PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Enhancing Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data
Version 1
: Received: 21 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (13:00:01 CEST)
How to cite:
Omoniyi, T.; Sims, A. Enhancing Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data. Preprints2024, 2024091741. https://doi.org/10.20944/preprints202409.1741.v1
Omoniyi, T.; Sims, A. Enhancing Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data. Preprints 2024, 2024091741. https://doi.org/10.20944/preprints202409.1741.v1
Omoniyi, T.; Sims, A. Enhancing Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data. Preprints2024, 2024091741. https://doi.org/10.20944/preprints202409.1741.v1
APA Style
Omoniyi, T., & Sims, A. (2024). Enhancing Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data. Preprints. https://doi.org/10.20944/preprints202409.1741.v1
Chicago/Turabian Style
Omoniyi, T. and Allan Sims. 2024 "Enhancing Precision of Forest Growing Stock Volume in the Estonian National Forest Inventory with Different Predictive Techniques and Remote Sensing Data" Preprints. https://doi.org/10.20944/preprints202409.1741.v1
Abstract
Estimating Forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly, however integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and Airborne Laser Scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia's geographical regions, RF consistently delivered the best performance. In the northwest (NW), RF achieved an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), it achieved an R² of 0.73 and an RMSE of 128.86 m³/plot. In the northeast (NE), RF achieved an R² of 0.64 and an RMSE of 133.77 m³/plot, and an R² of 0.70 and an RMSE of 120.72 m³/plot was achieved in the southeast (SE). These results underscore RF's precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.
Keywords
National Forest Inventory; Growing stock volume; Remote sensing; Sentinel-2; airborne laser scanning; Machine Learning; random forest; support vector regression; extreme gradient boosting
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
