Mangroves, vital salt-tolerant coastal forests, confer numerous societal benefits. However, mapping and monitoring these dense, coastal ecosystems is challenging due to limited field access. Leveraging recent hyperspectral remote sensing advancements, this study addresses this challenge. Hyperspectral imaging’s narrow contiguous bands are potent for characterizing vegetation structure. The research centers on evaluating hyperspectral remote sensing potential, identifying suitable vegetation indices (VI) for the study area, and employing a multistage Decision Tree (DT) by stacking Vis at each stage to extract distinctive mangrove features. Mangrove attributes like unique leaf greenness, high canopy moisture, coastal-induced temperature moderation, and distinct shortwave infrared absorption contrast them from terrestrial vegetation. Various spectral Vegetation Indices (VI) capture these features by utilizing hyperspectral image bands across wavelengths. The proposed study employs Decision Tree (DT) classifier to optimize feature extraction for mangrove mapping by employing VI-based decision functions to discriminate mangroves from non-mangrove classes. Focused on the Marine National Park (MNP) in Jamnagar, Gulf of Kutch, the study exploits AVIRIS-NG hyperspectral data. It employs the Atmospherically Resistant Vegetation Index (ARVI) for vegetation separation, the Normalized Difference Infrared Index (NDII) for elevated canopy moisture analysis, and Shortwave Infrared Absorption Depth (SIAD) to assess unique shortwave infrared absorption in mangroves. Moreover, it integrates the Mangrove Vegetation Index (MVI) and Normalized Difference Mangrove Index (NDMI) for mangrove recognition. Stacking these indices in a DT remarkably boosts classification accuracy, yielding a peak overall accuracy (OA) of 94.71% and a kappa value (k) of 0.86. Published data and reports corroborate these results. This milestone furnishes preliminary insights into mangrove presence, vital for remote sensing-driven mapping and monitoring within challenging field-access environments. Furthermore, these findings pave the way for species-level discrimination via spectral matching and Machine Learning (ML) algorithms.