scholarly journals Hindcast and Near Real-Time Monitoring of Green Macroalgae Blooms in Shallow Coral Reef Lagoons Using Sentinel-2: A New-Caledonia Case Study

2021 ◽  
Vol 13 (2) ◽  
pp. 211
Author(s):  
Maële Brisset ◽  
Simon Van Wynsberge ◽  
Serge Andréfouët ◽  
Claude Payri ◽  
Benoît Soulard ◽  
...  
Keyword(s):  
Green Algae ◽  
Algal Blooms ◽  
New Caledonia ◽  
Spectral Indices ◽  
Green Macroalgae ◽  
Spectral Bands ◽  
Algae Blooms ◽  
Trade Offs ◽  
Sentinel 2

Despite the necessary trade-offs between spatial and temporal resolution, remote sensing is an effective approach to monitor macroalgae blooms, understand their origins and anticipate their developments. Monitoring of small tropical lagoons is challenging because they require high resolutions. Since 2017, the Sentinel-2 satellites has provided new perspectives, and the feasibility of monitoring green algae blooms was investigated in this study. In the Poé-Gouaro-Déva lagoon, New Caledonia, recent Ulva blooms are the cause of significant nuisances when beaching. Spectral indices using the blue and green spectral bands were confronted with field observations of algal abundances using images concurrent with fieldwork. Depending on seabed compositions and types of correction applied to reflectance data, the spectral indices explained between 1 and 64.9% of variance. The models providing the best statistical fit were used to revisit the algal dynamics using Sentinel-2 data from January 2017 to December 2019, through two image segmentation approaches: unsupervised and supervised. The latter accurately reproduced the two algal blooms that occurred in the area in 2018. This paper demonstrates that Sentinel-2 data can be an effective source to hindcast and monitor the dynamics of green algae in shallow lagoons.

scholarly journals Retrieval of Leaf Area Index Using Sentinel-2 Imagery in a Mixed Mediterranean Forest Area

2020 ◽  
Vol 9 (11) ◽  
pp. 622
Author(s):  
Irene Chrysafis ◽  
Georgios Korakis ◽  
Apostolos P. Kyriazopoulos ◽  
Giorgos Mallinis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Model Development ◽  
Mixed Forest ◽  
Selection Procedure ◽  
Coefficient Of Determination ◽  
Spectral Indices ◽  
Area Index ◽  
Spectral Bands ◽  
Sentinel 2

Leaf area index (LAI) is a crucial biophysical indicator for assessing and monitoring the structure and functions of forest ecosystems. Improvements in remote sensing instrumental characteristics and the availability of more efficient statistical algorithms, elevate the potential for more accurate models of vegetation biophysical properties including LAI. The aim of this study was to assess the spectral information of Sentinel-2 MSI satellite imagery for the retrieval of LAI over a mixed forest ecosystem located in northwest Greece. Forty-eight field plots were visited for the collection of ground LAI measurements using an ACCUPAR LP-80: PAR & LAI Ceptometer. Spectral bands and spectral indices were used for LAI model development using the Gaussian processes regression (GPR) algorithm. A variable selection procedure was applied to improve the model’s prediction accuracy, and variable importance was investigated for identifying the most informative variables. The model resulting from spectral indices’ variables selection produced the most precise predictions of LAI with a coefficient of determination of 0.854. Shortwave infrared bands and the normalized canopy index (NCI) were identified as the most important features for LAI prediction.

A spectral index for the detection of algal blooms using Sentinel-2 Multispectral Instrument (MSI) imagery: a case study of Hulun Lake, China

2021 ◽  
Vol 42 (12) ◽  
pp. 4514-4535
Author(s):  
Mengmeng Cao ◽  
Song Qing ◽  
Eerdemutu Jin ◽  
Yanling Hao ◽  
Wenjing Zhao
Keyword(s):  
Spectral Index ◽  
Algal Blooms ◽  
Hulun Lake ◽  
Sentinel 2

scholarly journals Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal's fires of 2017

2020 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Konstantinos P. Ferentinos
Keyword(s):  
Large Scale ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Spectral Indices ◽  
Burnt Area ◽  
Vector Machines ◽  
European Forest ◽  
Burnt Areas ◽  
Sentinel 2

The recent launch of Sentinel missions offers a unique opportunity to assess the impacts of wildfires at higherspatial and spectral resolution provided by those Earth Observing (EO) systems. Herein, an assessment of theSentinel-1 & 2 to map burnt areas has been conducted. Initially the use of Sentinel-2 solely was explored, andthen in combination with Sentinel-1 and derived radiometric indices. As a case study, the large wildfire occurredin Pedrógão Grande, Portugal in 2017 was used. Burnt area estimates from the European Forest FiresInformation System (EFFIS) were used as reference. Burnt area was delineated using the Maximum Likelihood(ML) and Support Vector Machines (SVMs) classifiers, and two multi-index methods. Following this, burn severitywas assessed using SVMs and Artificial Neural Networks (ANNs), again for both standalone Sentinel-2 dataand in combination with Sentinel-1 and spectral indices. Soil erosion predictions were evaluated using theRevised Universal Soil Loss Equation (RUSLE) model. The effect of the land cover derived from CORINE operationalproduct was also evaluated across the burnt area and severity maps. SVMs produced the most accurateburnt area map, resulting a 94.8% overall accuracy and a Kappa coefficient of 0.90. SVMs also achieved thehighest accuracy in burn severity levels estimation, with an overall accuracy of 77.9% and a kappa of 0.710.From an algorithmic perspective, implementation of the techniques applied herein, is based on EO imageryanalysis provided nowadays globally at no cost. It is also robust and adaptable, being potentially integrated withother high EO data available. All in all, our study contributes to the understanding of Mediterranean landscapedynamics and corroborates the usefulness of Sentinels data in wildfire studies.

CAN REDOX CHANGES IN LAKEBED SEDIMENTS TRIGGER ALGAL BLOOMS? A CASE STUDY OF CARLYLE LAKE, ILLINOIS

2017 ◽  
Author(s):  
Andrew R. Shaughnessy ◽  
◽  
Aneesh Venkata ◽  
Elizabeth A. Hasenmueller ◽  
John J. Sloan ◽  
...  
Keyword(s):  
Algal Blooms

scholarly journals Entropy trade-offs in artistic design: A case study of Tamil kolam

2021 ◽  
Vol 3 ◽  
Author(s):  
N.-Han Tran ◽  
Timothy Waring ◽  
Silke Atmaca ◽  
Bret A. Beheim
Keyword(s):  
Trade Offs

Abstract

scholarly journals Decision Support for the (Inter-)Basin Management of Water Resources Using Integrated Hydro-Economic Modeling

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Gerald Norbert Souza da Silva ◽  
Márcia Maria Guedes Alcoforado de Moraes
Keyword(s):  
Decision Support ◽  
Water Resources ◽  
Water Allocation ◽  
Hydrological Modeling ◽  
Well Being ◽  
Water Transfer ◽  
Economic Modeling ◽  
Trade Offs ◽  
Study Results

The development of adequate modeling at the basin level to establish public policies has an important role in managing water resources. Hydro-economic models can measure the economic effects of structural and non-structural measures, land and water management, ecosystem services and development needs. Motivated by the need of improving water allocation using economic criteria, in this study, a Spatial Decision Support System (SDSS) with a hydro-economic optimization model (HEAL system) was developed and used for the identification and analysis of an optimal economic allocation of water resources in a case study: the sub-middle basin of the São Francisco River in Brazil. The developed SDSS (HEAL system) made the economically optimum allocation available to analyze water allocation conflicts and trade-offs. With the aim of providing a tool for integrated economic-hydrological modeling, not only for researchers but also for decision-makers and stakeholders, the HEAL system can support decision-making on the design of regulatory and economic management instruments in practice. The case study results showed, for example, that the marginal benefit function obtained for inter-basin water transfer, can contribute for supporting the design of water pricing and water transfer decisions, during periods of water scarcity, for the well-being in both basins.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

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