel approach. Statistical dispersion measures and statistical shape measures were calculated for each of the sets of sub-pixel values. This gave heterogeneity estimates for each cruise transit of a pixel area. The third bias reduction addressed bias of temporally oversampled seasons. For each transit-derived heterogeneity measure, the values within each season were averaged, before the annual average value was derived across all four seasons in 2011.</p><p>Significant associations were identified between satellite SST-derived SH values, and In-situ heterogeneity measures related to shape; absolute skewness (Spearman’s Rank, n=14, ρ[12]= +0.5755, P<0.05), and kurtosis (Spearman’s Rank, n=14, ρ[12] = 0.5446, P < 0.05). These are a consequence of (i) locally-extreme measurements, and/or (ii) increased presence of sharp transitions detected spatially by In-situ data. However, the number and location of suitable In-situ validation sites precluded a robust validation of the SH dataset (14 pixels located in Irish waters, for a dataset spanning the North Atlantic). This requires more targeted data. The approach would have benefited from more samples over the winter season, which would have enabled more offshore validation sites to be incorporated into the analysis. This is a challenge that faces satellite product developers, who want to deliver spatio-temporal information to new markets. There is a significant opportunity for dedicated, transit-measured (e.g. Ferry box data), validation sites to be established. These could potentially synergise with key nodes in global shipping routes to maximise data collected by vessels of opportunity, and ensure consistent data are collected over the same area at regular intervals.</p>

Spatio-temporal patterns in microplastic pollution of surface waters and sediments within the R. Thames (UK) and its tributaries

2021 ◽  
Author(s):  
Karolina Skalska ◽  
Annie Ockelford ◽  
James E. Ebdon ◽  
Andrew B. Cundy
Keyword(s):  
Marine Environment ◽  
Surface Waters ◽  
Water Samples ◽  
Limit Of Detection ◽  
Temporal Variations ◽  
Bulk Water ◽  
High Flow ◽  
Mitigation Measures ◽  
Future Research ◽  
Spatio Temporal

<p>It is currently predicted that rivers deliver as much as 80% of plastic waste into the marine environment, including microplastics (MP) <5 mm in size. Yet, the transfer mechanisms of MP in river systems remain poorly understood. While high flow events are thought to flush more microplastics into marine waters, their overall load may depend on factors such as river morphology, land-use, or local MP sources.</p><p>Microplastic concentrations were monitored on a seasonal basis (summer 2019 - winter 2020/2021) across 13 sites located across the R. Thames catchment, UK. Sites were selected to include rural, urban and industrial locations with different hydrological characteristics and proximities to potential MP inputs (e.g. sewage or industrial effluents). At each site, bed sediment samples were manually extracted (n=55 samples), and surface water samples collected in 5 L clean polyethylene bottles (n=22 samples) and using a 500-µm plankton net (n=12 samples). Microplastics were extracted from sediment and plankton net samples using density flotation, whilst bulk water samples were filtered with no prior extraction steps. All samples were visually inspected under a stereomicroscope and their morphology recorded. The chemical composition is to be further investigated using µFTIR as part of future research.</p><p>Sediment and water samples likely contained MP from different sources (e.g. in-situ breakdown of plastic litter, sewage effluent), which was reflected in the varying MP shapes and loads observed at the study sites. Microplastic levels ranged from <LoD (limit of detection) to 381 MP·100 g<sup>-1</sup> in sediments, <LoD to 16 MP ·L<sup>-1</sup> in bulk water samples and <LoD to 2 MP·m<sup>-3</sup> in plankton net samples and were highest at sites downstream of known sewage inputs. There was also a clear variation in particle shapes and levels with respect to site, with fibres and fragments representing the dominant MP type present along urban river stretches, and microbeads most abundant near industrial locations.</p><p>Microplastic levels varied on a temporal basis in both surface waters and sediments. Increasing river discharge generally had a diluting effect on MP levels observed in the water column (mean levels of 5 MP·L<sup>-1</sup> and 2 MP·L<sup>-1</sup> in summer 2019 and winter 2020, respectively). Mean microplastic levels in sediments also decreased from 15.1 MP·100 g<sup>-1</sup> in the summer to 9.4 MP·100 g<sup>-1 </sup>in the winter, although some local increases in microplastic pollution were observed during high flow period, particularly at sites situated in close proximity to reported sewage discharges (e.g. from Combined Sewer Overflows).</p><p>This study is one of the first few to report spatio-temporal variations in microplastic contamination of both river water and sediments. Our early findings suggest that variability in MP levels and composition in both media may correspond to local pollution sources, and plastic particles could be released from surface sediments during periods of increased precipitation, even in the absence of flooding. Understanding such patterns in MP flux will be crucial to accurately model plastic loads from terrestrial to marine environment and implement effective mitigation measures.</p>

scholarly journals The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Yixu Wang ◽  
Mingxue Xu ◽  
Jun Li ◽  
Nan Jiang ◽  
Dongchuan Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landscape Planning ◽  
Potential Gradient ◽  
Surface Characteristics ◽  
Ordinary Least Squares ◽  
Urban Agglomeration ◽  
Spatial Gradient ◽  
Gradient Effect

Although research relating to the urban heat island (UHI) phenomenon has been significantly increasing in recent years, there is still a lack of a continuous and clear recognition of the potential gradient effect on the UHI—landscape relationship within large urbanized regions. In this study, we chose the Beijing-Tianjin-Hebei (BTH) region, which is a large scaled urban agglomeration in China, as the case study area. We examined the causal relationship between the LST variation and underlying surface characteristics using multi-temporal land cover and summer average land surface temperature (LST) data as the analyzed variables. This study then further discussed the modeling performance when quantifying their relationship from a spatial gradient perspective (the grid size ranged from 6 to 24 km), by comparing the ordinary least squares (OLS) and geographically weighted regression (GWR) methods. The results indicate that: (1) both the OLS and GWR analysis confirmed that the composition of built-up land contributes as an essential factor that is responsible for the UHI phenomenon in a large urban agglomeration region; (2) for the OLS, the modeled relationship between the LST and its drive factor showed a significant spatial gradient effect, changing with different spatial analysis grids; and, (3) in contrast, using the GWR model revealed a considerably robust and better performance for accommodating the spatial non-stationarity with a lower scale dependence than that of the OLS model. This study highlights the significant spatial heterogeneity that is related to the UHI effect in large-extent urban agglomeration areas, and it suggests that the potential gradient effect and uncertainty induced by different spatial scale and methodology usage should be considered when modeling the UHI effect with urbanization. This would supplement current UHI study and be beneficial for deepening the cognition and enlightenment of landscape planning for UHI regulation.

scholarly journals A data acquisition setup for data driven acoustic design

2021 ◽  
pp. 1351010X2098690
Author(s):  
Romana Rust ◽  
Achilleas Xydis ◽  
Kurt Heutschi ◽  
Nathanael Perraudin ◽  
Gonzalo Casas ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Interdisciplinary Approach ◽  
Computational Design ◽  
Surface Structures ◽  
Future Research ◽  
Data Set ◽  
Process Measures ◽  
First Results ◽  
Spatio Temporal ◽  
Automated Data Acquisition

In this paper, we present a novel interdisciplinary approach to study the relationship between diffusive surface structures and their acoustic performance. Using computational design, surface structures are iteratively generated and 3D printed at 1:10 model scale. They originate from different fabrication typologies and are designed to have acoustic diffusion and absorption effects. An automated robotic process measures the impulse responses of these surfaces by positioning a microphone and a speaker at multiple locations. The collected data serves two purposes: first, as an exploratory catalogue of different spatio-temporal-acoustic scenarios and second, as data set for predicting the acoustic response of digitally designed surface geometries using machine learning. In this paper, we present the automated data acquisition setup, the data processing and the computational generation of diffusive surface structures. We describe first results of comparative studies of measured surface panels and conclude with steps of future research.

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