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

• URL: http://arxiv.org/abs/2506.20388v1
• Date: Wed, 25 Jun 2025 12:51:49 GMT
• Title: A Novel Large Vision Foundation Model (LVFM)-based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management
• Authors: Shen Tan, Xin Zhang, Liangxiu Han, Huaguo Huang, Han Wang,
• Abstract summary: High-resolution canopy height maps (CHMs) are essential for monitoring plantation aboveground biomass (AGB)<n>We developed a novel model for high-resolution CHM generation using a Large Vision Foundation Model (LVFM)<n>Tested in Beijing's Fangshan District using 1-meter Google Earth imagery, our model outperformed existing methods.
• Score: 6.293696981925574
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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