Urban settlements can support significant biodiversity and provide a wide range of ecosystem services. Remote sensing (RS) offers valuable tools for monitoring and conserving urban biodiversity. Our research, funded by the Italian Recovery and Resilience Plan (National Biodiversity Future Centre—Urban Biodiversity), undertakes a systematic scientific review to assess the current status and future prospects of urban biodiversity evaluation using RS. An extensive literature search of indexed peer-reviewed papers published between 2008 and 2023 was conducted on the Scopus database, using a selective choice of keywords. After screening the titles, abstracts, and keywords of 500 articles, 117 relevant papers were retained for meta-data analysis. Our analysis incorporated technical (e.g., sensor, platform, algorithm), geographic (e.g., country, city extent, population) and ecological (biodiversity target, organization level, biome) meta-data, examining their frequencies, temporal trends (Generalized Linear Model—GLM), and covariations (Cramer’s V). The rise in publications over time is linked to the increased availability of imagery, enhanced computing power, and growing awareness of the importance of urban biodiversity. Most research focused on the Northern Hemisphere and large metropolitan areas, with smaller cities often overlooked. Consequently, data coverage is predominantly concentrated on Mediterranean and temperate habitats, with limited attention given to boreal, desert, and tropical biomes. A strong association was observed between the source of RS data (e.g., satellite missions), pixel size, and the purpose of its use (e.g., modeling, detection). This research provides a comprehensive summary of RS applications for evaluating urban biodiversity with a focus on the biomes studied, biodiversity targets, and ecological organization levels. This work can provide information on where future studies should focus their efforts on the study of urban biodiversity using remote sensing instruments in the coming years.

Rapid human population growth accelerates biodiversity loss through urban habitat fragmentation, yet ecologically informed urban planning can mitigate these effects. This study evaluates whether and how vegetation characteristics, as captured by Earth observation data varies across forest habitats in a small Mediterranean city in Italy. The Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) for the Functional Urban Area of Campobasso were derived from multitemporal Landsat 8 imagery (2020–2023) acquired during the growing season and combined with elevation data to account for topographic gradients. Different forest habitats were identified using the regional coeval Carta della Natura (Map of Nature) and were sampled by a random stratified strategy yielding more than 900,000 observations. A linear mixed-effects model was used to model NDVI as a function of NDMI, LST, elevation, and habitat type, while accounting for temporal and spatial dependencies. The model explained a large proportion of NDVI variability (marginal R² = 0.75; conditional R² = 0.85), with NDMI emerging as the strongest predictor, followed by weaker effects of LST and elevation. Habitat differences were also evident: oak-dominated forests (i.e., Quercus frainetto, Q. cerris, and Q. pubescens dominated habitats) exhibited the highest NDVI values, while coniferous plantations (i.e., Pinus nigra dominated habitat) had the lowest; forests dominated by Robinia pseudoacacia and riparian Salix alba showed intermediate vegetation greenness values. These results highlight the ecological importance of oak forests in Mediterranean urban landscapes and demonstrate the value of satellite-based monitoring for capturing habitat variability. The reproducible workflow applied here provides a scalable tool to support habitat conservation and planning in urban environments, also accounting for impending climate change scenarios.

Urban areas are being recognized as ecosystems, where biodiversity is essential for sustaining their functionality. Healthy ecosystems offer numerous services that contribute to the well-being of human populations. The EU Biodiversity Strategy and the Nature Restoration law call for no-net loss of urban green spaces and a minimum 10% tree canopy cover in European cities. This commitment is driving a surge in city tree planting projects, with expectations for further increase in the coming years.In this context, we present an investigation focused on the multiscale mapping and monitoring of urban green biodiversity, with a primary emphasis on trees. Urban trees, besides providing various ecosystem services, play a crucial role in mitigating the urban heat load during summers, thereby alleviating adverse effects on human health, and reducing energy consumption. Unfortunately, the challenging conditions within urban environments, including increased temperatures and water scarcity due to impervious surfaces, can impact the phenology and physiology of trees, often compromising their health and functionality.In order to better explore these aspects, we introduce an application based on Google Earth Engine with the aim to extract geospatial data related to tree cover and temperatures across various spatial scales in urban environments. Utilizing machine learning algorithms, the application downscales thermal infrared satellite imagery and classifies vegetation features. Users have the flexibility to investigate the relationship between temperatures and vegetation by selecting specific time windows and areas of interest, along with access to significant spectral indices and correlation coefficients. In a pilot case study on the city of Milan, we use the application to perform a detailed analysis at the tree species level, involving the assessment of individual tree canopy temperature response in different areas of the city.This application aims to provide researchers, urban planners, green managers, and other professionals with a valuable tool to comprehend the spatial dynamics of vegetation in urban environments, assess the impacts of stressors on their fitness, and in the long run to evaluate the effectiveness of mitigation efforts, such as urban reforestations and tree planting.