Historical maps constitute an essential information for investigating the ecological and landscape features of a region over time. The integration of heritage maps in GIS models requires their digitalization and classification. This paper presents a semi-automatic procedure for the digitalization of heritage maps and the successive filtering of undesirable features such as text, symbols and boundary lines. The digitalization step is carried out using Object-based Image Analysis (OBIA) in GRASS GIS and R, combining image segmentation and machine-learning classification. The filtering step is performed by two GRASS GIS modules developed during this study and made available as GRASS GIS add-ons. The first module evaluates the size of the filter window needed for the removal of text, symbols and lines; the second module replaces the values of pixels of the category to be removed with values of the surrounding pixels. The procedure has been tested on three maps with different characteristics, the “Historical Cadaster Map for the Province of Trento” (1859), the “Italian Kingdom Forest Map” (1926) and the “Map of the potential limit of the forest in Trentino” (1992), with an average classification accuracy of 97%. These results improve the performance of classification of heritage maps compared to more classical methods, making the proposed procedure that can be applied to heterogeneous sets of maps, a viable approach.

The recovery of the 1936 Italian Kingdom Forest Map (IKFM) (276 sheets 1:100,000) is described. It is a unique document that describes the forest extent and species composition for the whole of Italy. The original document is available in paper format using a datum and a map projection no longer in use, therefore it is not suitable for analysis using current digital tools. Therefore, this map has been firstly converted to a digital image, georeferenced and reprojected as a raster map in the official Italian datum; secondly, has been digitalized in vector format. This map provides historical, ecological and landscape information and fills a great temporal gap in those portions of Italy where landscape maps are available for some earlier periods. Its importance is extended to parts of Croatia, Slovenia and France. The technical problems faced in the recovery and transformation of the cartography into a usable format are described and discussed. A first data overview and analysis based on a test study, and comparisons with current national forest inventory data aimed to highlight potential and limits of IKFM are presented. The map is available on-line through a WebGIS at the address carta1936.dicam.unitn.it

While the theory and implementation of geographic information systems (GIS) have a history of more than 50 years, the development of dedicated educational tools and applications in this field is more recent. This paper presents a free and open source software (FOSS) approach for education in the geospatial disciplines, which has been used over the last 20 years at two Italian universities. The motivations behind the choice of FOSS are discussed with respect to software availability and development, as well as educational material licensing. Following this philosophy, a wide range of educational tools have been developed, covering topics from numerical cartography and GIS principles to the specifics regarding different systems for the management and analysis of spatial data. Various courses have been implemented for diverse recipients, ranging from professional training workshops to PhD courses. Feedback from the students of those courses provides an invaluable assessment of the effectiveness of the approach, supplying at the same time directions for further improvement. Finally, lessons learned after 20 years are discussed, highlighting how the management of educational materials can be difficult even with a very open approach to licensing. Overall, the use of free and open source software for geospatial (FOSS4G) science provides a clear advantage over other approaches, not only simplifying software and data management, but also ensuring that all of the information related to system design and implementation is available.

Landscape metrics constitute one of the main tools for the study of the changes of the landscape and of the ecological structure of a region. The most popular software for landscape metrics evaluation is FRAGSTATS, which is free to use but does not have free or open source software (FOSS). Therefore, FOSS implementations, such as QGIS’s LecoS plugin and GRASS’ r.li modules suite, were developed. While metrics are defined in the same way, the “cell neighborhood” parameter, specifying the configuration of the moving window used for the analysis, is managed differently: FRAGSTATS can use values of 4 or 8 (8 is default), LecoS uses 8 and r.li 4. Tests were performed to evaluate the landscape metrics variability depending on the “cell neighborhood” values: some metrics, such as “edge density” and “landscape shape index”, do not change, other, for example “patch number”, “patch density”, and “mean patch area”, vary up to 100% for real maps and 500% for maps built to highlight this variation. A review of the scientific literature was carried out to check how often the value of the “cell neighborhood” parameter is explicitly declared. A method based on the “aggregation index” is proposed to estimate the effect of the uncertainty on the “cell neighborhood” parameter on landscape metrics for different maps.