2025/11/27 by José Manuel Álvarez‐Martínez, Tijana Nikolić Lugonja, Alicia Valdés +16 · 1 voice
Environmental Science · Engineering · #Remote Sensing in Agriculture #Remote-Sensing Image Classification #Species Distribution and Climate Change
paper · doi:10.1016/j.srs.2025.100341
Remote sensing (RS) has evolved from occasional mapping to continuous, indicator-based monitoring of terrestrial ecosystems. This review synthesizes four decades of global progress in RS to characterize natural and semi-natural ecosystems, examining how study purposes, sensor types and analytical methods have diversified from 1985 to 2025. A systematic literature review of 6856 publications (1567 selected) documents the transition from expert-based visual interpretation using aerial photography and early Landsat missions, to harmonized, AI-driven workflows that enable scalable and replicable ecosystem assessments. Advances in cloud computing, data cubes and open-access archives now allow wall-to-wall time series of analyses across regions and biomes. Yet, important challenges persist, including the underrepresentation of biodiversity-rich areas, limited in-situ calibration data and uncertainties related to phenological variability, image correction, or temporal mosaicking pipelines. Building on case studies from a global perspective, we outline design principles for policy-ready ecosystem indicators traceable to raw observations, comparable through time and space, and aligned with biodiversity policy frameworks. Integrating multi-sensor data (optical, radar, LiDAR, thermal), standardized in-situ observations and artificial intelligence/machine learning algorithms, RS provides a robust pathway towards operational ecosystem accounting and large-scale functional mapping and monitoring, strengthening conservation planning and ecosystem management worldwide. • We reviewed 1567 remote sensing studies on ecosystem monitoring (1985-2025) • We document the evolution of purpose, sensors and algorithms through time • Machine learning dominates mapping, while time-series analyses expand monitoring • Data gaps persist in biodiversity-rich but under-monitored regions • Reliable field data, multi-sensor fusion and AI will drive next-generation models