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AN INTERCOMPARISON OF PASSIVE TERRESTRIAL REMOTE SENSING
TECHNOLOGIES TO DERIVE LAI AND CANOPY COVER METRICS
W. L. Woodgate
Dept. of Infrastructure Engineering, The University of Melbourne, Australia — w.woodgate@pgrad.unimelb.edu.au
Technical Commission VIII, WG 7 (Forestry)
KEY WORDS: LIDAR, Forestry, Sustainable, Multisensor, Scale, Terrestrial
ABSTRACT:
Forest indicators such as Leaf Area Index (LAI) and vegetation cover type are recognised as Essential Climate Variables (ECVs) which
€
support the
...research, modelling, analysis, and capacity-building activities...
,
requirements of the United Nations Framework
Convention on Climate Change. This research compares the use of passive terrestrial remote sensing technologies for LAI and canopy
cover metrics. The passive sensors used are the LAI-2200 and Digital Hemispherical Photography (DHP). The research was conducted at
a Victorian reference site containing tree species with predominantly erectophile leaf angle distributions, which are significantly under-
represented in the literature. The reference site contributes to a network of sites representative of Victorian land systems and is
considered to be in good condition. Preliminary results indicate a low correlation (R’=0.46) between the LAI-2200 and DHP. Further
comparisons to be conducted include adding a passive CI-110 plant canopy analyser and an active Terrestrial Laser Scanner. The future
objective is to scale the in situ data to aerial and satellite remotely sensed datasets. The aerial remotely sensed data include LiDAR flown
by a Riegl LMS Q560, and high resolution multispectral and hyperspectral imagery flown by the ASIA Eagle and Hawk system. The in
situ data can be utilised for both calibration and validation of the coincident aerial imagery and LiDAR. Finally, the derived datasets are
intended for use to validate the global MODIS LAI product.
1. INTRODUCTION
Sustainable forest management is fundamental to the
preservation of biodiversity and mitigation of climate change
(Garnaut, 2008; Lanly, 1995). Delineating criteria and
indicators that provide valuable information on forests are
important to assist sustainable forest management (Raison et al,
1998). Leaf Area Index (LAI) has been recognised as a key
forest indicator and is one of the Essential Climate Variables
Which support the ‘...research, modelling, analysis, and
capacity-building activities...” requirements of the United
Nations Framework Convention on Climate Change
(UNFCCC) (GCOS, 2010). Another key forest indicator is
canopy cover which is a primary requirement for the definition
of forest as recognised by the United Nations Food and
Agricultural Organisation (FAO) (FAO, 2010).
LAI is a quantitative measure of the amount of leaf tissue in the
canopy per unit of ground area (GTOS, 2009). It is broadly
defined as ‘leaf area per unit area of land’ (Watson, 1947). LAI
is a non-dimensional measurement, but is usually quantified as
m’ of leaf area per m” of ground area. (Running ef al., 1986)
identified LAI as ‘the single variable both amenable to
measurement by satellite and of greatest importance for
quantifying energy and mass exchange by plant canopies over
landscapes’.
‘Canopy cover refers to the proportion of the forest floor
covered by the vertical projection of the tree crowns’ (Jennings
et al, 1999). Canopy cover and variations of cover such as
Foliage Projective Cover (FPC) provide a useful measure of the
amount and distribution of foliage and allow for analysis at a
number of spatial scales (White ef al., 2000).
Remote sensing technologies can be utilised to indirectly derive
both LAI and canopy cover metrics. Remote sensing
technologies enable the landscape to be analysed at multiple
scales from ground, airborne and spaceborne platforms (Zheng
& Moskal, 2009). The technologies can categorise their sensors
as being either passive or active. Terrestrial remote sensing
technologies used to derive LAI and canopy cover metrics are
important for calibration and validation of datasets derived
from the airborne and spaceborne platforms (Baret, 2007;
Morrisette, 2006).
Passive sensors, such as imagery, can only detect energy when
naturally occurring energy exists (Zheng & Moskal, 2009).
Whereas active sensors, such as LiDAR, emit their own energy
source and record the energy returned from objects of interest
(Zheng & Moskal, 2009). The advantage of an active sensor
over a passive sensor is that it is independent of the naturally
occurring energy in the environment. Active sensors are not
limited in their time of operation by environmental conditions
such as the amount of sunlight available.
Terrestrial remote sensing technologies such as Digital
Hemispherical Photography (DHP), ceptometers and Terrestrial
Laser Scanning (TLS) are utilised to provide LAI and canopy
cover at the in situ scale through gap fraction analysis (INRA,
2010; Zheng & Moskal, 2009). Gap fraction can be used to
derive other metrics such as foliage mean tip angle (MTA), the
fraction of absorbed photosynthetically active radiation
(FAPAR) and the fraction of vegetation cover (FCOVER)
