Two-stage-upscaling method based on UAV and stereo satellite imagery to optimize the forest canopy height model

The forest ecosystem possesses the ability to sequester carbon, which is essential for maintaining the balance of the global carbon cycle. Canopy height is a directly related growth parameter of forest that is crucial for accurately estimating forest carbon stocks. Particularly in extensively planted forests, an accurate estimation of canopy height can effectively evaluate their ecological value. However, obtaining a high-resolution canopy height model (CHM) through remote sensing technology remains a challenge, and surveying numerous field values is also difficult. We propose a solution based on unmanned aerial vehicle (UAV) and GaoFen-7 (GF7) stereo satellite data. Through a two-stage upscaling approach, a scale-matched canopy height dataset derived from CHMUAV was utilized to optimize and generate a wide-ranging satellite-scale CHM. The model (LASSO-CHM-Random Forest, LA-CHM-RF) proposed in this study performs well in optimizing the original CHMGF7 (R2=0.73, RMSE = 3.16 m, rRMSE = 25%). The results indicate that the two-stage-upscaling method enhanced the quality of CHMGF7, reducing errors by 19% at the UAV scale and by 33% in large areas. We demonstrate that forest density, rather than forest height, is the primary factor influencing the accuracy of the optimized model.