Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Source at https://doi.org/10.3390/w10101428.The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology

Above ground biomass and carbon sequestration estimation -Implementation of a sentinel-2 based exploratory workflow

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThis work presents a Sentinel-2 based exploratory work ow for the estimation of Above Ground Biomass (AGB) and Carbon Sequestration (CS) in a subtropical forest. In the last decades, remote sensing-based studies on AGB have been widely investigated alongside with a variety of sensors, features and Machine Learning (ML) algorithms. Up-to-date and reliable mapping of such measures have been increasingly required by international commitments under the climate convention as well as by sustainable forest management practices. The proposed approach consists of 5 major steps: 1) generation of several Vegetation Indices (VI), biophysical parameters and texture measures; 2) feature selection with Mean Decrease in Impurity (MDI), Mean Decrease in Accuracy (MDA), L1 Regularization (LASSO), and Principal Component Analysis (PCA); 3) feature selection testing with k-Nearest Neighbour (kNN), Random Forest (RF), Extreme Gradient Boosting (XGB), and Arti cial Neural Network (ANN); 4) hyper-parameters ne-tuning with Grid Search, Random Search and Bayesian Optimization; and nally, 5) model explanation with the SHapley Additive exPlanations (SHAP) package, which to this day has not been investigated in the context of AGB mapping. The following results were obtained: 1) MDI was chosen as the best performing feature selection method by the XGB and the Deep Neural Network (DNN), MDA was chosen by the RF and the kNN, while LASSO was chosen by the Shallow Neural Network (SNN) and the Linear Neural Network (LNN); 2) before hyper-parameters optimization, the Deep Neural Network (DNN) yielded the best performance with a Root Mean Squared Error (RMSE) of 42.30 t=ha; 3) after hyper-parameters ne-tuning with Bayesian Optimization, the XGB model yielded the best performance with a RMSE of 37.79 t=ha; 4) model explanation with SHAP allowed for a deeper understanding of the features impact on the model predictions. Finally, the predicted AGB throughout the study area showed an average value of 83 t=ha, ranging from 0 t=ha to 346.56 t=ha. The related CS was estimated by using a conversion factor of 0.47

The use of algorithms to predict surface seawater dimethyl sulphide concentrations in the SE Pacific, a region of steep gradients in primary productivity, biomass and mixed layer depth

Dimethyl sulphide (DMS) is an important precursor of cloud condensation nuclei (CCN), particularly in the remote marine atmosphere. The SE Pacific is consistently covered with a persistent stratocumulus layer that increases the albedo over this large area. It is not certain whether the source of CCN to these clouds is natural and oceanic or anthropogenic and terrestrial. This unknown currently limits our ability to reliably model either the cloud behaviour or the oceanic heat budget of the region. In order to better constrain the marine source of CCN, it is necessary to have an improved understanding of the sea-air flux of DMS. Of the factors that govern the magnitude of this flux, the greatest unknown is the surface seawater DMS concentration. In the study area, there is a paucity of such data, although previous measurements suggest that the concentration can be substantially variable. In order to overcome such data scarcity, a number of climatologies and algorithms have been devised in the last decade to predict seawater DMS. Here we test some of these in the SE Pacific by comparing predictions with measurements of surface seawater made during the Vamos Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) in October and November of 2008. We conclude that none of the existing algorithms reproduce local variability in seawater DMS in this region very well. From these findings, we recommend the best algorithm choice for the SE Pacific and suggest lines of investigation for future work

Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

Effects of large solar zenith angles and cloud cover on underwater irradiance

Le processus de la photosynthèse nécessite l'énergie de la lumière solaire et, dans l’océan, se déroule essentiellement dans la couche euphotique. Outre les autres variables (à savoir la chlorophylle a et les paramètres photosynthétiques), une connaissance appropriée du champ lumineux en termes de rayonnement incident disponible sur la photosynthèse (PAR) à un emplacement, une profondeur et une heure et une date donnés, est requise par les modèles d'écosystème marin. Le travail inclus dans cette thèse examine comment des angles de zénith solaires plus grands et différentes conditions nuageuses caractéristiques des régions de haute latitude, en particulier dans l'Arctique, peuvent affecter la précision des estimations de l'éclairement de surface et dans la colonne d’eau. L’accent est également mis sur les variations du champs lumineux à haute fréquence liées à la nébulosité sur les estimations de la productivité primaire. Les PAR de surface estimés à partir de différents modèles ont été comparés à des mesures en série chronologique in situ à haute fréquence de données de PAR d'une bouée située en mer Méditerranée. Nous avons examiné comment les incertitudes dues aux angles de zénith solaires plus grands, en conditions nuageuses variables, pouvaient affecter la précision des estimations de l'éclairement de surface. La méthode de classement objectif a été utilisée pour identifier les meilleures méthodes. Le produit PAR de la NASA-Ocean Biology Processing Group (OBPG) a montré les meilleures performances globales, tandis que les PAR basées sur la méthode de la table de conversion (LUT) ont présenté les meilleures performances en termes de différence carrée moyenne, de biais sous ciel clair et également par temps couvert. D'autres méthodes basées sur des formulations empiriques ont montré la troisième meilleure performance par temps clair, tandis que par temps nuageux, elles présentaient de plus grandes incertitudes. Trois méthodes testées par faible ensoleillement ont montré des incertitudes allant jusqu'à 50% dans toutes les conditions du ciel. Les performances du modèle dépendent des propriétés et des produits de nuage...The process of photosynthesis requires the energy from sunlight and takes place essentially in the euphotic layer of the oceans. In addition to other variables (i.e., chlorophyll a and photosynthetic parameters) a suitable knowledge of light field in terms of photosynthetically available radiation (PAR) at any given location, depth and time is an important input parameter required by marine ecosystem models. The work included in this thesis examines how larger solar zenith angles, different cloud conditions that are characteristic features of high latitude regions, especially in Arctic, might affect the accuracy of surface irradiance estimates. Further, main focus was on the effects of high frequency variations in the light field on primary production. Surface PAR estimated from different models were compared with high frequency in situ time series measurements of PAR a buoy located in Mediterranean Sea. It was examined how uncertainties due to larger solar zenith angles under varying cloud conditions might affect the accuracy of surface irradiance. Objective ranking method was used to identify the best methods. Methods tested under low sun elevations exhibited uncertainties as large as 50% under all sky conditions. Model performances were dependent on cloud properties and products. Accuracy of a semianalytical model for coefficient of vertical diffuse attenuation of surface irradiance (kd!) based on optical properties inherent to the water itself (absorption and scattering), and solar zenith angle was examined under larger solar zenith angels and cloud conditions. Extensive radiative transfer simulations were performed to quantify the uncertainties due to large solar zenith angles and clouds on the estimates of diffuse attenuation coefficient. The uncertainties under both these conditions are due to the variability in the proportions of direct and diffuse parts of the total irradiance reaching the surface and in the water column. Also, an improved model parameterization proposed to estimate !"# under large solar zenith angels and cloud conditions was evaluated with Arctic in situ data exhibited good performances..

Modeling the Response of Black Walnut -dominant Mixed Hardwoods to Seasonal Climate Effects with UAV-based Hyperspectral Sensor and Aerial Photogrammetry

The development of compact sensors in recent years has inspired the use of UAS-based hyperspectral and aerial imaging techniques for small-scale remote sensing applications. With increasing concerns about climate change, spectrally-derived vegetation indices (VIs) have proven useful for quantifying stress-induced vegetation response. The goal of this study was to develop predictive models and assess methodology for modeling the biological response of a black walnut -dominant mixed hardwood stand to seasonal climate events using UAV-based hyperspectral remote-sensing. The derived VIs were evaluated against the means of four seasonal measures of climate calculated for a two-week period prior to the flight date. A best subsets regression was used to create best fitting linear regression models according to Bayesian Information Criterion (BIC). The highest-ranked model for total precipitation had an AdjR² of 0.0839 and RMSE of 0.0827 inches. The highest-ranked model for maximum air temperature had an AdjR² of 0.9922 and RMSE of 0.5485 °F. The highest-ranked model for average air temperature had an AdjR² of 0.9987 and RMSE of 0.2256 °F. The highest-ranked model for total solar radiation had an AdjR² of 0.9961 and RMSE of 0.06405 MJ/M². The results indicate that select VIs measured at the canopy level may be useful in estimating the response to at least some measures seasonal climate. The proposed regression models could help local researchers and landowners in making short-term management decisions, as well as further our understanding of climate-induced tree stress for maintaining sustainable forests in Missouri

REMOTE SENSING OF WATER COLOR: MODEL SENSITIVITY ANALYSIS AND ESTIMATION OF PHYTOPLANKTON SIZE FRACTIONS

Indiana University-Purdue University Indianapolis (IUPUI)Phytoplankton size classes (pico-plankton, nano-plankton, and micro-plankton) provide information about pelagic ocean ecosystem structure, and their spatiotemporal variation is crucial in understanding ocean ecosystem structure and global carbon cycling. Remote sensing provides an efficient approach to estimate phytoplankton size compositions on global scale. In the first part of this thesis, a global sensitivity analysis method was used to determine factors mainly controlling the variations of remote sensing reflectance and inherent optical properties inverse algorithms. To achieve these purposes, average mass-specific coefficients of particles were first calculated through Mie theory, using particle size distributions and refractive indices as input; and then a synthesis remote sensing reflectance dataset was created using Hydrolight. Based on sensitivity analysis results, an algorithm for estimating phytoplankton size composition was proposed in the second part. This algorithm uses five types of spectral features: original and normalized remote sensing reflectance, two-band ratios, continuum removed spectra, and spectral curvatures. With the spectral features, phytoplankton size compositions were regressed using support vector machine. According to validation results, this algorithm performs well with simulated and satellite Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS), indicating that it is possible to estimate phytoplankton size compositions through satellite data on global scale

The application of ocean front metrics for understanding habitat selection by marine predators

Marine predators such as seabirds, cetaceans, turtles, pinnipeds, sharks and large teleost fish are essential components of healthy, biologically diverse marine ecosystems. However, intense anthropogenic pressure on the global ocean is causing rapid and widespread change, and many predator populations are in decline. Conservation solutions are urgently required, yet only recently have we begun to comprehend how these animals interact with the vast and dynamic oceans that they inhabit. A better understanding of the mechanisms that underlie habitat selection at sea is critical to our knowledge of marine ecosystem functioning, and to ecologically-sensitive marine spatial planning. The collection of studies presented in this thesis aims to elucidate the influence of biophysical coupling at oceanographic fronts – physical interfaces at the transitions between water masses – on habitat selection by marine predators. High-resolution composite front mapping via Earth Observation remote sensing is used to provide oceanographic context to several biologging datasets describing the movements and behaviours of animals at sea. A series of species-habitat models reveal the influence of mesoscale (10s to 100s of kilometres) thermal and chlorophyll-a fronts on habitat selection by taxonomically diverse species inhabiting contrasting ocean regions; northern gannets (Morus bassanus; Celtic Sea), basking sharks (Cetorhinus maximus; north-east Atlantic), loggerhead turtles (Caretta caretta; Canary Current), and grey-headed albatrosses (Thalassarche chrysostoma; Southern Ocean). Original aspects of this work include an exploration of quantitative approaches to understanding habitat selection using remotely-sensed front metrics; and explicit investigation of how the biophysical properties of fronts and species-specific foraging ecology interact to influence associations. Main findings indicate that front metrics, particularly seasonal indices, are useful predictors of habitat preference across taxa. Moreover, frontal persistence and spatiotemporal predictability appear to mediate the use of front-associated foraging habitats, both in shelf seas and in the open oceans. These findings have implications for marine spatial planning and the design of protected area networks, and may prove useful in the development of tools supporting spatially dynamic ocean management

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio