期刊
REMOTE SENSING
卷 4, 期 1, 页码 1-20出版社
MDPI
DOI: 10.3390/rs4010001
关键词
terrestrial LiDAR; forest inventory; point cloud slicing (PCS); heterogeneous forests; terrestrial laser scanning (TLS); vehicle-based laser scanning (VLS)
类别
资金
- NSF (NSF award) [0855690]
- state key fundamental science funds of China [2010CB950701]
- open research fund program of State Key Laboratory of Hydroscience and Engineering in Tsinghua University [sklhse-2012-B-04]
- University of Washington Precision Forestry Cooperative
We present the point cloud slicing (PCS) algorithm, to post process point cloud data (PCD) from terrestrial laser scanning (TLS). We then test this tool for forest inventory application in urban heterogeneous forests. The methodology was based on a voxel data structure derived from TLS PCD. We retrieved biophysical tree parameters including diameter at breast height (DBH), tree height, basal area, and volume. Our results showed that TLS-based metrics explained 91.17% (RMSE = 9.1739 cm, p < 0.001) of the variation in DBH at individual tree level. Though the scanner generated a high-density PCD, only 57.27% (RMSE = 0.7543 m, p < 0.001) accuracy was achieved for predicting tree heights in these very heterogeneous stands. Furthermore, we developed a voxel-based TLS volume estimation method. Our results showed that PCD generated from TLS single location scans only captures 18% of the total tree volume due to an occlusion effect; yet there are significant relationships between the TLS data and field measured parameters for DBH and height, giving promise to the utility of a side scanning approach. Using our method, a terrestrial LiDAR-based inventory, also applicable to mobile- or vehicle-based laser scanning (MLS or VLS), was produced for future calibration of Aerial Laser Scanning (ALS) data and urban forest canopy assessments.
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