Optimizing Aboveground Carbon Stock Estimation in Restoration Forests: A Multi-Scale Analysis of UAV Multispectral Imagery

The precise Measurement, Reporting, and Verification (MRV) of carbon stocks in small-scale afforestation and restoration forests can be served as the  foundation for subsequent carbon sink monitoring and benefit assessment.  Satellite remote sensing method, on the other hand, often  faces insufficient spatial resolution comparing to Unmanned Aerial Vehicle (UAV) imagery. UAV can capture fine details, but often results in "scale mismatch" and systematic estimation bias due to canopy shadows, background soil noise, and spectral saturation effects while applying estimation models directly. To address this technical bottleneck, this study aims to establish an automated carbon stock estimation workflow based on UAV multispectral imagery and to optimize estimation accuracy by identifying the optimal observational resolution through multi-scale analysis. 

The research methodology synchronizes field surveys with remote sensing modeling. First, a comprehensive tree-by-tree biomass inventory was conducted in sample plots. Allometric equations were used to calculate stand biomass, which was then converted into measured carbon stock to serve as Ground Truth for model validation. Subsequently, UAV multispectral images were acquired to calculate vegetation indices (e.g., NDVI) and establish regression models between spectral features and carbon stock. Furthermore, image resampling techniques were adopted to simulate multi-level spatial resolutions ranging from 0.03 to 5 m, systematically analyzing the impact of resolution changes on the Root Mean Square Error (RMSE) and the coefficient of determination (R²). This study clarifies the interference mechanism of spatial scale on canopy spectral signals and identifies the optimal aggregation scale to mitigate background noise. Ultimately, this research provides practical prediction formulas and a Standard Operating Procedure (SOP). In the future, applying this model to UAV-acquired imagery in similar restoration forests will enable rapid, automated carbon estimation without the need for time-consuming field surveys, significantly enhancing the efficiency and economic viability of carbon asset inventories.

Keywords

Aboveground Biomass (AGB), Multispectral UAV, NDVI, Allometric Biomass Model, Scale Effect, Restoration Forest, Carbon Sink Estimation