FOSS Tools and Applications for Education in Geospatial Sciences

Digitalization and Classification of Cesare Battisti’s Atlas of 1915

The paper deals with an automated methodology for the digital acquisition of thematic information from historical maps in order to use them for spatial analysis in a GIS software. This methodology has been applied to an early XIX c. map in order to assess the historical changes in the forest coverage in Trentino. Specifically, a tailored Object Based Image Analysis (OBIA) and filtering procedure has been applied to digitize and georeference Cesare Battisti’s map of forest density published in his atlas “Il Trentino. Economic Statistical Illustration” from 1915. According to the historical ecology approach, forest history can be analyzed and evaluated with the use of historical documentary sources. Following this approach, historical cartography is a precious information tool, and in many respects unique, through which it is possible to reconstruct the evolution of the forest cover of a given territory. Trentino, in particular, has a rich heritage of historical maps from which to draw useful information for the construction of a qualitative and quantitative diachronic picture of the evolutionary dynamics of wooded areas. In these territories, forest management is a topic of great importance both for its socio-economic implications and for the more strictly environmental ones, connected to the increasingly urgent need to implement mitigation and adaptation policies towards climate change. Thus, the paper presents the historical maps and illustrates the methodology used for the digitisation. Data extracted by the historical sources have been compared with the current one in order to identify changes in forest density in the last century.

Relevance of the Cell Neighborhood Size in Landscape Metrics Evaluation and Free or Open Source Software Implementations

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.

New Tools for the Classification and Filtering of Historical Maps

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 Role of African Emerging Space Agencies in Earth Observation Capacity Building for Facilitating the Implementation and Monitoring of the African Development Agenda: The Case of African Earth Observation Program

AU-Agenda 2063 was adopted at the 24th Ordinary Session of the African Heads of State and Government in 2015 as the blueprint for the future development of the continent. Built upon the continent’s past experiences, challenges, and successes, AU-Agenda 2063 comprehensively describes the strategic path for Africa’s future development in the next 50 years. Thus, the monitoring of its implementation in various African states is critical for ensuring sustainable development and track progress. However, the higher cost of collecting data for accurately and reliably monitoring the implementation of Agenda 2063 may hinder the progress towards achieving these goals. Satellite Earth observation provides ample data, and thus has provided opportunities for the development of novel products and services with the potential to support implementation, monitoring and reporting for AU-Agenda 2063 development imperatives. However, it has been limitedly exploited in Africa, as evidenced by lower research outputs and investments. This calls for increased capacity building in the use of available EO data and products for various users including decision makers to advance national, regional and continental priorities. The use of such data products is often hampered by the capability to understand the products and thus their value for addressing socio-economic challenges. This paper discusses the potential of Earth observation capacity building for supporting the implementation, monitoring of, and reporting towards achieving AU-Agenda 2063 development imperatives. Specifically, this paper identifies existing capacity building resources, including the role of open and free Earth observation data, open-source software, and product dissemination platforms that can be leveraged for supporting national development, service delivery and the achievement of AU-Agenda 2063 targets. Furthermore, the paper recognizes the importance of bilateral and multilateral partnerships in leveraging existing know-how, technology and other resources for advancing strategic goals of African emerging space agencies and promoting sustainable development, with examples from South African National Space Agency (SANSA). Then, the challenges and opportunities for capacity building and the wide adoption of EO in Africa are discussed in the context of AU-Agenda 2063. The paper thus concludes that EO capacity building is essential to address the skills and data gaps and increase the use of EO-based solutions for decision making in various sectors, critical for achieving AU-A2063.

Impact of the ARSET Program on Use of Remote-Sensing Data

We show that training activities conducted through the National Aeronautics and Space Administration (NASA)’s Applied Remote-Sensing Training (ARSET) program led to a significant increase in remote-sensing data use for decision-making. Our findings are based on survey data collected from 1041 ARSET participants from 117 countries who attended ARSET trainings between 2013 and 2016. To assess the impact of the ARSET program, we analyzed changes in three metrics. Results show that 83% of all respondents increased their knowledge of remote-sensing data products at least moderately, 79% increased their ability to access data, and 73% increased their ability to make decisions. We also examined how respondents are using remote-sensing data across 40 specific work tasks ranging from research to decision support applications. More than 50% of respondents reported an increase in data use for all except two of the tasks. ARSET will use these findings, together with participant data on future training needs, to set future directions for the program.

Open and Flexible Li-ion Battery Tester Based on Python Language and Raspberry Pi

Technology improvements and cost reduction allow electrochemical energy storage systems based on Lithium-ion cells to massively be used in medium-power applications, where the low system cost is the major constraint. Battery pack maintenance services are expected to be required more often in the future. For this reason, a low-cost instrumentation able to characterize the cells of a battery pack is needed. Several works use low-cost programmable units as Li-ion cell tester, but they are generally based on proprietary-software running on a personal computer. This work introduces an open-source software architecture based on Python language to control common low-cost commercial laboratory instruments. The Python software application is executed on a Raspberry Pi board, which represents the control block of the hardware architecture, instead of a personal computer. The good results obtained during the validation process demonstrate that the proposed cell station tester features measurement accuracy and precision suitable for the characterization of Li-ion cells. Finally, as a simple example of application, the state of health of twenty 40 Ah LiFePO4 cells belonging to a battery pack used in an E-scooter was successfully determined.