Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis

Abstract

Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR-2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha⁻¹ and R²: 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha⁻¹ and R² of 0.81 for the combination of optical and SAR data in the GBM model.

Keywords:

• Forest biomass
• random forest
• extreme gradient boosting
• pixel-based
• object-based

Acknowledgment

This project was supported by the Climate & Applied Forest Research Institute (CAFRI) at SUNY ESF with funding from the NYS Department of Environmental Conservation. A McIntire-Stennis grant funded through the United States Department of Agriculture, National Institute of Food and Agriculture (USDA-NIFA) also supported this research.

Disclosure statement

No potential conflict of interest was reported by the authors.