A Landscape Approach to Understanding Carbon Sequestration Assets at a State-Wide Scale for Sustainable Urban Planning
Siqi Lai (),
Le Zhang,
Yijun Zeng and
Brian Deal
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Siqi Lai: Department of Landscape Architecture, University of Illinois at Urbana-Champaign, 611 Taft Drive, Champaign, IL 61820, USA
Le Zhang: Center for Terrestrial Biodiversity of the South China Sea, Hainan University, Haikou 570228, China
Yijun Zeng: Department of Landscape Architecture, University of Illinois at Urbana-Champaign, 611 Taft Drive, Champaign, IL 61820, USA
Brian Deal: Department of Landscape Architecture, University of Illinois at Urbana-Champaign, 611 Taft Drive, Champaign, IL 61820, USA
Sustainability, 2024, vol. 16, issue 9, 1-16
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.
Keywords: carbon sequestration in Illinois; sustainable forest management; Kriging interpolation; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:9:p:3779-:d:1386618
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