Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 563 - 563

Published: March 24, 2025

Accurate forest monitoring is critical for achieving the objectives of European Green Deal. While national inventories provide consistent information on state forests, their temporal frequency inadequate fast-growing species with 15-year rotations when are conducted every 10 years. However, Earth observation (EO) satellite systems can be used to address this challenge. Remote sensing satellites enable continuous acquisition land cover data high (annually or shorter), at a spatial resolution 10-30 m per pixel. This study focused northern Spain, highly productive region. aimed improve models predicting variables in plantations Spain by integrating optical (Sentinel-2) and imaging radar (Sentinel-1, ALOS-2 PALSAR-2 TanDEM-X) datasets supported climatic terrain variables. Five popular machine learning algorithms were compared, namely kNN, LightGBM, Random Forest, MLR, XGBoost. The findings show an improvement R2 from 0.24 only Sentinel-2 MultiLinear Regression 0.49 XGboost multi-source EO data. It concluded that combination datasets, regardless model used, significantly enhances performance, TanDEM-X standing out remarkable ability valuable height volume, particularly complex such as Spain.

Language: Английский