Explore the vast range of titles available.

Climate change and human activities are increasingly threatening water resources. In particular sub-alpine lakes are fundamental not only for tourism or other economical activities, but also as a source of water. In this context, there is a strong need to monitor such resources to understand, study and react to known and unknown impacts, so that appropriate mitigation actions can be taken. Unfortunately, although monitoring data already exist for many of these lakes, the information is archived in different formats and servers undermining the full exploitation of data and preventing a more efficient data management. The aim of this work is to improve this situation by implementing a system that integrates and standardizes data coming from different sources. In addition, the system integrates web based tools that estimate lake state indicators using open source software and standard. Thanks to this system, it will be possible to exploit the data potential more fully. This paper focuses on the achievements reached by the research carried out on Lake Lugano in the context of the project SIMILE after two years of work.

The Covid-19 outbreak has greatly impacted society behaviours fostering proximity tourism and valorising the social role of peri-urban natural protected areas as key locations for outdoor activities. FOSS and FOSS4G can play a critical role to support the value creation for these sites. This work evaluates its application in the context of two different protected areas for the creation of 3D digital products, the monitoring of touristic fluxes and the conduction of parks management activities. To this aim three solutions that copes with the mentioned aspects are presented and gaps, weakness and limitations evaluated. The investigated solutions consists in: the data workflow from survey to 3D rendering using Blender and GIS plugin; the touristic fluxes monitoring system based on a machine learning algorithm for image recognition from captured video data streams and istSOS; and finally the park assets management system which is based on PostGIS and OpenLayers.

The continuous expansion of invasive Asian tiger mosquito, Aedes albopictus, combined to its ability to transmit arboviruses (e.g. dengue, chikungunya) is raising major public health concern in Europe. In Switzerland, the mosquito is firmly established in most urban areas of the Canton of Ticino, south of the Alps, and there is a real risk that it will colonize also urban areas north of the Alps in the next years. The spatial distribution and colonization of new areas by Ae. albopictus depends on several environmental parameters, such as winter and summer temperatures, and precipitation patterns. A key factor for Ae. albopictus to establish at higher latitudes is the capability to develop cold-tolerant overwintering diapausing eggs under specific environmental conditions. Weather-driven abundance models are used to map the areas of potential distribution and to predict temporal dynamics of Ae. albopictus and the transmission potential of arboviruses. This contribution presents the designed system that integrates low-cost and on-line IoT sensors to monitor temperature, humidity and light with istSOS an OGC Sensor Observation Service server implementation with a user friendly interface and rich feature collection to easily manage this sensor network and distribute data in a standard way (www.istsos.org).

Climate, together with human activities, is changing the natural dynamics in lake ecosystems and adding new challenges to the management of water resources. Recent studies on Lake Lugano, in Switzerland, showed for instance that the increased water temperature influence other processes such as lake stratification and mixing dynamics, algal blooms, colonisation by alien species, affecting the lake ecosystem as a whole. In such situation, real-time systems with high frequency measurements, together with the traditional discrete monitoring, can help in understanding dynamics and processes occurring on short time scales. To this aim, an open monitoring system largely composed by open source components is being developed for the high frequency monitoring of Lake Lugano. The system relies on the open source software istSOS either on the server and node sides applying the edge computing paradigm which is more and more adopted in the Internet of Things field. The implementation collects temperature and dissolved oxygen data from sensors positioned at six different depths of the lake and transmits them using the LoRa radio frequency to a data warehouse. At server side, the software architecture adopts the evolving technology based on containers where services can be grouped in a compose and easily deployed on a server. This paper aims to describe the adopted open source technology and demonstrate that it can be successfully used also in environmental monitoring where the accessibility is limited and the weather conditions can be unpredictable.

Lakes as ecosystems provide many goods and services. To benefit from them in long term we must assure sustainable management. SIMILE (informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) project is focused on developing efficient monitoring of lake water quality since it gives the critical input for adequate management. The lakes of interest for SIMILE are the Insubric lakes Como, Lugano, and Maggiore. The paper is focused on describing which tools are used in the SIMILE project to exploit different sources of lake water quality data: in-situ high-frequency monitoring (HFM) through sensors, satellite observations, and data collected by citizens. Even though the paper is focused on the SIMILE project, and thus on tools and procedures for the Insubric lakes, it can serve as an example for other lakes too, especially because the tools developed in the project, such as a collaborative platform for sharing satellite-derived water quality parameters, and mobile application and web administrator interface for citizen science, are free and open-source, they can be easily adapted if needed. Moreover, the procedures for the processing of data coming from different sources are based on free (and often also open source) software and are well documented. The tools and procedures described in this paper might be a foundation for similar practice for lakes worldwide, and thus a step forward the 6th Sustainable Development Goal (SDG) of the United Nations (“Ensure availability and sustainable management of water and sanitation for all”).

During the last decade, many climatic parameters are more and more deviating from the mean values calculated over historical climatic time-series. The World Meteorological Organization (WMO) stated that years 2015, 2016 and 2017 were the warmest since 1850. According to the preliminary data analysis of the last year, this trend is going to continue in 2018 as well. These climatic changes have accelerated the occurrence of droughts in many parts of the world including Europe and Asia. The North Western region is one such region in Sri Lanka which always affected by droughts due to its inherent dry weather condition. Although drought is considered as a normal part of the climate, its increasing negative impacts on human activities and the environment urges the application of novel technologies in drought monitoring. The 4onse project (analysis of Open, Non-conventional, Sustainable and Effective monitoring systems), funded by the Swiss National Science Foundation (SNSF), is a project began in this context to address the aforementioned mentioned issues by deploying 27 open technologies based Environmental Monitoring Systems (EMS) in Deduru Oya basin of Sri Lanka. These systems measure weather parameters such as temperature, humidity, pressure, rainfall, wind speed and wind direction. Thanks to the collected data, it was possible to calculate some drought indexes to evaluate the intensity of the dry periods. The Standard Precipitation Index (SPI) has been selected as the principal indicator to evaluate droughts by integrating the 4onse data together with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset.

An Environmental Monitoring System (EMS) is needed not only to prevent many natural risks such as droughts, flooding and landslides but also to provide information for a better management of water resources and crops irrigation and finally it helps to increase the reliability of weather and climatological models. In addition, a monitoring system can directly impact the economic, social and political spheres. Unfortunately, in most developing and low income countries, due to the high costs of hardware and software there is a lack of efficient monitoring systems. The aim of the 4onse project (analysis of Four times Open Non-conventional system for Sensing the Environment), funded by the Swiss National Science Foundation, is the development of a totally open solution to monitor the environment. As well as the hardware layer, a monitoring system needs a data management software usually hosted on a server structure. As a software platform which is SOS OGC compliant, istSOS is chosen to receive, manage, validate and distribute environmental data. In the following article a solution to support big data is presented to extend the istSOS capabilities. In fact, a sensors network can hardly stress a data management system because of the several concurrent users and sensors and the long time series which every weather station can easily produce. Thus, a software called istSOS-proxy is developed as a single access point over multiple instances of istSOS whose procedures are distributed to balance the total load. First results on the effectiveness of the solution are proved thanks to load testing simulations of different levels of concurrent users.

Understanding how people and vehicles move through public spaces is essential for designing inclusive, safe, and efficient urban spaces. In the Mendrisio district (Switzerland), we deployed a low-cost, AI-powered sensor to monitor pedestrian and vehicle flows in different seasons. The sensor uses camera-based image recognition to detect and classify objects in real time while preserving privacy by avoiding biometric or identity-related data capture, in full compliance with GDPR. The system was developed using open source hardware and software. Processes video frames on edge using a lightweight machine learning model optimized for embedded devices and periodically transmits summary data (object type, direction, timestamp) via NB-IoT to a centralized data platform. The collected data was used to generate temporal analyses and heatmaps of space usage and to validate the classification accuracy in different weather and lighting conditions. Field tests demonstrated the sensor’s capability to operate autonomously for extended periods with low power consumption, while highlighting limitations in NB-IoT connectivity in specific locations. Despite these constraints, the system provided valuable information on public space utilization, identifying peak hours and spatial patterns relevant for mobility planning and urban design. This approach offers a replicable and cost-effective solution for municipalities seeking data-driven support for decision-making. By combining privacy-sensitive AI, open technologies, and standard data models (such as SensorThings API), the project contributes to a more transparent and inclusive digital urban ecosystem.

This study developed within the INTERREG VI-A Italy–Switzerland project WINCA4TI (Water Interactions with Nature, Climate, and Agriculture for the Ticino Hydrographic Basin), investigates land use and land cover (LU/LC) changes focusing on builtup surface evolution in the Swiss portion of the Ticino watershed. LU/LC data are essential for tracking environmental change, modeling nutrient flows, and guiding sustainable land management. We analyzed and compared three key LU/LC datasets available for Switzerland: the national Arealstatistik, the European CORINE Land Cover (CLC), and the Ticino Cantonal Land Cover Cadastre. These datasets differ in spatial resolution, classification systems, and acquisition methodologies. Data were harmonized regarding the coordinate system, spatial extent, and thematic classification to ensure consistency. The study focused on the ”built-up surfaces” category defined in the MODIFFUS 3.1 nutrient model. The analysis used open-source tools, such as QGIS and open data, ensuring a reproducible and transferable workflow. Results reveal significant discrepancies in the absolute values and temporal trends of built-up surfaces, depending on the dataset used. While Cadastre and Arealstatistik yield similar estimates when filtered for building-related categories, CORINE reports higher values, reflecting its suitability for national-scale analysis but limited precision in smaller, alpine regions. The findings emphasize the importance of dataset selection and class harmonization in LU/LC-based analyses, especially for integration into environmental models like MODIFFUS. This preliminary work lays the foundation for a broader comparison involving all land use categories. It supports future modeling of diffuse nutrient loads and climate adaptation strategies across the transboundary Ticino basin.

Lake Lugano is increasingly experiencing new processes and dynamics due to the combined effects of climate change and human activities. In recent years, abnormal phenomena of algal proliferation, known as Harmful Algal Blooms (HABs), have appeared on the lake. Particularly in 2023, after several critical events, public awareness of this issue has risen, as it can potentially impact socio-economic aspects related to the use of this locally important water resource. In this study, we aim to share the development process of a monitoring system designed to promptly inform about the increase in concentration levels, thereby initiating a protocol to support decision-makers. This system, based on open-source and cost-effective devices, is designed to gather information on these blooms, which pose potential toxicity risks to humans and animals. The system uses a Raspberry Pi connected to a Trilux fluorimeter, measuring algal pigments and transmitting data via NB-IoT. The data is processed using istSOS software, stored locally, and transmitted to a data warehouse. Alerts are set up based on phycocyanin (PC) levels, indicating cyanobacterial biomass and potential microcystin toxicity. The open-source nature of the system allows for easy replication and expansion, aiding decision-makers and researchers, and increasing citizen awareness.