A classification method of unmanned-aerial-systems-derived point cloud for generating a canopy height model of Farm Forest

During the last decade unmanned aerial systems (UAS) have been intensively applied to create 3-dimensional models of land surface features for various applications. Although, UAS data can be jointly used with airborne LiDAR data to generate a canopy height model (CHM) of forest stands, it is rare to find research concerned the generation of forest CHM using only UAS data. This paper investigate a suitable method to classify UAS point cloud to create CHM data for forest inventory. Results showed that ground points over building areas, open land, and forestland can be successfully collected by appropriate terrain angles which define a threshold value of the angle between a point, its projection on the plane of a triangle, and the closest vertex of a TIN surface model. A conservative threshold value of 5 degrees was suggested due to its allowing critical ground points whilst excluding crown points being collected. The UAS-derived CHM was evaluated with an RMSE accuracy of 0.01, 0.20, and 0.42 m for road, buildings, and trees respectively.

[1]  M. Pierrot-Deseilligny,et al.  A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery , 2013 .

[2]  Adrien Michez,et al.  Discrimination of Deciduous Tree Species from Time Series of Unmanned Aerial System Imagery , 2015, PloS one.

[3]  Chien-Shun Lo,et al.  Growth-Competition-Based Stem Diameter and Volume Modeling for Tree-Level Forest Inventory Using Airborne LiDAR Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[4]  S. Piao,et al.  Changes in vegetation net primary productivity from 1982 to 1999 in China , 2005 .

[5]  Peter Axelsson,et al.  Processing of laser scanner data-algorithms and applications , 1999 .

[6]  J. V. van Aardt,et al.  Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data , 2010 .

[7]  R. Podlaski,et al.  Modelling irregular and multimodal tree diameter distributions by finite mixture models: an approach to stand structure characterisation , 2012, Journal of Forest Research.

[8]  Chinsu Lin,et al.  A flexible modeling of irregular diameter structure for the volume estimation of forest stands , 2014, Journal of Forest Research.

[9]  Guoqing Sun,et al.  Evaluation of UAV-based forest inventory system compared with LiDAR data , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[10]  Chinsu Lin,et al.  Deriving the Spatiotemporal NPP Pattern in Terrestrial Ecosystems of Mongolia Using MODIS Imagery , 2015 .

[11]  Arko Lucieer,et al.  Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[12]  P. Axelsson.  DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[13]  Pablo J. Zarco-Tejada,et al.  Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods , 2014 .