Data Fusion of Remote-sensing and In-lake chlorophyll a Data Using Statistical Downscaling

Chlorophyll a is a green pigment, used as an indirect measure of lake water quality. Its strong absorption of blue and red light allows for quantification through satellite images, providing better spatial coverage than traditional in-lake samples. However, grid-cell scale imagery must be calibrated spatially using in-lake point samples, presenting a change-of-support problem. This paper presents a method of statistical downscaling, namely a Bayesian spatially-varying coefficient regression, which assimilates remotely-sensed and in-lake data, resulting in a fully calibrated spatial map of chlorophyll a with associated uncertainty measures. The model is applied to a case study dataset from Lake Balaton, Hungary

Online impedance regulation techniques for compliant humanoid balancing

This paper presents three distinct techniques, aimed at the online active impedance regulation of compliant humanoid robots, which endeavours to induce a state of balance to the system once it has been perturbed. The presence of passive elastic elements in the drives powering this class of robots leads to under-actuation, thereby rendering the control of compliant robots an intricate task. Consequently, the impedance regulation procedures proposed in this paper directly account for these elastic elements. In order to acquire an indication of the robot’s state of balance in an online fashion, an energy (Lyapunov) function is introduced, whose sign then allows one to ascertain whether the robot is converging to or diverging from, a desired equilibrium position. Computing this function’s time derivative unequivocally gives the energy-injecting nature of the active stiffness regulation, and reveals that active damping regulation has no bearing on the system’s state of stability. Furthermore, the velocity margin notion is interpreted as a velocity value beyond which the system’s balance might be jeopardized, or below which the robot will be guaranteed to remain stable. As a result, the unidirectional and bidirectional impedance optimization methods rely upon the use of bounds that have been defined based on the energy function’s derivative, in addition to the velocity margin. Contrarily, the third technique’s functionality revolves solely around the use of Lyapunov Stability Margins (LSMs). A series of experiments carried out using the COmpliant huMANoid (COMAN), demonstrates the superior balancing results acquired when using the bidirectional scheme, as compared to utilizing the two alternative techniques

Optimising Multispectral Active Fluorescence to Distinguish the Photosynthetic Variability of Cyanobacteria and Algae

This study assesses the ability of a new active fluorometer, the LabSTAF, to diagnostically assess the physiology of freshwater cyanobacteria in a reservoir exhibiting annual blooms. Specifically, we analyse the correlation of relative cyanobacteria abundance with photosynthetic parameters derived from fluorescence light curves (FLCs) obtained using several combinations of excitation wavebands, photosystem II (PSII) excitation spectra and the emission ratio of 730 over 685 nm (Fo(730/685)) using excitation protocols with varying degrees of sensitivity to yanobacteria and algae. FLCs using blue excitation (B) and green–orange–red (GOR) excitation wavebands capture physiology parameters of algae and cyanobacteria, respectively. The green–orange (GO) protocol, expected to have the best diagnostic properties for cyanobacteria, did not guarantee PSII saturation. PSII excitation spectra showed distinct response from cyanobacteria and algae, depending on spec�tral optimisation of the light dose. Fo(730/685), obtained using a combination of GOR excitation wavebands, Fo(GOR, 730/685), showed a significant correlation with the relative abundance of cya�nobacteria (linear regression, p-value < 0.01, adjusted R2 = 0.42). We recommend using, in parallel, Fo(GOR, 730/685), PSII excitation spectra (appropriately optimised for cyanobacteria versus algae), and physiological parameters derived from the FLCs obtained with GOR and B protocols to assess the physiology of cyanobacteria and to ultimately predict their growth. Higher intensity LEDs (G and O) should be considered to reach PSII saturation to further increase diagnostic sensitivity to the cyanobacteria component of the communit

Stiffness evaluation of a novel ankle rehabilitation exoskeleton with a type-variable constraint

This paper presents a novel ankle rehabilitation exoskeleton with two rotational degrees of freedom, which is suitable for dynamical rehabilitation for patients with neurological impairments. Its stiffness performance is assessed in consideration that the interaction between the footplate and the ground may deflect the mechanism away from the desired/predefined motion patterns. The novel design employs a universal-prismatic-universal (U-P-U) joint link, whose constraint type changes between a couple and a line vector during manipulation of the exoskeleton. To conduct a stiffness analysis of such a mechanism with a type-variable constraint – for the first time – a modified screw-based method (SBM) is proposed. Comparisons with the results obtained from finite element analysis verified that, the modified SBM provides reliable estimates of the exoskeleton's stiffness within the complete workspace (covering the constraint-type transition configurations). The stiffness of the exoskeleton is further evaluated by acquiring the minimum/maximum stiffness values, after computing the distribution of the most crucial linear and angular stiffness parameters within the workspace. Moreover, the influence of the architectural parameters on the stiffness properties is considered for further design optimization

Improved hyperspectral inversion of aquatic reflectance under non-uniform vertical mixing

Estimating the concentration of water constituents by optical remote sensing assumes absorption and scattering processes to be uniform over the observation depth. Using hyperspectral reflectance, we present a method to direct the retrieval of the backscattering coefficient (bb(λ)) from reflectance (> 600 nm) towards wavebands where absorption by water dominates the reflectance curve. Two experiments demonstrate the impact of hyperspectral inversion in the selected band set. First, optical simulations show that the resulting distribution of bb(λ) is sensitive to particle mixing conditions, although a robust indicator of non-uniformity was not found for all scenarios of stratification. Second, in the absence of spectral backscattering profiles from in situ data sets, it is shown how substituting the median of bb(λ) into a near infra-red / red band ratio algorithm improved chlorophyll-a estimates (root mean square error 75.45 mg m−3 became 44.13 mg m−3). This approach also allows propagation of the uncertainty in bb estimates to water constituent concentrations

Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover

Atmospheric correction of remotely sensed imagery of inland water bodies is essential to interpret water-leaving radiance signals and for the accurate retrieval of water quality variables. Atmospheric correction is particularly challenging over inhomogeneous water bodies surrounded by comparatively bright land surface. We present results of AisaFENIX airborne hyperspectral imagery collected over a small inland water body under changing cloud cover, presenting challenging but common conditions for atmospheric correction. This is the first evaluation of the performance of the FENIX sensor over water bodies. ATCOR4, which is not specifically designed for atmospheric correction over water and does not make any assumptions on water type, was used to obtain atmospherically corrected reflectance values, which were compared to in situ water-leaving reflectance collected at six stations. Three different atmospheric correction strategies in ATCOR4 was tested. The strategy using fully image-derived and spatially varying atmospheric parameters produced a reflectance accuracy of �0.002, i.e., a difference of less than 15% compared to the in situ reference reflectance. Amplitude and shape of the remotely sensed reflectance spectra were in general accordance with the in situ data. The spectral angle was better than 4.1� for the best cases, in the spectral range of 450–750 nm. The retrieval of chlorophyll-a (Chl-a) concentration using a popular semi-analytical band ratio algorithm for turbid inland waters gave an accuracy of ~16% or 4.4 mg/m3 compared to retrieval of Chl-a from reflectance measured in situ. Using fixed ATCOR4 processing parameters for whole images improved Chl-a retrieval results from ~6 mg/m3 difference to reference to approximately 2 mg/m3. We conclude that the AisaFENIX sensor, in combination with ATCOR4 in image-driven parametrization, can be successfully used for inland water quality observations. This implies that the need for in situ reference measurements is not as strict as has been assumed and a high degree of automation in processing is possible

Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs

Satellite product uncertainty estimates are critical for the further development and evaluation of remote sensing algorithms, as well as for the user community (e.g., modelers, climate scientists, and decision-makers). Optical remote sensing of water quality is affected by significant uncertainties stemming from correction for atmospheric effects as well as a lack of algorithms that can be universally applied to waterbodies spanning several orders of magnitude in non-covarying substance concentrations. We developed a method to produce estimates of Chlorophyll-a (Chla) satellite product uncertainty on a pixel-by-pixel basis within an Optical Water Type (OWT) classification scheme. This scheme helps to dynamically select the most appropriate algorithms for each satellite pixel, whereas the associated uncertainty informs downstream use of the data (e.g., for trend detection or modeling) as well as the future direction of algorithm research. Observations of Chla were related to 13 previously established OWT classes based on their corresponding water-leaving reflectance (Rw), each class corresponding to specific bio-optical characteristics. Uncertainty models corresponding to specific algorithm - OWT combinations for Chla were then expressed as a function of OWT class membership score. Embedding these uncertainty models into a fuzzy OWT classification approach for satellite imagery allows Chla and associated product uncertainty to be estimated without a priori knowledge of the biogeochemical characteristics of a water body. Following blending of Chla algorithm results according to per-pixel fuzzy OWT membership, Chla retrieval shows a generally robust response over a wide range of class memberships, indicating a wide application range (ranging from 0.01 to 362.5 mg/m3). Low OWT membership scores and high product uncertainty identify conditions where optical water types need further exploration, and where biogeochemical satellite retrieval algorithms require further improvement. The procedure is demonstrated here for the Medium Resolution Imaging Spectrometer (MERIS) but could be repeated for other sensors, atmospheric correction methods and optical water quality variables

A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes

The land-affected signal in remotely sensed radiance from nearshore waters is a common problem for remote sensing, introducing uncertainty in atmospheric correction and subsequent water quality constituent concentration estimates. This study proposes a new method for identifying effects of land on satellite remote sensing of water quality. The new optical water types (OWT) containing the land-affected signal were derived from POLYMER-corrected imagery of the Medium Resolution Imaging Spectrometer in reduced resolution (MERIS RR) and Sentinel-3 Ocean and Land Colour Instrument (OLCI). These were then applied, as part of a larger set of existing OWTs corresponding to the variability observed in natural waters, to satellite images. The ability to identify pixels containing both water and land, and those contaminated with radiance from adjacent land, was evaluated. Our test sites include dark lakes of varying size in Sweden (Lakes Rusken, Bolmen, Ringsjon, ¨ and Ivosj ¨ on) ¨ where the classification showed high sensitivity to land near the lake shore. The land-affected signal is shown to lead to underestimations of chlorophyll-a concentration and Forel-Ule colour indices, and over estimations of turbidity in these lakes, which can be corrected after masking out the land-affected pixels. The land-affected signal is strongest in summer, both NDVI and sun zenith angle covaried with the seasonal variation of land-affected signal. Further, the results confirmed that satellite images with coarser spatial resolution are more prone to land-affected signal compared to images with finer spatial resolution, for small inland water bodies. We propose a data-driven approach for water quality processing with ‘land-affected water types’ as an effective way to improve the lake optical water quality monitoring from water colour sensors

State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality

Satellite remote sensing can provide indicative measures of environmental variables that are crucial to understanding the environment. The spatial and temporal coverage of satellite images allows scientists to investigate the changes in envi�ronmental variables in an unprecedented scale. However, identifying spatiotemporal patterns from such images is chal�lenging due to the complexity of the data, which can be large in volume yet sparse within individual images. This paper proposes a new approach, state space functional principal components analysis (SS-FPCA), to identify the spatiotemporal patterns in processed satellite retrievals and simultaneously reduce the dimensionality of the data, through the use of functional principal components. Furthermore our approach can be used to produce interpolations over the sparse areas. An algorithm based on the alternating expectation–conditional maximisation framework is proposed to estimate the model. The uncertainty of the estimated parameters is investigated through a parametric bootstrap procedure. Lake chlorophyll�a data hold key information on water quality status. Such information is usually only available from limited in situ sampling locations or not at all for remote inaccessible lakes. In this paper, the SS-FPCA is used to investigate the spatiotemporal patterns in chlorophyll-a data of Taruo Lake on the Tibetan Plateau, observed by the European Space Agency MEdium Resolution Imaging Spectromete

Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters

The relatively high spatial resolution, short revisit time and red-edge spectral band (705 nm) of the ESA Sentinel-2 Multi Spectral Imager makes this sensor attractive for monitoring water quality of coastal and inland waters. Reliable atmospheric correction is essential to support routine retrieval of optically active substance concentration from water-leaving reflectance. In this study, six publicly available atmospheric correction algorithms (Acolite, C2RCC, iCOR, l2gen, Polymer and Sen2Cor) are evaluated against above-water optical in situ measurements, within a robust methodology, in two optically diverse coastal regions (Baltic Sea, Western Channel) and from 13 inland waterbodies from 5 European countries with a range of optical properties. The total number of match-ups identified for each algorithm ranged from 1059 to 1668 with 521 match-ups common to all algorithms. These in situ and MSI match-ups were used to generate statistics describing the performance of each algorithm for each respective region and a combined dataset. All ACs tested showed high uncertainties, in many cases >100% in the red and >1000% in the near-infra red bands. Polymer and C2RCC achieved the lowest root mean square differences (~0.0016 sr−1) and mean absolute differences (~40–60% in blue/green bands) across the different datasets. Retrieval of blue-green and NIR-red band ratios indicate that further work on AC algorithms is required to reproduce the spectral shape in the red and NIR bands needed to accurately retrieve the chlorophyll-a concentration in turbid waters