Classification and Characterization of Vegetation Dynamics in Northeastern Mexico from 25-Year EVI Time Series

Abstract

Vegetation indices are used to analyze vegetation dynamics and primary productivity. However, most studies rely on short time series and peak or integral metrics, which limit the understanding of long-term vegetation dynamics in heterogeneous areas. This study aimed to classify a subarea of northeastern Mexico using a 25-year EVI time series and to characterize the resulting groups using growth parameters derived from temporal analysis. MODIS EVI mosaics from 2000 to 2024 were averaged and classified using the ISODATA algorithm, resulting in 16 groups. Smoothed EVI time series were analyzed with TIMESAT to extract growth parameters, which were compared among groups using Discriminant Function Analysis with cross-validation. Minimum primary productivity expressed as EVI base value (BVAL) explained most of the observed variance among groups (70.7\%). The classification exhibited robust statistical separability, achieving a cross-validated accuracy of 75.1\% (kappa = 0.73), and showed mesoscale spatial structure (similar to 12.5 km). The groups had moderate but significant associations (Cramer's V = 0.33) with existing vegetation and climate cartography. The results suggest that long-term BVAL is a stable and ecologically meaningful descriptor of landscape functioning. Overall, the proposed classification captures gradients and transition zones not represented in static cartographic products, revealing vegetation dynamics across heterogeneous landscapes.