Scalable Mapping of Tree Traits to Study the Dynamics of Protected Forest Areas in India

Abstract

Monitoring forest functional diversity is essential to understand ecosystem resilience in the face of rapid environmental change. While existing remote sensing approaches primarily track structural attributes such as canopy density and tree height, functional traits like leaf phenology (evergreen vs. deciduous) and leaf type (broadleaf vs. needleleaf) reveal more direct information about adaptive strategies of tree species. This study presents a scalable machine learning framework for mapping these traits across India at 10 m resolution using Google AlphaEarth Foundations (AEF) embeddings, which capture the complete annual spectral reflectance and radar signatures of the land surface. A key contribution we make is to curate an ML-ready training dataset by combining tree traits information with tree species occurrence data, and to obtain a diverse sample from this data based on spectral time-series to ensure the dataset captures a wide range of phenological dynamics. We then build cross-validation folds to specifically test for spatial generalizability across different eco-regions in India and temporal generalizability across different years, for classifiers learned from the data. Multiple classifiers are evaluated: Random Forest models trained on AEF embedding features achieved the best performance for both classification tasks, outperforming models trained on conventional Sentinel-1 and Sentinel-2 time series while offering seamless deployment in Google Earth Engine. Compared to publicly available land-cover products that encode leaf phenology and leaf type, our model yields significantly higher accuracy while providing outputs at substantially finer spatial resolution. We then observe the outputs of our model over several protected forest areas in India to understand their dynamics over the last 8 years. Our contribution is an analysis-ready open dataset to learn tree traits from remote sensed spectral data, a trained model that is spatially and temporally generalizable, and a demonstration of the insights the model can provide to understand the dynamics of protected forest areas, all of which can be replicated in other areas.