Forest Inventory, Modeling and Remote Sensing

This section highlights progress in the mensuration, modelling and monitoring of forests around the world. The total woody volume and stem volume of a tree, aboveground biomass and basal area are key forest inventory attributes that are required by forest managers. Reliable mapping of volumes and biomass facilitates the implementation of sustainable management strategies and practices. We encourage applications tackling issues of integrating ground and satellite data for the calibration and validation of remote sensing-based forest observations.

Contributions dealing with various types of sensors (active and passive) and carriers (terrestrial, airborne, unmanned aerial vehicles, space-borne) or combinations thereof are welcome. We solicit contributions describing innovative remote sensing-based approaches, data processing techniques and modelling tools, to improve understanding forest structure and composition supporting operational applications. We invite researchers to contribute original research articles as well as review articles. Potential topic areas include, but are not necessarily limited to:

• Multispectral (e.g., Sentinel-2, Landsat-8 OLI) and hyperspectral (e.g., PRISMA) data processing for forest monitoring;
• LiDAR processing in vegetation characterization and mensuration;
• New LiDAR metrics at stand and individual tree levels apart from height metrics (e.g., height heterogeneity, canopy gaps and LAI profiles) to predict canopy characteristics (i.e., wood volume, stem volume, aboveground biomass and basal area, among others) across a wide range of canopy structures;
• Methods for multi-source data fusion and integration for forest modelling, mensuration and inventory area (e.g., integration of satellite, aerial/drone and in situ observations);
• Analysis of forest conditions at multiple spatial and temporal scales and geostatistical analysis;
• Development of novel statistical or functional approaches for quantifying forest conditions;
• Forest monitoring and evolution simulation;
• Recent developments in artificial intelligence (AI) for forest modelling;
• Models combining field sample plots from National Forest Inventories with satellite and/or LiDAR data;
• Identification of forest areas at risk.