Multi-Level monitoring system for cork oak (Quercus suber L.) stands in Sicily

Abstract

Cork oak (Quercus suber L.) stands, while occupying about 6% (17,261 ha, IFNC 2015) of Sicily's forest area, are of considerable ecological, economic and social importance, including biodiversity conservation and sustainable forest production. They are historically managed as silvo-pastoral systems whose sustainability depends on balanced relationships among their components. In recent years, land use changes, rapid climate change and anthropogenic pressures on these systems have progressively led to their decline and loss of their functions. In order to understand the structure of cork stands, an integrated multilevel monitoring procedure was implemented in a sample area in Sicily (Ficuzza wood), considering the following components: (i) use of multispectral information from Unmanned Aerial Vehicle (UAV) using a Phantom4 Multispectral to analyze canopy cover (ii) Light Detection and Ranging (LiDAR) technology with Terrestrial Laser Scanning (TLS) tree level surveys; (iii) field data collection. The use of multi- and hyperspectral information of the area is closely related to the health status of each tree. This information can be obtained through the use of Vegetation Index (VI) and major vegetative characteristics that are closely related to productivity. TLS is a suitable tool for assessing forest structure with non-destructive, rapid and more accurate measurements; this system allows the acquisition of very high volumes of data and high-resolution point clouds that can be potentially and productively used to derive structural information about forests. We evaluated the main forest parameters, including diameter at the breast height (DBH), height, volume, basal area and stand density. The results of this study suggested that integration between different monitoring techniques could be a promising tool to describe and monitor canopy structure and productivity of cork oak stands and other forest formations. The current availability of sensors offers the opportunity for more in-depth analyses that could allow advanced and more precise estimates of forest attributes.