Macroscale factors affecting diatom abundance: a synergistic use of Continuous Plankton Recorder and satellite remote sensing data

Diatoms exist in almost every aquatic regime; they are responsible for 20% of global carbon fixation and 25% of global primary production, and are regarded as a key food for copepods, which are subsequently consumed by larger predators such as fish and marine mammals. A decreasing abundance and a vulnerability to climatic change in the North Atlantic Ocean have been reported in the literature. In the present work, a data matrix composed of concurrent satellite remote sensing and Continuous Plankton Recorder (CPR) in situ measurements was collated for the same spatial and temporal coverage in the Northeast Atlantic. Artificial neural networks (ANNs) were applied to recognize and learn the complex non-monotonic and non-linear relationships between diatom abundance and spatiotemporal environmental factors. Because of their ability to mimic non-linear systems, ANNs proved far more effective in modelling the diatom distribution in the marine ecosystem. The results of this study reveal that diatoms have a regular seasonal cycle, with their abundance most strongly influenced by sea surface temperature (SST) and light intensity. The models indicate that extreme positive SSTs decrease diatom abundances regardless of other climatic conditions. These results provide information on the ecology of diatoms that may advance our understanding of the potential response of diatoms to climatic change.

Remote sensing of assimilation number for marine phytoplankton

Estimating primary production at large spatial scales is key to our understanding of the global carbon cycle. Algorithms to estimate primary production are well established and have been used in many studies with success. One of the key parameters in these algorithms is the chlorophyll-normalised production rate under light saturation (referred to as the light saturation parameter or the assimilation number). It is known to depend on temperature, light history and nutrient conditions, but assigning a magnitude to it at particular space-time points is difficult. In this paper, we explore two models to estimate the assimilation number at the global scale from remotely-sensed data that combine methods to estimate the carbon-to-chlorophyll ratio and the maximum growth rate of phytoplankton. The inputs to the algorithms are the surface concentration of chlorophyll, seasurface temperature, photosynthetically-active radiation af the surface of the sea, sea surface nutrient concentration and mixed-layer depth. A large database of in situ estimates of the assimilation number is used to develop the models and provide elements of validation. The comparisons with in situ observations are promising and global maps of assimilation number are produced.

Volcanic ash supply to the surface ocean – remote sensing of biological responses and their wider biogeochemical significance

Transient micronutrient enrichment of the surface ocean can enhance phytoplankton growth rates and alter microbial community structure with an ensuing spectrum of biogeochemical feedbacks. Strong phytoplankton responses to micronutrients supplied by volcanic ash have been reported recently. Here we: (i) synthesize findings from these recent studies; (ii) report the results of a new remote sensing study of ash fertilization; and (iii) calculate theoretical bounds of ash-fertilized carbon export. Our synthesis highlights that phytoplankton responses to ash do not always simply mimic that of iron amendment; the exact mechanisms for this are likely biogeochemically important but are not yet well understood. Inherent optical properties of ash-loaded seawater suggest rhyolitic ash biases routine satellite chlorophyll-a estimation upwards by more than an order of magnitude for waters with <0.1 mg chlorophyll-a m-3, and less than a factor of 2 for systems with >0.5 mg chlorophyll-a m-3. For this reason post-ash-deposition chlorophyll-a changes in oligotrophic waters detected via standard Case 1 (open ocean) algorithms should be interpreted with caution. Remote sensing analysis of historic events with a bias less than a factor of 2 provided limited stand-alone evidence for ash-fertilization. Confounding factors were poor coverage, incoherent ash dispersal, and ambiguity ascribing biomass changes to ash supply over other potential drivers. Using current estimates of iron release and carbon export efficiencies, uncertainty bounds of ash-fertilized carbon export for 3 events are presented. Patagonian iron supply to the Southern Ocean from volcanic eruptions is less than that of windblown dust on thousand year timescales but can dominate supply at shorter timescales. Reducing uncertainties in remote sensing of phytoplankton response and nutrient release from ash are avenues for enabling assessment of the oceanic response to large-scale transient nutrient enrichment.

Uncertainty estimates of remote sensing reflectance derived from comparison of ocean color satellite data sets

Assigning uncertainty to ocean-color satellite products is a requirement to allow informed use of these data. Here, uncertainty estimates are derived using the comparison on a 12th-degree grid of coincident daily records of the remote-sensing reflectance RRS obtained with the same processing chain from three satellite missions, MERIS, MODIS and SeaWiFS. The approach is spatially resolved and produces σ, the part of the RRS uncertainty budget associated with random effects. The global average of σ decreases with wavelength from approximately 0.7– 0.9 10−3 sr−1 at 412 nm to 0.05–0.1 10−3 sr−1 at the red band, with uncertainties on σ evaluated as 20–30% between 412 and 555 nm, and 30–40% at 670 nm. The distribution of σ shows a restricted spatial variability and small variations with season, which makes the multi-annual global distribution of σ an estimate applicable to all retrievals of the considered missions. The comparison of σ with other uncertainty estimates derived from field data or with the support of algorithms provides a consistent picture. When translated in relative terms, and assuming a relatively low bias, the distribution of σ suggests that the objective of a 5% uncertainty is fulfilled between 412 and 490 nm for oligotrophic waters (chlorophyll-a concentration below 0.1 mg m−3). This study also provides comparison statistics. Spectrally, the mean absolute relative difference between RRS from different missions shows a characteristic U-shape with both ends at blue and red wavelengths inversely related to the amplitude of RRS. On average and for the considered data sets, SeaWiFS RRS tend to be slightly higher than MODIS RRS, which in turn appear higher than MERIS RRS. Biases between mission-specific RRS may exhibit a seasonal dependence, particularly in the subtropical belt.

THz Frequency Selective Surface Filters for Earth Observation Remote Sensing Instruments

The purpose of this paper is to review recent developments in the design and fabrication of Frequency Selective Surfaces (FSS) which operate above 300 GHz. These structures act as free space electromagnetic filters and as such provide passive remote sensing instruments with multispectral capability by separating the scene radiation into separate frequency channels. Significant advances in computational electromagnetics, precision micromachining technology and metrology have been employed to create state of the art FSS which enable high sensitivity receivers to detect weak molecular emissions at THz wavelengths. This new class of quasi-optical filter exhibits an insertion loss

Remote Sensing and GIScience in Geomorphological Mapping

The rapid proliferation of remote sensing and geographic information systems (GIS) into geomorphologic mapping has increased the objectivity and efficiency of landform segmentation, measurement, and classification. The near ubiquitous presence of Earth-observing satellites provides an array of perspectives to visualize the biophysical characteristics of landscapes, access inhospitable terrain on a predictable schedule, and study landscape processes when conditions are hazardous. GIS technology has altered the analysis, visualization, and dissemination of landform data due to the shared theoretical concepts that are fundamental to geomorphology and GIScience. The authors review geospatial technology applications in landform mapping (including emerging issues) within glacial, volcanic, landslide, and fluvial research.

Combining sampling, profiling, remote sensing and modelling to efficiently monitoring estuarine and coastal water quality

Monitoring of coastal and estuarine water quality has been traditionally performed by sampling with subsequent laboratory analysis. This has the disadvantages of low spatial and temporal resolution and high cost. In the last decades two alternative techniques have emerged to overcome this drawback: profiling and remote sensing. Profiling using multi-parameter sensors is now in a commercial stage. It can be used, tied to a boat, to obtain a quick “picture” of the system. The spatial resolution thus increases from single points to a line coincident with the boat track. The temporal resolution however remains unchanged since campaigns and resources involved are basically the same. The need for laboratory analysis was reduced but not eliminated because parameters like nutrients, microbiology or metals are still difficult to obtain with sensors and validation measurements are still needed. In the last years the improvement in satellite resolution has enabled its use for coastal and estuarine water monitoring. Although spatial coverage and resolution of satellite images in the present is already suitable to coastal and estuarine monitoring, temporal resolution is naturally limited to satellite passages and cloud cover. With this panorama the best approach to water monitoring is to integrate and combine data from all these sources. The natural tools to perform this integration are numerical models. Models benefit from the different sources of data to obtain a better calibration. After calibration they can be used to extend spatially and temporally the methods resolution. In Algarve (South of Portugal) a monitoring effort using this approach is being undertaken. The monitoring effort comprises five different locations including coastal waters, estuaries and coastal lagoons. The objective is to establish the base line situation to evaluate the impact of Waste Water Treatment Plants design and retrofitting. The field campaigns include monthly synoptic profiling, using an YSI 6600 multi-parameter system, laboratory analysis and fixed stations. The remote sensing uses ENVISAT\MERIS Level 2 Full Resolution data. This data is combined and used with the MOHID modelling system to obtain an integrate description of the systems. The results show the limitations of each method and the ability of the modelling system to integrate the results and to produce a comprehensive picture of the system.

Analyzing Geospatial patterns of syrian refugee flows in southeastern Turkey by use of remote sensing and complementary data

Crisis-affected communities and global organizations for international aid are becoming increasingly digital as consequence geotechnology popularity. Humanitarian sector changed in profound ways by adopting new technical approach to obtain information from area with difficult geographical or political access. Since 2011, turkey is hosting a growing number of Syrian refugees along southeastern region. Turkish policy of hosting them in camps and the difficulty created by governors to international aid group expeditions to get information, made such international organizations to investigate and adopt other approach in order to obtain information needed. They intensified its remote sensing approach. However, the majority of studies used very high-resolution satellite imagery (VHRSI). The study area is extensive and the temporal resolution of VHRSI is low, besides it is infeasible only using these sensors as unique approach for the whole area. The focus of this research, aims to investigate the potentialities of mid-resolution imagery (here only Landsat) to obtain information from region in crisis (here, southeastern Turkey) through a new web-based platform called Google Earth Engine (GEE). Hereby it is also intended to verify GEE currently reliability once the Application Programming Interface (API) is still in beta version. The finds here shows that the basic functions are trustworthy. Results pointed out that Landsat can recognize change in the spectral resolution clearly only for the first settlement. The ongoing modifications vary for each case. Overall, Landsat demonstrated high limitations, but need more investigations and may be used, with restriction, as a support of VHRSI.

UAV - based imagery processing using structure from motion and remote sensing technology

With the recent advances in technology and miniaturization of devices such as GPS or IMU, Unmanned Aerial Vehicles became a feasible platform for a Remote Sensing applications. The use of UAVs compared to the conventional aerial platforms provides a set of advantages such as higher spatial resolution of the derived products. UAV - based imagery obtained by a user grade cameras introduces a set of problems which have to be solved, e. g. rotational or angular differences or unknown or insufficiently precise IO and EO camera parameters. In this work, UAV - based imagery of RGB and CIR type was processed using two different workflows based on PhotoScan and VisualSfM software solutions resulting in the DSM and orthophoto products. Feature detection and matching parameters influence on the result quality as well as a processing time was examined and the optimal parameter setup was presented. Products of the both workflows were compared in terms of a quality and a spatial accuracy. Both workflows were compared by presenting the processing times and quality of the results. Finally, the obtained products were used in order to demonstrate vegetation classification. Contribution of the IHS transformations was examined with respect to the classification accuracy.

Using GPS, GIS & remote sensing to understand Niagara Terroir : Pinot noir in the Four Mile Creek & St. David's Bench sub-appellations

The relationships between vine water status, soil texture, and vine size were observed in four Niagara, Ontario Pinot noir vineyards in 2008 and 2009. The vineyards were divided into water status zones using geographic information systems (GIS) software to map the seasonal mean midday leaf water potential (,P), and dormant pruning shoot weights following the 2008 season. Fruit was harvested from all sentinel vines, bulked by water status zones and made into wine. Sensory analysis included a multidimensional sorting (MDS) task and descriptive analysis (DA) of the 2008 wines. Airborne multispectral images, with a spatial resolution of 38 cm, were captured four times in 2008 and three times in 2009, with the final flights around veraison. A semi-automatic process was developed to extract NDVI from the images, and a masking procedure was identified to create a vine-only NDVI image. 2008 and 2009 were cooler and wetter than mean years, and the range of water status zones was narrow. Yield per vine, vine size, anthocyanins and phenols were the least consistent variables. Divided by water status or vine size, there were no variables with differences between zones in all four vineyards in either year. Wines were not different between water status zones in any chemical analysis, and HPLC revealed that there were no differences in individual anthocyanins or phenolic compounds between water status zones within the vineyard sites. There were some notable correlations between vineyard and grape composition variables, and spatial trends were observed to be qualitatively related for many of the variables. The MDS task revealed that wines from each vineyard were more affected by random fermentation effects than water status effects. This was confirmed by the DA; there were no differences between wines from the water status zones within vineyard sites for any attribute. Remotely sensed NDVI (normalized difference vegetation index) correlated reasonably well with a number of grape composition variables, as well as soil type. Resampling to a lower spatial resolution did not appreciably affect the strength of correlations, and corresponded to the information contained in the masked images, while maintaining the range of values of NDVI. This study showed that in cool climates, there is the potential for using precision viticulture techniques to understand the variability in vineyards, but the variable weather presents a challenge for understanding the driving forces of that variability.

Using GPS, GIS & remote sensing to understand Niagara Terroir

The focus of this study was to detennine whether soil texture and composition variables were related to vine water status and both yield components and grape composition, and whether multispectral high definition airborne imagery could be used to segregate zones in vineyards to target fruit of highest quality for premium winemaking. The study took place on a 10-ha commercial Riesling vineyard at Thirty Bench Winemakers, in Beamsville (Ontario). Results showed that Soil moisture and leaf'l' were temporally stable and related to berry composition and remotely-sensed data. Remote-sensing, through the calculation of vegetation indices, was particularly useful to predict vine vigor, yield, fruit maturity as well as berry monoterpene concentration; it could also clearly assist in making wines that are more representative ofthe cultivar used, and also wines that are a reflection of a specific terroir, since calculated vegetation indices were highly correlated to typical Riesling.

Drought Climatology of Indo-Gangetic Region of India Using Remote Sensing and Crop Growth Simulation Models

Department of Atmospheric Sciences, Cochin University of Science and Technology