Hyperspectral in-situ attenuation depths and their relation to satellite imagery in two southeastern US estuaries

2018 ◽  
Author(s):  
Charles R. Bostater
Keyword(s):  
Satellite Imagery ◽  
Southeastern Us

Sentinel-2 mission Contribution for Supporting Bathymetric layers of SAFAGA coastal zone, Egypt

2021 ◽  
Author(s):  
Ramez Saeed ◽  
Saad Abdelrahman ◽  
Andrea Scozari ◽  
Abdelazim Negm
Keyword(s):  
Red Sea ◽  
Satellite Imagery ◽  
Data Science ◽  
New Technologies ◽  
Coefficient Of Determination ◽  
Change Environment ◽  
Bathymetric Data ◽  
Work Supports ◽  
Sentinel 2

<p><strong>ABSTRACT</strong></p><p>With the fast and highly growing demand for all possible ways of remote work as a result of COVID19 pandemic, new technologies using Satellite data were highly encouraged for multidisciplinary applications in different fields such as; agriculture, climate change, environment, coastal management, maritime, security and Blue Economy.</p><p>This work supports applying Satellite Derived Bathymetry (SDB) with the available low-cost multispectral satellite imagery applications, instruments and readily accessible data for different areas with only their benthic parameters, water characteristics and atmospheric conditions.  The main goal of this work is to derive bathymetric data needed for different hydrographic applications, such as: nautical charting, coastal engineering, water quality monitoring, sediment movement monitoring and supporting both green carbon and marine data science.  Also, this work proposes and assesses a SDB procedure that makes use of publicly-available multispectral satellite images (Sentinel2 MSI) and applies algorithms available in the SNAP software package for extracting bathymetry and supporting bathymetric layers against highly expensive traditional in-situ hydrographic surveys. The procedure was applied at SAFAGA harbor area, located south of Hurghada at (26°44′N, 33°56′E), on the Egyptian Red Sea coast.  SAFAGA controls important maritime traffic line in Red Sea such as (Safaga – Deba, Saudi Arabia) maritime cruises.  SAFAGA depths change between 6 m to 22m surrounded by many shoal batches and confined waters that largely affect maritime safety of navigation.  Therefore, there is always a high demand for updated nautical charts which this work supports.  The outcome of this work provides and fulfils those demands with bathymetric layers data for the approach channel and harbour usage bands electronic nautical chart of SAFAGA with reasonable accuracies.  The coefficient of determination (R<sup>2</sup>) differs between 0.42 to 0.71 after applying water column correction by Lyzenga algorithm and deriving bathymetric data depending on reflectance /radiance of optical imagery collected by sentinel2 missions with in-situ depth data values relationship by Stumpf equation.  The adopted approach proved to give  highly reasonable results that could be used in nautical charts compilation. Similar methodologies could be applied to inland water bodies.  This study is part of the MSc Thesis of the first author and is in the framework of a bilateral project between ASRT of Egypt and CNR of Italy which is still running.</p><p><strong>Keywords: Algorithm, Bathymetry, Sentinel 2, nautical charting, Safaga port, satellite imagery, water depth, Egypt.</strong></p>

Debris cover and the thinning of Kennicott Glacier, Alaska

2021 ◽  
Author(s):  
Leif S. Anderson ◽  
William H. Armstrong ◽  
Robert S. Anderson ◽  
Dirk Scherler
Keyword(s):  
Satellite Imagery ◽  
Hot Spots ◽  
High Mountain ◽  
Threshold Method ◽  
Ice Dynamics ◽  
Ice Flow ◽  
Debris Cover ◽  
A Chain ◽  
The Relationship

<p>Many glaciers in High Mountain Asia are experiencing the debris-cover anomaly. The Kennicott Glacier, a large Alaskan Glacier, is also thinning most rapidly under debris cover. This contradiction has been explained by melt hotspots, such as ice cliffs, streams, or ponds scattered within the debris cover or by declining ice flow in time. We collected abundant in situ measurements of debris thickness, sub-debris melt, and ice cliff backwasting, allowing for extrapolation across the debris-covered tongue. A newly developed automatic ice cliff delineation method is the first to use only optical satellite imagery. The adaptive binary threshold method accurately estimates ice cliff coverage even where ice cliffs are small and debris color varies. We also develop additional remotely-sensed datasets of ice dynamical variables, other melt hot spots, and glacier thinning.</p><p>Kennicott Glacier exhibits the highest fractional area of ice cliffs (11.7 %) documented to date. Ice cliffs contribute 26 % of total melt across the glacier tongue. Although the <em>relative</em> importance of ice cliffs to area-average melt is significant, the<em> absolute</em> area-averaged melt is dominated by debris. At Kennicott Glacier, glacier-wide melt rates are not maximized in the zone of maximum thinning. Declining ice discharge through time therefore explains the rapid thinning. Through this study, Kennicott Glacier is the first glacier in Alaska, and the largest glacier globally, where melt across its debris-covered tongue has been rigorously quantified.</p><p>We also carefully explore the relationship between debris, melt hotspots, ice dynamics, and thinning across the debris-covered tongue. In doing so we reveal a chain of linked processes that can explain the striking patterns expressed on the debris-covered tongue of Kennicott Glacier.</p>

scholarly journals Simulation of river water quality from in-situ data and satellite imagery, focus on organic pollutants

2017 ◽  
Author(s):  
Tran Thi Van ◽  
Ha Duong Xuan Bao ◽  
Pham Thi Anh My ◽  
Tran Lap Phong ◽  
Tran Viet Tri
Keyword(s):  
Water Quality ◽  
River Water ◽  
Organic Pollutants ◽  
Satellite Imagery ◽  
River Water Quality ◽  
In Situ Data

scholarly journals BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS

2019 ◽  
Vol 16 (1) ◽  
pp. 23
Author(s):  
Kuncoro Teguh Setiawan ◽  
Nana Suwargana ◽  
Devica Natalia Br. Ginting ◽  
Masita Dwi Mandini Manessa ◽  
Nanin Anggraini ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Coefficient Of Determination ◽  
Shallow Waters ◽  
Benthic Habitat ◽  
Benthic Habitats ◽  
Depth Data ◽  
Marine Habitats ◽  
Random Forest Method

The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery.

Coupling machine learning with high resolution satellite imagery to estimate spatiotemporal changes of salinity in water bodies

2020 ◽  
Author(s):  
Majid Bayati ◽  
Mohammad Danesh-Yazdi
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
High Resolution ◽  
Satellite Imagery ◽  
Water Bodies ◽  
Wide Range ◽  
High Resolution Satellite Imagery

<p>The spatiotemporal dynamics of salinity in hypersaline lakes is strongly dependent on the rate of water flow feeding the lake, evaporation rate, and the phenomena of precipitation and dissolution. Although in-situ observations are most reliable in quantifying water quality variables, the spatiotemporal distribution of such data are typically limited or cannot be readily extrapolated for long-term projections. Alternatively, remotely-sensed imagery has facilitated less expensive and stronger ability to estimate water quality over a wide range of spatiotemporal resolutions. This study introduces a machine learning model that leverages in-situ measurements and high-resolution satellite imagery to estimate the salinity concentration in water bodies. To this end, 123 points were sampled in April and July of 2019 across the Lake Urmia surface covering the wide range of salinity fluctuations. Among the artificial neural networks, ANFIS, and linear regression tools examined to determine the relationship between salinity and surface reflectance, artificial neural networks yielded the best accuracy evidenced by R<sup>2</sup> = 0.94 and RMSE = 6.8%. The results show that the seasonal change of salinity is linearly correlated with the volume of water feeding the lake, witnessing that dilution imposes a stronger control on the salinity than bed salt dissolution. The impact of disturbance in the lake circulation due to the causeway is also evident from the sharp changes of salinity around the bridge piers near spring when the mixing of fresh and hypersaline water from the southern and northern parts, respectively, takes place. The results of this study prove the promising potential of machine learning tools fed multi-spectral satellite information to map other water quality metrics than salinity as well.</p>

scholarly journals A Comparison of Three Trapezoid Models Using Optical and Thermal Satellite Imagery for Water Table Depth Monitoring in Estonian Bogs

2020 ◽  
Vol 12 (12) ◽  
pp. 1980 ◽  
Author(s):  
Iuliia Burdun ◽  
Michel Bechtold ◽  
Valentina Sagris ◽  
Viacheslav Komisarenko ◽  
Gabrielle De Lannoy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Table ◽  
Satellite Imagery ◽  
Landsat Imagery ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Temporal Correlation ◽  
Water Table Depth ◽  
P Value

This study explored the potential of optical and thermal satellite imagery to monitor temporal and spatial changes in the position of the water table depth (WTD) in the peat layer of northern bogs. We evaluated three different trapezoid models that are proposed in the literature for soil moisture monitoring in regions with mineral soils. Due to the tight capillary connection between water table and surface soil moisture, we hypothesized that the soil moisture indices retrieved from these models would be correlated with WTD measured in situ. Two trapezoid models were based on optical and thermal imagery, also known as Thermal-Optical TRApezoid Models (TOTRAM), and one was based on optical imagery alone, also known as the OPtical TRApezoid Model (OPTRAM). The models were applied to Landsat imagery from 2008 to 2019 and the derived soil moisture indices were compared with in-situ WTD from eight locations in two Estonian bogs. Our results show that only the OPTRAM index was significantly (p-value < 0.05) correlated in time with WTD (average Pearson correlation coefficient of 0.41 and 0.37, for original and anomaly time series, respectively), while the two tested TOTRAM indices were not. The highest temporal correlation coefficients (up to 0.8) were observed for OPTRAM over treeless parts of the bogs. An assessment of the spatial correlation between soil moisture indices and WTD indicated that all three models did not capture the spatial variation in water table depth. Instead, the spatial patterns of the indices were primarily attributable to vegetation patterns.

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