Integrating Multispectral Imagery with Lidar Enhances 3D Mobile Mapping of Vegetation Condition

Forests provide essential ecosystem services, including regulation of air and water quality, timber provision, and biodiversity support. However, climate extremes and increasing human land use threaten forest health, particularly through biotic and abiotic stressors such as pests, diseases, drought, and nutrient deficiency. Monitoring vegetation condition across forest strata, especially the understorey, remains challenging due to labour-intensive field surveys and limitations of traditional remote sensing. We present a self-built backpack system, with a colourisation method that preserves lidar intensity while extending each lidar point with multispectral camera bands. The altered simultaneous localisation and mapping (SLAM) algorithm outputs a multi-band point cloud alignment, combining close-range mobile laser scanning and spectrally informative imagery. Integration of lidar-derived geometry with multispectral colour information substantially enhances vegetation mapping by combining 3D geometry and position with physiologically meaningful spectral indicators. This establishes more efficient and informative mapping approaches for understorey and forest monitoring. Overall, this study establishes a transferable framework for close-range vegetation condition monitoring that investigates the advantages and limitations of non-calibrated multispectral data, highlights the importance of illumination influence, and closes the gap of multispectral camera and lidar fusion, beyond airborne remote sensing approaches.