Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data

Abstract

Assessing biomass is critical for accounting bioenergy potentials and monitoring forest ecosystem responses to global change and disturbances. Remote sensing, especially Light Detection and Ranging (LiDAR) data combined with field data, is being increasingly used for forest inventory purposes. We evaluated the feasibility of the combined use of freely available data, both remote sensing (LiDAR data provided by the Spanish National Plan for Aerial Ortophotography - PNOA - and Landsat vegetation spectral indices) and field data (from the National Forest Inventory) to estimate stand dendrometric and aboveground biomass variables of the most productive tree species in a pilot area in Galicia (northwestern Spain). The results suggest that the models can accurately predict dendrometric and biomass variables at plot level with an R2 ranging from 0.49 to 0.65 for basal area, from 0.65 to 0.95 for dominant height, from 0.48 to 0.68 for crown biomass and from 0.55 to 0.82 for stem biomass. Our results support the use of this approach to reduce the cost of forest inventories and provide a useful tool for stakeholders to map forest stand variables and biomass stocks.

Keywords

• Biomass Maps
• Forest Inventory
• LiDAR
• Landsat Vegetation Indices