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 (

SUBMITTER: Marcilio-Silva V 

PROVIDER: S-EPMC11848234 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature