Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

Development of a toolbox for the reduction of hordeins in barley malt beers

Gluten sensitive consumers and people suffering from coeliac disease account for up to 6% of the general population (Catassi et al., 2013). These consumers must avoid foods which contain gluten and related proteins found in wheat, rye or barley. Beer is produced from barley malt and therefore contains hordeins, (gluten like proteins). Beers labelled as gluten-free must contain below 10 mg/kg hordeins (10 mg/kg hordeins = 20 mg/kg gluten under current regulations) to be considered safe for gluten sensitive consumers. Currently there are a limited number of methods available for reducing beer hordeins, the studies outlined in this thesis provide a range of tools for the beverage industry to reduce the hordein content of beer It is well known, that during malting and brewing hordeins are reduced, but they still remain in beer at levels above 10 mg/kg. During malting, hordeins are broken down to form new proteins in the growing plant. Model malting and brewing systems were developed and used to test, how the modification of the malting process could be used to reduce beer hordeins. It was shown, that by using a controlled malting and brewing regime, a range of barley cultivars produced beer with significant differences in levels of hordeins. Beer hordeins ranged from 10 mg/kg to 60 mg/kg. Another study revealed that when malting was prolonged, to maximise breakdown of proteins, beer hordeins can be reduced by up to 44%. The natural breakdown of hordein during malting enhanced in a further study, when a protease was added to support the hordein degradation during steeping and germination. The enzyme addition resulted in a 46% reduction in beer hordeins 2 when compared to the control. All of the malt treatments had little or no impact on malt quality. The hordein levels can also be reduced during the beer stabilisation process. Levels of beer hordein were tested after stabilisation using two different concentrations of silica gel and tannic acid. Silica gel was very effective in reducing beer hordeins, 90% of beer hordeins were removed compared to the control beer. Beer hordeins could be reduced to below 10 mg/kg and the beer qualities such as foam, colour and flavour were not affected. Tannic acid also reduced beer hordein by up to 90%, but it reduced foam stability and affected beer flavours. A further study described treatment of beer with microbial transglutaminase (mTG), to create bonds between hordein proteins, which increased particle size and allowed removal during filtration. The addition of the mTG led to a reduction of the beer hordein by up to 96% in beer, and the impact on the resulting beer quality was minimal. These studies provide the industry with a toolbox of methods leading to the reduction of hordein in the final beer without negatively affecting beer quality.

Evaluation of physicochemical and glycaemic properties of commercial plant-based milk substitutes

The market for plant-based dairy-type products is growing as consumers replace bovine milk in their diet, for medical reasons or as a lifestyle choice. A screening of 17 different commercial plant-based milk substitutes based on different cereals, nuts and legumes was performed, including the evaluation of physicochemical and glycaemic properties. Half of the analysed samples had low or no protein contents (<0.5 %). Only samples based on soya showed considerable high protein contents, matching the value of cow’s milk (3.7 %). An in-vitro method was used to predict the glycaemic index. In general, the glycaemic index values ranged from 47 for bovine milk to 64 (almond-based) and up to 100 for rice-based samples. Most of the plant-based milk substitutes were highly unstable with separation rates up to 54.39 %/h. This study demonstrated that nutritional and physicochemical properties of plant-based milk substitutes are strongly dependent on the plant source, processing and fortification. Most products showed low nutritional qualities. Therefore, consumer awareness is important when plant-based milk substitutes are used as an alternative to cow’s milk in the diet.