Integrating remote sensing and field inventories to understand determinants of urban forest diversity and structure.

Abstract

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 with field inventories to comprehensively map and analyze urban forest attributes in forest patches across the Minneapolis-St. Paul Metropolitan Area (MSPMA) in a multistep process. First, we developed predictive machine learning models of forest attributes by integrating data from forest inventories (from 40 12.5-m-radius plots) with Global Ecosystem Dynamics Investigation (GEDI) observations and Sentinel-2-derived land surface phenology (LSP). These models enabled accurate predictions of forest attributes, specifically nine metrics of plant diversity (tree species richness, tree abundance, and understory plant abundance), structure (average canopy height, dbh, and canopy density), and structural complexity (variability in canopy height, dbh, and canopy density) with relative errors ranging between 11% and 21%. Second, we applied these machine learning models to predict diversity metrics for 804 additional plots from GEDI and Sentinel-2. Finally, we applied Bayesian multilevel models to the predicted diversity metrics to assess the influence of multiple factors-patch dimensions, landscape attributes, plot position, and jurisdictional agency-on these forest attributes across the 804 predicted plots. The models showed all predictors have some degree of effect on forest attributes, presenting varying explanatory power with R 2 values ranging from 0.071 to 0.405. Overall, plot characteristics (e.g., distance to nearest trail, proximity to forest edge) and jurisdictional agency explained a large portion of the variability across patches, whereas patch and landscape characteristics did not. The relative effect of plot versus management sets of predictors on the marginal ΔR 2 was heterogeneous across metrics and ecological subsections (an ecological classification designation). The multiplicity of determinants influencing urban forests emphasizes the intricate nature of urban ecosystems and highlights nuanced, heterogeneous relationships between urban ecological and anthropogenic factors that determine forest properties. Effectively enhancing biodiversity in urban forests requires assessments, management, and conservation strategies tailored for context-specific characteristics.

Plant phenotyping relevance

GEDI・Sentinel-2と機械学習を統合し、植物の多様性・構造属性を予測する測定手法を開発、誤差評価し、追加プロットへ適用しているため、表現型取得が中心的である。

Code and data availability

The paper's data availability statement provides three paper-specific public assets: the field vegetation inventory data on EDI, the machine learning ensemble R script on Zenodo, and the Bayesian model summaries on Zenodo.