Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest

Methods for accurately measuring biophysical parameters are a key component for quantitative evaluation regarding to various forest applications. Conventional in situ measurements of these parameters take time and expense, encountering difficultness at locations with heterogeneous microtopography. To obtain precise biophysical data in such situations, we deployed an unmanned aerial system (UAS) multirotor drone in a cypress forest in a mountainous area of Japan. The structure from motion (SfM) method was used to construct a three-dimensional (3D) model of the forest (tree) structures from aerial photos. Tree height was estimated from the 3D model and compared to in situ ground data. We also analyzed the relationships between a biophysical parameter, diameter at breast height (DBH), of individual trees with canopy width and area measured from orthorectified images. Despite the constraints of ground exposure in a highly dense forest area, tree height was estimated at an accuracy of root mean square error = 1.712 m for observed tree heights ranging from 16 to 24 m. DBH was highly correlated with canopy width (R2 = 0.7786) and canopy area (R2 = 0.7923), where DBH ranged from 11 to 58 cm. The results of estimating forest parameters indicate that drone-based remote-sensing methods can be utilized to accurately analyze the spatial extent of forest structures.

[1]  Juha Hyyppä,et al.  Advances in Forest Inventory Using Airborne Laser Scanning , 2012, Remote. Sens..

[2]  Masanobu Shimada,et al.  An Evaluation of the ALOS PALSAR L-Band Backscatter—Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  P. Surový,et al.  Determining tree height and crown diameter from high-resolution UAV imagery , 2017 .

[4]  Noriyuki Osada.  Crown exposure to light and tree allometry of 11 tree species in a snowy cool-temperate forest in Japan , 2012, Plant Ecology.

[5]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

[6]  Uldis Silins,et al.  Reducing stem bending increases the height growth of tall pines. , 2006, Journal of experimental botany.

[7]  M. Keller,et al.  Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+ , 2015, Carbon Balance and Management.

[8]  P. West,et al.  Tree and Forest Measurement , 2004, Springer Berlin Heidelberg.

[9]  P. Němec.  Comparison of modern forest inventory method with the common method for management of tropical rainforest in the Peruvian Amazon. , 2015 .

[10]  Marco Marchetti,et al.  REDD+: Quick Assessment of Deforestation Risk Based on Available Data , 2017 .

[11]  I. Woodhouse,et al.  Structure from Motion (SfM) Photogrammetry with Drone Data: A Low Cost Method for Monitoring Greenhouse Gas Emissions from Forests in Developing Countries , 2017 .

[12]  Lalit Kumar,et al.  Sub-Compartment Variation in Tree Height, Stem Diameter and Stocking in a Pinus radiata D. Don Plantation Examined Using Airborne LiDAR Data , 2014, Remote. Sens..

[13]  Luo Jianxin,et al.  Survey of structure from motion , 2014, Proceedings of 2014 International Conference on Cloud Computing and Internet of Things.

[14]  A. Sumida,et al.  Relationships of tree height and diameter at breast height revisited: analyses of stem growth using 20-year data of an even-aged Chamaecyparis obtusa stand , 2013, Tree physiology.

[15]  Stanley J. Zarnoch,et al.  The Forest Service Safety Survey: results from an employee-wide safety attitude survey , 2014 .

[16]  Shizhong Liu,et al.  Stand characteristics and water use at two elevations in a sub-tropical evergreen forest in southern China , 2014 .

[17]  Michael Bock,et al.  System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 , 2015 .

[18]  J. Fransson.  Estimation of stem volume in boreal forests using ERS-1 C- and JERS-1 L-band SAR data , 1999 .

[19]  Jonathan P. Dash,et al.  Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak , 2017 .

[20]  Mathias Schardt,et al.  HIGH-SCAN: The first European-wide attempt to derive single-tree information from laserscanner data , 2001 .

[21]  M. Honzak,et al.  Tropical Forest Biomass Density Estimation Using JERS-1 SAR: Seasonal Variation, Confidence Limits, and Application to Image Mosaics , 1998 .

[22]  Brian R. Sturtevant,et al.  Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data , 2009 .

[23]  Ryutaro Tateishi,et al.  Mapping Tropical Forest Biomass by Combining ALOS-2, Landsat 8, and Field Plots Data , 2016 .

[24]  Guang Zheng,et al.  Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest , 2011, Remote. Sens..

[25]  S. Magnussen,et al.  Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory , 2006 .

[26]  Bambang Supriadi,et al.  Backscattering characteristics of L-band polarimetric and optical satellite imagery over planted acacia forests in Sumatra, Indonesia , 2012 .

[27]  C CoopsNicholas,et al.  Unmanned aerial systems for precision forest inventory purposes: A review and case study , 2017 .

[28]  Juha Hyyppä,et al.  Evaluation of a Smartphone App for Forest Sample Plot Measurements , 2015 .

[29]  Ryutaro Tateishi,et al.  Simple Relationship Analysis between L-Band Backscattering Intensity and the Stand Characteristics of Sugi ( Cryptomeria japonica ) and Hinoki ( Chamaecyparis obtusa ) Trees , 2014 .

[30]  Ran Wang,et al.  Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images , 2016 .

[31]  Mark C. Vanderwel,et al.  Allometric equations for integrating remote sensing imagery into forest monitoring programmes , 2016, Global change biology.

[32]  Tetsuji Ota,et al.  Aboveground Biomass Estimation Using Structure from Motion Approach with Aerial Photographs in a Seasonal Tropical Forest , 2015 .

[33]  Timo Tokola,et al.  Effect of field plot location on estimating tropical forest above-ground biomass in Nepal using airborne laser scanning data , 2014 .

[34]  Ryutaro Tateishi,et al.  Estimation of CO2 Sequestration by the Forests in Japan by Discriminating Precise Tree Age Category using Remote Sensing Techniques , 2015, Remote. Sens..

[35]  T. Noland,et al.  Automated delineation of individual tree crowns from lidar data by multi-scale analysis and segmentation , 2012 .

[36]  Jaehoon Jung,et al.  Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm , 2013 .

[37]  Veronika Braunisch,et al.  Environmental predictors of species richness in forest landscapes: abiotic factors versus vegetation structure , 2016 .

[38]  L. Wallace,et al.  Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds , 2016 .

[39]  Oguz Gungor,et al.  Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud , 2018 .

[40]  K. A. Rahman,et al.  REMOTELY SENSED L-BAND SAR DATA FOR TROPICAL FOREST BIOMASS ESTIMATION , 2011 .

[41]  Takuhiko Murakami,et al.  Discontinuous DBH–height relationship of Cryptomeria japonica on Yakushima Island: effect of frequent typhoons on the maximum height , 2009, Ecological Research.

[42]  M. Keller,et al.  Estimating Canopy Structure in an Amazon Forest from Laser Range Finder and IKONOS Satellite Observations1 , 2002 .

[43]  Sébastien Bauwens,et al.  Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning , 2016 .

[44]  A. Arneth,et al.  Terrestrial biogeochemical feedbacks in the climate system , 2010 .