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Uncertainties in mapping forest carbon in urban ecosystems
Gang Chen1, Emre Ozelkan2, Kunwar K Singh3
1Laboratory for Remote Sensing and Environmental Change (LRSEC), Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
Remote sensing data resolution impacts urban forest carbon estimates. Higher uncertainty in carbon mapping occurs in disturbed urban neighborhoods, with LiDAR data more sensitive to connectivity and NAIP to shape complexity.
Area of Science:
- Urban forestry
- Remote sensing
- Geospatial analysis
Background:
- Accurate urban forest carbon estimation is vital for sustainable city planning and management.
- High-resolution remote sensing data is crucial but often limited by cost and availability for urban carbon mapping.
- Understanding data resolution impacts on carbon estimation in diverse urban landscapes is essential.
Purpose of the Study:
- To assess uncertainties in urban forest carbon estimation due to varying remote sensing data resolutions and neighborhood spatial patterns.
- To quantify the impact of LiDAR point density and National Agricultural Imagery Program (NAIP) image resolution on carbon mapping accuracy.
- To investigate how urban spatial patterns influence the reliability of carbon estimation across different built-up densities.
Main Methods:
- Resampling LiDAR point cloud data to densities of 1.2–5.8 pts/m² and NAIP imagery to resolutions of 1–20 m.
- Extracting urban spatial patterns (area, shape, dispersion, diversity, connectivity) from residential neighborhoods.
- Analyzing statistical relationships between data resolution, spatial patterns, and carbon estimation uncertainties.
Main Results:
- Varying remote sensing data resolutions introduce significant uncertainties in neighborhood-level forest carbon estimation.
- LiDAR resolution changes caused 8.7–11.0% variation, while NAIP resolution changes caused 6.2–8.6% variation.
- Higher anthropogenic disturbance in urban neighborhoods led to greater uncertainty; LiDAR results were affected by connectivity, NAIP by shape complexity.
Conclusions:
- Remote sensing data resolution is a critical factor influencing urban forest carbon estimation accuracy.
- Urban spatial patterns, particularly connectivity and shape complexity, interact with data resolution to affect estimation reliability.
- Future urban carbon mapping requires careful consideration of data resolution and landscape characteristics for improved accuracy.
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