Lidar detection of individual tree size in tropical forests

Abstract Characterization of tropical forest trees has been limited to field-based techniques focused on measurement of diameter of the cylindrical part of the bole, with large uncertainty in measuring large trees with irregular shapes, and other size attributes such as total tree height and the crown size. Here, we introduce a methodology to decompose lidar point cloud data into 3D clusters corresponding to individual tree crowns (ITC) that enables the estimation of many biophysical variables of tropical forests such as tree height, crown area, crown volume, and tree number density. The ITC-based approach was tested using airborne high-resolution lidar data collected over the 50-ha Center for Tropical Forest Science (CTFS) plot in the Barro Colorado Island, Panama. The lack of tree height and crown size measurements in the field prohibits the direct validation of the ITC metrics. We assess the reliability of our method by comparing the aboveground biomass (AGB) estimated using ground and lidar individual tree measurements at multiple spatial scales, namely 1 ha, 2.25 ha, 4 ha, and 6.25 ha. We examined four different lidar-derived AGB models, with three based on individual tree height, crown volume, and crown area, and one with mean top canopy height (TCH) calculated at the plot level using the lidar canopy height model. Results show that the predictive power of all models based on ITC size and TCH increases with decreasing spatial resolution from 16.9% at 1 ha for the worst model to 5.0% at 6.25 ha for the best model. The TCH-based model performed slightly better than ITC-based models except at higher spatial scales (~ 4 ha) and when errors due to edge effects associated with tree crowns were reduced. Unlike the TCH models that change regionally depending on forest type and structure allometry, the ITC-based models are derived as a function of individual tree allometry and can be extended globally to all tropical forests. The method for lidar detection of individual crown size overcome some limitations of ground-based inventories such as 1) it is able to access crowns of large trees and 2) it enables the assessment of directional changes in tree density, canopy architecture and forest dynamics over large and inaccessible areas to support robust tropical ecological studies.

[1]  R. Houghton,et al.  Characterizing 3D vegetation structure from space: Mission requirements , 2011 .

[2]  Karl J. Niklas,et al.  Erratum: Invariant scaling relations across tree-dominated communities (Nature (2000) 410 (655-660)) , 2003 .

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

[4]  T. Whitmore,et al.  Tropical rain forest: ecology and management. , 1984 .

[5]  Hanan Samet,et al.  Efficient Component Labeling of Images of Arbitrary Dimension Represented by Linear Bintrees , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Label object representation and manipulation with ITK , 2007, The Insight Journal.

[7]  S. Saatchi,et al.  Using Lidar and Radar measurements to constrain predictions of forest ecosystem structure and function. , 2011, Ecological applications : a publication of the Ecological Society of America.

[8]  M. Keller,et al.  Structural Dynamics of Tropical Moist Forest Gaps , 2015, PloS one.

[9]  Yosio Edemir Shimabukuro,et al.  Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. , 2012, Ecology letters.

[10]  M. Keller,et al.  Seeing the forest beyond the trees , 2015 .

[11]  D. A. King,et al.  Height-diameter allometry of tropical forest trees , 2010 .

[12]  Karl J. Niklas,et al.  Corrigendum: Invariant scaling relations across tree-dominated communities , 2003, Nature.

[13]  J. Dalling,et al.  Spatial scale and sampling resolution affect measures of gap disturbance in a lowland tropical forest: implications for understanding forest regeneration and carbon storage , 2014, Proceedings of the Royal Society B: Biological Sciences.

[14]  Stephanie A. Bohlman,et al.  Allometry, adult stature and regeneration requirement of 65 tree species on Barro Colorado Island, Panama , 2006, Journal of Tropical Ecology.

[15]  S. Goetz,et al.  Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change , 2011 .

[16]  Gregory P. Asner,et al.  Spatial variability in tropical forest leaf area density from multireturn lidar and modeling , 2015 .

[17]  S. Ernest,et al.  Relationships between body size and abundance in ecology. , 2007, Trends in ecology & evolution.

[18]  David A. Coomes,et al.  Disturbances prevent stem size‐density distributions in natural forests from following scaling relationships , 2003 .

[19]  Arko Lucieer,et al.  Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions , 2011 .

[20]  Maxim Neumann,et al.  Detecting tropical forest biomass dynamics from repeated airborne lidar measurements , 2013 .

[21]  Stephen J. Wright,et al.  Light-Gap disturbances, recruitment limitation, and tree diversity in a neotropical forest , 1999, Science.

[22]  Richard Condit,et al.  Tropical Forest Census Plots , 1998, Environmental Intelligence Unit.

[23]  Gaia Vaglio Laurin,et al.  Airborne LiDAR Detects Selectively Logged Tropical Forest Even in an Advanced Stage of Recovery , 2015, Remote. Sens..

[24]  Tetsuji Ota,et al.  Estimation of Aboveground Biomass Using Manual Stereo Viewing of Digital Aerial Photographs in Tropical Seasonal Forest , 2014 .

[25]  J. Chambers,et al.  Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. , 2007, Trends in ecology & evolution.

[26]  Karl J. Niklas,et al.  Invariant scaling relations across tree-dominated communities , 2001, Nature.

[27]  Fred Godtliebsen,et al.  Tropical Pacific–high latitude south Atlantic teleconnections as seen in δ18O variability in Antarctic coastal ice cores , 2009 .

[28]  David Kenfack,et al.  Comparing tropical forest tree size distributions with the predictions of metabolic ecology and equilibrium models. , 2006, Ecology letters.

[29]  Frédéric Bretar,et al.  3-D mapping of a multi-layered Mediterranean forest using ALS data , 2012 .

[30]  Jocelyn Chanussot,et al.  On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images , 2015 .

[31]  G. Prance.  Tropical Rain Forest: Ecology and Management , 1984 .

[32]  Y. Malhi,et al.  The allocation of ecosystem net primary productivity in tropical forests , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[33]  G. Asner,et al.  Evaluating uncertainty in mapping forest carbon with airborne LiDAR , 2011 .

[34]  S. Goetz,et al.  A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing , 2013 .

[35]  Valerie Ussyshkin,et al.  ALTM ORION: BRIDGING CONVENTIONAL LIDAR AND FULL WAVEFORM DIGITIZER TECHNOLOGY , 2010 .

[36]  Roberta E. Martin,et al.  High-fidelity national carbon mapping for resource management and REDD+ , 2013, Carbon Balance and Management.

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

[38]  F. Rovero,et al.  Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics , 2013 .

[39]  Yinghai Ke,et al.  A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing , 2011 .

[40]  Richard Condit,et al.  Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama and a Comparison with Other Plots , 1998 .

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

[42]  B. Nelson,et al.  Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.

[43]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[44]  P. Couteron,et al.  Closing a gap in tropical forest biomass estimation: accounting for crown mass variation in pantropical allometries , 2015 .

[45]  Uwe Soergel,et al.  A Marked Point Process for Modeling Lidar Waveforms , 2010, IEEE Transactions on Image Processing.

[46]  S. Popescu,et al.  A voxel-based lidar method for estimating crown base height for deciduous and pine trees , 2008 .

[47]  O. Phillips,et al.  The importance of crown dimensions to improve tropical tree biomass estimates. , 2014, Ecological applications : a publication of the Ecological Society of America.

[48]  S. Wofsy,et al.  Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2 , 2009 .

[49]  G. Asner,et al.  Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric , 2014 .

[50]  Bettina M. J. Engelbrecht,et al.  Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees , 2013, Proceedings of the National Academy of Sciences.

[51]  N. Picard,et al.  Site-specific versus pantropical allometric equations: Which option to estimate the biomass of a moist central African forest? , 2014 .

[52]  Gregory Asner,et al.  Tree Species Discrimination in Tropical Forests Using Airborne Imaging Spectroscopy , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[53]  Frédéric Bretar,et al.  Canopy Density Model: A New ALS-Derived Product to Generate Multilayer Crown Cover Maps , 2015, IEEE Transactions on Geoscience and Remote Sensing.