Application of Random Forest Method Based on Sensitivity Parameter Analysis in Height Inversion in Changbai Mountain Forest Area

Abstract

The vertical structure of forests, including the measurement of canopy height, helps researchers understand forest characteristics such as density and growth stages. It is one of the key variables for estimating forest biomass and is crucial for accurately monitoring changes in forest carbon storage. However, current technologies face challenges in achieving cost-effective, accurate measurement of canopy height on a widespread scale. This study introduces a method aimed at extracting accurate forest canopy height from The Global Ecosystem Dynamics Investigation (GEDI) data, followed by a comprehensive large-scale analysis utilizing this approach. Before mapping, verifying and analyzing the accuracy and sensitivity of parameters that may affect the precision of GEDI data extraction, such as slope, aspect, and vegetation coverage, can aid in assessment and decision-making, enhancing inversion accuracy. Consequently, a random forest method based on parameter sensitivity analysis is developed to break through the constraints of traditional issues and achieve forest canopy height inversion. Sensitivity analysis of influencing parameters surpasses the uniform parameter calculation of traditional methods by differentiating the effects of various land use types, thereby enhancing the precision of height inversion. Moreover, potential factors affecting the accuracy of GEDI data, such as vegetation cover density, terrain complexity, and data acquisition conditions, are thoroughly analyzed and discussed. Subsequently, large-scale forest canopy height estimation is conducted by integrating vegetation cover Normalized Difference Vegetation Index (NDVI), sun altitude angle and terrain data, among other variables, and accuracy validation is performed using airborne LiDAR data. With an R2 value of 0.64 and an RMSE of 8.62, the mapping accuracy underscores the resilience of the proposed method in delineating forest canopy height within the Changbai Mountain forest domain.

Keywords

• Changbai Mountain forest area
• GEDI LiDAR
• sensitivity analysis
• canopy height
• random forests