Biogeomorphic development of foredune trough blowouts quantified from medium-resolution satellite imagery

2021 ◽  
Author(s):  
Gerben Ruessink ◽  
Niels van Kuik ◽  
Job de Vries ◽  
Christian Schwarz
Keyword(s):  
Surface Area ◽  
Time Scales ◽  
Google Earth ◽  
Coastal Dune ◽  
Strong Wind ◽  
Medium Resolution ◽  
Coastal Resilience ◽  
Spatio Temporal ◽  
Padre Island ◽  
Sand Budget

<p>Foredune trough blowouts are wind-eroded trough-shaped hollows in the most seaward coastal dune with their adjoining depositional lobes. They evolve on time scales ranging from strong wind events, seasons to multiple decades due to biogeomorphic interactions. Trough blowouts play an essential role in the sand budget of many coastal dune systems by connecting the beach with the backdune. There, the deposited sand can lead to vegetation rejuvenation and an overall larger floral diversity. In Northwestern Europe, nature and coastal managers have started to experiment with constructing trough blowouts in the hope that a positive sand budget beyond the foredune in concert with enlarged biodiversity improves coastal resilience in times of climate change. The spatio-temporal evolution of trough blowouts and the factors driving this evolution are not well understood, despite their common natural occurrence and construction for nature-based management.</p><p>The aim of this contribution is to quantify the spatio-temporal development of selected trough-blowout systems around the globe utilizing cloud-free medium-resolution Landsat and Sentinel-2 spectral imagery available in the Google Earth Engine platform. Linear spectral unmixing was applied on a single image basis to extract blowout surface area over time at one man-made blowout system (Zuid-Kennemerland, Netherlands) and two natural systems (Haurvig, Denmark; Padre Island, Texas, USA), assigning pixels with a fractional vegetation cover less than 50% to the blowout. At Zuid-Kennemerland and Haurvig, the blowout surface area fluctuated predominantly on seasonal time scales, with the smallest and largest values in late summer/early autumn and late winter/early spring, respectively. This seasonal variability reflects plant phenology in combination with increased sand accumulation in winter because of the more energetic wind conditions. In summer, vegetation regrew mainly at the edges of the depositional lobes and on the foredune between individual blowouts. The blowout surface area at the subtropical Padre Island varied predominantly on a multi-annual time scale. Most notably, multi-annual area decay was observed when a blowout progressed inland and lost its open connection to the beach, likely resulting in less physical disturbance and hence a dominance of ecological processes. In future work, we will combine our results with auxiliary information (e.g., multitemporal digital elevation models, time series of external forcing conditions, plant species and traits) to develop and test an eco-geomorphological model for blowout evolution. Such a model is adamant to understand what factors contribute to the success or failure of dune restoration projects involving blowouts as nature-based solutions to increase coastal resilience.</p>

scholarly journals Depositional setting and limiting factors of early Late Cretaceous glaucony formation: implications from Cenomanian glauconitic strata (Elbtal Group, Germany)

Facies ◽  
2021 ◽  
Vol 67 (3) ◽  
Author(s):  
Markus Wilmsen ◽  
Udita Bansal
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Time Scales ◽  
Late Cretaceous ◽  
Limiting Factors ◽  
High Accumulation ◽  
Spatio Temporal ◽  
Facies Types ◽  
Facies Succession ◽  
Early Late

AbstractCenomanian strata of the Elbtal Group (Saxony, eastern Germany) reflect a major global sea-level rise and contain, in certain intervals, a green authigenic clay mineral in abundance. Based on the integrated study of five new core sections, the environmental background and spatio-temporal patterns of these glauconitic strata are reconstructed and some general preconditions allegedly needed for glaucony formation are critically questioned. XRD analyses of green grains extracted from selected samples confirm their glauconitic mineralogy. Based on field observations as well as on the careful evaluation of litho- and microfacies, 12 glauconitc facies types (GFTs), broadly reflecting a proximal–distal gradient, have been identified, containing granular and matrix glaucony of exclusively intrasequential origin. When observed in stratigraphic succession, GFT-1 to GFT-12 commonly occur superimposed in transgressive cycles starting with the glauconitic basal conglomerates, followed up-section by glauconitic sandstones, sandy glauconitites, fine-grained, bioturbated, argillaceous and/or marly glauconitic sandstones; glauconitic argillaceous marls, glauconitic marlstones, and glauconitic calcareous nodules continue the retrogradational fining-upward trend. The vertical facies succession with upwards decreasing glaucony content demonstrates that the center of production and deposition of glaucony in the Cenomanian of Saxony was the nearshore zone. This time-transgressive glaucony depocenter tracks the regional onlap patterns of the Elbtal Group, shifting southeastwards during the Cenomanian 2nd-order sea-level rise. The substantial development of glaucony in the thick (60 m) uppermost Cenomanian Pennrich Formation, reflecting a tidal, shallow-marine, nearshore siliciclastic depositional system and temporally corresponding to only ~ 400 kyr, shows that glaucony formation occurred under wet, warm-temperate conditions, high accumulation rates and on rather short-term time scales. Our new integrated data thus indicate that environmental factors such as great water depth, cool temperatures, long time scales, and sediment starvation had no impact on early Late Cretaceous glaucony formation in Saxony, suggesting that the determining factors of ancient glaucony may be fundamentally different from recent conditions and revealing certain limitations of the uniformitarian approach.

scholarly journals Design of a scanning laser meter for monitoring the spatio-temporal evolution of snow depth and its application in the Alps and in Antarctica

2016 ◽  
Vol 10 (4) ◽  
pp. 1495-1511 ◽  
Author(s):  
Ghislain Picard ◽  
Laurent Arnaud ◽  
Jean-Michel Panel ◽  
Samuel Morin
Keyword(s):  
Spatial Variability ◽  
Snow Depth ◽  
Laser Scanning ◽  
Single Point ◽  
Measurement Techniques ◽  
Strong Wind ◽  
Slow Increase ◽  
Automatic Instrument ◽  
Spatio Temporal ◽  
Wind Events

Abstract. Although both the temporal and spatial variations of the snow depth are usually of interest for numerous applications, available measurement techniques are either space-oriented (e.g. terrestrial laser scans) or time-oriented (e.g. ultrasonic ranging probe). Because of snow heterogeneity, measuring depth in a single point is insufficient to provide accurate and representative estimates. We present a cost-effective automatic instrument to acquire spatio-temporal variations of snow depth. The device comprises a laser meter mounted on a 2-axis stage and can scan  ≈  200 000 points over an area of 100–200 m2 in 4 h. Two instruments, installed in Antarctica (Dome C) and the French Alps (Col de Porte), have been operating continuously and unattended over 2015 with a success rate of 65 and 90 % respectively. The precision of single point measurements and long-term stability were evaluated to be about 1 cm and the accuracy to be 5 cm or better. The spatial variability in the scanned area reached 7–10 cm (root mean square) at both sites, which means that the number of measurements is sufficient to average out the spatial variability and yield precise mean snow depth. With such high precision, it was possible for the first time at Dome C to (1) observe a 3-month period of regular and slow increase of snow depth without apparent link to snowfalls and (2) highlight that most of the annual accumulation stems from a single event although several snowfall and strong wind events were predicted by the ERA-Interim reanalysis. Finally the paper discusses the benefit of laser scanning compared to multiplying single-point sensors in the context of monitoring snow depth.

scholarly journals Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

2021 ◽  
Vol 13 (19) ◽  
pp. 3870
Author(s):  
Hilma S. Nghiyalwa ◽  
Marcel Urban ◽  
Jussi Baade ◽  
Izak P. J. Smit ◽  
Abel Ramoelo ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Parametric Methods ◽  
Mixed Pixel ◽  
Medium Resolution ◽  
Long Time ◽  
Pixel Analysis ◽  
Spatio Temporal ◽  
Resolution Data

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

scholarly journals Multi-Term Attention Networks for Skeleton-Based Action Recognition

2020 ◽  
Vol 10 (15) ◽  
pp. 5326
Author(s):  
Xiaolei Diao ◽  
Xiaoqiang Li ◽  
Chen Huang
Keyword(s):  
Neural Network ◽  
Time Scales ◽  
Action Recognition ◽  
State Of The Art ◽  
Attention Networks ◽  
Weighted Fusion ◽  
Temporal Features ◽  
Benchmark Datasets ◽  
Spatio Temporal ◽  
Different Time Scales

The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.

scholarly journals An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

2020 ◽  
Vol 12 (4) ◽  
pp. 709 ◽  
Author(s):  
Abhishek Banerjee ◽  
Ruishan Chen ◽  
Michael E. Meadows ◽  
R.B. Singh ◽  
Suraj Mal ◽  
...  
Keyword(s):  
River Basin ◽  
Temporal Trends ◽  
Google Earth ◽  
Monthly Rainfall ◽  
Annual Rainfall ◽  
Google Earth Engine ◽  
Spatio Temporal ◽  
Uttarakhand Himalaya ◽  
Rainy Days

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.

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