Advanced Remote Sensing Data Analysis for Sustainable Development Goals – Life on Land: Biodiversity and Ecosystem

Remote sensing has become a cornerstone of data-driven decision-making for monitoring biodiversity and supporting the achievement of the Sustainable Development Goals (SDGs). By providing consistent, spatially explicit observations across scales, Earth observation (EO) technologies enable systematic assessment of environmental change and ecosystem dynamics. Within this context, the Essential Biodiversity Variables (EBV) framework offers a standardised approach to harmonising biodiversity observations from in-situ and remote sensing platforms, thereby enhancing interoperability and the effective use of biodiversity information for conservation and sustainable development. This paper focuses on two EBV classes of particular relevance to EO applications: Ecosystem structure and Species traits. We review recent advances in remote sensing techniques—particularly LiDAR, multispectral, hyperspectral, and radar data—and their capacity to monitor ecosystem vertical structure, ecosystem distribution, habitat suitability, and vegetation traits such as productivity, phenology, leaf area index, chlorophyll content, and functional traits. The integration of EO data with in-situ observations and machine learning approaches is highlighted as a key pathway for improving habitat modelling and biodiversity assessments at regional to continental scales, with direct relevance to SDG 15 (Life on Land). We further discuss current challenges, including data resolution limitations, standardisation, computational demands, and the translation of EO-derived indicators into policy-relevant metrics. Finally, we outline future perspectives, emphasising the role of emerging sensor technologies, artificial intelligence, FAIR data principles, and multi-source data integration in advancing EBV monitoring and strengthening the contribution of remote sensing to sustainable ecosystem management and global biodiversity targets.