Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy

Čerňava J., Tuček J., Koreň M., Mokroš M. (2017): Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy. J. For. Sci., 63: 433–441. Mobile laser scanning (MLS) is time-efficient technology of geospatial data collection that proved its ability to provide accurate measurements in many fields. Mobile innovation of the terrestrial laser scanning has a potential to collect forest inventory data on a tree level from large plots in a short time. Valuable data, collected using mobile mapping system (MMS), becomes very difficult to process when Global Navigation Satellite System (GNSS) outages become too long. A heavy forest canopy blocking the GNSS signal and limited accessibility can make mobile mapping very difficult. This paper presents processing of data collected by MMS under a heavy forest canopy. DBH was estimated from MLS point cloud using three different methods. Root mean squared error varied between 2.65 and 5.57 cm. Our research resulted in verification of the influence of MLS coverage of tree stem on the accuracy of DBH data.

[1]  Jianping Wu,et al.  A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data , 2013, Remote. Sens..

[2]  Xinyu Song,et al.  Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data , 2016, Remote. Sens..

[3]  Juha Hyyppä,et al.  Tree mapping using airborne, terrestrial and mobile laser scanning – A case study in a heterogeneous urban forest , 2013 .

[4]  Juha Hyyppä,et al.  Outlook for the Next Generation’s Precision Forestry in Finland , 2014 .

[5]  Norbert Pfeifer,et al.  OPALS - A framework for Airborne Laser Scanning data analysis , 2014, Comput. Environ. Urban Syst..

[6]  Erik Lithopoulos,et al.  Inertial / GPS System for Seismic Survey , 2000 .

[7]  J. Hyyppä,et al.  QUALITY ANALYSIS AND CORRECTION OF MOBILE BACKPACK LASER SCANNING DATA , 2016 .

[8]  Di Wang,et al.  Automatic and Self-Adaptive Stem Reconstruction in Landslide-Affected Forests , 2016, Remote. Sens..

[9]  Jan van Aardt,et al.  Single-Scan Stem Reconstruction Using Low-Resolution Terrestrial Laser Scanner Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Jussi Rasinmäki,et al.  A method for estimating tree composition and volume using harvester data , 2005 .

[11]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[12]  Norbert Pfeifer,et al.  ORIENTATION AND PROCESSING OF AIRBORNE LASER SCANNING DATA (OPALS) - CONCEPT AND FIRST RESULTS OF A COMPREHENSIVE ALS SOFTWARE , 2009 .

[13]  M. Holopainen,et al.  SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR , 2015 .

[14]  M. Vastaranta,et al.  Terrestrial laser scanning in forest inventories , 2016 .

[15]  Juha Hyyppä,et al.  The Use of a Mobile Laser Scanning System for Mapping Large Forest Plots , 2014, IEEE Geoscience and Remote Sensing Letters.

[16]  J. Holmgren,et al.  Tree Stem Diameter Estimation from Mobile Laser Scanning Using Line-Wise Intensity-Based Clustering , 2016 .