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.

The impacts of past land-use on the ecology of an ancient woodland in south-west Ireland

A multi-disciplinary study was conducted to compare stands of ancient and secondary origin within a single wood, the Gearagh woodland, County Cork. These sites were compared with adjacent areas of grassland, which provided a reference for the former land-use (pasture) of the secondary woodland. A historical study confirmed that while the core of the Gearagh has been subject to minimal human interference, other sections have been cleared in the past for agricultural purposes. Investigations into soil structure and composition showed that soil properties in these secondary woodland areas were significantly altered by this past woodland clearance and conversion to agriculture, while the soil of the ancient woodland showed little signs of disturbance. The vegetation community also differed between the two woodland areas, partly due to altered environmental conditions. Many of the ancient woodland plant species were unable to form a persistent seed bank, while there was increased representation of species associated with more open-habitat conditions in the seed bank of the secondary woodland. While germination of woodland species was low in all sites, overall, seeds tended to germinate more successfully in the ancient woodland. The ancient woodland also provided a suitable habitat for many soil and ground detritivores, most notably enchytraeids, although earthworms were not abundant. Past agricultural use, however, changed the decomposer community considerably, with increased representation of earthworm species and a decline in the abundance of enchytraeids in the secondary stands. In conclusion, the legacies of historical agricultural activities can continue to significantly affect the structure and composition of present-day woodlands so that they may differ considerably from undisturbed ancient woodland stands, even within the same woodland. A greater understanding of the origin, development and ecological functioning of ancient woodlands should aid in determining future conservation and management requirements.

Biomethane production from grass silage: laboratory assessment to maximise yields

On-farm biogas production is typically associated with forage maize as the biomass source. Digesters are designed and operated with the focus of optimising the conditions for this feedstock. Thus, such systems may not be ideally suited to the digestion of grass. Ireland has ca. 3.85 million ha of grassland. Annual excess grass, surplus to livestock requirements, could potentially fuel an anaerobic digestion industry. Biomethane associated with biomass from 1.1 % of grassland in Ireland, could potentially generate over 10 % renewable energy supply in transport. This study aims to identify and optimise technologies for the production of biomethane from grass silage. Mono-digestion of grass silage and co-digestion with slurry, as would occur on Irish farms, is investigated in laboratory trials. Grass silage was shown to have 7 times greater methane potential than dairy slurry on a fresh weight basis (107 m3 t-1 v 16 m3 t-1). However, comprehensive trace element profiles indicated that cobalt, iron and nickel are deficient in mono-digestion of grass silage at a high organic loading rate (OLR) of 4.0 kg VS m-3 d-1. The addition of a slurry co-substrate was beneficial due to its wealth of essential trace elements. To stimulate hydrolysis of high lignocellulose grass silage, particle size reduction (physical) and rumen fluid addition (biological) were investigated. In a continuous trial, digestion of grass silage of <1 cm particle size achieved a specific methane yield of 371 L CH4 kg-1 VS when coupled with rumen fluid addition. The concept of demand driven biogas was also examined in a two-phase digestion system (leaching with UASB). When demand for electricity is low it is recommended to disconnect the UASB from the system and recirculate rumen fluid to increase volatile fatty acid (VFA) and soluble chemical oxygen demand (SCOD) production whilst minimising volatile solids (VS) destruction. At times of high demand for electricity, connection of the UASB increases the destruction of volatiles and associated biogas production. The above experiments are intended to assess a range of biogas production options from grass silage with a specific focus on maximising methane yields and provide a guideline for feasible design and operation of on-farm digesters in Ireland.