Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: a possible contribution for updating forest management plans

TL;DR

This study demonstrates how hyperspectral and LiDAR data combined with machine learning can accurately classify tree species and estimate biomass and volume, aiding forest management planning.

Contribution

It introduces a methodology integrating remote sensing data with machine learning classifiers for detailed forest inventory analysis.

Findings

Support Vector Machine outperformed K-Nearest Neighbor in classification accuracy.

High correlation coefficients (0.94 for volume, 0.90 for biomass) validate the estimation approach.

Remote sensing combined with machine learning effectively supports forest management decisions.

Abstract

This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing solutions. In particular, data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed. Such studies are very important in the context of forest management scenarios. The method includes defining tree species ground-truth by outlining single tree crowns with polygons and labeling them. Successively two supervised machine learning classifiers, K-Nearest Neighborhood and Support Vector Machine (SVM) were used. The results show that, by setting specific hyperparameters, the SVM methodology gave the best results in classification of tree species. Biomass was estimated using canopy parameters and the Jucker equation for the above ground biomass (AGB) and that of Scrinzi for the tariff volume. Predicted values were compared with 11 field plots of fixed radius where volume and biomass were field-estimated in 2017. Results show significant coefficients of correlation: 0.94 for stem volume and 0.90 for total aboveground tree biomass.