Applying Remote Sensing for Improved Monitoring and Management of Forest Biodiversity: From Tree-Level Indicators to National Level

Even though forest biodiversity is currently at the center of international attention in the context of ecosystem restoration and global conservation strategies, quantifying forest biodiversity across large spatial extents remains a central challenge in ecological monitoring and forest management. The effective management of biodiversity in both protected areas and managed forests has long been constrained by a lack of methodologically consistent, and spatially continuous data. RNew high-resolution remote sensing data—including Vegetation Height Models, phenology time series, and detailed forest area maps—combined with standardized inventories, can bridge existing data gaps and substantially enhance forest ecosystem monitoring and biodiversity management. These base datasets have enabled the generation of new value products such as tree species mapping, standing deadwood, biomass mapping and phenology anomalies opening a new level of forest monitoring.  We present a multi-level showcase framework to demonstrate practical applications of these technologies in forest biodiversity and nature conservation: At the individual tree level, we explore the potential for delineating habitat tree priority areas by detecting indicators such as standing deadwood and crown dieback. These features serve as critical proxies for saproxylic insects, fungi, and cavity-nesting birds. While precise individual mapping remains a challenge, identifying these potential habitat backdrops enables a more targeted spatial approach to the conservation of rare or endangered tree species. At the stand level, structural heterogeneity and damages can be indicating biodiversity. A 26-class tree species map enables the assessment of compositional diversity and mixing degrees. Time-series of vegetation indices derived from sensors depict seasonal changes in forest phenology. This enables recognizing forest damages such as windthrow, bark beetle infestations, and even slow progressing tree pests. At the landscape and national levels, we utilize digital terrain models (DTM) and high-resolution vegetation height models to derive geodiversity and forest structure indices and identify micro-habitats. Landscape connectivity is addressed by mapping forest roads to measure fragmentation and planning ecological corridors between protected areas. Despite these opportunities, we address several critical limitations. While remote sensing offers scalability and objectivity, "ground truthing" - such as with the Austrian inventory data - remains an indispensable foundation for model validation. This necessitates a new profile of expertise: professionals who bridge deep ecological knowledge with data science. Only through interdisciplinary cooperation and a careful balance of technological gain versus energy consumption can digital models be meaningfully applied to protect our natural resources.