Forest Stand Volume Estimation by Species from Sentinel-2 and LiDAR Data Using Regression Models

The paper presents a novel forest stand volume (FSV) estimation approach based on remote sensing (RS) data when the forest inventory data used as reference are limited. The proposed approach consists of several steps, such as filtering of existing inventory data, identifying individual tree tops from the Canopy Height Model (CHM), classifying dominant tree species from Sentinel-2 data, and creating the polynomial regression model for stand volume estimation based on training data. The study area was located in the Zemgale region of Southeast Latvia, where the dominant tree species are Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), birch (Betula pendula Roth, Betula pubescens Ehrh.) and black alder (Alnus glutinosa (L.) Gaertn.). The FSV (m3/ha) for each dominant species was estimated, and the accuracy against the harvester data was evaluated by calculating the root mean square error (RMSE). Additionally, a cross-validation was performed using sparse and partially imprecise inventory data, and the RMSE errors were less than 20% for pine, 22% for spruce, 28% for birch, and 23% for black alder. In general, the developed approach can be used with species for which there is a sufficient number of inventory compartments in the analysis region where these species dominate. The proposed approach can be used in automatic workflows estimating forest inventory parameters from RS data.