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Mo-Bjørkelund T, Majaneva S, Fragoso GM, Johnsen G, Ludvigsen M. Multi-vehicle adaptive 3D mapping for targeted ocean sampling. PLoS One 2024; 19:e0302514. [PMID: 38718004 PMCID: PMC11078410 DOI: 10.1371/journal.pone.0302514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/07/2024] [Indexed: 05/12/2024] Open
Abstract
Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.
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Affiliation(s)
- Tore Mo-Bjørkelund
- Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Sanna Majaneva
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Geir Johnsen
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
- Arctic Biology Department, University Centre in Svalbard (UNIS), Longyearbyen, Norway
| | - Martin Ludvigsen
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
- Arctic Technology Department, University Centre in Svalbard (UNIS), Longyearbyen, Norway
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Mohseni F, Saba F, Mirmazloumi SM, Amani M, Mokhtarzade M, Jamali S, Mahdavi S. Ocean water quality monitoring using remote sensing techniques: A review. MARINE ENVIRONMENTAL RESEARCH 2022; 180:105701. [PMID: 35939895 DOI: 10.1016/j.marenvres.2022.105701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 07/09/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.
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Affiliation(s)
- Farzane Mohseni
- Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran; Department of Technology and Society, Faculty of Engineering, Lund University, P.O. Box 118, 221 00, Lund, Sweden.
| | - Fatemeh Saba
- Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - S Mohammad Mirmazloumi
- Centre Tecnològic de Telecommunications de Catalunya (CTTC/CERCA), Geomatics Research Unit, Av. Gauss 7, E-08860, Castelldefels, Barcelona, Spain.
| | - Meisam Amani
- Wood Environment and Infrastructure Solutions, Ottawa, ON, K2E 7L5, Canada.
| | - Mehdi Mokhtarzade
- Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Sadegh Jamali
- Department of Technology and Society, Faculty of Engineering, Lund University, P.O. Box 118, 221 00, Lund, Sweden.
| | - Sahel Mahdavi
- Wood Environment and Infrastructure Solutions, Ottawa, ON, K2E 7L5, Canada.
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Are Northern Lakes in Relatively Intact Temperate Forests Showing Signs of Increasing Phytoplankton Biomass? Ecosystems 2021. [DOI: 10.1007/s10021-021-00684-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression. SUSTAINABILITY 2021. [DOI: 10.3390/su13116416] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m−3 in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m−3 in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs.
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Smith JE, Stocker MD, Wolny JL, Hill RL, Pachepsky YA. Intraseasonal variation of phycocyanin concentrations and environmental covariates in two agricultural irrigation ponds in Maryland, USA. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:706. [PMID: 33064217 DOI: 10.1007/s10661-020-08664-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 10/05/2020] [Indexed: 06/11/2023]
Abstract
Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and assimilated into crops when present in irrigation waters. Phycocyanin is a pigment known only to occur in cyanobacteria and is often used to indicate cyanobacteria presence in waters. The objective of this work was to identify the most influential environmental covariates affecting the phycocyanin concentrations in agricultural irrigation ponds that experience cyanobacteria blooms of the potentially toxigenic species Microcystis and Aphanizomenon using machine learning methodology. The study was performed at two agricultural irrigation ponds over a 5-month period in the summer of 2018. Phycocyanin concentrations, along with sensor-based and fluorometer-based water quality parameters including turbidity (NTU), pH, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), conductivity, chlorophyll, color dissolved organic matter (CDOM), and extracted chlorophyll were measured. Regression tree analyses were used to determine the most influential water quality parameters on phycocyanin concentrations. Nearshore sampling locations had higher phycocyanin concentrations than interior sampling locations and "zones" of consistently higher concentrations of phycocyanin were found in both ponds. The regression tree analyses indicated extracted chlorophyll, CDOM, and NTU were the three most influential parameters on phycocyanin concentrations. This study indicates that sensor-based and fluorometer-based water quality parameters could be useful to identify spatial patterns of phycocyanin concentrations and therefore, cyanobacteria blooms, in agricultural irrigation ponds and potentially other water bodies.
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Affiliation(s)
- J E Smith
- Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, ARS-USDA, Beltsville, MD, USA.
- Department of Environmental Science and Technology, University of Maryland, College Park, MD, USA.
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA.
| | - M D Stocker
- Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, ARS-USDA, Beltsville, MD, USA
- Department of Environmental Science and Technology, University of Maryland, College Park, MD, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
| | - J L Wolny
- Resource Assessment Service, Maryland Department of Natural Resources, Annapolis, MD, USA
| | - R L Hill
- Department of Environmental Science and Technology, University of Maryland, College Park, MD, USA
| | - Y A Pachepsky
- Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, ARS-USDA, Beltsville, MD, USA
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Multispectral Remote Sensing Utilization for Monitoring Chlorophyll-a Levels in Inland Water Bodies in Jordan. ScientificWorldJournal 2020; 2020:5060969. [PMID: 32831806 PMCID: PMC7428877 DOI: 10.1155/2020/5060969] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 07/06/2020] [Accepted: 07/13/2020] [Indexed: 11/17/2022] Open
Abstract
This study focuses on the utilization of multispectral satellite images for remote water-quality evaluation of inland water body in Jordan. The geophysical parameters based on water's optical properties, due to the presence of optically active constituents, are used to determine contaminant level in water. It has a great potential to be employed for continuous and cost-effective water-quality monitoring and leads to a reliable regularly updated tool for better water sector management. Three sets of water samples were collected from three different dams in Jordan. Chl-a concentration of the water samples was measured and used with corresponding Sentinel 2 surface reflectance (SR) data to develop a predictive model. Chl-a concentrations and corresponding SR data were used to calibrate and validate different models. The predictive capability of each of the investigated models was determined in terms of determination coefficient (R2) and lowest root mean square error (RMSE) values. For the investigated sites, the B3/B2 (green/blue bands) model and the Ln (B3/B2) model showed the best overall predictive capability of all models with the highest R2 and the lowest RMSE values of (0.859, 0.824) and (30.756 mg/m3, 29.787 mg/m3), respectively. The outcome of this study on selected sites can be expanded for future work to cover more sites in the future and ultimately cover all sites in Jordan.
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Limaye SS, Mogul R, Smith DJ, Ansari AH, Słowik GP, Vaishampayan P. Venus' Spectral Signatures and the Potential for Life in the Clouds. ASTROBIOLOGY 2018; 18:1181-1198. [PMID: 29600875 PMCID: PMC6150942 DOI: 10.1089/ast.2017.1783] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 02/11/2018] [Indexed: 05/17/2023]
Abstract
The lower cloud layer of Venus (47.5-50.5 km) is an exceptional target for exploration due to the favorable conditions for microbial life, including moderate temperatures and pressures (∼60°C and 1 atm), and the presence of micron-sized sulfuric acid aerosols. Nearly a century after the ultraviolet (UV) contrasts of Venus' cloud layer were discovered with Earth-based photographs, the substances and mechanisms responsible for the changes in Venus' contrasts and albedo are still unknown. While current models include sulfur dioxide and iron chloride as the UV absorbers, the temporal and spatial changes in contrasts, and albedo, between 330 and 500 nm, remain to be fully explained. Within this context, we present a discussion regarding the potential for microorganisms to survive in Venus' lower clouds and contribute to the observed bulk spectra. In this article, we provide an overview of relevant Venus observations, compare the spectral and physical properties of Venus' clouds to terrestrial biological materials, review the potential for an iron- and sulfur-centered metabolism in the clouds, discuss conceivable mechanisms of transport from the surface toward a more habitable zone in the clouds, and identify spectral and biological experiments that could measure the habitability of Venus' clouds and terrestrial analogues. Together, our lines of reasoning suggest that particles in Venus' lower clouds contain sufficient mass balance to harbor microorganisms, water, and solutes, and potentially sufficient biomass to be detected by optical methods. As such, the comparisons presented in this article warrant further investigations into the prospect of biosignatures in Venus' clouds.
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Affiliation(s)
- Sanjay S. Limaye
- Space Science and Engineering Center, University of Wisconsin, Madison, Wisconsin
| | - Rakesh Mogul
- Chemistry and Biochemistry Department, California State Polytechnic University, Pomona, Pomona, California
| | - David J. Smith
- Space Biosciences Research Branch, NASA Ames Research Center, Moffett Field, California
| | - Arif H. Ansari
- Precambrian Palaeobotany Laboratory, Birbal Sahni Institute of Palaeosciences, Lucknow, India
| | | | - Parag Vaishampayan
- Biotechnology and Planetary Protection Group, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
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Selecting the Best Band Ratio to Estimate Chlorophyll-a Concentration in a Tropical Freshwater Lake Using Sentinel 2A Images from a Case Study of Lake Ba Be (Northern Vietnam). ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2017. [DOI: 10.3390/ijgi6090290] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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