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 semiautomated 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. Objectdetection precision was high (mAP₅₀ ≈ 0.995, where mAP₅₀ 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.