Dry forests provide essential ecological functions and sustain local livelihoods, yet they are increasingly threatened by land use and climate change. Their sustainable management requires understanding the links between structure, function, and use. This study develops a framework to characterize Neltuma flexuosa woodlands and guide management strategies. Field surveys, multivariate analyses, and remote sensing were combined in the lower Tunuyan River basin (Monte ecoregion, Argentina). We identified three environments based on an object-based classification of Sentinel-2 images: Dense Woodland (DW), Open Woodland (OW), and Shrubland with Emergents (SwE) (overall accuracy 82 %). DW showed the highest tree and canopy cover, strong regeneration, and greater product availability, but also high stump density, reflecting both conservation value and use pressure. OW exhibited intermediate density and cover, high multifustality, and medium product availability, while SwE was dominated by shrubs, with low tree density and limited potential. Multitemporal SATVI (SoilAdjusted Total Vegetation Index) analysis revealed that DW was directly linked to groundwater hydrology, whereas OW and SwE depended on rainfall and showed attenuated seasonal dynamics. Sentinel-2 imagery and classification algorithms enabled accurate mapping of the spatial distribution. The results support differentiated guidelines: protection in DW, low-intensity use in OW, and conservation of shrub cover in SwE. The framework provides scalable tools for adaptive strategies balancing biodiversity conservation and sustainable use in arid landscapes.