DepthCanopyNet: Toward High-Precision Canopy Height Mapping via Gradient-Enhanced Learning Using Single UAV Optical Imagery

Abstract

Tree height is a key indicator in both forestry ecosystems and tree breeding. Thus, accurately and rapidly monitoring the height distribution and dynamic changes of individual trees and forest stands is of vital importance. With the advancement of deep learning, there has been growing attention on canopy height mapping using a single remote sensing image. However, current research primarily relies on satellite or airborne laser scanning (ALS) data for wall-to-wall canopy estimation. In recent years, close-range remote sensing technologies, particularly unmanned aerial vehicle (UAV)-based methods, have demonstrated significant potential in forest inventory, monitoring, and high-throughput phenotyping of trees. Thus, this work focuses on canopy height mapping using UAV-acquired imagery. To address challenges such as the loss of canopy details and the insufficient precision in crown height variation extraction, we propose a transformer-based method for canopy height mapping, named DepthCanopyNet, specifically designed for ultrahigh-resolution UAV imagery. The DepthCanopyNet integrates a bidirectional gradient-enhancement module into a conditional random field, leveraging gradient-based structural knowledge to emphasize height details, particularly the variations in the tree crown and the edges between the tree crown and the background. Furthermore, a lightweight global stepwise aggregation (GSA) module is employed for multilevel feature aggregation, progressively integrating low-level details with high-level global semantic features. This facilitates the flow of multiscale information across different layers, thereby enhancing the ability of the model to represent tree structures with varying sizes and spatial distributions. A two-stage training strategy is further introduced to alleviate the domain gap between the pretrained model and our depth mapping task. Comprehensive experiments conducted on two UAV datasets—one from a coniferous forest with moderate stand density and another from a broad-leaved forest with high stand density—demonstrate that the proposed method outperforms state-of-the-art architectures. On the coniferous forest dataset, the absolute relative loss decreased by 0.0128, while on the broad-leaved forest dataset, it decreased by 0.0076. In addition, cross-dataset out-of-distribution transfer experiments validate the generalization capability of the proposed method. This work marks a significant advancement in applying monocular depth estimation to centimeter-level remote sensing imagery and highlights the potential of using a single image for extracting individual tree-level crown parameters.

Plant phenotyping relevance

UAV単画像から個体木の樹冠高を抽出する深層学習手法を開発し、複数データセットとクロスデータセット実験で検証しており、植物表現型取得が中心である。

Code and data availability

The paper evaluates on two private UAV datasets (UAVTC 1, UAVTC 2) and a public NEON-derived dataset from prior work (Tolan et al.). No code, model checkpoint, or dataset deposit with an authors' public URL is stated anywhere in the supplied blocks; the only public URL is the WRAP repository page hosting the manuscript

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