This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands.

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

This paper focuses on the historic and cultural dimension of landscape, in relation to the holistic and dynamic approach today required in landscape research. In this direction, the Authors investigate the present role played by historical rural landscapes in relation to their multifaceted character and function. In recent years there has been an ever-growing appreciation of their cultural value, depending on the differential speed of environmental change (relatively high) and people’s adaptation to it (relatively slow), as a matter of compensation. Although reference is often made to the global and the European contexts, Italy is given special attention, since the long history of civilization known by its rural landscapes makes them particularly worthy of consideration and offers a wide variety of examples assuming a more general interest. The main changes occurred in the Italian rural landscapes over the last two centuries are described by referring to their main causes and to the parallel change observed in the urban-rural dialectic. The notion of “cultural landscape”, as it emerges from the international debate and documents, is applied to historic rural landscapes, according to a varied range of conditions and characteristics. Knowledge, identification, interpretation and characterization are fundamental actions to define sustainable landscape management strategies. These last should be diversified according to landscape character, functionality, integrity and obsolescence, not being limited to outstanding landscapes only but considering all rural landscapes as heritage. To this end, heritage strategies and policies must go together with agricultural sectorial policies, since agriculture and farmers are the major actors of rural landscape protection and transformation. EU Common Agricultural Policy is considered by focusing on its effect on rural landscape and proposals coming from the heritage experts for its future implementation are examined. Emerging strategic measures and action criteria are singled out and presented. In any case, to protect historic rural landscapes, balanced change-dynamics and development vitality should be strengthened if present, introduced if not, so as to assure resilience. Neo-rurality today expresses the search for sustainable lifestyles, green development models and a better quality of life, implicitly offering new opportunities for the revitalisation of historic rural landscapes. Finally, a holistic approach and multidisciplinary cooperation are needed to allow for an effective synthesis of the many cultural visions, which today concern the theme of landscape.

Abstract. The most recent and significant transformations of European landscapes have occurred as a consequence of a series of diffused, varied and often connected phenomena: urban growth and sprawl, agricultural intensification in the most suitable areas and agricultural abandonment in marginal areas. These phenomena can affect dramatically ecosystems' structure and functioning, since certain modifications cause landscape fragmentation while others tend to increase homogeneity. Thus, a thorough comprehension of the evolution trends of landscapes, in particular those linked to urban-rural relations, is crucial for a sustainable landscape planning.In this framework, the main objectives of the present paper are: (a) to investigate Land Use/Land Cover (LULC) transformations and dynamics that occurred over the period in the municipality of Serra San Bruno (Calabria, Italy), an area particularly representative of the Mediterranean mountainous landscape; (b) to compare the settlement growth with the urban planning tools in charge in the study area; (c) to examine the relationship between urbanrural gradient, landscape metrics, demographic and physical variables; (d) to investigate the evolution of urban-rural gradient composition and configuration along significant axes of landscape changes.Data with a high level of detail (minimum mapping unit 0.2 ha) were obtained through the digitisation of historical aerial photographs and digital orthophotos identifying LULC classes according to the Corine Land Cover legend. The investigated period was divided into four significant time intervals, which were specifically analysed to detect LULC changes.Differently from previous studies, in the present research the spatio-temporal analysis of urban-rural gradient was performed through three subsequent steps: (1) kernel density analysis of settlements; (2) analysis of landscape structure by means of metrics calculated using a moving window method; (3) analysis of composition and configuration of the urbanrural gradient within three landscape profiles located along significant axes of LULC change.The use of thematic overlays and transition matrices enabled a precise identification of the LULC changes that had taken place over the examined period. As a result, a detailed description and mapping of the landscape dynamics were obtained. Furthermore, landscape profiling technique, using continuous data, allowed an innovative and valuable approach for analysing and interpreting urban-rural gradient structure over space and time.

This paper presents the results of a change‐detection study of the historical agricultural terraced landscape in “Costa Viola” (Calabria, South Italy). During the last century, because of the loss of economic competitiveness, it has undergone progressive abandonment, followed by landscape degradation. Taking into consideration the very steep slopes of Costa Viola and the need to analyse with high precision the historical evolution of the terraced landscape, research methods were implemented coupling advanced geomatic techniques with in situ detailed surveys. Based on historical aerial photographs, orthophotos, and numeric cartography, we analysed the land use/land cover change in the period 1955–2014 using photogrammetric and geoprocessing techniques, focusing particularly on trajectories in agricultural terraces. Area covered by active terraces decreased dramatically between 1955 and 2014, from 813.25 to 118.79 ha (−85.4%). The implemented spatial database was built in a free open‐source software taking into consideration spatial accuracies and completeness. Spatial comparison among land use/land cover maps was carried out using a postclassification comparison technique that can provide complete cross‐tabulation matrices. These data were compared with socio‐economic statistics concerning demography and trends of farms with vineyards. The evolutionary dynamics of the active agricultural terraces were also analysed trough the definition of 6 types of spatio‐temporal patterns recognised in the analysed period. These methods allowed to highlight the ongoing dynamics of abandonment of agricultural terraces in relation to their main causes and effects. Although tailored for the specific case study, they can be applied to many other terraced agricultural landscapes presenting similar characteristics and problems.