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    Accurately measuring the biophysical dimensions of urban trees, such as crown diameter, stem diameter, height, and biomass, is essential for quantifying their collective benefits as an urban forest. However, the cost of directly measuring thousands or millions of individual trees through field surveys can be prohibitive. Supplementing field surveys with remotely sensed data can reduce costs if measurements derived from remotely sensed data are accurate. This study identifies and measures the errors incurred in estimating key tree dimensions from two types of remotely sensed data: high-resolution aerial imagery and LiDAR (Light Detection and Ranging). Using Sacramento, CA, as the study site, we obtained field-measured dimensions of 20 predominant species of street trees, including 30–60 randomly selected trees of each species. For each of the 802 trees crown diameter was estimated from the aerial photo and compared with the field-measured crown diameter. Three curve-fitting equations were tested using field measurements to derive diameter at breast height (DBH) (r2 = 0.883, RMSE = 10.32 cm) from the crown diameter. The accuracy of tree height extracted from the LiDAR-based surface model was compared with the field-measured height (RMSE = 1.64 m). We found that the DBH and tree height extracted from the remotely sensed data were lower than their respective field-measured values without adjustment. The magnitude of differences in these measures tended to be larger for smaller-stature trees than for larger stature species. Using DBH and tree height calculated from remotely sensed data, aboveground biomass (r2 = 0.881, RMSE = 799.2 kg) was calculated for individual tree and compared with results from field-measured DBH and height. We present guidelines for identifying potential errors in each step of data processing. These findings inform the development of procedures for monitoring tree growth with remote sensing and for calculating single tree level carbon storage using DBH from crown diameter and tree height in the urban forest.

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    Lee, Jun-Hak; Ko, Yekang; McPherson, E. Gregory. 2016. The feasibility of remotely sensed data to estimate urban tree dimensions and biomass. Urban Forestry & Urban Greening. 16: 208-220.


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    Aerial images, Crown diameter, DBH, LiDAR, Tree height, Urban tree biomass

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