A landscape classification map of Ireland and its potential use in national land use monitoring. (1st July 2021) - Carlier, J.; Doyle, M.; Finn, J.A.; Ó hUallacháin, D.; Moran, J.
Abstract: This study presents a novel landscape classification map of the Republic of Ireland and is the first to identify broad landscape classes by incorporating physiographic and land cover data. The landscape classification responds to commitments to identify and classify the Irish landscape as a signatory to the European Landscape Convention. The methodology applied a series of clustering iterations to determine an objective multivariate classification of physiographic landscape units and land cover datasets. The classification results determined nine statistically significant landscape classes and the development of a landscape classification map at a national scale. A statistical breakdown of land cover area and diversity of each class was interpreted, and a comparison was extended using independent descriptive variables including farmland use intensity, elevation, and dominant soil type. Each class depicts unique spatial and composition characteristics, from coastal, lowland and elevated, to distinct and dominating land cover types, further explained by the descriptive variables. The significance of individual classes and success of the classification is discussed with particular reference to the wider applicability of the map. The transferability of the methodology to other existing physiographic maps and environmental datasets to generate new landscape classifications is also considered. This novel work facilitates the development of a strategic framework to efficiently monitor, compare and analyse ecological and other land use data that is spatially representative of the distribution and extent of land cover in the Irish countryside. Highlights: We present a unique landscape classification and map for the Republic of Ireland. A multivariate classification incorporated physiographic and land cover data. Nine new landscape classes are determined, described and compared. The use of the classification is discussed from a strategic and policy perspective. The methodology applied is highly replicable with other similar, existing datasets.