Abstract
Forests are an important component of urban ecosystems that offer a variety of ecological services; however, there are few reports on the application of high-precision three-dimensional (3D) laser-scanned point cloud data to quantify the 3D green volume. To quantitatively study the 3D green quantity of urban forests in Xi'an, China, aerial remote sensing and high-precision 3D laser-scanned point cloud data were used to build an estimation model using a differential method. The results show that the 3D green quantity model has a high estimation accuracy of 88.07%, with an R value of 0.89, and can be used as an inverse model for estimating the 3D standing wood volume. The method can be automated for data processing and has a high measurement efficiency, making it applicable to real situations and significantly improving the measurement accuracy of single-tree 3D green quantification. The total 3D green quantity in the main urban area of Xi’an is 37,475,294.7 m3, where areas containing the greatest green volume follow the order scenic sites > cultural and educational areas > traffic routes > residential areas > industrial areas. This method can be used to improve urban design, policy making, and healthy urban forests.
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