Biennial sub-meter tree coverage dataset of Orlando (2013-2021).

Area of Science:

• Urban Ecology
• Remote Sensing
• Environmental Monitoring

Background:

• Urban development leads to significant vegetation loss, particularly urban trees.
• Cities recognize the importance of tree canopy cover for environmental challenges but lack monitoring resources.
• Spatiotemporal monitoring is crucial for balancing urban growth with environmental sustainability.

Purpose of the Study:

• To develop high-resolution biennial tree canopy maps for the Orlando metropolitan area (2013-2021).
• To provide a reliable dataset for urban forest management and planning.
• To support environmental and public health monitoring initiatives.

Main Methods:

• Utilized National Agricultural Imagery Program (NAIP) orthoimagery for map development.
• Addressed challenges including sensor inconsistencies, shadows, variable observation times, and environmental factors.
• Conducted extensive ground-truth validation to assess accuracy.

Main Results:

• Generated high-resolution biennial tree canopy maps for 2013-2021.
• Achieved 78%-89% accuracy in tree detection, outperforming existing products.
• Developed a dataset crucial for linking urban planning with actionable management strategies.

Conclusions:

• The developed tree canopy maps offer a valuable tool for urban forest management.
• The dataset supports informed urban planning, environmental monitoring, and public health initiatives.
• High-accuracy remote sensing data is essential for urban sustainability efforts.