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Atlas of the Irish rural landscape
\"The second edition of Atlas of the Irish Rural Landscape is a magnificently illustrated, beautifully written, and thoroughly updated introduction to the hidden riches of the Irish landscape. Topics include archaeology, field and settlement patterns, houses, demesnes, villages and small towns, monuments, woodland, bogs, roads, canals, and a host of other features. The Atlas combines superbly chosen illustrations and cartography with a text amenable to a general reader. Hundreds of maps, diagrams, photographs, and paintings present accessible information suitable for any school, college, or home. New content in the contemporary section takes into account the Celtic Tiger and explores six fresh case studies - Tory Island (Donegal), the Wicklow Uplands, Inistiogue (County Kilkenny), Aughris (County Sligo), Clonfert (County Galway), and Point Lance in Newfoundland. This second edition of the award-winning Atlas of the Irish Rural Landscape continues to increase the visibility of the landscape within national heritage while establishing a proper basis for conservation and planning\"--Publisher description.
Book
Persistence in cultural landscapes: a pan-European analysis
Persistence in landscapes is determined by land cover types that remain unchanged over a certain period of time. Mapping and analyzing landscape persistence brings a new view to landscape-ecological research. Insights on processes, factors, or driving forces stabilizing landscapes can contribute to protect and manage valuable landscapes in a rapidly changing world. We analyzed and compared landscape persistence in Europe (EU27 plus Switzerland), as well as in six case studies using historical maps which were harmonized regarding their thematic, spatial, and temporal resolution. The spatial resolution was adjusted to the resolution of the least detailed case study map. To get a thematically harmonized map set, the legend classes were assigned to a common map legend. For enabling a thorough comparison, a persistence index has been developed, taking onto account differences in size of the study regions and in timing of the available historical maps. The persistence index is expressed in number of years that would be needed for the complete transformation of the land cover in a considered area if the transformation would occur at the same rate as it occurred in the considered time period. For the whole area of Europe, the persistence index is 198 years. However, change is not happening uniformly across Europe: the persistence hotspots are located in the Alps, north and south Italy, north Ireland, Sweden, south Finland, Romania, and UK where the persistence index is higher than 500 years. There is a variation between the persistence of different land cover types as well. Natural rocks, quarries, and beaches are fully persistent. Settlements and forests are highly persistent. Least persistent are grasslands and croplands, except olive groves.
Journal Article
Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies
Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.
Journal Article