scholarly journals InSAR Coherence Analysis for Wetlands in Alberta, Canada Using Time-Series Sentinel-1 Data

2021 ◽  
Vol 13 (16) ◽  
pp. 3315
Author(s):  
Meisam Amani ◽  
Valentin Poncos ◽  
Brian Brisco ◽  
Fatemeh Foroughnia ◽  
Evan R. DeLancey ◽  
...  
Keyword(s):  
Open Water ◽  
Added Value ◽  
Wetland Classification ◽  
Interferometric Synthetic Aperture Radar ◽  
Wetland Mapping ◽  
Change Analysis ◽  
Future Studies ◽  
Multi Temporal ◽  
Trends Over Time ◽  
Reference Samples

Wetlands are valuable natural resources which provide numerous services to the environment. Many studies have demonstrated the potential of various types of remote sensing datasets and techniques for wetland mapping and change analysis. However, there are a relatively low number of studies that have investigated the application of the Interferometric Synthetic Aperture Radar (InSAR) coherence products for wetland studies, especially over large areas. Therefore, in this study, coherence products over the entire province of Alberta, Canada (~661,000 km2) were generated using the Sentinel-1 data acquired from 2017 to 2020. Then, these products along with large amount of wetland reference samples were employed to assess the separability of different wetland types and their trends over time. Overall, our analyses showed that coherence can be considered as an added value feature for wetland classification and monitoring. The Treed Bog and Shallow Open Water classes showed the highest and lowest coherence values, respectively. The Treed Wetland and Open Wetland classes were easily distinguishable. When analyzing the wetland subclasses, it was observed that the Treed Bog and Shallow Open Water classes can be easily discriminated from other subclasses. However, there were overlaps between the signatures of the other wetland subclasses, although there were still some dates where these classes were also distinguishable. The analysis of multi-temporal coherence products also showed that the coherence products generated in spring/fall (e.g., May and October) and summer (e.g., July) seasons had the highest and lowest coherence values, respectively. It was also observed that wetland classes preserved coherence during the leaf-off season (15 August–15 October) while they had relatively lower coherence during the leaf-on season (i.e., 15 May–15 August). Finally, several suggestions for future studies were provided.

scholarly journals Wetland Mapping Using SAR Data from Sentinel-1A and TanDEM-X Missions: Comparative Study in the Biebrza Floodplain (Poland)

2017 ◽  
Author(s):  
Magdalena Mleczko ◽  
Marek Mróz
Keyword(s):  
Good Accuracy ◽  
Biodiversity Loss ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Dual Polarization ◽  
X Band ◽  
Multi Temporal ◽  
Set Up ◽  
Flooded Areas ◽  
Wide Swath

This research is related to the eco-hydrological problems of herbaceous wetland drying and biodiversity loss in the floodplain lakes of the Middle Basin of the Biebrza river (Poland). An experiment was set up, whose main goals were: (i) mapping the vegetation types and the temporarily or permanently flooded areas, and (ii) comparing the usefulness of C-band Sentinel-1A (S1A) and X-band TerraSAR-X/TanDEM-X (TSX/TDX) for mapping purposes. The S1A imagery was acquired on a regular basis using the dual polarization VV/VH and the Interferometric Wide Swath Mode. The TSX/TDX data were acquired in quad-pol, a fully polarimetric mode, during the Science Phase. The paper addresses the following aspects: i) wetland mapping with S1A multi-temporal series; ii) wetland mapping with fully polarimetric TSX/TDX data; iii) comparing the wetland mapping using dual polarization TSX/TDX subsets, i.e. HH-HV, HH-VV and VV-VH; iv) comparing wetland mapping using S1A and TSX/TDX data based on the same polarization (VV-VH); v) studying the suitability of the Shannon Entropy for wetland mapping; and vi) assessing the contribution of interferometric coherence for wetland classification. The experimental results show main limitations of the S1A dataset, while they highlight the good accuracy that can be achieved using the TSX/TDX data, especially those taken in fully polarimetric mode.

scholarly journals Multi-source global wetland mapping: combining surface water imagery and groundwater constraints

2018 ◽  
Author(s):  
Ardalan Tootchi ◽  
Anne Jost ◽  
Agnès Ducharne
Keyword(s):  
Surface Area ◽  
Land Surface ◽  
Large Scale ◽  
Soil Surface ◽  
Open Water ◽  
Wetland Classification ◽  
Wetland Mapping ◽  
Coastal Zones ◽  
Land Area ◽  
Shallow Water Table

Abstract. Wetlands are important players in the Earth climate system because of their effect on ecosystems, river discharge, water quality, and through their feedback effects on atmosphere by increasing methane emission and evapotranspiration. Many datasets have been developed for open water and wetland mapping, based on three main methods: (i) compiling national/regional wetland maps; (ii) identifying inundated areas by satellite imagery; (iii) delineating wetlands as areas with shallow water table depths. There is a massive disagreement, however, between the resulting wetland extent estimates (from 3 to 21 % of the land surface area). To reconcile these differences, we propose composite wetland (CW) maps consisting of two classes of wetlands: (1) regularly flooded wetlands (RFWs) which are obtained by overlapping selected open-water and inundation datasets, and cover 9.7 % of the land surface area; (2) scattered groundwater wetlands (SGWs), derived either from direct groundwater modelling or simplified modelling based on the topographic index (TI), using several variants. In this framework, wetlands are defined as zones that are either inundated or where the groundwater is sufficiently close to the surface to maintain near saturated soil surface. By combining RFW and different SGW maps, seven CW maps are generated, which correspond to contemporary potential wetlands, i.e. the areas that would turn into actual wetlands under the present climate assuming no human influence. They are produced at the 15 arc-sec resolution (almost 500 m at the Equator) using geographic information system (GIS) tools. Two CW maps, showing the best overall match with the available evaluation datasets, are eventually selected. Wetlands in these maps respectively cover 21.1 and 21.6 % of the global land area, which is in the high end of the literature range, along with recent estimates also recognizing the contribution of groundwater-driven wetlands. The two proposed composite maps agree massively about six major wetland hotspots, which include 75 % of the global wetlands, and concentrate in the boreal, tropical, and coastal zones. The high wetland density in the tropics is brought by the SGWs, which allows detecting wetlands under dense canopy and cloud cover. Another major feature of the two CW maps, brought by the SGWs and the high resolution of the maps, is the identification of many small and scattered wetlands, which cover less than 5 % of the land area, but are very important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and SGWs globally based on uniform principles, we eventually propose a simple wetland classification focused on hydrologic functioning, believed to be very useful for large-scale land surface modelling.

Wetland mapping with multi-temporal sentinel-1 & -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta

2021 ◽  
pp. 1-23
Author(s):  
Alex Okiemute Onojeghuo ◽  
Ajoke Ruth Onojeghuo ◽  
Michelle Cotton ◽  
Johnathan Potter ◽  
Brennan Jones
Keyword(s):  
Lidar Data ◽  
Wetland Mapping ◽  
Natural Region ◽  
Multi Temporal

Landsat thermal infrared to detect sub-canopy riparian flooding

2021 ◽  
Author(s):  
Emanuel Storey ◽  
Witold Krajewski ◽  
Efthymios Nikolopoulos
Keyword(s):  
Open Water ◽  
Thermal Infrared ◽  
Short Wave ◽  
Mapping Technique ◽  
Landsat 8 ◽  
Infrared Observations ◽  
Plant Canopies ◽  
Multi Temporal ◽  
Flood Detection

<p>Satellite based flood detection can enhance understanding of risk to humans and infrastructures, geomorphic processes, and ecological effects.  Such application of optical satellite imagery has been mostly limited to the detection of water exposed to sky, as plant canopies tend to obstruct water visibility in short electromagnetic wavelengths.  This case study evaluates the utility in multi-temporal thermal infrared observations from Landsat 8 as a basis for detecting sub-canopy fluvial inundation resulting in ambient temperature change.</p><p>We selected three flood events of 2016 and 2019 along sections of the Mississippi, Cedar, and Wapsipinicon Rivers located in Iowa, Minnesota, and Wisconsin, United States.  Classification of sub-canopy water involved logical, threshold-exceedance criteria to capture thermal decline within channel-adjacent vegetated zones.  Open water extent in the floods was mapped based on short-wave infrared thresholds determined parametrically from baseline (non-flooded) observations.  Map accuracy was evaluated using higher-resolution (0.5–5.0 m) synchronic optical imagery.</p><p>Results demonstrate improved ability to detect sub-canopy inundation when thermal infrared change is incorporated: sub-canopy flood class accuracy was comparable to that of open water in previous studies.  The multi-temporal open-water mapping technique yielded high accuracy as compared to similar studies.  This research highlights the utility of Landsat thermal infrared data for monitoring riparian inundation and for validating other remotely sensed and simulated flood maps.</p>

scholarly journals Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests

2019 ◽  
Vol 11 (6) ◽  
pp. 670 ◽  
Author(s):  
Sarah Banks ◽  
Lori White ◽  
Amir Behnamian ◽  
Zhaohua Chen ◽  
Benoit Montpetit ◽  
...  
Keyword(s):  
Land Cover ◽  
Random Forests ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Wetland Classification ◽  
Model Based ◽  
Angle Data ◽  
Significant Difference ◽  
Multi Temporal ◽  
Sar Data

To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here.

scholarly journals Promoting Reminiscences with Virtual Reality Headsets: A Pilot Study with People with Dementia

2020 ◽  
Vol 17 (24) ◽  
pp. 9301
Author(s):  
Tiago Coelho ◽  
Cátia Marques ◽  
Daniela Moreira ◽  
Maria Soares ◽  
Paula Portugal ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Added Value ◽  
Personal Relevance ◽  
Behavioral Symptoms ◽  
Mixed Methods Design ◽  
Post Intervention ◽  
Future Studies ◽  
Reminiscence Therapy ◽  
People With Dementia

This study aimed to explore the feasibility and effects of promoting reminiscences, using virtual reality (VR) headsets for viewing 360° videos with personal relevance, with people with dementia. A study with a mixed methods design was conducted with nine older adults diagnosed with dementia. Interventions consisted of four sessions, in which the participants’ engagement, psychological and behavioral symptoms, and simulation sickness symptoms were evaluated. Neuropsychiatric symptomatology and quality of life were measured pre- and post-intervention. Caregivers were interviewed regarding the effect of the approach. In most cases, participants appeared to enjoy the sessions, actively explored the 360° environment, and shared memories associated with the depicted locations, often spontaneously. There were no cases of significant increases in simulator sickness and psychological and behavioral symptoms during sessions, with only some instances of minor eyestrain, fullness of head, anxiety, irritability, and agitation being detected. Although there were no significant changes in the measured outcomes after intervention, the caregivers assessed the experience as potentially beneficial for most participants. In this study, promoting reminiscences with VR headsets was found to be a safe and engaging experience for people with dementia. However, future studies are required to better understand the added value of immersion, using VR, in reminiscence therapy.

scholarly journals Distinct Transcriptional Signatures of Monocytes Treated with α-linolenic Acid and Docosahexaenoic Acid

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1270-1270
Author(s):  
Samantha Pauls ◽  
Christopher Pascoe ◽  
Lisa Rodway ◽  
Carla Taylor ◽  
Harold Aukema ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fatty Acid ◽  
Docosahexaenoic Acid ◽  
Linolenic Acid ◽  
Cholesterol Biosynthesis ◽  
Interaction Network ◽  
Principal Component ◽  
Change Analysis ◽  
Future Studies ◽  
Engineering Research

Abstract Objectives Docosahexaenoic acid (DHA) can be obtained directly from the diet or produced by elongation and desaturation of α-linolenic acid (ALA). Both are proposed to reduce inflammation associated with obesity, however, fewer studies have investigated ALA. The objective of this study was to evaluate the gene expression changes in monocytes induced by each fatty acid and to compare the predicted functional outcomes. Methods RNA was extracted from THP-1 monocytes treated with ALA, DHA or vehicle for 48 h and then transcriptomics profiles were assessed by microarray. Multiple tools were used for data interpretation, including fold change analysis, Principal Component Analysis (PCA), Variable Importance Projection (VIP), Ingenuity Pathway Analysis (IPA) and Network Analyst. Results We found that the ALA and DHA treatments produced distinct profiles with many individual genes making small contributions to the separation between groups. Relative to vehicle treatment, many downregulated targets were similarly affected by both ALA and DHA. Several of these downregulated genes are involved in cholesterol synthesis and are regulated by miR-335–5p, a microRNA upregulated by both treatments. Consistently, IPA predicted similar pathways and functions are decreased by ALA and DHA, most notably cholesterol biosynthesis. In contrast, ALA and DHA upregulated unique gene sets and in agreement IPA predicted each treatment would activate distinct pathways and functions. ALA was strongly and uniquely predicted to increase infection responses while only DHA was predicted to increase oxidative phosphorylation. Finally, analysis of the protein-protein interaction network involving the genes modified by each fatty acid treatment allowed us to predict the most functionally important gene targets, which will be tested in future studies. Conclusions These analyses have revealed both unique and overlapping effects of ALA and DHA on the monocyte gene expression profile, providing further evidence that they have distinct bioactivities. Many novel predictions were made and these will form the basis for future studies investigating the effects of ALA and DHA on human physiology. Funding Sources Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research.

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