Image processing for smart browsing of ocean colour data products and subsequent incorporation into a multi-modal sensing framework

Ocean colour is defined as the water hue due to the presence of tiny plants containing the pigment chlorophyll, sediments and coloured dissolved organic material and so water colour can provide valuable information on coastal ecosystems. The ‘Ocean Colour project’ collects data from various satellites (e.g. MERIS, MODIS) and makes this data available online. One method of searching the Ocean Colour project data is to visually browse level 1 and level 2 data. Users can search via location (regions), time and data type. They are presented with images which cover chlorophyll, quasi-true colour and sea surface temperature (11 μ) and links to the source data. However it is often preferable for users to search such a complex and large dataset by event and analyse the distribution of colour in an image before examination of the source data. This will allow users to browse and search ocean colour data more efficiently and to include this information more seamlessly into a framework that incorporates sensor information from a variety of modalities. This paper presents a system for more efficient management and analysis of ocean colour data and suggests how this information can be incorporated into a multi-modal sensing framework for a smarter, more adaptive environmental sensor network

Evaluating the spatial transferability and temporal repeatability of remote sensing-based lake water quality retrieval algorithms at the European scale:a meta-analysis approach

Many studies have shown the considerable potential for the application of remote-sensing-based methods for deriving estimates of lake water quality. However, the reliable application of these methods across time and space is complicated by the diversity of lake types, sensor configuration, and the multitude of different algorithms proposed. This study tested one operational and 46 empirical algorithms sourced from the peer-reviewed literature that have individually shown potential for estimating lake water quality properties in the form of chlorophyll-a (algal biomass) and Secchi disc depth (SDD) (water transparency) in independent studies. Nearly half (19) of the algorithms were unsuitable for use with the remote-sensing data available for this study. The remaining 28 were assessed using the Terra/Aqua satellite archive to identify the best performing algorithms in terms of accuracy and transferability within the period 2001–2004 in four test lakes, namely Vänern, Vättern, Geneva, and Balaton. These lakes represent the broad continuum of large European lake types, varying in terms of eco-region (latitude/longitude and altitude), morphology, mixing regime, and trophic status. All algorithms were tested for each lake separately and combined to assess the degree of their applicability in ecologically different sites. None of the algorithms assessed in this study exhibited promise when all four lakes were combined into a single data set and most algorithms performed poorly even for specific lake types. A chlorophyll-a retrieval algorithm originally developed for eutrophic lakes showed the most promising results (R2 = 0.59) in oligotrophic lakes. Two SDD retrieval algorithms, one originally developed for turbid lakes and the other for lakes with various characteristics, exhibited promising results in relatively less turbid lakes (R2 = 0.62 and 0.76, respectively). The results presented here highlight the complexity associated with remotely sensed lake water quality estimates and the high degree of uncertainty due to various limitations, including the lake water optical properties and the choice of methods

A plankton guide to ocean physics: Colouring in the currents round South Africa and Madagascar

The ocean colour sensor SeaWiFS, launched in August 1997, has been a great boon to those researching large-scale oceanic biological productivity. The sensor can detect variations in the colour of the water due to the presence of chlorophyll in phytoplankton, which essentially changes the water colour from blue to green. SeaWiFS has provided measurements of chlorophyll concentration over nearly all the world’s oceans, and because of their association with fronts, eddies and regions of upwelling, these records of phytoplankton abundance reveal much about physical processes occurring within the ocean

Satellite ocean colour sensors

The 70% of the earth’s surface is covered by the ocean and the life inhabiting the oceans play an important role in shaping the earth’s climate. Phytoplankton, also known as microalgae, are the single celled, autotrophic components of the plankton community and a key part of oceans, seas and freshwater basin ecosystems. They are significant factor in the ocean carbon cycle and, hence, important in all pathways of carbon in the ocean. Phytoplankton contain chlorophyll pigments for photosynthesis, similar to terrestrial plants and require sunlight in order to live and grow. Most of them are buoyant and float in the upper part of the ocean, where plenty of sunlight is available. They also require inorganic nutrients such as nitrates, phosphates, and sulphur which they convert into proteins, fats, and carbohydrates. In a balanced ecosystem, phytoplankton are the base of the food web and provide food for a wide range of sea creatures (NOAA). The measurement of phytoplankton can be indexed as chlorophyll concentration and is important as they are fundamental to understanding how the marine ecosystem responds to climate variability and climate change

A visible record of eddies in the southern Mozambique Channel

The flows around Madagascar feed into the Agulhas Current, but there have been few hydrographic studies of the flow within the Mozambique Channel. Some cruise and altimetric data point to this being a region of high mesoscale activity, with eddies migrating through the area. Here we show how ocean colour data throw light on the behaviour of eddies in the southern Mozambique Channel

Integration of Remote Sensing-GIS Techniques for Mapping and Monitoring Seagrass and Ocean Colour off Malaysian Coasts

This paper describes seagrass and ocean colour mapping off Peninsular Malaysia. The seagrass were extracted from visible bands of Landsat TM using the depth invariant index of the scabottom type. The ocean colour which much referred to plankton concentration is derived by regressing samples from known site collected at time of satellite overpass. Out these information were then input into GIS database which were also being established to assist the Marine Fisheries Management and Development Centre in managing and monitoring coastal areas This paper also addresses the experience gained in building spatial database for coastal areas various dala collected from various mapping environments were carried out

MERIS-based ocean colour classification with the discrete Forel-Ule scale

Multispectral information from satellite borne ocean colour sensors is at present used to characterize natural waters via the retrieval of concentrations of the three dominant optical constituents; pigments of phytoplankton, non-algal particles and coloured dissolved organic matter. A limitation of this approach is that accurate retrieval of these constituents requires detailed local knowledge of the specific absorption and scattering properties. In addition, the retrieval algorithms generally use only a limited part of the collected spectral information. In this paper we present an additional new algorithm that has the merit of using the full spectral information in the visible domain to characterize natural waters in a simple and globally valid way. This Forel–Ule MERIS (FUME) algorithm converts the normalized multiband reflectance information into a discrete set of numbers using uniform colourimetric functions. The Forel–Ule (<i>FU</i>) scale is a sea colour comparator scale that has been developed to cover all possible natural sea colours, ranging from indigo blue (the open ocean) to brownish-green (coastal water) and even brown (humic-acid dominated) waters. Data using this scale have been collected since the late nineteenth century, and therefore, this algorithm creates the possibility to compare historic ocean colour data with present-day satellite ocean colour observations. The FUME algorithm was tested by transforming a number of MERIS satellite images into Forel–Ule colour index images and comparing in situ observed <i>FU</i> numbers with <i>FU</i> numbers modelled from in situ radiometer measurements. Similar patterns and <i>FU</i> numbers were observed when comparing MERIS ocean colour distribution maps with ground truth Forel–Ule observations. The <i>FU</i> numbers modelled from in situ radiometer measurements showed a good correlation with observed <i>FU</i> numbers (<i>R</i>² = 0.81 when full spectra are used and <i>R</i>² = 0.71 when MERIS bands are used)

Ocean colour changes in the North Pacific since 1930

In this paper we present an analysis of historical ocean colour data from the North Pacific Ocean. This colour is described by the Forel-Ule colour index, a sea colour comparator scale that is composed of 21 tube colours that is routinely measured since the year 1890. The main objective of this research is to characterise colour changes of the North Pacific Ocean at a timescale of decades. Next to the seasonal colour changes, due to the yearly cycle of biological activity, this time series between 1930 and 1999 might contain information on global changes in climate conditions. From seasonal independent analyses of the long-term variations it was found that the greenest values, with mean Forel-Ule scale ((FU) ̅) of 4.1 were reached during the period of 1950-1954, with a second high ((FU) ̅ = 3) in the period 1980-1984. The bluest ocean was encountered during the years 1990-1994. The data indicate that after 1955 a remarkable long bluing took place till 1980

Segmentation of multispectral images and prediction of ChI-a concentration for effective ocean colour remote sensing

With the development of new sensors and data processing techniques, ocean colour remote sensing has undergone rapid development in more accurately measurement of coastal shelf classification and concentration of chlorophyll. In this paper, multispectral images are employed to achieve these targets, using techniques including region-growing based segmentation for pixel classification and support vector regression for ChI-a prediction. Interesting results are reported to show the great potential in using state-of-the-art data analysis techniques for effective ocean colour remote sensing

Evaluation of Satellite Retrievals of Chlorophyll-a in the Arabian Gulf

The Arabian Gulf is a highly turbid, shallow sedimentary basin whose coastal areas have been classified as optically complex Case II waters (where ocean colour sensors have been proved to be unreliable). Yet, there is no such study assessing the performance and quality of satellite ocean-colour datasets in relation to ground truth data in the Gulf. Here, using a unique set of in situ Chlorophyll-a measurements (Chl-a; an index of phytoplankton biomass), collected from 24 locations in four transects in the central Gulf over six recent research cruises (2015–2016), we evaluated the performance of VIIRS and other merged satellite datasets, for the first time in the region. A highly significant relationship was found (r = 0.795, p < 0.001), though a clear overestimation in satellite-derived Chl-a concentrations is evident. Regardless of this constant overestimation, the remotely sensed Chl-a observations illustrated adequately the seasonal cycles. Due to the optically complex environment, the first optical depth was calculated to be on average 6–10 m depth, and thus the satellite signal is not capturing the deep chlorophyll maximum (DCM at ~25 m). Overall, the ocean colour sensors’ performance was comparable to other Case II waters in other regions, supporting the use of satellite ocean colour in the Gulf. Yet, the development of a regional-tuned algorithm is needed to account for the unique environmental conditions of the Gulf, and ultimately provide a better estimation of surface Chl-a in the region