Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta

Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature.

[1]  N. Brokaw,et al.  The definition of treefall gap and its effect on measures of forest dynamics. , 1982 .

[2]  Roberta E. Martin,et al.  Forest Canopy Gap Distributions in the Southern Peruvian Amazon , 2013, PloS one.

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

[4]  Guillermo Castilla,et al.  Seismic lines in the boreal and arctic ecosystems of North America: environmental impacts, challenges, and opportunities , 2018, Environmental Reviews.

[5]  Arko Lucieer,et al.  A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[6]  T. Pock,et al.  Point Clouds: Lidar versus 3D Vision , 2010 .

[7]  Erik Næsset,et al.  Vertical Height Errors in Digital Terrain Models Derived from Airborne Laser Scanner Data in a Boreal-Alpine Ecotone in Norway , 2015, Remote. Sens..

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

[9]  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 .

[10]  J. Mccarthy,et al.  Gap dynamics of forest trees: A review with particular attention to boreal forests , 2001 .

[11]  Martina L. Hobi,et al.  Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing , 2015 .

[12]  Benoît St-Onge,et al.  Spatially explicit characterization of boreal forest gap dynamics using multi-temporal lidar data , 2008 .

[13]  Mary H. Nichols,et al.  Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States , 2018, Front. Plant Sci..

[14]  G. McDermid,et al.  UAV Remote Sensing Can Reveal the Effects of Low‐Impact Seismic Lines on Surface Morphology, Hydrology, and Methane (CH4) Release in a Boreal Treed Bog , 2018 .

[15]  Fabio Remondino,et al.  State of the art in high density image matching , 2014 .

[16]  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 .

[17]  Gregory J. McDermid,et al.  Assessing the Value of UAV Photogrammetry for Characterizing Terrain in Complex Peatlands , 2017, Remote. Sens..

[18]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[19]  L. Waser,et al.  Assessing the structure of primeval and managed beech forests in the Ukrainian Carpathians using remote sensing , 2017 .

[20]  Katarzyna Zielewska-Büttner,et al.  Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery , 2016, Remote. Sens..

[21]  Ronald J. Hall,et al.  Large fires as agents of ecological diversity in the North American boreal forest , 2008 .

[22]  W. Cohen,et al.  Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A. , 1995 .

[23]  M. L. Cadenasso,et al.  PATCH DYNAMICS AND THE ECOLOGY OF DISTURBED GROUND: A FRAMEWORK FOR SYNTHESIS , 1999 .

[24]  Philippe Lejeune,et al.  Canopy Gap Mapping from Airborne Laser Scanning: An Assessment of the Positional and Geometrical Accuracy , 2015, Remote. Sens..

[25]  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 .

[26]  Alberta. Natural regions and subregions of Alberta , 2006 .

[27]  Guillermo Castilla,et al.  Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry , 2017, Remote. Sens..

[28]  M. Wulder,et al.  Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data , 2011 .

[29]  Nikolay S. Strigul,et al.  Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring , 2018, Remote. Sens..

[30]  Jonathan P. Dash,et al.  Comparison of high-density LiDAR and satellite photogrammetry for forest inventory , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[31]  Tiit Nilson,et al.  Estimating canopy cover in Scots pine stands , 2005 .

[32]  J. Clevers,et al.  The robustness of canopy gap fraction estimates from red and near-infrared reflectances: A comparison of approaches , 1995 .

[33]  Keqi Zhang,et al.  Identification of gaps in mangrove forests with airborne LIDAR , 2008 .

[34]  D. Langor,et al.  Edge influence of low-impact seismic lines for oil exploration on upland forest vegetation in northern Alberta (Canada) , 2017 .

[35]  K. Moffett,et al.  Remote Sens , 2015 .

[36]  Cornelius Senf,et al.  Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. , 2017, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[37]  James R. Runkle,et al.  Guidelines and sample protocol for sampling forest gaps. , 1992 .

[38]  Glenn D. Sutherland,et al.  Canopy Gaps and the Landscape Mosaic in a Coastal Temperate Rain Forest , 1996 .

[39]  George Alan Blackburn,et al.  Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS , 2004 .

[40]  Alessandro Cescatti,et al.  Indirect estimates of canopy gap fraction based on the linear conversion of hemispherical photographs Methodology and comparison with standard thresholding techniques , 2007 .

[41]  Matti Maltamo,et al.  Using airborne laser scanning data for detecting canopy gaps and their understory type in mature boreal forest , 2011, Annals of Forest Science.

[42]  M. Lefsky,et al.  Comparison and integration of lidar and photogrammetric point clouds for mapping pre-fire forest structure , 2019, Remote Sensing of Environment.

[43]  Nicholas C. Coops,et al.  Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest , 2019, Remote. Sens..

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

[45]  Ranz,et al.  World Map of the Köppen-Geiger climate classification updated — Source link , 2006 .

[46]  David A. Coomes,et al.  Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion , 2019, Remote. Sens..

[47]  Juha Hyyppä,et al.  Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests , 2018 .

[48]  Jing Liu,et al.  Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics , 2018 .

[49]  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 .

[50]  M. Hebblewhite Billion dollar boreal woodland caribou and the biodiversity impacts of the global oil and gas industry , 2017 .

[51]  E. Næsset,et al.  Comparison of four types of 3D data for timber volume estimation , 2014 .

[52]  Rachel Gaulton,et al.  LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques , 2010 .

[53]  Xin Shen,et al.  Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests , 2019, Forests.

[54]  Guoqing Sun,et al.  Features of point clouds synthesized from multi-view ALOS/PRISM data and comparisons with LiDAR data in forested areas , 2014 .

[55]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[56]  Juha Hyyppä,et al.  FOREST INVENTORY ATTRIBUTE ESTIMATION USING AIRBORNE LASER SCANNING, AERIAL STEREO IMAGERY, RADARGRAMMETRY AND INTERFEROMETRY–FINNISH EXPERIENCES OF THE 3D TECHNIQUES , 2015 .

[57]  S. Boutin,et al.  Persistence and developmental transition of wide seismic lines in the western Boreal Plains of Canada. , 2006, Journal of environmental management.

[58]  Viliam Pichler,et al.  Canopy gap dynamics and tree understory release in a virgin beech forest, Slovakian Carpathians , 2018 .

[59]  T. Groen,et al.  Detection of forest canopy gaps from very high resolution aerial images , 2018, Ecological Indicators.

[60]  Gregory J. McDermid,et al.  A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles , 2017, Remote. Sens..

[61]  Harle Light regimes beneath closed canopies and tree-fall gaps in temperate and tropical forests , 2010 .

[62]  Martin Isenburg,et al.  A comparison between LiDAR and photogrammetry digital terrain models in a forest area on Tenerife Island , 2013 .

[63]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

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

[65]  Bin Zou,et al.  Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling , 2016, Remote. Sens..

[66]  Joanne C. White,et al.  Comparison of airborne laser scanning and digital stereo imagery for characterizing forest canopy gaps in coastal temperate rainforests , 2018 .

[67]  F. Putz,et al.  Natural Disturbance and Gap‐Phase Regeneration in a Wind‐Exposed Tropical Cloud Forest , 1988 .