Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

This is the author's preprint. The final version is available from the publisher via the DOI in this record.The authors would like to thank Dr. Peter Land for useful discussions on reflectance spectra of ground targets. Fig. 9 contains Ordnance Survey OpenData © Crown copyright and database right 2013. The hyperspectral data used in this report were collected by the Natural Environment Research Council Airborne Research and Survey Facility.Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points. © 2013 Elsevier Ltd

Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone

Seagrass ecosystems are highly sensitive to environmental change. They are also in global decline and under threat from a variety of anthropogenic factors. There is now an urgency to establish robust monitoring methodologies so that changes in seagrass abundance and distribution in these sensitive coastal environments can be understood. Typical monitoring approaches have included remote sensing from satellites and airborne platforms, ground based ecological surveys and snorkel/scuba surveys. These techniques can suffer from temporal and spatial inconsistency, or are very localised making it hard to assess seagrass meadows in a structured manner. Here we present a novel technique using a lightweight (sub 7 kg) drone and consumer grade cameras to produce very high spatial resolution (∼4 mm pixel−1) mosaics of two intertidal sites in Wales, UK. We present a full data collection methodology followed by a selection of classification techniques to produce coverage estimates at each site. We trialled three classification approaches of varying complexity to investigate and illustrate the differing performance and capabilities of each. Our results show that unsupervised classifications perform better than object-based methods in classifying seagrass cover. We also found that the more sparsely vegetated of the two meadows studied was more accurately classified - it had lower root mean squared deviation (RMSD) between observed and classified coverage (9–9.5%) compared to a more densely vegetated meadow (RMSD 16–22%). Furthermore, we examine the potential to detect other biotic features, finding that lugworm mounds can be detected visually at coarser resolutions such as 43 mm pixel−1, whereas smaller features such as cockle shells within seagrass require finer grained data (<17 mm pixel−1)

A sensitivity analysis of the impact of rain on regional and global sea-air fluxes of CO2 (dataset)

Directories containing the results from several different runs of the FluxEngine software (see Shutler et al 2015 http://www.oceanflux-ghg.org). These directories are named according o the parameterisation used to derive the results within. 'SOCAT' or 'takahashi' refers to the source of pCO2 climatology used in the software. 'Nonlinear raink' or 'raink' refer to the parameterisation used for estimating rain enhancement of gas transfer velocity (Harrison et al 2012 & Ho et al. 2004 respectively). 'wetdep' and 'wet deposition' refer to the direct deposition of carbon to the surface ocean by rain. 'reference' data sets do not include the effects of rain. Within the directories, results are in netCDF files within sub-directories for year and month. Net Fluxes and summary statistics have been calculated and are provided as text files. The names are again according to the parameterisation used to derive them. More details are in the associated paper References: Shutler JD, Land PE, Piolle J-F, Woolf DK, Goddijn-Murphy L, Paul F, et al. FluxEngine: A flexible processing system for calculating atmosphere-ocean carbon dioxide gas fluxes and climatologies. Journal of Atmospheric and Oceanic Technology. 2015; (Early release). doi: 10.1175/JTECH-D-14-00204.1. Harrison EL, Vernon F, Ho DT, Reid MR, Orton P, McGillis WR. Nonlinear interaction between rain- and wind-induced air-water gas exchange. Journal of Geophysical Research. 2012;117(C03034). doi: 10.1029/2011JC007693. Ho DT, Zappa CJ, McGillis WR, Bliven LF, Ward B, Dacey JWH, et al. Influence of rain on air-sea gas exchange: Lessons from a model ocean. Journal of Geophysical Research. 2004;109(C08S18). doi: 10.1029/2003JC001806.The article associated with this dataset is available in ORE at http://hdl.handle.net/10871/22888Data sets calculated using the FluxEngine software to examine the sensitivity of global estimates of CO2 exchange between ocean and atmosphere to rainfall. These data contribute to the publication 'A sensitivity analysis of the impact of rain on regional and global sea-air fluxes of CO2', accepted for publication by PlosOneThis work was funded by the European Space Agency (ESA) Support to Science Element (STSE) through the OceanFlux Greenhouse Gases project (contract 4000104762/11/I-AM) and the OceanFlux Greenhouse Gases Evolution project (contract 4000112091/14/I-LG). http://due.esrin.esa.int/stse

Estimation of Ocean Surface Currents from Maximum Cross Correlation applied to GOCI geostationary satellite remote sensing data over the Tsushima (Korea) Straits

Attempts to automatically estimate surface current velocities from satellite-derived thermal or visible imagery face the limitations of data occlusion due to cloud cover, the complex evolution of features and the degradation of their surface signature. The Geostationary Ocean Color Imager (GOCI) provides a chance to reappraise such techniques due to its multi-year record of hourly high-resolution visible spectrum data. Here we present the results of applying a Maximum Cross Correlation (MCC) technique to GOCI data. Using a combination of simulated and real data we derive suitable processing parameters and examine the robustness of different satellite products, those being water-leaving radiance and chlorophyll concentration. These estimates of surface currents are evaluated using High Frequency (HF) radar systems located in the Tsushima (Korea) Strait. We show the performance of the MCC approach varies depending on the amount of missing data and the presence of strong optical contrasts. Using simulated data it was found that patchy cloud cover occupying 25% of the image pair reduces the number of vectors by 20% compared to using perfect images. Root mean square errors between the MCC and HF radar velocities are of the order of 20 cm s−1. Performance varies depending on the wavelength of the data with the blue-green products out-performing the red and near infra-red products. Application of MCC to GOCI chlorophyll data results in similar performance to radiances in the blue-green bands. The technique has been demonstrated using specific examples of an eddy feature and tidal induced features in the region. This article is protected by copyright. All rights reserved

The OceanFlux Greenhouse Gases methodology for deriving a sea surface climatology of CO2 fugacity in support of air–sea gas flux studies

Climatologies, or long-term averages, of essential climate variables are useful for evaluating models and providing a baseline for studying anomalies. The Surface Ocean CO2 Atlas (SOCAT) has made millions of global underway sea surface measurements of CO2 publicly available, all in a uniform format and presented as fugacity, fCO2. As fCO2 is highly sensitive to temperature, the measurements are only valid for the instantaneous sea surface temperature (SST) that is measured concurrently with the in-water CO2 measurement. To create a climatology of fCO2 data suitable for calculating air–sea CO2 fluxes, it is therefore desirable to calculate fCO2 valid for a more consistent and averaged SST. This paper presents the OceanFlux Greenhouse Gases methodology for creating such a climatology. We recomputed SOCAT's fCO2 values for their respective measurement month and year using monthly composite SST data on a 1° × 1° grid from satellite Earth observation and then extrapolated the resulting fCO2 values to reference year 2010. The data were then spatially interpolated onto a 1° × 1° grid of the global oceans to produce 12 monthly fCO2 distributions for 2010, including the prediction errors of fCO2 produced by the spatial interpolation technique. The partial pressure of CO2 (pCO2) is also provided for those who prefer to use pCO2. The CO2 concentration difference between ocean and atmosphere is the thermodynamic driving force of the air–sea CO2 flux, and hence the presented fCO2 distributions can be used in air–sea gas flux calculations together with climatologies of other climate variables

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points

Derivation of seawater &lt;i&gt;p&lt;/i&gt;CO&lt;sub&gt;2&lt;/sub&gt; from net community production identifies the South Atlantic Ocean as a CO&lt;sub&gt;2&lt;/sub&gt; source

A key step in assessing the global carbon budget is the determination of the partial pressure of CO2 in seawater (pCO2 (sw)). Spatially complete observational fields of pCO2 (sw) are routinely produced for regional and global ocean carbon budget assessments by extrapolating sparse in situ measurements of pCO2 (sw) using satellite observations. As part of this process, satellite chlorophyll a (Chl a) is often used as a proxy for the biological drawdown or release of CO2. Chl a does not, however, quantify carbon fixed through photosynthesis and then respired, which is determined by net community production (NCP). In this study, pCO2 (sw) over the South Atlantic Ocean is estimated using a feed forward neural network (FNN) scheme and either satellite-derived NCP, net primary production (NPP) or Chl a to compare which biological proxy produces the most accurate fields of pCO2 (sw) . Estimates of pCO2 (sw) using NCP, NPP or Chl a were similar, but NCP was more accurate for the Amazon Plume and upwelling regions, which were not fully reproduced when using Chl a or NPP. A perturbation analysis assessed the potential maximum reduction in pCO2 (sw) uncertainties that could be achieved by reducing the uncertainties in the satellite biological parameters. This illustrated further improvement using NCP compared to NPP or Chl a. Using NCP to estimate pCO2 (sw) showed that the South Atlantic Ocean is a CO2 source, whereas if no biological parameters are used in the FNN (following existing annual carbon assessments), this region appears to be a sink for CO2. These results highlight that using NCP improved the accuracy of estimating pCO2 (sw) and changes the South Atlantic Ocean from a CO2 sink to a source. Reducing the uncertainties in NCP derived from satellite parameters will ultimately improve our understanding and confidence in quantification of the global ocean as a CO2 sink

A generic approach for the development of short-term predictions of Escherichia coli and biotoxins in shellfish

Microbiological contamination or elevated marine biotoxin concentrations within shellfish can result in temporary closure of shellfish aquaculture harvesting, leading to financial loss for the aquaculture business and a potential reduction in consumer confidence in shellfish products. We present a method for predicting short-term variations in shellfish concentrations of Escherichia coli and biotoxin (okadaic acid and its derivates dinophysistoxins and pectenotoxins). The approach was evaluated for 2 contrasting shellfish harvesting areas. Through a meta-data analysis and using environmental data in situ, satellite observations and meteorological nowcasts and forecasts), key environmental drivers were identified and used to develop models to predict E. coli and biotoxin concentrations within shellfish. Models were trained and evaluated using independent datasets, and the best models were identified based on the model exhibiting the lowest root mean square error. The best biotoxin model was able to provide 1 wk forecasts with an accuracy of 86%, a 0% false positive rate and a 0% false discovery rate (n = 78 observations) when used to predict the closure of shellfish beds due to biotoxin. The best E. coli models were used to predict the European hygiene classification of the shellfish beds to an accuracy of 99% (n = 107 observations) and 98% (n = 63 observations) for a bay (St Austell Bay) and an estuary (Turnaware Bar), respectively. This generic approach enables high accuracy short-term farm-specific forecasts, based on readily accessible environmental data and observations

Assessing risks and mitigating impacts of harmful algal blooms on mariculture and marine fisheries

Aquaculture is the fastest growing food sector globally and protein provisioning from aquaculture now exceeds that from wild capture fisheries. There is clear potential for the further expansion of marine aquaculture (mariculture), but there are associated risks. Some naturally occurring algae can proliferate under certain environmental conditions, causing deoxygenation of seawater, or releasing toxic compounds (phycotoxins), which can harm wild and cultured finfish and shellfish, and also human consumers. The impacts of these so‐called harmful algal blooms (HABs) amount to approximately 8 $billion/yr globally, due to mass mortalities in finfish, harvesting bans preventing the sale of shellfish that have accumulated unsafe levels of HAB phycotoxins and unavoided human health costs. Here, we provide a critical review and analysis of HAB impacts on mariculture (and wild capture fisheries) and recommend research to identify ways to minimise their impacts to the industry. We examine causal factors for HAB development in inshore versus offshore locations and consider how mariculture itself, in its various forms, may exacerbate or mitigate HAB risk. From a management perspective, there is considerable scope for strategic siting of offshore mariculture and holistic Environmental Approaches for Aquaculture, such as offsetting nutrient outputs from finfish farming, via the co‐location of extractive shellfish and macroalgae. Such pre‐emptive, ecosystem‐based approaches are preferable to reactive physical, chemical or microbiological control measures aiming to remove or neutralise HABs and their phycotxins. To facilitate mariculture expansion and long‐term sustainability, it is also essential to evaluate HAB risk in conjunction with climate change