IP and LAI-2200 are 
y Coops et al., (2004), 
DHP and LAI-2000 
ear regression with an 
lies may be explained 
ements, measurement 
ng software to derive 
in the Coops et al, 
s and the Eucalyptus 
nificantly taller and 
) the species found in 
I values reported in 
1 the study area in 
er biomass of the tree 
et al., (2004) also 
this study compared 
ot. Lastly, different 
JAI from DHP in the 
emiView 2.1 software 
efine a threshold for 
sky elements for gap 
software used in this 
a 
ve 
Rushworth 
an increased coverage 
ing the measurement 
iparison of results at a 
s (Nelder, 1962). The 
t level of a variety of 
o be undertaken at a 
leasurements. 
1al point scale will be 
metrics for both the 
| same metrics from 
stermining the degree 
fferent metrics will be 
mine the degree of 
the ratio of woody to 
. will enable the ratio 
calculated. This ratio 
ink between LAI and 
nents for comparison 
with each other over 
s each instrument has 
- example, FCOVER 
is derived over a FOV, 
gregated of the same 
FCOVER can be 
and then compared to 
for testing of various 
os, will enable the 
  
minimum number of measurements and their location to be 
determined in order to characterise the variability of each 
metric for the entire plot. This will provide insight as to the 
approximate sampling scheme required for each plot location 
as each site is relatively homogenous. 
A further comparison will be made between the height profile 
from the modified FPC and the bottom of canopy returns from 
the TLS data. The dense TLS dataset will provide a reference 
dataset for the modified FPC measurements and give an 
indication of the robustness of the FPC sample density. 
An investigation will be conducted io determine whether a 
more accurate extent of the individual FOV point measurement 
from the passive sensors can be derived from TLS. The 
restricted FOV from each passive instrument will be projected 
out from the centre of the TLS point cloud to determine a 
theoretical extent of the sampling radius. The restricted FOV 
will be 115 degrees, as the 57.5 degrees azimuth angle was 
found to be favourable to derive LAI (Wilson, 1963). Once the 
approximate sample radius is calculated, it will be utilised as a 
more geometrically accurate sample size at each point 
measurement for validation and calibration of aerial and 
satellite remote sensing datasets. 
2.5 Future Planned Research 
As Project 2.07 is still in the first year of a three year study, the 
majority of the research is yet to be conducted. The two study 
areas that are the focus of this future research are Rushworth 
and Watts Creek. Both areas provide for a good comparison as 
the vegetation species and distribution are different, as well as 
climatic factors. Below is an outline of a subset of future 
planned activates, intended research and preliminary 
methodology. 
Airborne data collection: Aerial data consisting of ALS and 
imagery will be acquired at the 5x5km extent for each site. The 
ALS scanner to be flown is a Riegl LMS Q560. The 
approximate flying height is 1km and a ground sample density 
of 20 points/m^. The absolute horizontal and vertical accuracies 
expected are +0.3m and +0.2m respectively. The ASIA Eagle 
and Hawk passive scanners are to be flown to provide a 
seamless high spatial resolution (<Im pixel size) dataset 
(NERC, 2012). The sensors provide radiometric resolution 
from 400nm to 2500nm. 
Study Area Scale Characterisation: A major aim is to collect 
the in situ data for scaling to aerial and satellite remotely 
sensed datasets. The in situ data will be utilised for both 
calibration and validation of the aerial imagery and LiDAR 
captured for Project 2.07. The site sampling scheme is pivotal 
fo ensure the in situ data will provide a robust scaling method. 
The centre 3x3km of the site is to be stratified into nine 1km 
grids, each with one randomly selected plot location. The 
sampling design for each plot will follow the SLATS Star 
Transect protocol (TERN, 2012). The design consists of three 
100m long transects centred at the middle of the plot (Figure 4). 
The first transect is aligned in the North-South direction, where 
the next two transects are placed at 60? rotations from the 
centre point. 
    
      
    
   
    
    
    
    
   
    
   
   
   
   
   
   
    
   
   
  
  
   
     
    
     
     
     
   
    
   
     
  
  
  
   
    
  
    
  
  
   
   
    
   
   
  
    
   
  
  
    
  
For each of the three 100m transects, an LAI value is recorded 
at every meter interval using the LAI-2200, totalling 300 
measurements. The DHP and CI-110 capture one image at the 
centre of the plot, one image at each point 25m away from the 
centre, and one image at the end of each arm, totalling 13 
images from each instrument. The TLS is to complete one scan 
at the centre point. However, due to logistical constraints the 
TLS will be utilised at only a subset of the nine plots for each 
site. 
s 
a 
Figure 4: SLATS Star Transect Sample Design. 
240° 
  
180* 
The in situ measurements will be related to the passive aerial 
datasets though transfer functions (Zheng & Moskal, 2009). 
Transfer functions enable the metric to be derived at the scale 
of the data captured by remote sensing instrument. ALS can 
derive gap fraction through ratios of below canopy returns to 
total returns (Morsdorf ef al., 2006). A future aim is to compare 
the LAI derived dataset at the site scale against the MODIS 
LAI product. Furthermore, the MODIS product could be 
validated from the in situ measurements collected from this 
research. 
3. CONCLUSION 
LAI and canopy cover are important for forest managers and 
decision making. This paper outlined a comparison between 
two passive terrestrial remote sensing technologies used to 
derive LAI metrics. The area investigated contained dry 
sclerophyll forest is representative of vegetation found in 
Victorian land systems. Preliminary results indicated a poor 
correlation between two passive instruments for deriving LAI. 
Future research will be conducted to examine the differences in 
instruments and methods, and include other active and passive 
terrestrial sensors for comparison. The in situ data can be 
utilised for both calibration and validation of the coincident 
aerial imagery and LiDAR. 
4. REFERENCES 
Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, 
M. et al (2007). LAI, fAPAR and fCover 
CYCLOPES global products derived from 
VEGETATION. Part 1: Principles of the algorithm. 
Remote Sensing of Environment, 110(3), 275-286. 
Caldas, L. S., Luttge, U., Franco, A. C., & Haridasan, M. 
(1997). Leaf heliotropism in Pterodon pubescens, a