Employing Synthetic Canopy Height Model Data to Enhance Tree Identification in High-Resolution Satellite Imagery

Abstract

This paper investigates a novel approach for detecting trees in high-resolution satellite imagery by leveraging synthetic canopy height model (CHM) data. We demonstrate that integrating fine-scale CHM maps significantly enhances both the quality and accuracy of tree detection. Additionally, the study conducts a comparative evaluation of several neural network architectures for delineating tree crowns in imagery. Experimental results confirm the effectiveness of the proposed method, underscoring its potential to streamline satellite data processing workflows and strengthen the reliability of automated vegetation monitoring systems.