Theme 4 examines the consequences of biodiversity for ecosystem structure and function and its response to global change.

One of our goals in theme 4 is to evaluate the effect of plant biodiversity on ecosystem properties and processes. In this study, we are investigating how urban forest canopy and understory composition, structure and diversity in urban forest patches influence belowground soil processes in Minneapolis Saint Paul. To map urban forest structure and diversity across the metropolitan area, the ASCEND team has developed predictive models using spaceborne data coupled with ground-based measurements.

About the map

Understanding the determinants of urban forest diversity and structure is important for preserving biodiversity and sustaining ecosystem services in cities. However, comprehensive field assessments are resource-intensive, and landscape-level approaches may overlook heterogeneity within urban regions. To address this challenge, we combined remote sensing tools with field inventories to comprehensively map urban forest attributes across the Minneapolis-St. Paul Metropolitan Area (MSPMA). We used machine learning ensemble models to integrate data from forest inventories (40 12.5-meter radius plots; Marcilio-Silva et al. 2022), GEDI observations, and Sentinel-2 derived land surface phenology. These models enabled accurate predictions of the nine forest attributes presented here: plant diversity (tree species richness, tree abundance, and understory plant abundance), structure (average canopy height, diameter at breast height (DBH), and canopy density), and structural complexity (variability in canopy height, DBH, and canopy density) with relative errors ranging between 11% to 21%. Each point in this map shows the predicted forest attributes based on our models. The layer named “Footprints” shows the in-groud size of the GEDI footprints and surveyed plots (12.5-radius circle), thus it is only visible at higher zoom views.