Utilizing LiDAR to quantify aboveground tree biomass within an urban university

Abstract

Universities, often situated at the heart of metropolitan areas, have the unique opportunity to leverage effective urban forestry methods to promote ecological and economic conservation. Simply identifying what trees to plant where can have major effects such as reducing temperature, flood impacts, habitat fragmentation, and carbon emissions in urban areas. Detailed mapping of tree biomass allows researchers to spatially identify carbon sinks and analyze the associated ecological benefits at an urban or intra-urban level. This study utilizes non-destructive field measurements and aerial Light Detection and Ranging (LiDAR) remote sensing to estimate biomass on the University of Alabama in Huntsville (UAH) campus. A field survey of campus trees was performed to calculate the observed biomass through allometry. These values were then used as an input in a regression analysis along with LiDAR-derived canopy metrics to evaluate LiDAR’s ability to estimate biomass. The result yielded biomass equations specific to 14 tree species found on the UAH campus. It was found that the regression models showed a high fit (R2 = 0.62–0.98) for most species. Some trees such as the Pinus taeda, Pinus echinata, and Lagerstroemia indica had lower R2 values (0.26–0.43) most likely due to overlapping tree canopies; a known limitation in urban biomass studies that can be solved with more intricate segmentation methods. Nonetheless, this methodology is an avenue for urban planners to estimate biomass without intensive and costly field surveys, particularly in north Alabama where there is a lack of urban biomass research.