Quantitative Landscape Analysis Using Earth-Observation Data: An Example from Chania, Crete, Greece

Pursuing sustainability in a challenging world and under the influence of numerous natural and anthropogenic drivers of change presents one of the major global concerns. The transition towards a more sustainable development requires a harmonious balance between human well-being and environmental management. The concept of landscape is at the core of such a process. Hence, evaluating the different aspects of the landscape and their components is crucial for policy making, planning and management. In fact, landscape quality assessment has become a special focus of interest, especially with the directives of the European Landscape Convention. This research work aims to analyze the rural landscape of the Chania prefecture, West Crete, Greece, taking into account its multifold dimensions. The analysis was carried out using a series of quantitative spatial indicators. Consecutively, structural (mean patch area, contagion index, edge density and percentage of landscape occupied by a class of the highest share), ecological (density of ecological barriers, Modified Shannon diversity index), visual (share of positive land-cover forms, share of negative land-cover forms, form and color disharmony index, shape disharmony index) and cultural indices (historical monuments index) were estimated and analyzed in a GIS environment. The overall methodology incorporated different land-use/land-cover data (multitemporal Corine data and land use derived from the classification of Earth-observation (EO) data). The historical and current analysis of the landscape within Chania revealed quite high structural and visual values. The ecological dimension is rather stable, with a potential decrease by the year 2045. Additionally, the structural dimension seemed to be sensitive to the spatial resolution of the data source. The spatial extent, at which the landscape is evaluated, seemed to impact the landscape's ecological, visual and cultural values.