Abstract
Surface roughness parameters, such as roughness length and displacement height, impact the estimation of surface moisture, and the frontal areas of buildings and trees are two components that contribute to surface roughness in urban areas. Research on tree frontal area has not been conducted in urban areas before, and we hope to fill that gap in the literature with this study by using Terrestrial Light Detection and Ranging (LiDAR) data to estimate tree frontal areas in Warren Township, Indianapolis, IN, USA. We first estimated the frontal areas of individual trees based on their morphology, then calibrated a regression model to estimate the tree frontal area in 30 m pixels using parameters derived from LiDAR data and tree inventory data. The parameters included tree crown base area, height, width, conditions, defects, maintenances, genera, and land use. The validation shows that R2 yielded values ranging from 0.84 to 0.88, and RMSEs varied with tree category. The tree categories were identified based on the height and broadness of the canopy, which indicated the degree of resistance to air flow. This type of model can be used to empirically determine local roughness values at the tree-level for any city with a complete tree inventory. With the strong correlation between trees’ frontal area and crown base area, this model may also be used to determine local roughness value at 30 m resolution with NLCD (National Land Cover Database) tree canopy cover data as a component. A proper tree categorization according to the vertical air resistance, e.g., height and canopy density, was effective to reduce the RMSE in tree frontal area estimation. Geometric parameters, such as height, crown base height, and crown base area extracted from Airborne LiDAR, which demand less storage and computation capacity, may also be sufficient for tree frontal area estimation in the areas where Terrestrial LiDAR is not available.
Highlights
A better knowledge of the controls of the urban heat island effect is important for estimating the impact of global climate change on urban areas
This study presents the first attempt to estimate the frontal areas of trees in an urban area using Terrestrial Light Detection and Ranging (LiDAR), tree inventory data, Normalized Difference Vegetation Index (NDVI), and NLCD tree canopy cover
Instead of using conventional methods based on LULC types or geometrical models, Terrestrial LiDAR data offered great potentials to extract true and timely individual tree frontal areas in urban settings
Summary
A better knowledge of the controls of the urban heat island effect is important for estimating the impact of global climate change on urban areas. The limited number of weather stations in urban areas is often not sufficient to delineate the spatial distribution of air temperature, and the estimation of. Land Surface Temperature (LST) from remotely sensed data has attracted more and more attention [1]. Factors that link air temperature and LST are important in urban heat island studies [2,3]. The Two Source Energy Balance (TSEB) Model has been used in forest and agricultural areas to estimate evapotranspiration over soil and vegetation surfaces. When applying the TSEB to urban areas to estimate evaporation over impervious surfaces, a great challenge
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