Assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos and 3D airborne laser scanning (ALS) point clouds can reliable assess the biodiversity potential of forests. We introduce the BioVista dataset, comprising 44378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multimodal fusion approaches. Using deep neural networks (ResNet for orthophotos and PointVector for ALS point clouds), we investigate each data modality's ability to assess forest biodiversity potential, achieving overall accuracies of 76.7% and 75.8%, respectively. We explore various 2D and 3D fusion approaches: confidence-based ensembling, feature-level concatenation, and end-to-end training, with the latter achieving an overall accuracies of 82.0% when separating low- and high potential forest areas. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in the assessment of forest biodiversity potential.

翻译：森林生物多样性评估对于生态系统管理与保护至关重要。传统的野外调查虽能提供高质量评估，但具有劳动密集性和空间局限性。本研究探讨了基于深度学习的二维正射影像与三维机载激光扫描点云近程传感数据融合方法，能否可靠评估森林的生物多样性潜力。我们引入了BioVista数据集，包含来自丹麦温带森林的44378组正射影像与ALS点云配对样本，旨在探索多模态融合方法。通过深度神经网络（正射影像采用ResNet，ALS点云采用PointVector），我们分别评估了各数据模态对森林生物多样性潜力的预测能力，总体准确率分别达到76.7%和75.8%。我们探索了多种二维与三维融合方法：基于置信度的集成、特征级拼接以及端到端训练，其中端到端训练在区分低潜力与高潜力森林区域时达到82.0%的总体准确率。研究结果表明，正射影像的光谱信息与ALS点云的结构信息在森林生物多样性潜力评估中能有效互补。