Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment

A Canopy Height Profile (CHP) procedure presented in Harding et al. (2001) for large footprint LiDAR data was tested in a closed canopy environment as a way of extracting vertical foliage profiles from LiDAR raw-waveform. In this study, an adaptation of this method to small-footprint data has been shown, tested and validated in an Australian sparse canopy forest at plot- and site-level. Further, the methodology itself has been enhanced by implementing a dataset-adjusted reflectance ratio calculation according to Armston et al. (2013) in the processing chain, and tested against a fixed ratio of 0.5 estimated for the laser wavelength of 1550nm. As a by-product of the methodology, effective leaf area index (LAIe) estimates were derived and compared to hemispherical photography-derived values. To assess the influence of LiDAR aggregation area size on the estimates in a sparse canopy environment, LiDAR CHPs and LAIes were generated by aggregating waveforms to plot- and site-level footprints (plot/site-aggregated) as well as in 5m grids (grid-processed). LiDAR profiles were then compared to leaf biomass field profiles generated based on field tree measurements. The correlation between field and LiDAR profiles was very high, with a mean R2 of 0.75 at plot-level and 0.86 at site-level for 55 plots and the corresponding 11 sites. Gridding had almost no impact on the correlation between LiDAR and field profiles (only marginally improvement), nor did the dataset-adjusted reflectance ratio. However, gridding and the dataset-adjusted reflectance ratio were found to improve the correlation between raw-waveform LiDAR and hemispherical photography LAIe estimates, yielding the highest correlations of 0.61 at plot-level and of 0.83 at site-level. This proved the validity of the approach and superiority of dataset-adjusted reflectance ratio of Armston et al. (2013) over a fixed ratio of 0.5 for LAIe estimation, as well as showed the adequacy of small-footprint LiDAR data for LAIe estimation in discontinuous canopy forests.

[1]  Jan Verbesselt,et al.  Effects of clumping on modelling LiDAR waveforms in forest canopies , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[2]  Guang Zheng,et al.  Computational-Geometry-Based Retrieval of Effective Leaf Area Index Using Terrestrial Laser Scanning , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Michael A. Lefsky,et al.  Optimization of Geoscience Laser Altimeter System waveform metrics to support vegetation measurements , 2011 .

[4]  Erik Næsset,et al.  Mapping LAI in a Norway spruce forest using airborne laser scanning , 2009 .

[5]  Emilio Chuvieco,et al.  Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests , 2004 .

[6]  W. Cohen,et al.  Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest , 2005 .

[7]  M. Lefsky.  Application of Lidar remote sensing to the estimation of forest canopy and stand structure , 1997 .

[8]  Alan H. Strahler,et al.  Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing , 2009 .

[9]  N. Coops,et al.  Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests , 2003 .

[10]  John D. Aber,et al.  A Method for Estimating Foliage-Height Profiles in Broad-Leaved Forests , 1979 .

[11]  J. Chen,et al.  Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand , 1991 .

[12]  Jeffrey P. Walker,et al.  Effective LAI and CHP of a Single Tree From Small-Footprint Full-Waveform LiDAR , 2014, IEEE Geoscience and Remote Sensing Letters.

[13]  Erik Næsset,et al.  Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning , 2006 .

[14]  K. Itten,et al.  Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction , 2006 .

[15]  N. Breda.  Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[16]  M. Lefsky,et al.  Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests , 2001 .

[17]  Felix Morsdorf,et al.  Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling , 2009 .

[18]  Natascha Kljun,et al.  Integrating terrestrial and airborne lidar to calibrate a 3D canopy model of effective leaf area index , 2013 .

[19]  Håkan Olsson,et al.  Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data , 2012 .

[20]  J. W. Wilson,et al.  Stand Structure and Light Penetration. I. Analysis by Point Quadrats , 1965 .

[21]  Kenji Omasa,et al.  Estimation and Error Analysis of Woody Canopy Leaf Area Density Profiles Using 3-D Airborne and Ground-Based Scanning Lidar Remote-Sensing Techniques , 2010, IEEE Transactions on Geoscience and Remote Sensing.

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

[23]  L. Monika Moskal,et al.  Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR , 2009 .

[24]  Daniel Zelterman,et al.  Applied Linear Models with SAS: Review of Methods , 2010 .

[25]  Bryan Blair,et al.  Retrieval of vertical LAI profiles over tropical rain forests using waveform lidar at La Selva, Costa Rica , 2012 .

[26]  S. Running,et al.  Measuring Fractional Cover and Leaf Area Index in Arid Ecosystems: Digital Camera, Radiation Transmittance, and Laser Altimetry Methods , 2000 .

[27]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[28]  Stuart R. Phinn,et al.  Direct retrieval of canopy gap probability using airborne waveform lidar , 2013 .

[29]  J. Wilson,et al.  ANALYSIS OF THE SPATIAL DISTRIBUTION OF FOLIAGE BY TWO‐DIMENSIONAL POINT QUADRATS , 1959 .

[30]  Alan H. Strahler,et al.  Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-Waveform Ground-Based Lidar , 2011 .

[31]  S. T. Gower,et al.  Leaf area index of boreal forests: theory, techniques, and measurements , 1997 .

[32]  Thomas J. Jackson,et al.  The third Soil Moisture Active Passive Experiment , 2011 .

[33]  W. Cohen,et al.  Estimates of forest canopy height and aboveground biomass using ICESat , 2005 .

[34]  Jeffrey P. Walker,et al.  Preliminary leaf area index estimates from airborne small footprint full-waveform LiDAR data , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[35]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[36]  R. McMurtrie,et al.  Estimation of leaf area index in eucalypt forest using digital photography , 2007 .

[37]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[38]  C. Woodcock,et al.  Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar , 2011 .

[39]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[40]  Guoqing Sun,et al.  Inversion of a lidar waveform model for forest biophysical parameter estimation , 2006, IEEE Geoscience and Remote Sensing Letters.

[41]  A. Lang,et al.  Validity of surface area indices of Pinus radiata estimated from transmittance of the sun's beam , 1991 .

[42]  Guoqing Sun,et al.  Modeling lidar returns from forest canopies , 2000, IEEE Trans. Geosci. Remote. Sens..

[43]  C. Woodcock,et al.  Measuring Gap Fraction, Element Clumping Index and LAI in Sierra Forest Stands Using a Full-Waveform Ground-Based Lidar , 2012 .

[44]  Andrew T. Hudak,et al.  Discrete return lidar-based prediction of leaf area index in two conifer forests , 2008 .

[45]  J. Chen,et al.  Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .

[46]  John Armston,et al.  Sensitivity of direct canopy gap fraction retrieval from airborne waveform lidar to topography and survey characteristics , 2014 .

[47]  P. Levy,et al.  Direct and indirect measurements of LAI in millet and fallow vegetation in HAPEX-Sahel , 1999 .

[48]  Graham Brodie,et al.  Allometric relationships for estimating biomass in grey box (Eucalyptus microcarpa) , 2005 .

[49]  T. A. Black,et al.  Characteristics of shortwave and longwave irradiances under a Douglas-fir forest stand , 1991 .

[50]  Ying Gao,et al.  The Soil Moisture Active Passive Experiments (SMAPEx): Toward Soil Moisture Retrieval From the SMAP Mission , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[51]  Jeffrey P. Walker,et al.  Analysis of full-waveform LiDAR data for classification of an orange orchard scene , 2013 .

[52]  R. Macarthur,et al.  Foliage Profile by Vertical Measurements , 1969 .