Dense Canopy Height Model from a low-cost photogrammetric platform and LiDAR data

Key messageLow-cost methodology to obtain CHMs integrating terrain data from LiDAR into photogrammetric point clouds with greater spatial, radiometric and temporal resolution due to a correction model.AbstractThis study focuses on developing a methodology to generate a Dense Canopy Height Model based on the registration of point clouds from LiDAR open data and the photogrammetric output from a low-cost flight. To minimise georeferencing errors from dataset registration, terrain data from LiDAR were refined to be included in the photogrammetric point cloud through a correction model supported by a statistical analysis of heights. As a result, a fusion point cloud was obtained, which applies LiDAR to characterize the terrain in areas with high vegetation and utilizes the greater spatial, radiometric and temporal resolution of photogrammetry. The obtained results have been successfully validated: the accuracy of the fusion cloud is statistically consistent with the accuracies of both clouds based on the principles of classical photogrammetry and LiDAR processing. The resulting point cloud, through a radiometric and geometric segmentation process, allows a Dense Canopy Height Model to be obtained.

[1]  Jan Helge Bøhn.  Automatic CAD-model repair , 1993 .

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

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

[4]  J. Means,et al.  Predicting forest stand characteristics with airborne scanning lidar , 2000 .

[5]  Johnie N. Jenkins,et al.  A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications , 2012 .

[6]  G. Hay,et al.  Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery , 2008 .

[7]  Richard A. Fournier,et al.  Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS , 2008, Sensors.

[8]  Ross Nelson,et al.  A Portable Airborne Laser System for Forest Inventory , 2003 .

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

[10]  Ross Nelson,et al.  Estimating forest biomass and volume using airborne laser data , 1988 .

[11]  Micha Sharir,et al.  Filling gaps in the boundary of a polyhedron , 1995, Comput. Aided Geom. Des..

[12]  Zuyuan Wang,et al.  COLOR AND LIDAR DATA FUSION: APPLICATION TO AUTOMATIC FOREST BOUNDARY DELINEATION IN AERIAL IMAGES , 2007 .

[13]  K. Kraus.  Photogrammetry: Geometry from Images and Laser Scans , 2007 .

[14]  Sandeep Gupta,et al.  Comparative Analysis of Clustering-Based Approaches for 3-D Single Tree Detection Using Airborne Fullwave Lidar Data , 2010, Remote. Sens..

[15]  Zuyuan Wang,et al.  Automatic Forest Boundary Delineation in Aerial Images , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[16]  Subodh Kumar,et al.  Repairing CAD models , 1997, Proceedings. Visualization '97 (Cat. No. 97CB36155).

[17]  Erik Næsset,et al.  Efficient multiresolution spatial predictions for large data arrays , 2007 .

[18]  J. Quero,et al.  On the importance of topography, site quality, stock quality and planting date in a semiarid plantation: Feasibility of using low-density LiDAR , 2014 .

[19]  A. Dolenc,et al.  Some Efficient Procedures for Correcting Triangulated Models , 1993 .

[20]  Diego González-Aguilera,et al.  Development of an All-Purpose Free Photogrammetric Tool , 2016 .

[21]  Joanne C. White,et al.  A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach , 2013 .

[22]  Aerial photo-interpretation using Z/I DMC images for estimation of forest variables , 2007 .

[23]  Juha Hyyppä,et al.  Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR , 2013, Remote. Sens..

[24]  Javier Estornell,et al.  Assessment of factors affecting shrub volume estimations using airborne discrete-return LiDAR data in Mediterranean areas , 2012 .

[25]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[26]  Michael J. Wozny,et al.  Generating Topological Information from a "Bucket of Facets" , 1992 .

[27]  David Hernández-López,et al.  An Automatic Approach to UAV Flight Planning and Control for Photogrammetric Applications: A Test Case in the Asturias Region (Spain) , 2013 .

[28]  Marco Attene,et al.  A lightweight approach to repairing digitized polygon meshes , 2010, The Visual Computer.

[29]  Randolph H. Wynne,et al.  Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size , 2002 .

[30]  Yi Lin,et al.  A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements , 2010 .

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

[32]  Wesley Ketchum,et al.  Comparative Direct Analysis of Type Ia Supernova Spectra , 2006 .

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

[34]  E. Næsset.  Estimating timber volume of forest stands using airborne laser scanner data , 1997 .

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

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

[37]  Joanne C. White,et al.  Lidar sampling for large-area forest characterization: A review , 2012 .

[38]  Terje Gobakken,et al.  Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses , 2004 .

[39]  Nicholas C. Coops,et al.  Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots , 2013 .

[40]  S. Magnussen,et al.  Predictions of Forest Inventory Cover Type Proportions Using Landsat TM , 2000 .

[41]  M. d'Oliveira,et al.  Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon , 2012 .

[42]  Pablo Rodríguez-Gonzálvez,et al.  Survey and Classification of Large Woody Debris (LWD) in Streams Using Generated Low-Cost Geomatic Products , 2014, Remote. Sens..

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

[44]  Wang Tao,et al.  Dense point cloud extraction from UAV captured images in forest area , 2011, Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services.

[45]  Zoë J. Wood,et al.  Topological Noise Removal , 2001, Graphics Interface.

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

[47]  C. Mallet,et al.  Processing full-waveform lidar data: Modelling raw signals , 2015 .

[48]  M. Benko,et al.  Prošlost, sadašnjost i budućnost primjene metoda daljinskih istraživanja pri inventuri šuma u Hrvatskoj , 2011 .

[49]  Frédéric Bretar,et al.  Full-waveform topographic lidar : State-of-the-art , 2009 .

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

[51]  R. G. Oderwald,et al.  Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass , 1997 .

[52]  Terje Gobakken,et al.  Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data , 2005 .

[53]  K. Lim,et al.  Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators , 2004 .

[54]  A. Bachelor.  GLOSSARY OF TERMS GLOSSARY OF TERMS , 2010 .

[55]  E. Næsset.  Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .

[56]  M. Benko,et al.  Past, present and future of application of remote sensing methods in Croatian forest inventory , 2011 .

[57]  I. Kolingerová,et al.  SIMPLE HOLES TRIANGULATION IN SURFACE RECONSTRUCTION , 2022 .

[58]  P. Milano,et al.  FULLY AUTOMATIC UAV IMAGE-BASED SENSOR ORIENTATION , 2010 .

[59]  M. Pierrot Deseilligny,et al.  APERO, AN OPEN SOURCE BUNDLE ADJUSMENT SOFTWARE FOR AUTOMATIC CALIBRATION AND ORIENTATION OF SET OF IMAGES , 2012 .

[60]  Nellie Clarke Brown.  Trees , 1896, Savage Dreams.

[61]  Jun Zhou,et al.  Maximizing spectral radius of unoriented Laplacian matrix over bicyclic graphs of a given order , 2008 .

[62]  Jungho Im,et al.  Forest biomass estimation from airborne LiDAR data using machine learning approaches , 2012 .

[63]  L. Monika Moskal,et al.  Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR , 2011 .

[64]  M. Vastaranta,et al.  Status and prospects for LiDAR remote sensing of forested ecosystems , 2013 .

[65]  Beat Koch,et al.  Development of Filtering , Segmentation and Modelling Modules for Lidar and Multispectral Data as a Fundament of an Automatic Forest Inventory System , 2004 .