Robotic research for forest monitoring and wildfire prevention relies on robust sensing systems, yet challenges arise from sensor limitations and poor Global Navigation Satellite System (GNSS) coverage in dense environments. This work presents a portable multimodal sensing system for precise 3D data acquisition in forestry applications. Within a unified framework, the system integrates multiple sensor modalities, including Red, Green, Blue, and Depth (RGBD), Laser imaging, Detection, and Ranging (LiDAR), Inertial Measurement Unit (IMU), and GNSS-RTK data. Extrinsic calibration methods align these sensors to a common reference frame, ensuring accurate spatial registration and acquisition of precise 3D position and orientation.Field experimentation in woodland and forest environments supports the system’s effectiveness, demonstrating reliable multimodal data acquisition. The collected datasets are available at https://zenodo.org/records/13757335 , offering a testbed for research in localization, mapping, and segmentation of forested areas. The findings indicate that the proposed system provides a reliable, high-accuracy collection solution for forest robotics, offering comprehensive multisensory datasets to further precision forestry research.