Journal of Experimental Botany, Vol. 58, No. 4, pp. 881–898, 2007
Imaging Stress Responses in Plants Special Issue
doi:10.1093/jxb/erl142 Advance Access publication 9 October, 2006

SPECIAL ISSUE PAPER

3D lidar imaging for detecting and understanding plant
responses and canopy structure
Kenji Omasa*, Fumiki Hosoi and Atsumi Konishi
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo,113-8657 Japan
Received 5 May 2006; Accepted 24 July 2006

Abstract
Understanding and diagnosing plant responses tostress will beneﬁt greatly from three-dimensional (3D)
measurement and analysis of plant properties because
plant responses are strongly related to their 3D structures. Light detection and ranging (lidar) has recently
emerged as a powerful tool for direct 3D measurementof plant structure. Here the use of 3D lidar imaging toestimate plant properties such as canopy height,
canopy structure, carbon stock, and species is demonstrated, and plant growth and shape responses areassessed by reviewing the development of lidar systems and their applications from the leaf level tocanopy remote sensing. In addition, the recent creationof accurate 3D lidar images combined with natural
colour, chlorophyll ﬂuorescence, photochemical reﬂectance index, and leaf temperature images is demonstrated, thereby providing information on responsesof pigments, photosynthesis, transpiration, stomatal
opening, and shape to environmental stresses; thesedata can be integrated with 3D images of the plants
using computer graphics techniques. Future lidar
applications that provide more accurate dynamic estimation of various plant properties should improve ourunderstanding of plant responses to stress and ofinteractions between plants and their environment.
Moreover, combining 3D lidar with other passive andactive imaging techniques will potentially improve theaccuracy of airborne and satellite remote sensing,and make it possible to analyse 3D information on

ecophysiological responses and levels of various
substances in agricultural and ecological applications
and in observations of the global biosphere.
Key words: Carbon stock, canopy structure, chlorophyll ﬂuorescence, leaf temperature, lidar, photosynthesis, plant growth,
stomatal response, three-dimensional (3D) imaging, remotesensing.

Introduction
Passive and active imaging techniques have come into
widespread use for plant analysis at the subcellular level, thewhole-plant level, and the ecosystem and remote-sensing
levels (Mayers, 1983; Hashimoto et al., 1990; Hobbs andMooney, 1990; Omasa, 1990, 2006; Häder, 1992, 2000;
Lichtenthaler et al., 1996; Ustin et al., 1999; Buschmann et al.,
2000; Govindjee and Nedbal, 2000; Lefsky et al., 2002b;
Omasa et al., 2002a, 2005, 2006; Jones, 2004; Oxborough,2004; Papageorgiou and Govindjee, 2004; Zarco-Tejada
et al., 2004; Chaerle et al., 2005; Kolber et al., 2005; Schurr
et al., 2006). One recent trend in imaging techniques is the
adoption of three-dimensional (3D) imaging.
The 3D structure of leaves and plants plays an importantrole in sustaining plant functions such as photosynthesis andtranspiration and in determining the suitability of different
habitats for various species. Therefore, it is necessary toobtain 3D information to improve understanding of plant
functioning and habitats. The 3D information can also serve
as a good indicator of plant stress and improve understanding of stress responses. For example, stresses such as a water* To whom correspondence should be addressed. E-mail: aomasa@mail.ecc.u-tokyo.ac.jp
Abbreviations: CHP, canopy height proﬁle; DBH, diameter at breast height; DCHM, digital canopy-height model; DEM, digital elevation model; DTM, digitalterrain model; FP-mode, ﬁrst-pulse mode; GPP, gross primary production; GPS, global positioning system; IMU, internal measurement unit;
InSAR, interferometric synthetic-aperture radar; LAD, leaf area density; LAI, leaf area index; lidar, light detection and ranging; LIF, laser-induced
ﬂuorescence; LIFT, laser-induced ﬂuorescence transient; LP-mode, last-pulse mode; NPP, net primary production; PPFD, photosynthetic photon ﬂux
density; PRI, photochemical reﬂectance index; RMSE, root-mean-square error; SAR, synthetic-aperture radar; VCP, voxel-based canopy proﬁling.
ª The Author [2006]. Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved.
For Permissions, please e-mail: journals.permissions@oxfordjournals.org

882 Omasa et al.

deficit and severe heat can change the shape and morphology
of individual plants. On a large scale, environmental changes
can induce extensive plant responses, such as changes inforest growth, species composition and global carbon
cycling. These responses also involve changes in the 3D
structure of plants and vegetation canopies. Therefore, 3D
plant analysis is indispensable for understanding responsesof ecosystems and individual plants.
Methods for 3D imaging are divided broadly into
two types: passive and active. Passive methods includestereovision and shape-from-x (where x represents focus,
shading, texture, contour, and other parameters) algorithms.
In particular, stereovision has been used in a wide range ofscientific research and industrial applications. It allows
3D reconstruction of an object based on the use ofgeometrical rules of perspective and based on the difference
between two or more images of objects taken fromdifferent positions. Although some studies have reported
the application of stereovision with small plants (He et al.,
2003; Andersen et al., 2005) and field crops (Ivanov et al.,
1994, 1995), plants are a difficult target for stereovisionbecause they have complex structures; leaves have a rangeof shapes and textures, and create discontinuities (i.e. they
may hide each other). Mismatches between the corresponding points in different images (the so-called ‘correspondence problem’) and self-occlusion within an object remain
major problems in stereovision, and increase with increasing complexity of plant shape. Dynamic stereo isanother trend in stereovision, in which motion is used toprovide a more reliable 3D reconstruction (Negahdaripour
et al., 1995; Brooks et al., 1998). In one example of thisapproach, Schurr et al. (2001) employed a single camerathat they moved continuously from one stereo position toanother. They simplified the correspondence problem bytracking each pixel throughout the acquired image sequence and used this improved information to calculate the3D position of the leaf surface. However, it is difficult tohandle the large amount of data from multiple frames
generated by this technique. Airborne or satellite-basedstereoscopic methods have been widely used for large-scaleterrestrial and forest observations (Gong et al., 2002;
Zomer et al., 2002; Hay et al., 2004; Eckert et al., 2005).
Although their accuracy has been improved by using
sensors with high spatial resolution (Sheng et al., 2001;
Toutin, 2004), the resolution and accuracy (several metres)
remain insufficient for obtaining detailed 3D informationfor vegetation. In addition, image quality is affected bysunlight and weather conditions. Shape-from-x algorithms
are not commonly used compared with stereovisionbecause of limitations in the technique. For example, ashape-from-focus algorithm proved to be suitable for use
with a light microscope, but was limited to use at shortdistances (within 20–30 m) with a 3D camera (Omasa,
2000, 2006). The use of such passive methods is limited
to objects with clearly defined and fine-grained texture.

Active methods are not limited to specific surface
textures because they are designed to generate wavelengths
of electromagnetic radiation (rather than relying on reflected or emitted ambient radiation) that are suitable for the
specific type of 3D measurement. Synthetic-aperture radar(SAR) is a form of active sensor that allows measurementof the physical characteristics of the ground surface using
microwaves during all weather conditions, as well as during
the day and night. In particular, interferometric syntheticaperture radar (InSAR) has been applied for determining
the height of vegetation canopies (Hagberg et al., 1995;
Askne et al., 1997; Papathanassiou and Cloude, 2001;
Kellndorfer et al., 2004). InSAR can estimate groundelevations based on phase differences in images acquired
from different locations. However, the complexity ofthe scattering process within vegetation makes it difficult
to separate physical forest parameters based on the interferometric data (Papathanassiou and Cloude, 2001). Furthermore, the method is useful for large-scale observation,
but its accuracy is of the order of several metres and is thus
insufficient to provide a detailed description of the 3D
structure of plants.
Recently, light detection and ranging (lidar) using laser
scanners has emerged as a powerful active sensing tool for
direct 3D measurement of plant shapes and canopy
structures. Lidar can obtain accurate 3D information forplants by measuring the distance between the sensor anda target. The scales of application have ranged from small
plants to forest stands. Numerous studies have shown theusefulness of lidar for the estimation of 3D plant properties(Næsset, 1997; Means et al., 1999; Omasa et al., 2000,
2002b; Hyyppä et al., 2001; Lefsky et al., 2002b; Næsset
et al., 2004; Yu et al., 2004; Reutebuch et al., 2005).
However, the potential of lidar has not yet been fully
exploited for monitoring plant responses to stress.
In the present paper, recent research on lidar imaging isreviewed, including aerial remote sensing for 3D measurements of plant shape and canopy structure, and then thepotential of lidar for monitoring plant responses to stress is
described. A demonstration is also provided of the potentialof composite imaging that combines lidar data with data
provided by other imaging techniques.
Development of lidar systems
Lidar is also called a ‘laser radar’, ‘laser scanner’, ‘laser
profiler’, ‘range finder’, or ‘laser ranger’. Recently, it has
been used as a novel active sensing tool for 3D measurement of plant shapes and canopy structures. Lidar can accurately measure the distance between the sensor and atarget based on the elapsed time between the emission andreturn of laser pulses (the ‘time of flight’ method) or basedon trigonometry (the ‘optical probe’ or ‘light section’methods). The accuracies of airborne and ground-based
lidar systems are typically ;0.1–1 m and ;0.05–10 cm,

3D lidar imaging of plant responses and canopy structure

respectively, so lidar can replace conventional passive
methods for 3D measurement. Lidar systems are divided
into several categories according to their characteristics.
Early studies of non-scanning airborne lidar systems
Airborne lidar systems were initially developed for bathymetric work (Hickman and Hogg, 1969; Hoge et al., 1980).
In the 1980s, lidar systems were applied in topographic
mapping. Krabill et al. (1984) used an airborne lidar system
to map the topography of a watershed near Memphis,
Tennessee. Schreier et al. (1984) determined, in a terrain
mapping study in Canada, that 95% of all laser terrain
elevations were within 1.8 m of photogrammetricallyderived values. In this application, vegetation was regarded
as an obstruction and a source of noise when terrain elevations were calculated based on laser profiles. After the
mid-1980s, lidar was also used for the determination ofvegetation canopy heights. Nelson et al. (1984) utilized
lidar on a heavily forested hardwood site in Pennsylvania
to show that changes in the laser canopy profile corresponded to changes in canopy density. They found that themean tree height estimates were within 60 cm of thephotogrammetric values, but that the laser estimates weremore precise. Some biophysical properties of forests, suchas gross merchantable timber volume and biomass, havebeen estimated from lidar-derived tree heights (Maclean
and Krabill, 1986; Nelson et al., 1988). These experiments
showed that lidar can be a useful tool for estimating forest
properties.
Airborne small-footprint scanning lidar systems
Lidar systems in the 1980s were non-scanning types, andonly a single line of data directly beneath the aircraft could
be obtained as a cross-sectional profile. The systems thus
had limitations for covering wide areas. In the mid-1990s,
airborne-scanning lidar became available and this technology was applied for topographic terrain mapping and forest
measurements (Flood and Gutelius, 1997; Næsset, 1997;
Hyyppä and Inkinen, 1999; Omasa et al., 2000). Scanningsystems can sweep a swath of terrain by means of lateraldeflection of the emitted laser beams during forward
movement of the aircraft. Consequently, a large area canbe scanned as a series of swaths. The footprint diameter (i.e.
the diameter of the laser beam on the ground) is relatively
small, typically ;10–30 cm. The position of the laser beamon the ground can be determined with an absolute accuracyof <0.5 m and a relative accuracy of 0.15 m (Omasa et al.,2000) by recording the movements of an aircraft and its
lidar instrument using ground-based and aerial global
positioning system (GPS) receivers and an aerial internalmeasurement unit (IMU). The range resolution and accuracy of the lidar system are within 1 cm and 15 cm, respectively. The lidar system functions as a discrete-return
recording device, since it only receives a single return883

signal or a small number of return signals from the ground.
Figure 1 shows a schematic diagram of 3D measurement
using a lidar system. The illustrated helicopter-borne lidar
system has two receiving modes (Fig. 1A): a first-pulse
mode (FP-mode), in which the first returned pulses arereceived, and a last-pulse mode (LP-mode), in which thelast returned pulses are received.
In the 1990s, the density of laser pulses provided byordinary small-footprint airborne scanning lidar systems
was less than several pulses mÿ2 on the ground, with apulse repetition frequency of 1–25 kHz. Recent advances in lidar technology have increased this value to >10
pulses mÿ2 with a pulse repetition frequency of up to 100
kHz. Moreover, the density can be increased several-foldusing slower flight speeds (e.g. using helicopters rather
than airplanes to carry the scanning lidar). These systems
can provide high-resolution images of individual trees.
Several studies have shown the ability of lidar systems toprovide measurements of individual trees (Hyyppä andInkinen, 1999; Omasa et al., 2000, 2003; Hyyppä et al.,
2001; Brandtberg et al., 2003; Holmgren and Persson,
2004; Næsset et al., 2004; Yu et al., 2004; Reutebuch
et al., 2005).
Airborne large-footprint scanning lidar systems

Airborne lidars with a large footprint and a large scan width
have been developed for a target of forest remote sensing onlarge scales and from satellites (Blair et al., 1999; Lefsky
et al., 1999b, 2002b; Means et al., 1999; Drake et al.,2002). These systems scan with a large footprint, typically;10–25 m in diameter, and are obtained from a higher
altitude, resulting in more complete sampling of the canopy
and a wider image swath. The system includes a waveformrecording device that digitizes the power level of the entire
return laser signal and captures both the vertical distributionof the backscatter of laser illumination from all canopy
elements (both foliar and woody) and reflection from theground. The range resolution and accuracy are both ;10cm (Blair and Hofton, 1999). The position of the laser beamon the ground can be determined to within 5–10 m (Meanset al., 1999).
Ground-based non-scanning lidar systems

Liu (1995) described an example of a non-scanning
ground-based lidar system. The system has a laser sourceand detector for measuring distances, a compass formeasuring azimuth angles, and a tilt sensor for measuring
zenith angles. From these measurements, the position of atarget is calculated as orthogonal coordinates. Distance ismeasured using the time-of-flight method, and ranging
accuracy is ;10 cm. The system is portable, so a major useof the system (and of comparable non-scanning lidar
systems) is for forest surveys, such as traverses of forest stands, stem mapping, and dendrometry (Liu, 1995;

884 Omasa et al.

Fig. 1. A schematic diagram of 3D remote sensing using helicopter-borne and ground-based scanning lidar systems. (A) Data processing ina helicopter-borne scanning lidar system. (B) Data processing in a ground-based scanning lidar system. FP-mode, first-pulse mode; LP-mode, last-pulse
mode; DEM, digital elevation model; DTM, digital terrain model; DCHM, digital canopy-height model.

Peet et al., 1997; Williams et al., 1999). Another use
involves measurements of vertical foliage profiles usinga ground-based lidar system with the beam direction set
vertically and the operator manually repositioning the lidar
head by walking through the forest (Radtke and Bolstad,
2001; Parker et al., 2004).
Ground-based scanning lidar systems

In ground-based scanning lidar systems, rotating or oscillating mirrors within the lidar head or built-in stepping
motors automate the horizontal and vertical scanning.
These systems can record the configuration of whole plants
and foliage structures as a 3D ‘point-cloud image’ by merging lidar data measured from different positions (Fig. 1B).
In stand-scale measurement from a distance >10 m, thetime-of-flight method is most often available, and provides
a ranging accuracy of ;1 cm (Omasa et al., 2002b; Uranoand Omasa, 2003). However, the ranging accuracy is
insufficient to capture foliage and leaf shapes precisely.
The phase-shift detection method (Vanderbilt, 1985) andthe optical-probe method (Shirai, 1972) are more suitablefor precise measurements from <10 m. In the former
method, the system modulates the amplitude of the laser
beam and converts the difference in the phases of themodulation in the sent and received laser beams into a

distance value. In the latter method, a laser beam is projected on an object to create a small spot, and the beam is
detected by a photo-detector placed at a certain distancefrom the laser source but outside the line connecting thelaser source and target. The laser spot, laser source, anddetector thus form a triangle, and the distance to the object
can be calculated by means of trigonometry. For example,
one system that uses the optical-probe method can measure
foliage and leaf shapes with range accuracies of 0.5 mm at
a distance of 3.5 m, and 5 mm at a distance of 10 m undersunlight using a near-infrared laser with a wavelength of
785 nm (maximum power 30 mW).

Applications
Canopy height

Canopy height is an important variable needed to estimate
the 3D properties of trees. Lidar-derived tree heights can beused to estimate various biophysical properties of trees
based on allometric relationships between the biophysicalproperties and lidar-derived tree heights. In small-footprint
scanning lidar systems with a pulse density of <1 pulse
mÿ2, underestimation of tree heights was a critical problem
because the pulse density was insufficient to detect the3D lidar imaging of plant responses and canopy structure

actual tree tops. Næsset (1997) reported that the mean treeheight in coniferous stands derived from airborne lidar witha pulse density of 0.1 pulses mÿ2 was underestimated by
4.1–5.5 m compared with ground-measured data. In an
attempt to solve this problem, Næsset divided forest stands
into regular grids with cells of equal size and extracted only
the maximum estimated height value within each cell. The
lidar-derived mean height of stands was then computed as
the arithmetic mean of these cell maxima. This method
decreased the bias in mean tree height to <1.9 m. However,
the area-based approach cannot satisfy the growing demand
for feature extraction based on individual trees.
Small-footprint lidar systems with a high pulse density
provide another solution for underestimation. The highpulse density increases the probability of laser hits on theactual tops of the trees and consequently reduces themagnitude of the underestimation. In a study by Maltamo
et al. (2004b), tree heights of several species of Norway
spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestrisL.), silver birch (Betula pendula Roth.), and downy birch(Betula pubescens Ehrh.) were estimated at a pulse densityof 10 pulses mÿ2 (with a pulse repetition frequency of 83
kHz). The underestimation of tree height decreased to ;1 m.
Omasa et al. (2000, 2003) used a lidar system with a pulse
density of ;30 pulses mÿ2 to estimate tree height in

885Japanese coniferous and broadleaved forests. Their systemincreased the pulse density both by using a high pulse
repetition frequency (25 kHz) and by using a helicopter
with low flight speed as the scanning platform. This systemallowed scanning of the entire ground surface, since theinterval between neighbouring footprints was nearly equal
to the footprint diameter. The accuracy of tree heightmeasurement improved to <47 cm [with a root-mean-squareerror (RMSE) of 19 cm] for coniferous trees (five species)and 40 cm (RMSE=12 cm) for broadleaved trees (sixspecies). These studies showed that the underestimation oftree height can be reduced by increasing the pulse density
on the ground. The systems with a high pulse density not
only reduced the underestimation of tree height but also
allowed the production of high-resolution 3D images of eachindividual tree. Figure 2 shows 3D images of a woody
canopy and of the terrain (ground surface) obtained by ahelicopter-borne scanning lidar with a high pulse density(;30 pulses mÿ2) (Omasa et al., 2000). The image
processing process is illustrated in Fig. 1A. First- and last
pulse-mode digital elevation models (FP-mode DEM,
Fig. 2A; and LP-mode DEM, Fig. 2B) were produced
from lidar data derived by first and last returned pulses,
respectively. The digital terrain model (DTM, Fig. 2C) ofa small valley was estimated with an accuracy of ;15 cm

Fig. 2. 3D false-colour images (Omasa et al., 2000) of a small valley produced using high-resolution helicopter-borne small-footprint scanning lidar
with a high pulse density (;30 pulses mÿ2) and a 15 mm range accuracy. (A) FP-mode DEM. (B) LP-mode DEM. (C) DTM. (D) DCHM. In the images,
coniferous and broadleaved trees, roads, houses, farmlands, streams, and other features can be seen.

886 Omasa et al.

by interpolating only LP-mode data reaching the groundsurface. The net height of the woody canopy was calculated
to produce a digital canopy-height model (DCHM, Fig. 2D)
by subtracting the DTM elevations from the FP-mode DEM
values.
An approach based on individual trees uses imageprocessing techniques to estimate the heights of individual
trees together with tree position and crown area automatically. Several algorithms have been developed for thispurpose, including the watershed algorithm (Hyyppä et al.,2001), the morphological computer vision method (Persson
et al., 2002), the crown extraction filtering method (Omasa
et al., 2003; details have not yet been published), and the
local maximum method (Popescu et al., 2003; Popescu andWynne, 2004). In some studies, basal area, stem volume,and stem density were regressed against lidar-derived
individual tree heights and crown areas (Hyyppä et al.,
2001; Persson et al., 2002; Omasa et al., 2003; Maltamo
et al., 2004a). However, these algorithms can only be usedwith single-stemmed trees such as conifers.
Ground-based scanning lidar can provide more precise
3D images of individual trees with a typical resolution of
0.05–10 cm. 3D images generated from such systems
provide much information about the trees, and each treeheight can be estimated from the image, together with othervariables (Omasa et al., 2002b; Urano and Omasa, 2003).
The problem with operational measurements using groundbased lidar systems is that the laser beam may be unable toilluminate all the target trees because parts of some target
trees may be hidden by other trees. To resolve this problem,
target trees can be scanned from several measuring
positions surrounding the target trees, and images acquired
from these several points can be co-registered and merged,
as shown in Fig. 1B (Hopkinson et al., 2004; Yoshimi
et al., 2004; Hosoi et al., 2005). The multiple images
compensate for blind regions in some of the images,
permitting accurate estimation of tree heights.
Canopy structure

Vegetation canopies play important roles in the interaction
between plants and their environment through their effects
on photosynthesis and transpiration. Therefore, changes in
canopy structure can provide a sensitive indicator of
responses to stress and adaptation of plants to their environment. However, it is still a challenge to measure canopy structure accurately due to its complex 3D structure.
Recently, the ability of lidar to provide 3D data has been
applied to the measurement of vertical foliage distribution,a key mensurational parameter used to represent the 3D
canopy structure.
Magnussen and Boudewyn (1998) demonstrated that the
distribution of canopy surface heights derived from a fieldtrial of an airborne small-footprint lidar system in a
Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] forest

was related to the vertical distribution of foliage area. The
vertical distribution of cumulative leaf area index (LAI)
was estimated using the correlation between the height andfoliage area distributions. In the study, the frequency oflaser interception by the canopy was utilized as an index offoliage area at each height. In another study, gap probability
was used instead of the frequency of interception (Lovell
et al., 2003; Houldcroft et al., 2005). Gap probability isdefined as the probability of non-interception of laser
beams by the canopy. This probability was calculated ateach height by tracing rays between points where the laser
contacted the canopy and the lidar position. The probability
was then related to LAI at each height using an exponential
transformation on the assumption that foliage is randomly
distributed throughout the canopy (Norman and Campbell,
1989; Welles and Norman, 1991). However, there havebeen some difficulties in accurately estimating the verticalfoliage distribution using airborne small-footprint lidar
systems. The vertical foliage profiles derived using such
systems showed little response to the middle canopy levels
that are present in actual foliage profiles because thesystems can receive only the first and last returned pulses,
and thus fail to record any reflections between the first andlast returns (Lovell et al., 2003). Furthermore, nonuniformity in the leaf distribution, changes in beam angle,
and the energy threshold of the returned pulse affect theaccuracy of these estimates (Houldcroft et al., 2005). The
presence of non-photosynthetic tissues (i.e. stem orbranches) would also affect estimation accuracy.
The waveform-recording capability of airborne largefootprint lidar systems has been used to conduct canopy
height profile (CHP) surveys, which represent the vertical
distribution of canopy components. The CHP can becalculated by correcting for the effects of shading of upper
foliage by lower foliage on the returned energy profile
using an exponential transformation and the assumption ofa uniform horizontal distribution of foliage (MacArthurand Horn, 1969; Lefsky et al., 1999a; Means et al., 1999).
Although the CHP values are useful in estimating vertical
canopy profiles over large scales, the accuracy of CHP isaffected by non-uniformity of the horizontal distribution of
actual foliage and the approach is thus less useful on
smaller scales. In addition, the CHP does not represent
the foliage height profile directly because of the effect of
non-photosynthetic tissues within the canopy.
The optical point quadrat method is a method forroughly estimating vertical foliage profiles from the ground(MacArthur and Horn, 1969). The method is based on measuring heights to the lowest leaves above a set of sample
points established on the ground beneath the canopy. A
telephoto lens or clinometers with trigonometry was traditionally used to measure heights to the lowest leaves. A
more modern version of this technique replaces these piecesof equipment with a non-scanning ground-based lidar
system (Radtke and Bolstad, 2001; Parker et al., 2004).

3D lidar imaging of plant responses and canopy structure

Ground-based scanning lidar systems have also been
applied to the gap probability method (Welles and Cohen,
1996; Lovell et al., 2003; Tanaka et al., 2004). A conventional method using gap probability is a popular technique for ground-based measurement of LAI, in which
sunlight transmission beneath the canopy is measured by
ceptometers (Norman and Campbell, 1989; Welles andNorman, 1991). Sunlight has been replaced by laser beams
in the lidar equivalent of this technique. Studies based on
the optical point quadrat and gap probability methods have
improved the efficiency of data collection. However, it isstill difficult for these methods to account for the effects of
non-uniformity of the actual foliage distribution and thepresence of non-photosynthetic tissue.
As described above, accurate estimation of the verticalfoliage distribution is still difficult with either airborne or
ground-based lidar systems. Recently, a voxel-based canopy profiling method (VCP method), in which the 3D spaceis divided into ‘volume elements’ (voxels) that are the 3D
equivalent of the pixels in a two-dimensional (2D) image,has been developed for estimating vertical foliage profiles
with reduced effects of any non-uniformity in the foliage
distribution and of non-photosynthetic tissue (Hosoi andOmasa, 2006). An individual bambooleaf oak tree
(Quercus myrsinaefolia Blume; LAI=4.9) was scanned
from four measuring points during both the leaf-on andleaf-off using high-resolution ground-based scanning lidar,
as shown in Fig. 3A (leaf-on) and 3B (leaf-off), respectively. The laser beam was able to illuminate the tree fully
inside the canopy, as shown by the cross-sectional images
presented in Fig. 4. By subtracting the leaf-off image fromthe leaf-on image, non-photosynthetic tissues can beeliminated from the data. The vertical foliage profile
obtained in this manner was accurately estimated basedon voxels estimated from the lidar-derived point-cloud
data, and is shown in Fig. 5. The mean absolute percentage
error, which was the arithmetic mean of the absolute
percentage error between the actual and predicted (lidarderived) value for each height, was 22%. The actual value887

of each height was measured by stratified clipping of thefoliage. This method thus appears to be applicable formonitoring changes in canopy structure, although further
improvement may be necessary and may be challenging
to attain.
It has recently been demonstrated that species can beclassified using 3D lidar data with a pulse density of 5.2–12
pulses mÿ2. Brandtberg et al. (2003) scanned several leafoff deciduous trees and extracted the vertical structure of
the branches of each species using airborne small-footprint
lidar. The absence of leaves in the canopy facilitatedpenetration by the laser beam in the deciduous forest sothat the vertical structure of the branches could be moreclearly extracted. Based on a lidar-derived height distribution and the proportion of laser return from the branches,
the authors performed linear discriminant analysis on eachindividual tree for classification. Although the classification
accuracy was only 60%, the potential of the lidar data toclassify species was nonetheless apparent in this earlywork. Similarly, Holmgren and Persson (2004) demonstrated that airborne small-footprint lidar could distinguishbetween Norway spruce and Scots pine under leaf-on
conditions. The lidar-derived variables used in this study
were the proportion of laser returns, measurements ofheight distribution, canopy geometry, and intensity of
returned pulses. Linear and quadratic discriminant analyses
were performed on these lidar-derived variables for eachindividual tree. The results showed a 95% ability to
distinguish between the two species correctly. Althoughthese two studies focused on species classification, future
works may allow this approach not only to classify species
but also to estimate quantitatively the biophysical properties of each species.
Carbon stocks
Increases in atmospheric CO2 concentration and climatechanges vary the terrestrial carbon cycle and forest carbon
stocks (Levy et al., 2004; Law, 2005; Shimizu et al., 2005).

Fig. 3. 3D point-cloud images of an individual bambooleaf oak tree (Quercus myrsinaefolia Blume; LAI=4.9) growing in the field, obtained using
high-resolution ground-based scanning lidar with an 8 mm range accuracy. (A) Leaf-on image. (B) Leaf-off image. Points a–d and a9–d9 represent thepositions of the horizontal cross-sections displayed in Fig. 4.

888 Omasa et al.

Fig. 4. Horizontal cross-sectional images of the bambooleaf oak tree
(Quercus myrsinaefolia Blume) at each of the positions (a–d and a9–d9)
shown in Fig. 3. (A) Leaf-on image. (B) Leaf-off image. These images
show that lidar can obtain information within the canopy.

Accurate estimation of forest carbon stock capacity is requested by the Kyoto protocol to reduce greenhouse gases
and global warming. It is also crucial for studying thefunctioning of forests and in studies of the terrestrial global
carbon budget. In many cases, forest carbon stocks are
estimated from limited site data, and the representativenessof such data is always uncertain because of the heterogeneity of forests. By contrast, remote sensing using passive
optical systems or active radar sensors has allowed
extraordinary advances in the modelling, mapping, andunderstanding of ecosystems over larger areas. However,these approaches have significant limitations in forestry
applications because their sensitivity and accuracy haverepeatedly been shown to decrease as above-ground biomass increases (Waring et al., 1995; Turner et al., 1999).
Airborne and ground-based scanning lidar has provided
novel alternatives for accurate and efficient large-scaleestimation of forest carbon stocks.
Forest carbon stocks are defined as the amount of carbon
per unit area (kg C mÿ2 or t C haÿ1), and also defined as
the amount of carbon per tree (kg C treeÿ1) for detailed
representation of carbon stocks in each tree. Also, biomass
can be converted into carbon stocks using a conversion
factor based on the carbon content. Estimates of carbon
stock can be regressed against lidar-derived variables. Total
carbon stocks (stem, branches, foliage, and roots) ofindividual Japanese cedar [Cryptomeria japonica (L.f.)

Fig. 5. Comparison between lidar-derived and actual profiles of leaf area
density (LAD) of the bambooleaf oak tree (Quercus myrsinaefolia Blume)
shown in Figs 3 and 4. The lidar-derived profile was estimated by meansof a voxel-based canopy profiling method (Hosoi and Omasa, 2006);
the actual profile was measured by stratified clipping of the foliage.

D. Don] trees could be mapped using a high-resolution
DEM (Fig. 6) obtained using an airborne small-footprint
lidar system with a high pulse density (;30 pulses mÿ1),with the results shown in Fig. 7 (Omasa et al., 2003). Eachtree height was estimated after segmentation of each tree
canopy into individual trees. The carbon stock could thenbe estimated for each tree using allometric relationships
between carbon stocks and lidar-derived tree height. The
lidar-derived tree height has also been used for theestimation of carbon stocks in another study (Lim et al.,2003). For more heterogeneous forest, such as a forest with
mixed species and ages, the distributions of lidar-derived
canopy heights becomes a good indicator of the vertical
canopy structure and can thus be used to estimate carbon
stocks. Patenaude et al. (2004) estimated above-groundcarbon stocks in mixed deciduous woodlands using the
distributions of lidar-derived canopy heights rather thanlidar-derived heights of dominant trees because the lattermight not reflect the height distribution of a forest with
mixed species and ages.
Means et al. (1999) and Lefsky et al. (1999a, b, 2002a)
have also performed regressions of biomass against the
canopy height derived using large-footprint lidar. Furthermore, data collected using such systems have been used

3D lidar imaging of plant responses and canopy structure

Fig. 6. 3D view of a DCHM obtained for a Japanese cedar (Cryptomeria
japonica (L. f.) D. Don) forest using a helicopter-borne small-footprint
scanning lidar system with 15 cm range accuracy (Omasa et al., 2003).
The mesh of the DCHM was produced with a very high resolution(10 cm).

Fig. 7. Estimated total carbon stocks (stem, branches, foliage, and roots;
kg C treeÿ1) for each individual tree shown in Fig. 6 (Omasa et al., 2003).
Each polygon represents the total carbon stock of a single tree and covers
the area occupied by the tree’s canopy.

to estimate the gross primary production (GPP) and net
primary production (NPP) of forests. The former is definedas the total amount of carbon that is fixed by plant photosynthesis in a certain period of time. The latter is definedas the net amount of primary production after the costs ofplant respiration have been subtracted from GPP. GPP and
NPP can be utilized as indicators of global plant responses.
Kotchenova et al. (2004) used lidar-derived CHPs fora mixed-deciduous forest, calibrated using the total canopy
field-measured LAIs, as input data for the vertical foliage
distribution and coupled this distribution with models of
GPP. They found that vertical foliage profiles weresignificantly correlated with GPP. The study also suggested
that accounting for the actual vertical foliage profile canincrease the accuracy of estimated daily GPP
in photosynthesis models. Lefsky et al. (2005) used integrated lidar and Landsat data sets to characterize NPP overa spatially extensive set of plots in western Oregon. Standage was mapped by means of iterative unsupervisedclassification of a multitemporal sequence of Landsat TM
images (Cohen et al., 2002). NPP was then calculated as theaverage increment in lidar-estimated biomass during standdevelopment. Both studies demonstrated the ability of889

lidar-based remote sensing to assess global-scale plant
responses.
In ground-based scanning lidar systems, information on
the underparts of trees becomes available. Based on thisinformation, lidar-derived diameters at breast height(DBHs) can be used to estimate carbon stocks. Hopkinson
et al. (2004) estimated the DBHs of trees in a red pine
(Pinus resinosa Ait.) plantation and in a mixed deciduous
stand dominated by sugar maple (Acer saccharum Marsh.)by selecting all lidar points between a height of 1.25 m and1.75 m above the lowest point, then fitting a cylinder to thepoints. In this study, the authors measured the trees withina plot from several measuring points, thereby minimizing
the number of blind regions (i.e. areas in which tree stems
were obscured by other trees) and facilitating the extractionof DBH values. However, it is often difficult to scan alltrees from several points in dense forest. Omasa et al.
(2002b) scanned the stems in a Japanese larch (Larix
leptolepis Gordon) forest with dense undergrowth (Fig. 8)
from only one point, then estimated the stem diameter of
each observable tree at a measurable height. The DBH ofeach tree was estimated accurately from the stem diameterat the measurable height using the relationship between
stem diameter at specific height and DBH that was obtained
from ground-truthing data. Although the DBHs of sometrees within the surveyed area could not be estimatedbecause of obstruction by dense undergrowth or other trees,
this method nonetheless demonstrated the adaptability ofground-based lidar under difficult measuring conditions.
Lidar-derived DBH can be converted into the carbon stockof each tree using allometric relationships between DBH
and the carbon stock. The above-ground carbon stock ofeach larch tree in Fig. 8 was mapped (Fig. 9) and wascorrelated with the lidar-derived DBH (R2=0.96), resulting
in a 2.7% error in the average carbon stock (kg C mÿ2) oflarch trees within 30 m from the lidar position (Omasa
et al., 2002b). Urano and Omasa (2003) applied thismethod to estimate the carbon stocks of stems, branches,leaves, and roots of Japanese cedars with an RMSE of 11.5
kg C treeÿ1 (total carbon stock=56.1–528.2 kg C treeÿ1).
The use of ground-based scanning lidar thus permits
accurate and cost-effective estimation of individual treecarbon stock, and would therefore be applicable for the
repeated measurements used in the studies of carbon stockmonitoring.
Plant growth and shape changes

Plant growth is affected by various biotic stresses, such as
diseases and insects, as well as by abiotic (environmental)
stresses. Furthermore, global-scale plant growth has beenshown to be affected by global climate change (Myneni
et al., 1997; Menzel and Fabian, 1999; Andalo et al., 2005).
Recently, plant growth at the forest-stand level has beenmonitored using airborne small-footprint lidar. Yu et al.
(2004) measured individual trees between 1998 and 2000

890 Omasa et al.

Fig. 8. False-colour image of a Japanese larch (Larix leptolepis Gordon) forest measured using a ground-based scanning lidar system with 8 mm range
accuracy (Omasa et al., 2002b). Numerical values in the colour scale represent the distance from the lidar system.

Fig. 9. Estimated above-ground carbon stock (kg C treeÿ1) and standposition of each Japanese larch (Larix leptolepis Gordon) tree shown in
Fig. 8. The x- and y-axes represent the horizontal plane in Fig. 8, and theorigin represents the lidar position.

to monitor the growth of deciduous forests using smallfootprint lidar with a sample density of 10 pulses mÿ2.
They developed a tree-to-tree matching algorithm to compare individual trees between survey dates. The growth ofeach tree was extracted by subtracting the tree heightobtained in 1998 from the height measured in 2000 basedon the matching results. The growth of the whole stand or in
the individual plots (;25330 m2) was obtained using themean height difference for all matched trees. The accuracyof the estimated height growth was ;5 cm at the stand level
and ;10–15 cm at the plot level. To monitor forest growth

in a large area, Naesset and Gobakken (2005) used a smallfootprint lidar system with a sampling density of 0.9–1.2
pulses mÿ2 in 1999 and 2001. Mean tree height, basal area,and stem volume were regressed against several lidarderived variables (i.e. height percentiles, mean and maximum heights, coefficients of variation of the heights, andcanopy density at different heights above the ground) basedon the 1999 and 2001 lidar data. Forest growth was assumed to be solely responsible for the difference between
the estimated biophysical variables in 2001 and 1999.
Although the precision of the study was low, the study
demonstrated the potential of this approach for monitoring
plant growth over large areas.
Crops and vegetables grow much faster than forest trees,
thus their growth rate becomes a sensitive and directindicator of stress. Using high-precision ground-based
scanning lidar, the growth of these plants can be depicted
as a 3D shape change. Figure 10 shows an example of 3D
growth monitoring for a small aubergine (Solanum melongena L.) seedling obtained using the above-mentioned
optical probe-based scanning lidar, with a range accuracy of
0.5 mm at a distance of 3.5 m. Natural colour information
was added to the images by means of a texture-mapping
method (Heckbert, 1986; Haeberli and Segal, 1993; Soucyet al., 1996; Omasa, 2000). As shown in Fig. 10, the lidar
could clearly capture plant growth from 34 (Fig. 10A) to 52
d (Fig 10.C) after seeding by capturing the 3D expansionof leaves and stems and changes in the natural colour
and texture of the plant elements.
Some stresses, such as water deficiency or severe heat,
induce conformational responses in plants, such as leaf
inclination, rolling, and wilting. Although 2D image
analysis has been used for monitoring the responses to3D lidar imaging of plant responses and canopy structure891

Fig. 10. Monitoring of aubergine (Solanum melongena L.) growth using high-resolution optical probe-based scanning lidar with a range accuracyof 0.5 mm at a distance of 3.5 m. Natural colour textural information was added to each lidar-derived image using a texture-mapping technique. A, B, andC are images of the aubergines at 34, 42, and 52 d after seeding, respectively. Growth conditions were 12 h of daylight with a photosynthetic photon
flux density (PPFD) of 300 lmol mÿ2 sÿ1, day/night temperatures and relative humidities of 26/22 8C and 40/60%, respectively.

such plant stresses (Kurata and Yan, 1996; Kacira et al.,
2002; Foucher et al., 2004), it is difficult to monitor the
shape responses fully solely by means of 2D imaging.
Figure 11 shows an attempt at 3D monitoring of the shape
response of a tomato (Lycopersicum esculentum Mill.)plant to water stress using the optical probe-based scanninglidar approach described above. The shape change (wilting)
was clearly captured. The shape response to water stress is
as sensitive an indicator as changes in stomatal response(Fujino et al., 2002).
Physiological responses and substances in leaves

Spectral analyses of reflection, radiation, and fluorescencefrom leaves have been widely used for imaging to detectphotosynthesis, transpiration, stomatal response, and substances in the leaves (Omasa, 1990, 2002; Wessman, 1990;
Omasa and Croxdale, 1992; Ustin et al., 1999; Buschmann
et al., 2000; Govindjee and Nedbal, 2000; Kim et al.,
2002; Omasa and Takayama 2002; Osmond and Park,
2002; Jones, 2004; Papageorgiou and Govindjee, 2004;
Zarco-Tejada et al., 2004; Chaerle et al., 2005). Recent
developments in this area of research are introduced in the
Focus Papers in this issue. However, the majority of suchresearch has been limited to 2D imaging. Mapping spectral
images to an accurate 3D lidar image using computer
graphics techniques such as texture mapping for natural
colour images, as shown in Fig. 10, makes it possible tocombine 3D images with biotic and physiological information. The composite image may provide more
effective information for detecting and understanding 3D
plant responses to stress.
For example, changes in 3D composite images of natural
colour, chlorophyll a fluorescence intensity (‘P’ at the peakof the Kautsky effect), photochemical reflectance index(PRI), and leaf temperature of a sunflower (Helianthus
annuus L.) plant in response to treatment with the herbicide

Fig. 11. Changes in the 3D shape of a tomato (Lycopersiconesculentum Mill.) plant in response to water stress obtained using opticalprobe-based scanning lidar with a 0.5 mm range accuracy. The upper andlower figures are top and side views, respectively. The water potentials of
the plant in images A, B, and C were about ÿ0.4, ÿ0.7, and ÿ1.5 MPa,
respectively. The water deficit was caused by eliminating the watersupply to the plant pot. Leaf inclination clearly changes as the water
potential decreases. Growth conditions were the same as those describedin Fig. 10.

glufosinate-ammonium (Basta) are shown in Figs 12 and13. Basta is the most popular commercially available foliar
herbicide used for weed control in many geneticallyengineered crops that have resistance to the herbicide(Lea and Ridley, 1989). Visible injury was not observed
during the experiment. Natural colour is an indicator of
the concentration of photosynthetic pigments such as892 Omasa et al.

Fig. 12. Changes in 3D composite images of (A) the natural colour of a sunflower (Helianthus annuus L.) plant, (B) of chlorophyll (Chl.) a fluorescenceintensity (‘P’ at the peak of the Kautsky effect), and (C) of the photochemical reflectance index (PRI) before (top row) and after (bottom row) treatment
with glufosinate-ammonium (Basta) herbicide. The original 3D images were measured using an optical probe-based scanning lidar with 0.5 mm range
accuracy. The composite images were obtained using a texture-mapping technique that mapped the natural colour, chlorophyll a fluorescence intensity,and PRI images to each 3D image. Broken lines show the part of the leaf to which the herbicide was applied. The upper images were measured ;180 min
before the herbicide treatment and the lower ones were recorded ;570 min after the treatment. PPFD for fluorescence measurement (Omasa et al., 1987)
was 250 lmol mÿ2 sÿ1. Temperature and relative humidity were 25 8C and 50%, respectively. Growth conditions were the same as those describedin Fig. 10.

Fig. 13. Changes in 3D composite images of (A) leaf temperature and (B) PRI of the same plant shown in Fig. 12. The upper images were obtained justbefore the treatment and the lower ones were obtained 90 min after the treatment. Light was provided at a PPFD of 450 lmol mÿ2 sÿ1 from 180 min
before the treatment, and stomata were mostly open by the time of the treatment. Temperature and relative humidity were 25 8C and 50%, respectively.
Growth conditions were the same as those described in Fig. 10.

3D lidar imaging of plant responses and canopy structure

chlorophylls and carotenoids. Chlorophyll fluorescence,
the first transient (OJIP) of a dark-adapted leaf, reflects
the successive reduction of the electron acceptor poolin photosystem II (QA, the one-electron acceptor-boundplastoquinone; QB, the two-electron acceptor-bound plastoquinone; and mobile plastoquinone molecules), and ‘P’
may reflect the peak concentration of QAÿ, QB2ÿ, andplastoquinone PQH2 (Govindjee, 2004). PRI has a negative
correlation with the de-epoxidation state of the xanthophyll
cycle from violaxanthin to zeaxanthin (Gamon et al., 1992;
Peñuelas et al., 1997; Evain et al., 2004) and it is calculatedusing the equation (R531–R570)/(R531+R570), where Rk is thereflectance at a wavelength of k nm. Leaf temperatureprovides a measure of stomatal response, transpiration,
absorption of CO2 (photosynthesis), and absorption of air
pollutants under constant thermal environments (Jones,
1983, 2004; Omasa, 1990, 2002).
Figure 12 represents 3D composite images of the natural
colour, chlorophyll fluorescence intensity at ‘P’, and PRI
before treatment with Basta on the central part of a leaf
(broken lines in the figure) and at 570 min after thetreatment. The active ingredient of Basta is glufosinate,
which inhibits the activity of glutamine synthetase, whichis essential for the removal of toxic ammonia producedby plant metabolism (Lea and Ridley, 1989). The application of glufosinate increases levels of ammonia in the planttissues, and this reaction stops photosynthesis and causes
mortality within a few days (Lea and Ridley, 1989). In Fig.
12, the natural colour and PRI images show little change,
but a large change is evident in fluorescence intensity
because of photosynthetic inhibition by 570 min after thetreatment. Changes are also evident in other young leaves
that had not been treated; the reason for these changes is notclear. Figure 13 shows 3D composite images of leaf
temperature and PRI just before the treatment and 90 min
after the treatment. Because illumination was provided
starting at 180 min before the treatment, stomata had mostly
opened before the treatment. The treatment caused stomatalclosure and a temperature increase in the treated area.
However, leaf temperature outside the treated area continued to decrease because of transpirational cooling permitted
by the open stomata. As in Fig. 12, PRI at 90 min after thetreatment showed little change from the pretreatment
values. The difference in the spatial responses to Basta
treatment was confirmed by simultaneous 3D imaging of
natural colour, chlorophyll fluorescence, PRI, and leaf
temperature.
Foliar chlorophyll contents are strongly related to thephotosynthetic ability of plants. Leaf spectral reflectanceis one means of assessing the contents of different chlorophylls. For example, chlorophyll a content was estimated(R2=0.90) using the ratio of reflectance at 550 and 900 nm(Omasa and Aiga, 1987). The change in red edge, which is
the region of the reflectance curve from 690 to 740 nm, also
depends on the chlorophyll content (Curran et al., 1990;

893

Jago et al., 1999). These analyses have been used to detect
plant injuries that result from nutrient deficiency, air
pollution, and other biotic and abiotic stresses that cancause changes in the chlorophyll content (Omasa and Aiga,
1987; Wessman, 1990; Ustin et al., 1999). Recent advancesin hyperspectral imaging have provided additional useful
information on physiological and ecophysiological reactions
and on levels of various substances in plants (Wessman,
1990; Ustin et al., 1999; Zarco-Tejada et al., 2004). Thus,
it may be possible to construct 3D composite images that
include additional useful information by means of computer
graphics. The spectral characteristics of plants have also been
coupled with their 3D structure. Integrated analyses ofspectral reflectance images and 3D lidar images provide
more precise estimation of structural, physiological, andecophysiological indices and levels of various substances inplants in wide-area imaging such as remote sensing. For
example, Blackburn (2002) integrated an airborne multispectral sensor with an airborne small-footprint lidar system
to estimate forest chlorophyll contents. Combining the 3D
tree structure derived from the lidar image with the multispectral image improved the strength of the relationship between chlorophyll content per unit of leaf mass and thewavelength position of the red edge to R2=0.85 for the
coniferous stands.
Spectral analysis of steady-state fluorescence in theregion between ultraviolet and red wavelengths (300–800 nm) has also been used for the detection of internal
responses related to changes in plant pigments, cellstructure, and membranes (Lang et al., 1991; Lichtenthaler
et al., 1996; Buschmann et al., 2000; Kim et al., 2002).
Chlorophyll a fluorescence imaging, which was started by
Omasa et al. (1987) and Daley et al. (1989), has been used
as a powerful tool for non-invasive analysis of photosynthesis. The imaging reveals the distribution of photosynthetic activity within the leaf and permits early detectionof stress before the appearance of visible injury on theleaf surface (Govindjee and Nedbal, 2000; Omasa and
Takayama, 2002; Osmond and Park, 2002; Papageorgiou
and Govindjee, 2004; Chaerle et al., 2005). Furthermore,because of the growing demand for large-scale monitoringof ecosystems in times of global change, the applicationof this form of imaging has been expanded from the leaf
to whole plants and even forest stands. In particular, laserinduced fluorescence (LIF) imaging has been applied forthe determination of substances in whole plants (Edneret al., 1994; Johansson et al., 1996; Saito et al., 2000)
and photosynthetic activity of whole plants as techniquesof laser-induced fluorescence transients (LIFTs) (Omasa,
1988, 1998; also see Omasa and Takayama, 2002; Kolber
et al., 1998). Recently, more extensive characterization ofphotosynthetic properties, such as the efficiency of photosynthetic light utilization, the quantum yield of photosynthesis, and the kinetics of photosynthetic electron transport,
has been proposed using LIFT, which is based on the894 Omasa et al.

principles of fast-repetition fluorescence (Kolber et al.,1998) for large-scale measurement at a range of 5–30 m(Ananyev et al., 2005; Kolber et al., 2005).
Leaf temperature is a useful indicator of stomatal
response, transpiration, and absorption of CO2 (photosynthesis) and of air pollutants (Jones, 1983, 2004; Omasa,
1990, 2002; Omasa and Croxdale, 1992) under constantthermal conditions. In the early 1980s, Omasa et al. (1981a,b; also see Omasa and Croxdale, 1992) estimated dynamic
responses in leaf images of stomatal resistance (i.e. theinverse of stomatal conductance), transpiration rate, andabsorption rates of O3, NO2, and SO2 from leaf-temperature
images under a range of values of light intensity, airtemperature, humidity, and air current. Recently, thistechnique was applied to simultaneous imaging of stomatal
conductance, non-photochemical quenching, and photochemical yield of photosystem II in intact leaves (Omasa
and Takayama, 2003). However, it is limited to imaging ofa single leaf in a controlled environment. Chaerle et al.
(2003) also applied thermal and chlorophyll fluorescenceimaging for monitoring the effects of phenylurea herbicide
on plant leaves. Further developments are presented inother Focus Papers in this issue.
Thermal imaging can permit the early detection of plantstress because stomatal closure occurs before the appearance of visible injury and this phenomenon is revealed bydifferences in temperature between stressed and nonstressed plants (Omasa, 1990, 2002; Jones, 2004). It isdifficult to evaluate stomatal conductance and transpiration
rates of plants under growing conditions in the field usingleaf-temperature images because leaf temperature depends
not only on stomatal opening but also on changeable
thermal conditions such as air temperature, humidity,
radiation, and air current. However, our previous studies
have provided early detection of environmental stress inindividual trees using ground-based or airborne (helicopterbased) thermal imaging under cloudy, slightly windy, andsteady-state thermal conditions (Omasa and Aiga, 1987;
Omasa et al., 1990, 1993; Omasa, 2002). Jones et al.
(1997) and Jones (1999a, b) proposed a much more userfriendly approach based on the use of wet and dry reference
surfaces to normalize the temperature of the leaf to accountfor changing environmental conditions and applied thetechnique to vegetation canopies in the field. Jones (2004)
presents a detailed review of the application of thermal
imaging and infrared sensing in plant physiology andecophysiology.
Although fluorescence and thermal images provide useful information on plant stress, they are limited to 2D
images. However, it is possible to create 3D images thatwill be more useful for detecting and understanding plantstress by compositing the 2D images with 3D lidar images,
as shown in Figs 12 and 13. Furthermore, the use of 3D
lidar images in mixel analysis in the 2D images (a mixel isa pixel whose value is a mixture of the different land coverradiance values) may improve the analytical results in widearea imaging such as the spectral reflectance analysis
described herein.
Concluding remarks and perspectives
for the future
In this paper, a wide range of research on 3D lidar imaging
from the leaf level to remote sensing of landscapes wasreviewed. Numerous studies during the past decade haveshown the applicability of lidar-based remote sensing toestimate plant properties such as canopy height, canopy
structure, carbon stock, and species. Several studies havealso demonstrated the usefulness of lidar in assessing largescale plant growth responses. However, the potential of
3D lidar has not yet been fully exploited for monitoring ofplant responses to stress. Future lidar applications, including more accurate dynamic estimation of plant properties,
will improve our understanding of plant responses to stressand will improve our knowledge of interactions between
plants and their environment.
The spectral properties of reflection, radiation, andfluorescence from leaves provide useful information on
physiological responses and levels of various substances inleaves. However, until recently, most research on imaging
these spectral properties has been limited to 2D imaging.
Current passive imaging techniques such as multi- or
hyperspectral imaging, thermal imaging, and active fluorescence imaging techniques can provide a variety of information on physiological conditions and levels of various
substances in plants, including plant pigments, stomatal
responses, transpiration, photosynthesis, and gas exchange.
Therefore, the composite use of 3D lidar with 2D datacollected using passive and active imaging techniques may
improve the accuracy of airborne and satellite remotesensing and make it possible to analyse 3D information
on physiological conditions and levels of various substances in agricultural and ecological applications and global
biosphere observations.
To demonstrate the potential usefulness of composite
imaging techniques, it was shown how accurate lidar
images and optical images of natural colour, chlorophyll fluorescence, PRI, and leaf temperature can be
combined to provide information on pigments, photosynthesis, transpiration, stomatal responses, and other factors
at the level of individual plants using computer graphics
techniques. The resulting 3D composite images will increasingly improve our understanding of and ability todiagnose plant responses to stress. Furthermore, the combination of lidar with other active imaging techniques isadvantageous. In particular, the LIF and LIFT techniques
are active forms of remote sensing that provide estimatesof levels of various substances in leaves and of photosynthetic activity. Combination of these active imaging
techniques with lidar may allow 3D estimates of levels

3D lidar imaging of plant responses and canopy structureof various substances and of plant responses at multiple
scales. In the future, composite 3D imaging techniques may
be applied not only to plant science and global observations, but also to education, precision agriculture, andforestry.

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