Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds
This work addresses the need for efficient, scalable forest biodiversity assessment for ecosystem management and conservation, though it is incremental in applying existing fusion methods to a new dataset.
This study tackled the problem of assessing forest biodiversity by using deep learning to fuse 2D orthophotos and 3D airborne laser scanning point clouds, achieving overall accuracies up to 81.4% with multimodal fusion approaches.
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 44 378 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, achieving overall accuracies of 80.5%, 81.4% and 80.4% respectively. Our results demonstrate that spectral information from orthophotos and structural information from ALS point clouds effectively complement each other in forest biodiversity assessment.