Use of multi-temporal UAV-derived imagery for estimating individual tree growth in Pinus pinea stands

High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments) to determine the tree positions and to estimate individual tree height (h), diameter (d), biomass (wa), as well as changes in these variables between 2015 and 2017. We used Structure from Motion (SfM) and 3D point cloud filtering techniques to generate the canopy height model and object-based image analysis to delineate individual tree crowns (ITC). ITC results were validated using accurate field measurements over a subsample of 50 trees. Comparison between SfM-derived and field-measured h yielded an R2 value of 0.96. Regressions using SfM-derived variables as explanatory variables described 79% and 86–87% of the variability in d and wa, respectively. The height and biomass growth estimates across the entire study area for the period 2015–2017 were 0.45 m ± 0.12 m and 198.7 ± 93.9 kg, respectively. Significant differences (t-test) in height and biomass were observed at the end of the study period. The findings indicate that the proposed method could be used to derive individual-tree variables and to detect spatio-temporal changes, highlighting the potential role of UAV-derived imagery as a forest management tool.

[1]  S. Shapiro,et al.  A Comparative Study of Various Tests for Normality , 1968 .

[2]  William A. Bechtold,et al.  Five instruments for measuring tree height: an evaluation , 1994 .

[3]  Emmanuel P. Baltsavias,et al.  A comparison between photogrammetry and laser scanning , 1999 .

[4]  N. Comerford,et al.  Biomass development for intensively managed loblolly pine plantations growing on Spodosols in the southeastern USA , 2002 .

[5]  Erik Næsset,et al.  Determination of Mean Tree Height of Forest Stands by Digital Photogrammetry , 2002 .

[6]  E. Næsset.  Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[7]  Åsa Persson,et al.  Detecting and measuring individual trees using an airborne laser scanner , 2002 .

[8]  R. Calama,et al.  Inter-regional variability in site index models for even-aged stands of stone pine (Pinus pinea L.) in Spain , 2003 .

[9]  Cédric Véga,et al.  Measuring individual tree height using a combination of stereophotogrammetry and lidar , 2004 .

[10]  J. Hyyppä,et al.  Automatic detection of harvested trees and determination of forest growth using airborne laser scanning , 2004 .

[11]  K. Johnsen,et al.  Production, allocation, and stemwood growth efficiency of Pinus taeda L. stands in response to 6 years of intensive management , 2004 .

[12]  I. Korpela.  Individual tree measurements by means of digital aerial photogrammetry , 2004, Silva Fennica Monographs.

[13]  I. Burke,et al.  Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests , 2005 .

[14]  Terje Gobakken,et al.  Estimating forest growth using canopy metrics derived from airborne laser scanner data , 2005 .

[15]  M. Coleman,et al.  Forest production responses to irrigation and fertilization are not explained by shifts in allocation , 2005 .

[16]  J. Hyyppä,et al.  DETECTING AND ESTIMATING ATTRIBUTES FOR SINGLE TREES USING LASER SCANNER , 2006 .

[17]  J. Hyyppä,et al.  Change Detection Techniques for Canopy Height Growth Measurements Using Airborne Laser Scanner Data , 2006 .

[18]  S. Popescu.  Estimating biomass of individual pine trees using airborne lidar , 2007 .

[19]  Ronald J. Hall,et al.  The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets , 2008 .

[20]  Y. Hu,et al.  Mapping canopy height using a combination of digital stereo‐photogrammetry and lidar , 2008 .

[21]  M. Coleman,et al.  Above- and below-ground biomass accumulation, production, and distribution of sweetgum and loblolly pine grown with irrigation and fertilization , 2008 .

[22]  Lars T. Waser,et al.  High‐quality image matching and automated generation of 3D tree models , 2008 .

[23]  Cédric Véga,et al.  Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models , 2008 .

[24]  J. Hyyppä,et al.  Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests , 2008 .

[25]  S. Popescu,et al.  Lidar remote sensing of forest biomass : A scale-invariant estimation approach using airborne lasers , 2009 .

[26]  Modelação do crescimento e da produção de pinha no pinheiro manso , 2009 .

[27]  Vicente S. Monleon,et al.  Challenges to Estimating Tree Height via LiDAR in Closed-Canopy Forests: A Parable from Western Oregon , 2010, Forest Science.

[28]  F. M. Danson,et al.  Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data , 2010 .

[29]  João Santos Pereira,et al.  Biomass allometry and carbon factors for a Mediterranean pine (Pinus pinea L.) in Portugal , 2010 .

[30]  S. Linder,et al.  Manipulating nutrient and water availability in a maritime pine plantation: effects on growth, production, and biomass allocation at canopy closure , 2008, Annals of Forest Science.

[31]  João H. N. Palma,et al.  Resource communication. sIMfLOR - platform for Portuguese forest simulators. , 2012 .

[32]  Harold E. Burkhart,et al.  Modeling Forest Trees and Stands , 2012, Springer Netherlands.

[33]  Arko Lucieer,et al.  Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..

[34]  Jörgen Wallerman,et al.  Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM , 2012 .

[35]  A. Fernández-Landa,et al.  Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements , 2012 .

[36]  Eduardo González-Ferreiro,et al.  Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities , 2012 .

[37]  Sakari Tuominen,et al.  Forest variable estimation using a high-resolution digital surface model , 2012 .

[38]  Piermaria Corona,et al.  Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy , 2012 .

[39]  R. Calama,et al.  Mediterranean Stone Pine: Botany and Horticulture , 2012 .

[40]  Joanne C. White,et al.  The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning , 2013 .

[41]  Erle C. Ellis,et al.  High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision , 2013 .

[42]  Eduardo González-Ferreiro,et al.  A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations , 2013 .

[43]  Joanne C. White,et al.  Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update , 2013 .

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

[45]  Michele Dalponte,et al.  Airborne laser scanning of forest resources: An overview of research in Italy as a commentary case study , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[46]  B. Koch,et al.  UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS – TREE STEM MAPPING IN OPEN STANDS IN COMPARISON TO TERRESTRIAL LASER SCANNER POINT CLOUDS , 2013 .

[47]  D. Pitt,et al.  A Comparison of Point Clouds Derived from Stereo Imagery and Airborne Laser Scanning for the Area-Based Estimation of Forest Inventory Attributes in Boreal Ontario , 2014 .

[48]  Barbara Koch,et al.  Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds , 2014 .

[49]  Arko Lucieer,et al.  Direct Georeferencing of Ultrahigh-Resolution UAV Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[50]  C. Hugenholtz,et al.  Remote sensing of the environment with small unmanned aircraft systems ( UASs ) , part 1 : a review of progress and challenges 1 , 2014 .

[51]  E. Næsset,et al.  Forestry Applications of Airborne Laser Scanning , 2014, Managing Forest Ecosystems.

[52]  WhiteheadKen,et al.  Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges1 , 2014 .

[53]  Eduardo González-Ferreiro,et al.  Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data , 2014 .

[54]  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 .

[55]  B. St-Onge,et al.  Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds , 2015 .

[56]  Marc Olano,et al.  Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure , 2015, Remote. Sens..

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

[58]  Zuyuan Wang,et al.  A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds , 2015 .

[59]  Joanne C. White,et al.  Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment , 2015 .

[60]  Terje Gobakken,et al.  Inventory of Small Forest Areas Using an Unmanned Aerial System , 2015, Remote. Sens..

[61]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[62]  E. Næsset,et al.  Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data , 2015 .

[63]  Erle C. Ellis,et al.  Using lightweight unmanned aerial vehicles to monitor tropical forest recovery , 2015 .

[64]  L. Tang,et al.  Drone remote sensing for forestry research and practices , 2015, Journal of Forestry Research.

[65]  Optical Remote Sensing of Tree and Stand Heights , 2015 .

[66]  Terje Gobakken,et al.  Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland , 2016, Remote. Sens..

[67]  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 .

[68]  Maria Margarida Branco de Brito Tavares Tomé,et al.  Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal: Short Communication , 2016 .

[69]  Derek Irby,et al.  Measuring Douglas-Fir Crown Growth with Multitemporal LiDAR , 2016 .

[70]  Joanne C. White,et al.  Remote Sensing Technologies for Enhancing Forest Inventories: A Review , 2016 .

[71]  M. Coleman,et al.  Growth responses of narrow or broad site adapted tree species to a range of resource availability treatments after a full harvest rotation , 2016 .

[72]  Fabio Meloni,et al.  Direct Measurement of Tree Height Provides Different Results on the Assessment of LiDAR Accuracy , 2016 .

[73]  Juha Hyyppä,et al.  Evaluating the performance of high-altitude aerial image-based digital surface models in detecting individual tree crowns in mature boreal forests , 2016 .

[74]  Susana Barreiro,et al.  StandsSIM-MD: a Management Driven forest SIMulator , 2016 .

[75]  Adam J. Mathews,et al.  Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem , 2016, Remote. Sens..

[76]  Jorge García-Gutiérrez,et al.  Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest , 2016 .

[77]  H. L. Allen,et al.  Tamm Review: Light use efficiency and carbon storage in nutrient and water experiments on major forest plantation species , 2016 .

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

[79]  M. Mokroš,et al.  Accuracy of Photogrammetric UAV-Based Point Clouds under Conditions of Partially-Open Forest Canopy , 2017 .

[80]  F. Agüera-Vega,et al.  Accuracy of Digital Surface Models and Orthophotos Derived from Unmanned Aerial Vehicle Photogrammetry , 2017 .

[81]  F. López-Granados,et al.  Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling , 2017, Plant Methods.

[82]  Reiner Zimmermann,et al.  Measurement of Within-Season Tree Height Growth in a Mixed Forest Stand Using UAV Imagery , 2017 .

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

[84]  Joanne C. White,et al.  Assessing Precision in Conventional Field Measurements of Individual Tree Attributes , 2017 .