Understory trees in airborne LiDAR data — Selective mapping due to transmission losses and echo-triggering mechanisms

Understory trees in multilayered stands are often ignored in forest inventories. Information about them would benefit silviculture, wood procurement, and biodiversity management. Cost-efficient inventory methods are needed and airborne LiDAR is a promising addition to fieldwork. The overstory, however, obstructs wall-to-wall sampling of the understory using LiDAR, because transmission losses affect echotriggering probabilities and intensity (peak amplitude) observations. We examined the potential of LiDAR in mapping of understory trees in pine (Pinus sylvestris L.) stands (62°N, 24°E), using careful experimentation. We formulated a conceptual model for the transmission losses and illustrated that loss normalization is highly ill-posed, especially for vegetation. The losses skew the population of targets that produce a subsequent echo. Losses up to 10–15% can occur even if an overstory echo is not triggered. In LiDAR sensors, quantized intensity values start from binary zero, but actually should include an offset, the noise level. We estimated these empirically. Constraining to low-loss pulses and ground data, we estimated parameters for compensation models that were based on the radar equation and employed the geometry of the pulse, as well as the overstory intensity observations as predictors. Intensity variation of second-return data was reduced, but, the intensity data were deemed of low value in species discrimination. Our results highlight differences between sensors in near-ground echo-triggering and height data. Area-based LiDAR height metrics from the understory had reasonable correlation with the density and mean height of the understory trees, whereas tree species seemed out of reach even if the transmission losses were compensated for. We conclude that transmission losses are a general impediment for radiometric analysis of multi-echo pulses in discrete- return and waveform LiDAR data.

[1]  Markus Holopainen,et al.  Airborne small-footprint discrete-return LiDAR data in the assessment of boreal mire surface patterns, vegetation, and habitats , 2009 .

[2]  J. Hyyppä,et al.  Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies , 2010 .

[3]  B. Koetz,et al.  Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning , 2010 .

[4]  I. Korpela,et al.  Appraisal of seedling stand vegetation with airborne imagery and discrete-return LiDAR : an exploratory analysis , 2008 .

[5]  A. Kuusk,et al.  Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates , 2006 .

[6]  W. Wagner,et al.  Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner , 2006 .

[7]  Eija Honkavaara,et al.  Variation and directional anisotropy of reflectance at the crown scale — Implications for tree species classification in digital aerial images , 2011 .

[8]  E. Næsset,et al.  Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data , 2009 .

[9]  E. Næsset.  Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data , 2009 .

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

[11]  E. Næsset,et al.  Estimating tree heights and number of stems in young forest stands using airborne laser scanner data , 2001 .

[12]  Demetrios Gatziolis.  Dynamic Range-based Intensity Normalization for Airborne, Discrete Return Lidar Data of Forest Canopies , 2011 .

[13]  Juha Hyyppä,et al.  Calibration of the optech ALTM-3100 laser scanner intensity data using brightness targets , 2006 .

[14]  Erik Næsset,et al.  Using remotely sensed data to construct and assess forest attribute maps and related spatial products , 2010 .

[15]  K. Korhonen,et al.  Hämeen-Uudenmaan metsäkeskuksen alueen metsävarat 1965–99 , 1970 .

[16]  Timo Kuuluvainen,et al.  Structural heterogeneity and spatial autocorrelation in a natural mature Pinus sylvestris dominated forest , 1998 .

[17]  C. Hopkinson.  The influence of flying altitude, beam divergence, and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution , 2007 .

[18]  Richard K. Broughton,et al.  Mapping the understorey of deciduous woodland from leaf-on and leaf-off airborne LiDAR data: A case study in lowland Britain , 2009 .

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

[20]  Miina Rautiainen,et al.  Seasonal reflectance dynamics of common understory types in a northern European boreal forest , 2011 .

[21]  Jari Vauhkonen,et al.  Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data , 2010 .

[22]  M. Maltamo,et al.  Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics , 2010 .

[23]  Timothy J. Malthus,et al.  A Multispectral Canopy LiDAR Demonstrator Project , 2011, IEEE Geoscience and Remote Sensing Letters.

[24]  J. Hyyppä,et al.  Tree species classification using airborne LiDAR - effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type , 2010 .

[25]  A. Jelalian.  Laser radar systems , 1980 .

[26]  Matti Maltamo,et al.  Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index , 2011 .

[27]  M. Rautiainen,et al.  BRDF measurement of understory vegetation in pine forests: dwarf shrubs, lichen, and moss , 2005 .

[28]  Ilkka Korpela,et al.  Mapping forest plots: an efficient method combining photogrammetry and field triangulation , 2007 .

[29]  E. Næsset.  Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[30]  Juha Hyyppä,et al.  Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data , 2005 .

[31]  W. Wagner,et al.  Area-based parameterization of forest structure using full-waveform airborne laser scanning data. , 2008 .

[32]  Nicholas C. Coops,et al.  Assessment of forest structure with airborne LiDAR and the effects of platform altitude , 2006 .

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

[34]  Ilkka Korpela,et al.  Mapping of understory lichens with airborne discrete-return LiDAR data , 2008 .

[35]  A. Hudak,et al.  Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability , 2009 .

[36]  Miina Rautiainen,et al.  Coupling forest canopy and understory reflectance in the Arctic latitudes of Finland , 2007 .

[37]  Aloysius Wehr,et al.  Airborne laser scanning—an introduction and overview , 1999 .

[38]  Juha Hyyppä,et al.  Toward Hyperspectral Lidar: Measurement of Spectral Backscatter Intensity With a Supercontinuum Laser Source , 2007, IEEE Geoscience and Remote Sensing Letters.

[39]  D. Donoghue,et al.  Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data , 2007 .