A Novel Large Vision Foundation Model (LVFM)-based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management

TL;DR

This paper introduces a novel large vision foundation model (LVFM) for generating high-resolution canopy height maps from RGB imagery, enabling cost-effective and accurate plantation monitoring for forestry management.

Contribution

The paper presents a new LVFM-based model that improves canopy height estimation accuracy and generalization over existing methods, using a self-supervised feature enhancement module.

Findings

Achieved a mean absolute error of 0.09 m in canopy height estimation.

Enabled over 90% success in individual tree detection.

Demonstrated effective tracking of plantation growth and carbon sequestration evaluation.

Abstract

Accurate, cost-effective monitoring of plantation aboveground biomass (AGB) is crucial for supporting local livelihoods and carbon sequestration initiatives like the China Certified Emission Reduction (CCER) program. High-resolution canopy height maps (CHMs) are essential for this, but standard lidar-based methods are expensive. While deep learning with RGB imagery offers an alternative, accurately extracting canopy height features remains challenging. To address this, we developed a novel model for high-resolution CHM generation using a Large Vision Foundation Model (LVFM). Our model integrates a feature extractor, a self-supervised feature enhancement module to preserve spatial details, and a height estimator. Tested in Beijing's Fangshan District using 1-meter Google Earth imagery, our model outperformed existing methods, including conventional CNNs. It achieved a mean absolute error of 0.09 m, a root mean square error of 0.24 m, and a correlation of 0.78 against lidar-based CHMs. The resulting CHMs enabled over 90% success in individual tree detection, high accuracy in AGB estimation, and effective tracking of plantation growth, demonstrating strong generalization to non-training areas. This approach presents a promising, scalable tool for evaluating carbon sequestration in both plantations and natural forests.