Abstract
Accurate forest monitoring is critical for achieving the objectives of the European Green Deal. While national forest inventories provide consistent information on the state of forests, their temporal frequency is inadequate for monitoring fast-growing species with 15-year rotations when inventories are conducted every 10 years. However, Earth observation (EO) satellite systems can be used to address this challenge. Remote sensing satellites enable the continuous acquisition of land cover data with high temporal frequency (annually or shorter), at a spatial resolution of 10-30 m per pixel. This study focused on northern Spain, a highly productive forest region. This study aimed to improve models for predicting forest variables in forest plantations in northern Spain by integrating optical (Sentinel-2) and imaging radar (Sentinel-1, ALOS-2 PALSAR-2 and TanDEM-X) datasets supported by climatic and terrain variables. Five popular machine learning algorithms were compared, namely kNN, LightGBM, Random Forest, MLR, and XGBoost. The study findings show an improvement in R2 from 0.24 when only Sentinel-2 data are used with MultiLinear Regression to 0.49 when XGboost is used with multi-source EO data. It can be concluded that the combination of multi-source datasets, regardless of the model used, significantly enhances model performance, with TanDEM-X data standing out for their remarkable ability to provide valuable radar information on forest height and volume, particularly in a complex terrain such as northern Spain.
| Original language | English |
|---|---|
| Article number | 563 |
| Journal | Forests |
| Volume | 16 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Apr 2025 |
| MoE publication type | A1 Journal article-refereed |
Funding
This research was supported by the research project of code PID2020-112839RB-I00 funded by the Spanish State Research Agency (AEI) of the Ministry of Science and Innovation (MCIN/AEI /10.13039/501100011033). The work of O.A. and J.M. was supported by the European Space Agency (ESA), contract 4000135015/21/I-NB—Forest Carbon Monitoring, under the EOEP5 program. This work was carried out while the first author was conducting a research stay at VTT, funded by the Government of the Principality of Asturias, Grants for Short Stays at Research Centers (code EB24-26). While undertaking the present study, the first author was in receipt of a Severo Ochoa Fellowship from the Government of Asturias (ref. BP21-125).
Keywords
- forest inventory
- kNN
- LightGBM
- optical
- random forest
- synthetic aperture radar
- XGBoost
