Deer forests, game shooting and landed estates in the South West of Ireland, 1840 - 1970

This thesis is concentrated on the historical aspects of the elitist field sports of deer stalking and game shooting, as practiced by four Irish landed ascendancy families in the south west of Ireland. Four great estates were selected for study. Two of these were, by Irish standards, very large: the Kenmare estate of over 136,000 acres in the ownership of the Roman Catholic Earls of Kenmare, and the Herbert estate of over 44,000 acres in the ownership of the Protestant Herbert family. The other two were, in relative terms, small: the Grehan estate of c.7,500 acres in the ownership of the Roman Catholic Grehan family, and the Godfrey estate of c.5,000 acres, in the ownership of the Protestant Barons Godfrey. This mixture of contrasting estate size, owner's religions, nobleman, minor aristocrat and untitled gentry should, it is argued, yield a diversity of the field sports and lifestyles of their owners, and go some way to assess the contributions, good or bad, they have bequeathed to modern Ireland. Equally, it should help in assessing what importance, if any, applied to hunting. In this context, hunting is here used in its broadest meaning, and includes deer stalking and game shooting, as well as hunting with dogs and hounds on foot and horseback. Where a specific type of hunting is involved, it is so described; for example, fox hunting, stag hunting, hare hunting. Similarly, the term game is sometimes used in sporting literature to encompass all species of quarry killed, and can include deer, ground game (hares and rabbits), waterfowl, and various species of game birds. Where it refers to specific species, these are so described; for example grouse, pheasants, woodcork, wild duck, etc. Since two of these estates - the Kenmare and Herbert - each created a deer forest, unique in mid-19th century Ireland, they form the core study estates; the two smaller estates serve as comparative studies. And, equally unique, as these two larger estates held the only remnant population of native Irish red deer, the survival of that herd itself forms a concomitant core area of analysis. The numerary descriptions applied to these animals in popular literature are critically reassessed against prime source historical evidence, as are the so-called deer forest 'clearances'. The core period, 1840 to 1970, is selected as the seminal period, spanning 130 years, from the creation of the deer forests to when a fundamental change in policy and administration was introduced by the state. Comparison is made with similar estates elsewhere, in Britain and especially in Scotland. Their influence on the Irish methods and style of hunting is historically examined.

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.