Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest

Remote Sensing

L. Monika Moskal¹ 

[1] Remote Sensing and Geospatial Analysis Laboratory, Precision Forestry Cooperative, School of Forest Resources, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA

关键词: terrestrial LiDAR;    forest inventory;    point cloud slicing (PCS);    heterogeneous forests;    terrestrial laser scanning (TLS);    vehicle-based laser scanning (VLS);   

DOI  :  10.3390/rs4010001

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.