Floodplain vegetation in dryland environments supports wildlife and provides ecosystem functions and productivity. Detecting, analyzing, and mapping the spatial distribution and structural metrics of floodplain vegetation provide effective indicators, for evaluating ecological responses to water management, which is vital for balancing dryland ecosystems. This study presents interpretable condition metrics and an approach, integrating Gaussian Filtering Model (GFM), Canopy Height Area Model (CHAM), and Visible Light Difference Vegetation Index (VDVI), using drone Lidar-RGB data, for classifying woody floodplain vegetation. By combining structural, spectral, and spatial information, the GFM-CHAM-VDVI approach enables direct estimation of field-relevant vegetation indicators, including growth conditions (vigorous and dormant), percent cover, openness, and height, delivering an interpretable alternative to single-index or black-box deep learning models. Applied across 17 plots (50 & times; 50 m) in floodplain shrublands of the Mallee region, Australia, dominated by Duma florulenta (lignum), Eucalyptus camaldulensis, and Eucalyptus largiflorens (trees), the approach achieved high classification performance with overall accuracy (0.9205), Kappa (0.8930), and true-positive rates exceeding 95% for shrubs and trees. The approach demonstrated strong agreement (approximate to 78.5%) with published CNN results. Its robustness was illustrated through sensitivity and spatial autocorrelation analyses by comparing the fixed CHM threshold (3 m) with Otsu and K-means derived thresholds, VDVI threshold variations (0.01176 +/- 10%) and Moran's I. The developed condition indicators revealed clear and ecologically meaningful relationships with flooding history. The study provides an interpretable and easy-to-use alternative to field-based monitoring and rapid assessment for improving the evaluation of floodplain vegetation responses to water management in dryland environments.