A new generation of sensors and monitoring tools to support climate-smart forestry practices

• Published in: Canadian journal of forest research Vol. 51; no. 12; pp. 1751 - 1765
• Main Authors: Torresan, Chiara, Benito Garzón, Marta, O’Grady, Michael, Robson, Thomas Matthew, Picchi, Gianni, Panzacchi, Pietro, Tomelleri, Enrico, Smith, Melanie, Marshall, John, Wingate, Lisa, Tognetti, Roberto, Rustad, Lindsey E., Kneeshaw, Dan
• Format: Journal Article
• Language: English
• Published: Ottawa NRC Research Press 01.12.2021; Canadian Science Publishing NRC Research Press
• Subjects: Big Data; Biodiversity; Climate change; Climate change mitigation; Data collection; Data management; ecosystems; Environmental Sciences; Forest management; Forest productivity; Forest Science; Forestry; Forests; information management; Internet; Internet of Things; Methods; Monitoring; Real time; Remote sensing; Sensors; Skogsvetenskap; stakeholders; Wireless sensor networks; Canada; United States; Europe; ecosystem regime shifts; wireless sensor network; citizen science; green technologies; Internet of Things; early warning signals; climate change
• ISSN: 0045-5067; 1208-6037; 1208-6037
• DOI: 10.1139/cjfr-2020-0295

Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e., a wireless sensor network (WSN), that can be part of a worldwide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scales of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime shifts in terms of forest productivity, health, and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.