We present an empirical application of multispectral laser scanning for
monitoring the seasonal and spatial changes in pine chlorophyll (
The photosynthetic activity of leaves within a tree canopy is an indicator
of tree health. Vigorous trees with high foliar biomass and chlorophyll
content have high carbon assimilation capacity. Stress in vegetation has
been shown to induce changes in the photosynthetically active pigments such
as chlorophyll
The leaf properties and the distribution of chlorophylls and nutrients
within a canopy vary as a function of time and space, and depending on the
resource availability (Wang and Schjoerring, 2012; Peltoniemi et al., 2012).
Plant phenology and seasonal chlorophyll content cycle are correlated to the
CO
One way to provide simultaneous structural and spectral information is lidar combined with hyperspectral passive sensing (e.g. Thomas et al., 2006; Asner et al., 2007; Jones et al., 2010). New applications using multi- or hyperspectral laser scanning have become more common recently. Hancock et al. (2012) demonstrated the potential of dual-wavelength, large-footprint, spaceborne lidar to separate ground and canopy returns using the extra information contained in a spectral ratio to complement the canopy height from laser scanning. Three-dimensional (3-D) distributions of vegetation biochemical properties were measured with spectral indices developed for the Salford Advanced Laser Canopy Analyser (SALCA), which is also a dual-wavelength lidar (Gaulton et al., 2013). A similar approach was used in the Dual-Wavelength Echidna Lidar (DWEL) (Douglas et al., 2012). A multispectral canopy lidar has also been introduced for simultaneous retrieval of vegetation structure and spectral indices (Woodhouse et al., 2011). In this approach, a tunable laser operating at four wavelengths was used.
In this technical note, an application of the recently developed
hyperspectral lidar instrument (HSL) (Hakala et al., 2012) is presented for
monitoring the seasonal and spatial changes in pine total chlorophyll
content (chlorophyll
The HSL is a prototype laser scanning instrument (Hakala et al., 2012) utilizing a supercontinuum laser. White laser (420–1680 nm) pulses are transmitted to a target and the distances of reflected echoes are determined from time of flight. A spectrograph and an avalanche photodiode (APD) array connected to a high-speed digitizer are used to determine the spectrum of each returning echo by measuring the intensity of the echo at multiple wavelengths. Also, the intensity of each transmitted laser pulse is measured and used to normalize the echo intensity. Current prototype configuration uses a 16-element APD array and an 8-channel digitizer, enabling us to measure at 8 wavelength bands: 545, 641, 675, 711, 742, 778, 978 and 1292 nm, with full width at half maximum of 20 nm. Before the target is measured, a reference target with known reflectance (Spectralon) is measured at distance intervals of approximately 30 cm, and these data are used to calibrate the reflectance over the whole measurement range. Additionally, the Spectralon is placed in the scanned area during the actual measurement to validate the calibration. The instrument and data processing are presented in more detail in Hakala et al. (2012).
A Scots pine (
Needle samples were taken immediately after the scan for laboratory analysis. Six branches were selected and the samples were taken from these branches according to needle cohorts (current year needles, and 1-, 2- and 3-year-old needles). Two needle pairs were taken from each cohort of each selected branch. Analysis of the chlorophyll contents followed the protocol described in Wellburn (1994) for extraction with dimethyl sulfoxide (DMSO). After extraction, the chlorophyll concentrations were determined from solvents spectrophotometrically using wavelengths of 480.0, 649.1 and 665.1 nm (resolution 0.1–0.5 nm).
Two of the six sampled branches were clearly identifiable from the multispectral point cloud, having enough point density and long enough growth of the branch. Previous-year cohorts were selected for further analysis, since they had needles present during all measurements. Therefore the following analysis is performed for two cohorts and five measurement dates. The parts of the point cloud containing the selected cohorts were isolated in post processing. Three spectral indices were tested for determining chlorophyll content of the needles. Since it was not possible to tune all required wavelengths to optimal positions for every index, we used the nearest available band.
The modified chlorophyll absorption ratio index (Eq. 1) using reflectance at
705 and 750 nm (referred here as MCARI750) was first presented by Wu et al. (2008). Contrary to the original MCARI (Daughtry et al., 2000), MCARI750 uses
reflectance at 705 and 750 nm, which have shown better sensitivity to high
chlorophyll contents (Wu et al., 2008). MCARI was designed to measure
the depth of the maximum chlorophyll absorption at 670 nm relative to green
reflectance peak at 550 and reflectance at 700 nm, at canopy scale
(Daughtry et al., 2000).
The limitation of empirical vegetation indices estimating chlorophyll content is that they are also affected by the canopy structural properties. In addition, they can be affected by the internal structure, size, surface and shape of leaves and can thus be species-specific, requiring calibration when applied to specific species (Zhang et al., 2008).
The benefit of active measurement systems, such as HSL, is that they measure backscattered signal that has the potential to eliminate many of the multiple scattering and geometric viewing effects caused by the canopy structure (Gaulton et al., 2013; Morsdorf et al., 2009). The major factors affecting the backscattered signal are the local incidence angle of the target and the area of effective backscattering surface (Gaulton et al., 2013). These factors are also present in this study, as one 5 mm footprint may include one or several needles with varying incidence angles. However, the influence of these factors is similar with different wavelengths measured at the same optical path. Thus, by calculating spectral ratios (i.e. vegetation indices), the influence of the incidence angle and target area can be reduced (Eitel et al., 2011; Gaulton et al., 2013).
A 3-D point cloud of the tree and changes in structure (such as the growth of new shoots) from May to November can be observed in Fig. 1, where no spectral information is used. The changes in the structure of one branch are visible in the coloured point clouds in Fig. 2, where we plot the NDVI time series of the pine branch from 15 May to 6 November 2013. The outbreak and growth of new shoots (May–June 2013) can be observed, as well as the year 2 cohorts defoliating (September–October 2013) and falling off completely (November 2013).
The available channel wavelengths (nm) and the nominal wavelengths (nm) of the spectral indices. The closest available channel was used.
Co-registered point clouds from the 15 May 2013 scan (grey) and the 11 June 2013 scan (red). Growth of the tree is visible and also some movement of the branches can be observed. The height of the tree is approximately 5.5 m.
NDVI (see the colour bar for values) point clouds of a sample branch M2. The growth of new needles (starting 27 May), already clearly visible new branch tips 19 June, fully grown new needles 12 September and dying and falloff of old needles (low NDVI in 12 September and 03 October) are visible in the data measured at different times. The measurement dates are shown in the plot titles.
Top row: distribution of MCARI750 spectral index during separate
scans – the central mark is the median, the edges of the box are the 25th and
75th percentiles, and the whiskers extend to the most extreme data points not
considered outliers. Middle row: laboratory measurements of chlorophyll
Same as previous figure (top and bottom rows; laboratory data are the same as in previous figure) but this time using the MSR2 spectral index.
Same as previous figure but this time using the SR6 spectral index.
Correlation of spectral index and laboratory measurement for combined M2_1 and M3_1 data. Left: MSR2; middle: MCARI750; right: SR6. Blue x: M3_1; red circle: M2_1.
To validate the capability of the HSL to estimate the chlorophyll content
using spectral indices, we compared the lidar data with laboratory analysis
over the growing season. We present data for two branch cohorts, denoted
M2_1 and M3_1 (1-year-old part of M2 and
M3), which were best visible in the multispectral point clouds. The trends
in the chlorophyll content and the indices MCARI750, MSR2 and SR6 from HSL
data are well reproduced for the individual branches (Figs. 3–5). For all
three indices, the sample branch M2_1 was best correlated
with the laboratory measurements with
In Figs. 3–5, branch M2_1 and M3_1 laboratory measurements consist of two separate needles only. More sampling should have been performed; however, the number of needles in each branch cohort is limited and the tree had to be sampled several times during the year (this emphasizes the need for non-destructive methods). The number of laser echoes from year 0 and 2 were highly variable; in the spring lidar point clouds, the year 0 growths were very small, providing very few echoes. The year 2 and older cohorts started dropping needles before September measurement, thus reducing the number of echoes during autumn compared to spring. Therefore we only used year 1 laboratory measurement of needles in plots 3–5 for whole tree (right column), since the weight of the year 0 and 2 laboratory measurements would have been higher compared to the lidar point cloud (lidar point density variable and laboratory sample number constant). Some lidar echoes still originate from the year 0 and 2 needles, reducing the overall correlation between laboratory and lidar data for the whole tree.
The changes in the structure of the tree are visible in Fig. 1. The fact that the structure of the tree can be retrieved from laser scanner point clouds has been shown before in numerous studies (see Kaasalainen et al., 2014, and references therein). We have also shown in our previous study that the tree structure and its changes can be quantified from laser scanner point clouds using quantitative structure modelling (QSM) designed to retrieve tree branching structures (Raumonen et al., 2013; Kaasalainen et al., 2014). As the scope of this note was to show the added value of spectral data in the chlorophyll distribution monitoring, the changes in tree structure will be an object of our future study.
We have shown that the multispectral lidar provides an empirical approach
for efficiently mapping the spatial distributions of tree physiological
parameters that are correlated with reflectance of the foliage (such as
chlorophyll
We demonstrated that the seasonal changes in the structure and physiology of
tree canopy, needles and branches are visible in 3-D; parameters affecting
tree physiology can be quantified with spectral indices and linked to a
specific location in the tree canopy using the multispectral point cloud. We
validated the method with reference measurements of chlorophyll
Although the influence of multiple scattering effects caused by canopy structure can be reduced using multispectral lidar and ratios of backscattered reflectance, it is not completely removed. Further study would be required to produce a physically based model that would properly account for the multiple scattering of needles within a single laser footprint and its effect to the measured backscattered reflectance. In addition, some of the limitations of vegetation indices in chlorophyll estimation, such as robustness and portability to different measurement configuration and wavelengths, might be overcome by using inversion of radiative transfer models, such as the LIBERTY (Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields) (Dawson et al., 1998), which is specifically developed for needles, , or by utilizing PROSPECT (leaf optical properties) model to optimize spectral indices (Féret et al., 2011).
The tree was scanned from two directions only. Increasing the number of scans from different directions around the tree will improve the results by increasing the point coverage. This will require some instrument development to allow for more efficient field use. Increasing the point density is also an important object of instrument improvement. However, the prototype instrument was capable of showing the potential of 3-D spectral measurements.
A major factor causing error and uncertainty in this research was the use of the nearest possible channel in vegetation index calculation instead of the band that the index was designed to use. This affects the performance of the vegetation indices, especially with indices requiring channels at the red edge, where even a small shift in channel wavelength causes a large change in reflectance. However, this was not considered to be major problem, as the aim of this study was to test the ability of our HSL instrument in chlorophyll estimation and not to optimize the performance of the indices.
Further work is needed to find the best spectral indices for different applications (e.g. monitoring the 3-D effects of drought or limited amount of light on the physiology of a tree) and then optimize the spectral channels to match with these indices. This will improve the precision of the results. Increasing the number of spectral channels would also improve the channel optimization and efficiency. Once the approach is well established and calibrated, it has potential for replacing a number of laborious and destructive manual experiments, and hence providing a new tool for remote observations of tree physiology. Although the first results show the potential of the approach, further studies on backscatter of the supercontinuum laser from the canopy are needed to establish the method physically.
This study was funded by the Academy of Finland research projects “New techniques in active remote sensing: hyperspectral laser in environmental change detection” and “Mobile hyperspectral laser remote sensing”. Edited by: A. MacArthur