A cross-comparison of field, spectral, and lidar estimates of forest canopy cover

A common challenge when comparing forest canopy cover and similar metrics across different ecosystems is that there are many field- and landscape-level measurement methods. This research conducts a cross-comparison and evaluation of forest canopy cover metrics produced using unmixing of reflective spectral satellite data, light detection and ranging (lidar) data, and data collected in the field with spherical densiometers. The coincident data were collected across a ~25 000 ha mixed conifer forest in northern Idaho. The primary objective is to evaluate whether the spectral and lidar canopy cover metrics are each statistically equivalent to the field-based metrics. The secondary objective is to evaluate whether the lidar data can elucidate the sources of error observed in the spectral-based canopy cover metrics. The statistical equivalence tests indicate that spectral and field data are not equivalent (slope region of equivalence = 43%). In contrast, the lidar and field data are within the acceptable error margin of most forest inventory assessments (slope region of equivalence = 13%). The results also show that in plots where the mean lidar plot heights are near zero, each of modeled remotely sensed estimates continues to report canopy cover >21% for lidar and >30% for all investigated spectral methods using near-infrared bands. This suggests these metrics are sensitive to the presence of herbaceous vegetation, shrubs, seedlings, saplings, and other subcanopy vegetation.

[1]  E. Strand,et al.  Net changes in aboveground woody carbon stock in western juniper woodlands, 1946–1998 , 2008 .

[2]  B. Haack,et al.  Percentage canopy cover – using Landsat imagery to delineate habitat for Myanmar's endangered Eld's deer (Cervus eldi) , 2005 .

[3]  M. Falkowski,et al.  Parcel-based classification of agricultural crops via multitemporal Landsat imagery for monitoring habitat availability of western burrowing owls in the Imperial Valley agro-ecosystem , 2010 .

[4]  Kurtis J. Thome,et al.  Effects of assumed solar spectral irradiance on intercomparisons of Earth-observing sensors , 2001, Remote Sensing.

[5]  P. Treitz,et al.  Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density , 2006 .

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

[7]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[8]  Paul E. Johnson,et al.  Simple algorithms for remote determination of mineral abundances and particle sizes from reflectance spectra , 1992 .

[9]  Ruiliang Pu,et al.  Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: Comparison of various indices through correlation analysis , 2003 .

[10]  M. Cochrane.  Linear mixture model classification of burned forests in the Eastern Amazon , 1998 .

[11]  C. Hopkinson,et al.  Testing LiDAR models of fractional cover across multiple forest ecozones , 2009 .

[12]  J. Means.  Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon , 1999 .

[13]  G. Asner,et al.  Net changes in regional woody vegetation cover and carbon storage in Texas Drylands, 1937–1999 , 2003 .

[14]  J. Marshall,et al.  A regression-based equivalence test for model validation: shifting the burden of proof. , 2005, Tree physiology.

[15]  S. Garman,et al.  Comparison of five canopy cover estimation techniques in the western Oregon Cascades , 2006 .

[16]  P. Morgan,et al.  Evaluation of linear spectral unmixing and ΔNBR for predicting post‐fire recovery in a North American ponderosa pine forest , 2007 .

[17]  Nicole A. Lazar,et al.  Testing Statistical Hypotheses of Equivalence , 2003, Technometrics.

[18]  S. Reutebuch,et al.  Estimating forest canopy fuel parameters using LIDAR data , 2005 .

[19]  J. C. Taylor,et al.  Sensitivity of mixture modelling to end‐member selection , 2003 .

[20]  M. Finney.  FARSITE : Fire Area Simulator : model development and evaluation , 1998 .

[21]  Andrew Thomas Hudak,et al.  A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[22]  R. Lawrence,et al.  Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape : Mount St. Helens, Washington , 1998 .

[23]  L. Rowan,et al.  Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data , 2003 .

[24]  Xuexia Chen,et al.  Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest , 2004 .

[25]  Paul E. Johnson,et al.  Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis , 1985 .

[26]  Andrew P. Robinson,et al.  The relationship between effective plant area index and Landsat spectral response across elevation, solar insolation, and spatial scales in a northern Idaho forest , 2004 .

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

[28]  B. Xu,et al.  Oakwood crown closure estimation by unmixing Landsat TM data , 2003 .

[29]  Paul E. Johnson,et al.  Quantitative analysis of planetary reflectance spectra with principal components analysis , 1985 .

[30]  P. Gessler,et al.  Wavelet estimation of plant spatial patterns in multitemporal aerial photography , 2005 .

[31]  J. Eitel,et al.  Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status , 2007 .

[32]  A. Yüksel,et al.  Using ASTER Imagery in Land Use/cover Classification of Eastern Mediterranean Landscapes According to CORINE Land Cover Project , 2008, Sensors.

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

[34]  S. A. Lewis,et al.  The Relationship of Multispectral Satellite Imagery to Immediate Fire Effects , 2007 .

[35]  S. Ustin,et al.  Generation of crown bulk density for Pinus sylvestris L. from lidar , 2004 .

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

[37]  Paul E. Gessler,et al.  Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling , 2005 .

[38]  P. Gessler,et al.  Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data , 2006 .

[39]  W. Walker,et al.  Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems , 2005 .

[40]  Alex C. Lee,et al.  A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests , 2007 .

[41]  Alistair M. S. Smith,et al.  Remote sensing for prediction of 1-year post-fire ecosystem condition , 2009 .

[42]  C. Elvidge.  Visible and near infrared reflectance characteristics of dry plant materials , 1990 .

[43]  G. R E G O R,et al.  Net Changes in Regional Woody Vegetation Cover and Carbon Storage in Texas Drylands, 1937±1999 , 2022 .

[44]  C. Messier,et al.  Predicting and managing light in the understory of boreal forests , 1999 .

[45]  J. Martonchik,et al.  Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer , 2008 .

[46]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[47]  M. Rautiainen,et al.  Estimation of forest canopy cover: A comparison of field measurement techniques , 2006 .

[48]  Michael J. Falkowski,et al.  Production of vegetation spatial-structure maps by per-object analysis of juniper encroachment in multitemporal aerial photographs , 2008 .

[49]  S. Magnussen,et al.  Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators , 1998 .

[50]  W. Verstraeten,et al.  Nonlinear Hyperspectral Mixture Analysis for tree cover estimates in orchards , 2009 .

[51]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[52]  Andrew P. Robinson,et al.  Model validation using equivalence tests , 2004 .

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

[54]  P. Gessler,et al.  Evaluating ASTER satellite imagery and gradient modeling for mapping and characterizing wildland fire fuels , 2004 .

[55]  Paul E. Gessler,et al.  The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data , 2008 .

[56]  D. Sheil,et al.  Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures , 1999 .

[57]  J. Greenberg,et al.  Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis , 2005 .

[58]  Michael J. Falkowski,et al.  Automatic detection of shrub location, crown area, and cover using spatial wavelet analysis and aerial photography , 2008 .

[59]  J. Settle,et al.  Mapping Vegetation, Soils, and Geology in Semiarid Shrublands Using Spectral Matching and Mixture Modeling of SWIR AVIRIS Imagery , 1999 .