Whiteboard photography: A field method with automated image processing to measure vertical vegetation features for ecological research in open habitats

Accurate measurement of vertical vegetation structure is essential for modeling habitat variation and species interactions. The study's objective to present an improved whiteboard photography method combined with a semi-automated image processing pipeline to standardize vegetation measurements in open habitats. The workflow includes whiteboard localization using a neural network for real-time object detection; geometric correction based on reference points; pixel-level vegetation classification; and calculation of structural metrics such as leaf area (LA: total leaf surface), maximum height of vegetation (MHV: tallest point), height of closed vegetation (HCV: lowest continuous cover), and foliage height diversity (FHD: height variation). All steps are integrated into a web interface that enables both user-controlled and fully automated image processing within seconds per image. Geometric transformation accuracy and noise sensitivity were evaluated using 120 calibration images. Object-detection precision was high (mAP?? ? 0.995, where mAP?? represents mean average precision at 50 % intersection-over-union). Over 92.9 % of cases exhibited geometric error ? 5 cm, regardless of whiteboard or image quality. Results indicated that renewing the board and using higher-resolution images improved measurement consistency, while moderate occlusion had minimal impact on transformation accuracy. The method was applied to 99 Hungarian grassland plots to model Balkan Wall Lizard density using N-mixture models. Lizard density increased with greater MHV and HCV but decreased with higher LA after accounting for temperature-dependent detectability. This low-cost, non-destructive, and replicable approach provides a reliable tool for measuring vertical vegetation structure and supports ecological monitoring and habitat modeling in open landscapes.