A Landscape Approach to Understanding Carbon Sequestration Assets at a State-Wide Scale for Sustainable Urban Planning

Siqi Lai, Le Zhang, Yijun Zeng, Brian Deal

Research output: Contribution to journalArticlepeer-review

Abstract

This study presents a refined approach to spatially identify carbon sequestration assets, crucial for effective climate action planning in Illinois. By integrating landscape analytical methods with species-specific carbon assessment techniques, we deliver a nuanced evaluation of forest area sequestration potential. Our methodology employs a combination of landscape imagery, deep learning analytics, Kriging interpolation, and i-Tree Planting tools to process forest sample data. The results reveal a spatial variability in sequestration capacities, highlighting significant carbon sinks in southern Illinois. This region, known for its historical woodland richness, showcases the distinct carbon sequestration abilities of various tree species. Findings emphasize the role of biodiversity in the carbon cycle and provide actionable insights for forest management and carbon neutral strategies. This study demonstrates the utility of advanced spatial analysis in environmental research, underscoring its potential to enhance accuracy in ecological quantification and conservation efforts.
Original languageEnglish (US)
Article number3779
JournalSustainability
Volume16
Issue number9
DOIs
StatePublished - May 2024

Keywords

  • carbon sequestration in Illinois
  • deep learning
  • Kriging interpolation
  • sustainable forest management

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