nt 101 (2006) 495–518www.elsevier.com/locate/rse

Remote Sensing of Environme

Assessment of the MODIS LAI product for Australian ecosystems

Michael J. Hill a,b,⁎, Udaya Senarath a,b, Alex Lee a,c, Melanie Zeppel a,d, Joanne M. Nightingale e,Richard (Dick) J. Williams f, Tim R. McVicar g

a Cooperative Research Centre for Greenhouse Accounting, Research School of Biological Sciences, Australian National University,Canberra, ACT, 0200, Australia

b Bureau of Rural Sciences, PO Box 858, Canberra, ACT, 2601, Australiac School of Resources, Environment and Society, Australian National University, Canberra, ACT, 0200, Australia

d University of Technology NSW, Sydney, Australiae College of Forestry, Oregon State University, Corvallis, OR 97331, USA

f CRC for Tropical Savanna Management and CSIRO Sustainable Ecosystems, Winnellie, NT, Canadag CSIRO Land and Water, PO Box 1600, Canberra, ACT, 2601, Australia

Received 8 November 2005; received in revised form 12 January 2006; accepted 14 January 2006

Abstract

The leaf area index (LAI) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) is important for monitoring andmodelling global change and terrestrial dynamics at many scales. The algorithm relies on spectral reflectances and a six biome land coverclassification. Evaluation of the specific behaviour and performance of the product for regions of the globe such as Australia are needed to assistwith product refinement and validation. We made an assessment of Collection 4 of the MODIS LAI product using four approaches: (a) assessmentagainst a continental scale Structural Classification of Australian Vegetation (SCAV); (b) assessment against a continental scale land useclassification (LUC); (c) assessment against historical field-based measurement of LAI collected prior to the Terra Mission; and (d) directcomparison of MODIS LAI with coincident field measurements of LAI, mostly from hemispherical photography. The MODIS LAI productproduced a wide variety of geographically and structurally specific temporal response profiles between different classes and even for sub-groupswithin classes of the SCAV. Historical and concurrent field measurements indicated that MODIS LAI was giving reasonable estimates for LAI formost cover types and land use types, but that major overestimation of LAI occurs in some eastern Australian open forests and woodlands. The sixbiome structural land cover classification showed some significant deviations in class allocation compared to the SCAV particularly wheregrasslands are allocated to shrubland, savanna woodlands are allocated to shrubland, savanna and broadleaf forest, and open forests are allocatedto savanna and broadleaf forest. The land cover and LAI products could benefit from some additional examination of Australian data addressingthe structural representation of Eucalypt canopies in the “space of canopy realisation” for savanna and broadleaf forest classes.© 2006 Elsevier Inc. All rights reserved.

Keywords: Leaf area index; Savanna; Forest; Grassland; Land cover classification; Canopy cover; Structure; Strata

1. Introduction

Leaf area index (LAI) has become a key descriptor ofvegetation condition over a wide variety of spatial scales andeco-physiological contexts (e.g., Kang et al., 2003). Since theplant canopy intercepts radiation and provides photosyntheticfunction that drives vegetation growth, calculation of LAI is an

⁎ Corresponding author. Bureau of Rural Sciences, PO Box 858, Canberra,ACT, 2601, Australia. Tel.: +61 2 62725317.

E-mail address: michael.hill@brs.gov.au (M.J. Hill).

0034-4257/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2006.01.010

integral component of most ecosystem models. Where spatialapplication is desired or required, remote sensing has providedthe basic input for calculation of LAI through derived measuresof greenness and canopy cover. The Normalised DifferenceVegetation Index (NDVI) is the most commonly used basis forcalculation of LAI from remote sensing (Tucker & Sellers,1986; Myneni & Williams, 1994; Chen et al., 2002) althoughsimple ratios between near-infrared (NIR) and red bands arealso used (Asrar et al., 1984). The methods based on vegetationindices from Landsat Thematic Mapper or AVHRR (AdvancedVery High Resolution Radiometer) NDVI have significant

496 M.J. Hill et al. / Remote Sensing of Environment 101 (2006) 495–518

limitations (Asrar et al., 1992; Gobron et al., 1997) due to theneed to generate empirical relationships between NDVI andLAI for each biome of interest. The Moderate ResolutionImaging Spectroradiometer (MODIS) provides LAI and FPAR(Fraction of absorbed Photosyntetically Active Radiation) at 8-day intervals and 1 km pixel resolution for the whole globe(Myneni et al., 1997). The method developed for estimation ofcanopy leaf area from MODIS data represents a substantialadvance over the old approaches (Knyazikhin et al., 2002;Myneni et al., 1997).

The synergistic LAI algorithm for estimation of LAI fromMODIS data uses both spectral and angular information(Knyazikhin et al., 2002). In the case of MODIS, the givenmean spectral information is insufficient to solve the complexthree-dimensional inversion problem unless the range ofvariation in structural variables is limited. Hence, retrieval ofLAI and FPAR for the MODIS products is based upon a six-biome land cover structural classification (6BSLCC) defined bya suite of canopy structural attributes representing a pattern oftree and canopy architecture and spectral reflectance andtransmittance (Lotsch et al., 2003; Myneni et al., 1997). Thekey construct in the MODIS LAI algorithm is the use of a“space of canopy realisation” which relates tree and canopyarchitecture to spectral leaf albedo for MODIS bands (Knya-zikhin et al., 2002). The ground reflectances can fit only onepattern of spectral leaf albedo per biome although ground cover,LAI and effective ground reflectance can vary within the givenbiome-dependent space. The algorithm uses distribution func-tions to identify acceptable candidate estimates. If the inverseproblem cannot be solved, a backup algorithm is triggered toestimate LAI from vegetation indices.

The sources of uncertainty in the LAI and FPAR retrievalsrelate to the input data of surface reflectance and land cover andthe radiative transfer model underlying the algorithm (Shabanovet al., 2004). The anomalies in data from Collection 3 andCollection 4 are based on:

1. uncertainty in input land cover particularly between grassesand crops;

2. uncertainties in input surface reflectances—coefficient ofvariation (CV) in surface reflectance above 0.14 lead tosignificant increases in CVs. of LAI;

3. model uncertainties due to mismatch between simulatedreflectance based on SeaWiFS (Sea-viewing Wide Field-of-view Sensor) 8 km data and actual MODIS reflectances—meaning that space of canopy realisation in the algorithm isnot correctly aligned with the actual NIR-Red bi-directionalreflectance function (BRF) response space from MODIS foreach biome.

Collection 4 data address some of these issues in relation tooverestimation of grasses and croplands, while Collection 5data should improve optimisation of the algorithm for woodyvegetation (Shabanov et al., 2003, 2004). Research iscontinuing in an effort to improve the inversion accuracy ofthe LAI algorithm, for example mixing radiative transfer andnon-parametric regression (Fang & Liang, 2005).

The MODIS LAI product is important for monitoring andmodelling global change and terrestrial dynamics at manyscales and provides the basis for future operational retrievalsthrough NPOESS (National Polar-orbiting Operational Envi-ronmental Satellite System). Therefore, there is a need toevaluate the specific behaviour and performance of the productsfor individual biomes across the globe and such site or regionbased comparisons are gradually being completed (Berterretcheet al., 2005; Cohen et al., 2003; Fensholt et al., 2004; Tan et al.,2005; Tian et al., 2002a,2002b; Verbyla, 2005; Wang et al.,2004). Many of these studies involve detailed in situmeasurement of LAI by destructive harvesting (direct physicalmeasurement or leaf litter relationships with foliar subsamples),allometry (using species specific relationships based uponphysical dimensions) or by remote methods (point quadrats,LiCor LAI 2000 or hemispherical photography; Asner et al.,2003; Whitford et al., 1995). The assessment of the MODISLAI product using field measurements is subject to uncertain-ties and biases introduced by ground-based sampling methods(e.g. Whitford et al., 1995) and the scale of ground samples inrelation to the 1 kmMODIS LAI resolution. A recent validationstudy using field data from croplands has provided quantitativeestimates of accuracy (0.3 units), precision (0.23 units) anduncertainty (0.38 units) for the Collection 4 MODIS LAIproduct (Tan et al., 2005).

The definition of LAI and the specific leaf orientation andcanopy architecture of individual species are important elementsin the accurate estimation of LAI for continental Australia. Leafarea index is generally defined as the single sided area of leavesper unit of ground area, however, there are a number of otherdefinitions depending upon the specific purpose of calculation(Asner et al., 2003). The difficulties associated with the commondefinition of LAI are described by Asner et al. (2003). Theseinclude the definition of one-side for needleleaf trees and theaccommodation of leaf inclination in drooping canopies. Theseparticular issues are important when considering Australianvegetation where needleleaf forms occur among Callitris,Casuarina and Acacia species, and the most widespreadEucalyptus genus has a more vertical leaf inclination than otherbroadleaf species (Anderson, 1981). This fairly verticalinclination reduces maximum sensible heat load at noon andacts as a transpiration-limiting adaptation to the hot andrelatively arid climate of Australia (Anderson, 1981). InAustralia, projected foliage cover (PFC) is often used insteadof LAI as it may relate more directly through allometry to treedensity and biomass (Hassett et al., 2000).

Direct or remote ground-based measurements of LAI are notin abundance for Australian vegetation for the period since thelaunch of MODIS, although we do provide some directcomparisons here. Accordingly, we have established a frame-work for assessment of Collection 4 of the MODIS LAI productat 1 km pixel resolution with four components (Table 1):

1. Assessment against a continental scale Structural Classifi-cation of Australian Vegetation (SCAV; AUSLIG, 1990)which can be correlated to steady-state LAI for the differentnatural vegetation types.

Table 1Framework for assessment of MODIS LAI product

Data source Data used Nature of assessment

Atlas of Australian Resources.Vegetation 1988 vegetation1 :5,000,000

Vegetation class polygons for rangelandsand undisturbed forests—matrix representingfloristics, growth form and foliage cover

Relativities between average LAIs inrelation to growth form and foliage covercodes

National Land Use Map 1kmresolution grid cells

Land use classes for agricultural anddisturbed areas constrained by StatisticalLocal Area boundaries

Relativities between average and seasonalLAI patterns and general knowledge ofexpected values for major crops and pastures

Historical transect and point data(see Table 4) Northern AustralianTropical Transect (NATT; R.Williams, personal communication)NSW east–west transect(McVicar et al., 1996)

Point estimates of LAI from variousmeasurement methods collected prior to theactivation of the MODIS sensor on TERRA

Relationship between point-basedmeasurements and average annual LAIresponse bands from 4 years of MODISdata

Recent hemispherical and other pointestimates of LAI

Point estimates of tree canopy LAI collectedsince the activation of MODIS

Direct comparison of point-basedmeasurements and temporal traces ofMODIS LAI.

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2. Assessment against a continental scale land use classification(LUC; Stewart et al., 2001) originally derived fromapplication of the SPREAD algorithm (Walker & Mal-lawaarachchi, 1998) to an annual time series of AVHRRNDVI data for 1997.

3. Assessment against historical field-based measurement ofLAI collected prior to the Terra Mission, assuming that thesehave not changed considerably in recent years.

4. Direct comparison of MODIS LAI with coincident fieldmeasurements of LAI using hemispherical photography.

2. Methods

2.1. MODIS LAI product

We acquired 4 years of the Collection 4 (MOD15A2) 8-daycomposite LAI/FPAR product for the period February 2000 toFebruary 2004 from the NASA Distributed Active ArchiveCentre. The data were mosaicked using the MODIS reprojec-tion tool and imported into the ENVI® image processingpackage to create a 4-year data stack.

2.2. Data for LAI assessment

The data used for assessment of the MODIS LAI product aredescribed in Table 1. The SCAV for the Australian continent in1988 is described by 1–2.5 million scale map (AUSLIG, 1990;Fig. 1a). The vegetation is classified on the basis of floristics,growth form and foliage cover with both overstorey andunderstorey definition where present. The floristics describemajor dominant species, e.g. Astrebla grassland (a), Eucalypttrees (e), Chenopod shrubs (k), Triodia grasslands (t), Acaciatrees or shrubs (w), Mixed stands (x), other grasslands (y);growth forms describe major structural types, e.g., tall trees (T),medium trees (M), low trees (L), tall shrubs (S), low shrubs (Z),hummock grasses (H), tussock grasses (G), other herbaceousplants (F); and foliage cover is grouped into four classes, e.g., 1(<10%), 2 (30–10%), 3 (70–30%) and 4 (>70%) (AUSLIG,1990).

The LUC of the Australian continent in 1996–1997 (Stewartet al., 2001) is described by a raster map at 1 km resolution (Fig.1b). The classification was constructed by automated analysis ofa 1 year sequence of NDVI images for 1996 using control sitesto provide agricultural land uses at known locations andagricultural census data for 1996–1997 to provide areaconstraints (Walker & Mallawaarachchi, 1998; Stewart et al.,2001).

The site-based data were made up of:

(a) a collection of points derived from a literature survey(Table 2; Fig. 2);

(b) two major historical transects running east-west in NSW,and north–south in the Northern Territory (Fig. 2);

(c) and field sampling of LAI concurrent with the MODISrecord for locations in northern Victoria, northern NSW,central Queensland and the tropical rainforest zone ofnorthern Queensland (Fig. 2).

Historical LAI data were collated from a wide search of theliterature. The data were based on field measurements fromdocumented experimental plots for a range of forest andagricultural sites. The nature of the literature-based historicaldata was variable with sources ranging from sampling of nativeforests to sampling of establishing tree plantations, and recordsfrom small plot agricultural trials with crop plants (Table 2). Thedata varied from single date and point measurements withhemispherical photography to time series of physical measure-ment from crop establishment to harvest. The range of measuredLAI values for each historical record is given in Table 2.

The two historical national transect data sets used in theassessment consisted of: (a) an east-west transect of LAImeasurements across New SouthWales (NSW) gathered in 1990for assessment of LAI estimation fromAVHRRNDVI (McVicaret al., 1996); and (b) the Northern Australia Tropical Transect(NATT), a 1000 km long, 250 km wide transect with a series ofresearch sites along a gradient of decreasing mean annual rainfallfrom 1600 mm in the north to 500 mm in the south (R.J.Williams, personal communication). Indirect measurements of

Fig. 1. (a) Carnahan classification of current vegetation for Australia (AUSLIG, 1990) and (b) land use classification for Australia (Stewart et al., 2001).

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tree LAI were collected in late 1999 using tree basal area-LAIrelationships (Williams, unpublished data). Measurements ofunderstorey LAI were collected early in 2000 by biomass assayusing biomass-LAI relationships established for six tussockgrass species (Williams, unpublished data).

Field measurements, concurrent with the MODIS program,were collected by the authors in separate field campaigns. Datafor a disturbed old-growth tropical rainforest site in far northQueensland were collected using hemispherical photography(Nightingale, 2004). Leaf area index of the tree overstorey inremnant forest in northern NSW was measured using a Li-Cor2000 Plant Canopy Analyser, with one wand used for bothabove- and below-canopy measurements. Measurements of

above-canopy light were taken in a clearing next to the remnantforest, and the below-canopy estimates were measured in sevenrepresentative locations within the remnant forest. Measure-ments were taken in diffuse light, at dusk, so time limitationsmeant that no more than 7 measurements could be taken on 1day before light conditions became inappropriate.

Data for the Injune region of central Queensland and for anarea of North East Victoria were collected for both tree andunderstorey canopies using hemispherical photography. Duringfield campaign to the Injune study area in 2004, hemisphericalphotographs were taken within 31 field plots at 10 m intervalsalong three 50 m north–south transects established within eachplot at 10, 25 and 40 m intervals eastwards from the south–west

Table 2Historical LAI measurements for agriculture (A), forests (F), natural vegetation (N), and plantations (P) extracted from literature

Site Type Long Lat Vegetation Dates LAI range Reference

Wagga, Bullenbung,Browning, Urana,NSW

A 147.33 −35.06 Pasture, wheat, oats,triticalee

1994, 1995 0.2–4.0 Leuning et al. (2005)

Walpeup, Vic (MalleeResearch Station)

A 141.98 −35.12 Wheat, field peas,indian mustard,Medicago truncatula

1992, 1993, 1994 0.1–4.2 Zhang et al. (1999)

Hillston, NSW A 145.85 −33.37 Wheat, oats, lucerne 1992–1995 0.1–5.3 Zhang et al. (1999)Warwick, Qld A 152.10 −28.30 Barley 1990–1991 0.3–10 Goyne et al. (1993)Beverley, WA, 25 km east A 117.17 −32.13 Lupinus angustifolius 1993 0–7.2 Dracup et al. (1998)Lawes, Qld A 152.83 −27.55 Vicia faba 1998/99 0–6.1 Turpin et al. (2002)Macknade, Qld A 146.00 −18.60 Irrigated sugarcane NA Muchow et al.

(1993a,b); Robertsonet al. (1996a,b)

Ayr, Qld A 147.40 −19.57 Irrigated sugarcane NA Muchow et al. (1993a)Bambaroo, Qld A 146.20 −18.90 Rainfed sugarcane NA Inman-Bamber et al.

(2002)Ayr, Qld A 147.40 −19.57 Irrigated sugar cane-

water stress trial1995–1997 0.89 (dry)–4.95

(wet)Robertson et al. (1999)

Gatton, Qld A 150.33 −27.57 Phasey bean, sesbania,vigna, soybean

1990 0–9.2 Pengelly et al. (1999)

Dalby, Qld A 151.35 −27.18 Soybean Phasey beanSesbania Vigna

1988 NA Pengelly et al. (1999)

Toowoomba, Qld A 151.87 −27.57 Wheat-irrigated (I)/dryland (D) with high(H)/low (0) N

0–7.0 (IN) 0–1.0(D0)

Meinke et al. (1998)

Burdekin River IrrigationArea, Qld

A 146.27 −20.05 Irrigated Rice 1989–1990 0.4–7.7 Borrell et al. (1997)

Northam, WA A 116.68 −31.72 Faba beans GAI 1993 0.2–4.9 Loss and Siddique(1997); Loss et al.(1997)

Merredin, WA A 118.20 −33.48 Faba beans 1993 0.3–3.5 Loss and Siddique(1997); Loss et al.(1997)

Wagga, NSW A 147.30 −35.17 Wheat, canola, oats 1992, 1993 0.1–2.2 Dawes et al. (1997)Dwellingup, WA F 116.22 −32.62 Eucalyptus marginata Prior to 1995 1.05–1.14 1.38–1.82 Whitford et al. (1995)Bago-Maragle State

Forest NSWF 148.14 −35.62 E. delegatensis,

E. pauciflora,E. dalrympeana,E. radiata, E. viminalis,E. macroryncha, E. albens

1999, 2000, 2001 0.73–2.05 0.90–2.070.83–2.01

Coops et al. (2004)LiCor (P),HemiView (P),PointQuadrat (L)

Dwellingup, WA F 116.08 −32.70 Eucalyptus marginatus 1977 3.7 ( 2.7 plus 1.0) Hingston et al. (1981)Yass F 145.33 −35.25 E. rossii/E.maculata 1979 0.8–1.56 Anderson (1981)Batemans Bay F 150.25 −35.25 E. maculata / E.gummifera 1979–1980 1.10–1.96 Anderson (1981)Darling Range, WA F 116.34 −32.50 E. marginatus; E. calopylla 1.5 0–2.4 Carbon et al. (1979)Gordon Field Obs. Site,

WA, see mapF 116.36 −32.15 E. marginatus 1993; 1994 0.88–1.32 Silberstein et al. (2001)

Kioloa, NSW F 150.32 35.55 E. maculata 3.0 Dunin et al. (1985)Esperance,Tasmania F 146.88 −43.26 E. obliqua 1985–1988 1.6–4.4 Honeysett et al. (1992)Pemberton, WA, 9.6 Km

northwestF 115.94 −34.43 E. diversicolor 2.6 Hingston et al. (1979)

Pemberton, WA, 11 km east F 116.12 −34.46 E. diversicolor; E. calophylla 5.0 (4.2 plus 0.8) Hingston et al. (1979)Kimba, Parilla, Walpeup

Dergholm, RoseworthyF 136.83 −33.17 E. socialis E. dumosa

E. salmonophloia E. baxteri,E. faciculosa

1997 0.56–1.21 Ellis et al. (2005)

Wakool, NSW N 143.39 −34.88 Grazed Atriplex nummularia 1994 0.35 Slavich et al. (1999)Gnangara Reserve,

Perth, WAN 115.68 −31.92 Banksia woodland-Banksia

attenuata; B. menziesii1985–1986 0.04–0.12 Farrington et al. (1989)

Port Hedland, WA N 118.67 −20.50 Mangroves on tidal flats;Avicennia marina andCeriops tagal

1992–1995 1.0–2.1 Gordon et al. (1995)

Darkan, Mummbalup,Manjimup,Northcliffe, WA

P 116.76 −33.35 E. globulus 1991–1993 2.7–3.8 Hingston et al. (1998)

(continued on next page)

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Table 2 (continued)

Site Type Long Lat Vegetation Dates LAI range Reference

North Maroondah, Vic. P 145.54 −37.62 E. regnans 4.2 Vertessy et al. (1995)Gippsland, Vic. P 146.50 −38.23 E. globulus 1995 1.7–4.2 Bennett et al. (1997)Hilton, WA P 116.32 −33.99 E. globulus 2.7–4.6 MacFarlane and

Adams (1998)Gympie, Qld P 152.77 −26.12 E. grandis 5.2 Cromer et al. (1983)Wagga Wagga, NSW P 147.48 −35.16 E. grandis 1992; 1993 2.5 6.5 Myers et al. (1996)Bunbury, Busselton Collie,Cowaramup CundinupGrimwade MandurahNorthcliffe Scott RiverAlbany WA

P 115.72 −33.15 E. globulus(6–8 years old)

1997 1.5–3.5 (P)2.3–5.3 (L)

MacFarlane et al.(2000) HemisphericalL and P

Kyabrum, Victoria P 145.04 −36.31 Eucalypt plantation.E. camaldulensis,grandis, globulus,botryoides, saligna.

1995–1997 1.52–2.0 Silberstein et al.(1999) LiCor 2000(30% underestimate),

Toolara Forest, Gympie,Qld

P 152.75 −26.00 Eucalyptus grandis 1987–1990 0–5.2(from seed)

Cromer et al. (1993)

Bannister, WA P 116.53 −32.69 Mixed eucalypt, pine,casuarina

1976–1981 0.1–4.3 Biddiscombeet al. (1985)

The latitude and longitude represent the location of the first mentioned site. Single value for LAI range indicates single measurement. The sites are mapped in Fig. 4.

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corner. The field plot and transect layout utilised thoseestablished during a previous field campaign in 2000, whichis described in detail in Tickle et al. (2006). Photos werecollected using a 10.5 mm full frame hemispherical lens andNikon D70 digital SLR camera. For each field plot the photoclosest to the centre was used to estimate foliage cover and LAIusing Gap Light Analyser (Version 2) software (Frazer et al.,1999). In plots with highly variable cover, that is, the photoestimate deviated from the field transect or LIDAR coverestimate by more than 15%, then the centre photo from each ofthe three transects was selected and the cover and LAI estimatecalculated as the average of the three photos. At 22 NEVictorian sites, hemispherical photos were taken at 10 mintervals along 4 transects 45 m in length, in north, east, westand south directions from the plot centre, with an image alsotaken at plot centre. The plot layout is described in detail in BRS(2006). The plot estimate of LAI was derived from an averageof the centre photo and 4 photos, located 15 m from the centrealong each transect. All measurements were taken at approx-imately 1 m above the ground.

2.3. Measurement issues

The measurement of LAI by ground techniques is affectedby uncertainties and biases (Coops et al., 2004; Whitford etal., 1995). Comparison of the MODIS LAI product toground-based measurements is essentially a comparison ofalternative methods with different uncertainties and errorstructures, where the truth is unknowable except where largescale direct destructive harvesting with sufficient sample sizehas been conducted. This is possible with agricultural plantsbut virtually impossible in indigenous vegetation standsparticularly woodlands and forests. In Australia, all remotemethods whether satellite or ground based are affected by leafinclination. A general range for leaf inclination in eucalyptsof 60–80° was measured by Anderson (1981) for some south-

eastern Australian forests. Ground-based measurements usinggap fraction, allometric, plant canopy analysers (LiCor 2000),camera-based point quadrat or hemispherical photographyapproaches all provide biased estimates of LAI (Coops et al.,2004), with biases potentially large in sparse canopysituations (Whitford et al., 1995). The methods requirecalibration to produce an estimate of canopy LAI close tothe truth. However, with the vertical leaf inclination, and ahigher proportion of direct beam transmitted to the forestfloor, projected foliage cover adjusted for stems and branchesmay provide the best correlate for assessment of satellite-based LAI products.

2.4. Sampling rationale

The SCAV for 1988 (AUSLIG, 1990) provides an effectivedescription of vegetation in the pastoral zone. However, thesedata have a relatively simplistic classification of vegetation inthe agricultural zone. The LUC (Stewart et al., 2001) provides abetter definition of variation within the agricultural zone byclassification of major crop and pasture types. Therefore, themap-based sampling strategy involved selection of vegetationpolygons from the SCAV for the less disturbed areas ofAustralia, and selection of LUC classes constrained by censusregions for the agricultural zone. A subset of the possiblefloristic and structural types was selected from the SCAV torepresent: (a) tropical and temperate forests; (b) eucalypt treesranging in density from major temperate forest to open savannawoodland; (c) a range of shrublands with and without a lowerstratum; and (d) major grassland types (Table 3). The polygonsizes for the vegetation classification vary widely; we chosesmaller polygons for each vegetation type to reduce potentialspatial variation due to climate. The sample polygons weredistributed throughout central and northern Australia, and downthe east coast into Tasmania following the major forest types(Fig. 3).

Fig. 2. Site-based LAI measurements. (a) NAT transect and NSW transect; (b)historical literature; (c) concurrent measurements.

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The LUC provided definition of cereal cropping, dryland andirrigated cotton, dryland and irrigated sugar cane, modified(improved or oversown to some degree) pastures and “livestockgrazing” (areas where understory vegetation is native grassland)

(Fig. 1). Some of these areas may have trees but usually at a lowdensity, and the predominant signal provided to a satellitecomes from the grassland component. In order to constrain theland use types to a small region and to provide a transectsampling system across the agricultural zone, we usedStatistical Local Area polygons (these are the main reportingregions for the national agricultural census and provide areference to production statistics) to define the areas of land usetype. These polygons represent the highest level of spatialaggregation for reporting of the national agricultural census.Within each polygon, regions of interest were created from eachrelevant land use class and used to sample the MODIS LAI timeseries (Fig. 4). Since the land use data were created fromAVHRR NDVI profiles for 1996, it is recognised that therewould have been some changes in land use for the classifiedpixels in the interim, and that there is also some cyclical changebetween pasture and crop within the cereal zone. It is alsoacknowledged that the classification has a level of error anduncertainty resulting from misallocation of type in the originalclassification procedure (Stewart et al., 2001). We are thereforemainly interested in the general profile of MODIS LAI fromthese regions of interest and whether they appeared to bereasonably representative of the land use type for thatgeographical location. For the major land uses types—cerealcropping, modified pastures and livestock grazing, sampling ofMODIS LAI was carried out along transects running fromcentral and southern Queensland, through New South Wales(NSW), Victoria (VIC) and South Australia (SA) and finishingin the south–west of Western Australia (WA; Fig. 3). In WA,some of the major wool growing areas within the pastoral zonewere included in the livestock grazing transect. The cottonregions are highly localised in northern NSW. A transect ofsugar cane regions ran down the east coast from northernQueensland to north-eastern NSW (Fig. 4). For the site-basedmeasurements of LAI, MODIS LAI data were extracted for asingle coincident pixel.

2.5. MODIS LAI product data quality

In all cases, MODIS LAI Product quality flags wereextracted for the regions and interest and sample points. The4 years of MODIS LAI data were examined to generalise thequality problems for Australia. MODIS LAI Quality data arestored in bitfields of 8-bitwords. The binary values wereconverted into decimals and then classified into five classeswith most weight given to the SCF_QC bitfield. This bitfielddescribes the performance of the main radiative transferalgorithm and whether the backup algorithm was used. Thefive classes were used to distinguish between data originatingfrom the main algorithm with best possible results (Type 1) orminor saturation (Type 2) and cloud (Type 3) problems, and therest of the data where failure of the main algorithm throughgeometry of other problems led to use of the backup algorithm(Type 4), or no retrieval occurred (Type 5; Table 4). Data fallinginto Types 4 and 5 were classified as “poor” quality. Theseclassified quality data were then grouped on the basis of thepercentage of the dates in the 4-year record at each pixel that

Table 3Description of major vegetation classes in the pastoral and relatively undisturbed areas of Australia used to assess MODIS LAI (See Fig. 2 for map)

Code Vegetation name Floristic-overstorey

Growth form-overstorey

Foliage cover Growth form-understorey

Average LAI andtemporalstandard deviation

xM4 Tropical forest Mixed tropical forest Medium trees >70% cover None 4.71±0.37eT3L Tall forest Eucalypts Tall trees 30–70% cover Low trees 4.62±0.36eT3M Tall forest Eucalypts Tall trees 30–70% cover Medium trees 4.62±0.36eM3L Forest Eucalypts-southern Medium trees 30–70% cover Low trees 3.92±0.45eM3L Woodland Eucalypts l Medium trees 30–70% cover Low trees 1.89±0.64eM2S Open forest/Woodland Eucalypts-southern Medium trees 10–30% cover Tall shrubs 4.36±0.46eM2S Woodland Eucalypts-northern Medium trees 10–30% cover Tall shrubs 0.86±0.26eM1yG Open savanna Eucalypts Medium trees <10% cover Tussock grasses 0.85±0.28wS1yG Acacia open savanna Acacias Tall shrubs <10% cover Tussock grasses 0.38±0.09kZ2yG Shrubland (chenopod) Saltbush/bluebush Low shrubs 10–30% cover Tussock grasses 0.36±0.07wZ1 Shrubland Acacia Low shrubs <10% cover None 0.36±0.07xZ3 Shrubland Mixed Low shrubs 30–70% cover None 2.65±0.43aG2-3 Mitchell grassland Mitchell grass Tussock grasses 10–30% or 30–70% cover None 0.39±0.18tH2 Hummock grassland Spinifex Hummock grasses 10–30% cover None 0.47±0.13yG1-3 Tussock grassland Other tall/short grass Tussock grasses <10% to 30–70% cover None 0.50±0.20

LAI values represent means across all sample polygons and over the whole 4-year time series.

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were classified as poor quality (Fig. 5). The analysis indicatedthat the frequency of product dates with poor data was high incoastal and high elevation locations such as the south west coastof WA, the west of Tasmania and the southern and easterncoasts of Australia probably due to cloud. The 6BSLCC has aclass for bare areas, but the MODIS LAI algorithm does notmake retrievals for this class. Hence, the high frequency ofproduct dates with poor data for the bare and salt pan areas ofcentral southern Australia was expected. The 5–25% dataquality class (blue areas, Fig. 5) mostly indicate isolatedinstances of poor quality data (since the percentage of datesaffected is highly skewed to the low end) and are not asignificant impediment to data use.

Fig. 3. Map of vegetation polygons us

3. Results

3.1. Natural vegetation

We examined the LAI profiles for the natural vegetation bycreating a sequence of plots from low shrubland (x/w/kZ1-3G)through tall shrubland (wS1yG) to savanna (eM1) and openwoodland (eM2S; Fig. 6); then from more dense woodland(eM3L) to forests (eM3L, eT3L/M, xM4; Fig. 7) and finally tograssland (yG, aG, tH; Fig. 8). The profiles show the range inspatial mean LAI across all sample polygons and the overallmean LAI for that SCAV type. The overall average LAI acrossall dates and all sample polygons for each SCAV type is given in

ed in assessment of MODIS LAI.

Fig. 4. Map of SLA-constrained land use types used in assessment of MODIS LAI.

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Table 3. Where two distinct profile types are shown, theserepresent the different patterns from northern and southernlocations with summer and winter wet seasons respectively. Atemporal profile of the percentage of pixels with good (Quality1; Table 4) and poor (Quality 4 and 5; Table 4) quality in a singlerepresentative sample polygon is given below each LAI graphfor each SCAV type. The quality issues raised in Fig. 5 are

Fig. 5. Map of MODIS LAI time series quality for Australia between 2000 and 2004.was 65 or greater indicating use of the back-up algorithm, algorithm failure or clou

emphasised in the data for forests (eM3L, eT3L and xM4) ineastern Australia (Fig. 7). Average LAI is about 3.92 for eM3Land about 4.6–4.7 for eT3L and xM4 (Table 3). The savanna andshrubland groups show strong seasonal patterns with theresponse signals mostly associated with summer rainfall seasons(Fig. 6). The data show that woodland, with the same SCAVclassification of eM2S, is actually quite different in LAI

The data indicate the percentage of dates in the time series where the quality flagd.

Fig. 6. MODIS LAI for a range of SCAV vegetation types. The graphs show the MODIS LAI in the top pane, and the percentage area affected by different levels of data quality for a single example polygon in the bottompane. The shaded profiles show the range of average values over all sample polygons for that type. The central dotted line represents the mean LAI over all sample polygons for that type. Distinct profile groups withSCAV types are plotted separately where they occur. See legend within graphs. See Table 3 for key to codes. (a) Shrublands—mixed dense (xZ3) and low Acacia or chenopod types (k or wZ1yG); (b) Shrubby savanna—Acacia shrubs with grasses (wS1yG); (c) Open savanna—sparse eucalypts with grasses (eM1yG); and (d) Savanna woodland—eucalypt trees with shrubs both southern woodland and northern savanna types (Em2S).

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Fig. 7. MODIS LAI for a range of SCAV vegetation types. Conventions as for Fig. 6. (a) Forest/woodland—southern type: eucalypt trees with shrub understorey (eM3L); (b) seasonal woodland—northern type: eucalypttrees with shrub understorey (eM3L); and (c) tall eucalypt forest (eT3L/M); and (d) tropical forest (xM4).

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Fig. 8. MODIS LAI for a range of SCAV vegetation types. Conventions as for Fig. 6. (a) Tussock grasslands (yG1-3); (b) Mitchell grasslands (aG2-3); and (c)hummock grasslands (tH2).

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Table 4Classification of MODIS LAI quality flags

Category Decimal values Remark

Class 1 0, 1, 2, 8, 9, 10,16, 17, 18

Main Radiative TransferAlgorithm used with thebest possible results.

Class 2 26, 33, 34, 41, 42 Main Radiative TransferAlgorithm used with saturation.

Class 3 49, 50 Main Radiative TransferAlgorithm used with saturationand mixed cloud present on pixel.

Class 4 65, 66, 73, 74,82, 83, 97, 105,106, 113, 114

Main Radiative Transfer Algorithmfailed due to geometry problemsor due to problems other thangeometry, empirical modelling used.

Class 5 159 Couldn't retrieve pixel.

Quality data are stored in bitfields of 8-bitwords. The binary values wereconverted into decimals and then classified into five classes with most weightgiven to the SCF_QC bitfield.

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response pattern between southern (high LAI) and northern(seasonal LAI increase) locations; this may reflect the more opennature of the northern woodland with tree cover closer to 10%,and very seasonal canopy development in both under- andoverstorey.

The pattern of LAI for shrubland types reveals a markeddistinction between the xZ3 closed shrubland, with foliagecover between 30 and 70% and an LAI around 2–3, and the lowopen kZ type shrubland with or without grasses located in veryarid areas and having a foliage cover between 10% and 30% andan average LAI of <1 (Fig. 6). There is one example of a wZ1with an LAI of around 0.2 which is consistent with a singlestratum ground cover of 10% or less.

The pattern for grassland reflects increasing aridity as typemoves from tussocks (yG) to Mitchell grass (aG) to hummocks(tH) (Fig. 8). The grassland samples are all taken from central tonorthern parts of Australia (Fig. 3) and hence show seasonalpatterns representative of summer rainfall. The Mitchell grass-lands (aG) grasslands show a distinct trend of decreasingresponsiveness in LAI as the sites move from central to westernQueensland and rainfall becomes less reliable. The hummockgrasslands (tH) are confined to arid locations and have limitedresponsiveness with only the northernmost sample sites, closerto the monsoon zone, showing major seasonal patterns.

In summary, at the level of broad vegetation types, theCollection 4 MODIS LAI appears to produce plausible patternsof LAI for grasslands (tG, aG, yG), shrublands (kZ, wZ, xZ),savannas(eM1G) and open woodlands (eM2S and eM3Lwoodland). However, forest estimates are strongly affected bycloud and failure of the main algorithm resulting in a veryvariable time series (Figs. 6 and 7). The range of values for themedium forest (eM3L; Fig. 7) shows the effect of clearing inone sample polygon, as minimum values of the range decline toaround 1.5 in the last part of the record. The average LAI of 4.7for tropical rainforest approximately accords with figures in theliterature (Nightingale, 2004). However, the value of 3.9–4.6for southern temperate forests is high relative to measures fromthe literature such as 1–2 for tall eucalypt forest (range ofspecies) at Tumbarumba, NSW (Coops et al., 2004), 3 for E.

maculata forest at Kioloa, NSW (Dunin et al., 1985) and 0.9–2.4 for E. marginata forest near Perth, WA (Carbon et al., 1979;Silberstein et al., 2001). On the other hand, estimates of 4.2 forE. regnans in Victoria, and 4.6 for E. globulus plantations inWestern Australia are also observed. Direct comparisons forselected locations will be addressed in a following section.

3.2. Agricultural lands

The agricultural LAI profiles are grouped into five land usetypes; cotton, sugar cane; cereal cropping and modified pastures(Fig. 9). The profiles show the range in spatial mean LAI acrossall sample polygons and the overall mean LAI for that LUCclass. A single representative quality profile is also shown (asfor previous figures). The cotton profiles show a distinctseasonality with additional peaks indicative of winter croppingeither in rotation or in sequence with cotton. The sugar caneprofiles show a strong, regular, seasonal response with LAIreaching value around 4 at the height of growth. The coastallocation means that retrievals are affected by cloud and the mainalgorithm often fails during the height of the wet season.Irrigated sugar cane shows a distinct early growth peak in thelast two seasons. The growth drop off out of season is markedlygreater in the seasonally dry tropic and sub-tropical locationscompared to the wet tropic locations.

The cereal profiles show a synchronised winter wet seasonresponse, but several profiles from northern NSW and southernQueensland, also show a summer cropping pattern reflecting theopportunistic management response to seasonal conditions inthese locations (Fig. 9). The LAI values range in magnitudefrom about 0.7–2.5. The profiles for modified pastures andlivestock grazing (data not given) are quite similar; this reflectsthe difficulty of distinguishing the two types when seasonalclimate signals are the main driver, and pastures in easternAustralia form something of a continuum from native tomodified and highly improved. For the purposes of thiscomparison, it is not instructive to distinguish between thetypes. The results merely indicate that the peak seasonal LAI isgenerally between 2.5 and 3.0, and that the responses are similarfor the locations throughout the agricultural zone. The lowerresponses correspond to the areas inWA in the southern pastoralzone inland from the agricultural zone (Fig. 4).

In summary, the pattern of LAI for agricultural land usesappears to be plausible. Since we cannot guarantee that land useduring the period 2000–2003 is exactly the same as thatassigned in the classification based on AVHRR NDVI for 1996,these profiles provide only a guide. The high variability in valuefrom one time period to the next, in the absence of any qualityissue, is a general feature for the locations with higher amplitudeof response.

3.3. Historical site data

3.3.1. LiteratureThe historical site data were examined in two groups; the

data collected from the literature; and the transect data. Theliterature data were split into forest and other natural vegetation;

Fig. 9. MODIS LAI for a range of LUC classes constrained to SLA polygons (see Fig. 3). Conventions as for Fig. 6. (a) Cotton; (b) sugarcane; (c) cereal cropping; and (d) modified pasture.

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and agricultural sites (Table 2). For the forest and naturalvegetation, the mean and standard deviation of the MODIS LAIfor the 4-year period was extracted and compared with the rangeof the measured values; any profiles with an agricultural pattern,particularly in WA, were excluded from analysis as land coverchange was assumed (Fig. 10). The MODIS LAI showed goodcorrespondence with historical field estimates at 9 sites atlocations where data quality was high. These included E.marginatus and E. globulus sites in WA, E. obliqua inTasmania, E. delegatensis sites in coastal NSW and E. socialisin Queensland (Fig. 10). However, at sites with poorer dataquality (failure of the main algorithm) average MODIS LAI wassignificantly higher than historical field measurements. Thesesites included E. diversicolor at Pemberton, WA and a variety ofsites in eastern NSW. If these sites were filtered to remove mostof the poor data, average MODIS LAI was reduced but stillsignificantly exceeded measured values (Fig. 10). In summary,data quality limits capacity to assess MODIS LAI at some ofthese forest sites, but in locations where quality is reasonable,MODIS LAI estimates appear to be in the right range usinghistorical data as a guide.

Many of the agricultural sites represent research trials whereLAI was measured on a variety of crop treatments. These dataprovide an indication of the magnitude and temporal pattern ofLAI that might be expected at these locations. Measured LAItraces were comparedwithMODIS LAI profiles averaged acrossyears for locations in northern Victoria, southern NSW, south-eastern Queensland and the wheat belt in WA (Fig. 11). At thesouthern sites of Walpeup, Wagga, Merredin and Northam (Fig.

Fig. 10. Comparison of mean MODIS LAI for a 4-year period (March 2000 toFebruary 2004) with the range of historical measured LAI extracted from theliterature. Data are not concurrent. Measurements. The clear histograms indicatethe mean LAI for all MODIS data; the shaded histograms indicate the meanMODIS LAI for data adjusted to remove saturated and cloud affected dates forselected locations with the highest deviation from measured values.

11a, c, f and g), there is generally a good correspondencebetween the timing andmagnitude ofMODIS LAI andmeasureddata. The sites at Toowoomba, Gatton and Lawes in south-eastern Queensland (Fig. 11b, d and e) show poor correspon-dence between MODIS LAI and measured values principallybecause of a combination of drought in the MODIS collectionperiod, particularly in winter when wheat and faba beans aregrown, and the low frequency of phasey bean and soybean cropsaround Gatton where the experimental measurements weretaken. In summary, it is likely that the MODIS product providesreasonable estimates of LAI for agricultural locations althoughthese data provide only an approximate guide.

3.3.2. TransectsThe mean and standard deviation of MODIS LAI was

compared with measured values collected along transects in theNorthern Territory (NATT) and NSW (Fig. 12). For the NATTtransect (Fig. 12a), tree and ground stratum LAI were measuredat different locations and on different days and are plottedseparately. With the exception of the dense savanna aroundDarwin (about 11°N at the top of the transect), average MODISLAI values are generally much higher then measured data, withground measurements generally being greater than one standarddeviation lower than the MODIS average (Fig. 12a). Along theNSW transect (Fig. 12b), there was good correspondencebetween average MODIS LAI and measured values withmeasurement often very close to the MODIS average andusually well inside one standard deviation. Assuming thatmaximum and minimum values of LAI have not varied sincethe transects were measured, these data suggest that MODISLAI broadly captures the annual magnitude of LAI along atransect traversing arid shrubland, grassland, cropland andwoodland and forest, but that the timing and nature of the fieldsampling available to us may not be adequate for definitivecomparison with MODIS LAI in the tropical savannas.

3.4. Concurrent field measurements

Concurrent field measurements of MODIS LAI were onlyavailable for woodland and forest sites at four locations. Themeasurements were largely based on hemispherical photogra-phy (3 sites) with the LiCOR 2000 plant canopy analyser usedat the NSW site. The field measurements were collected forrelatively small areas, considerably smaller than a MODISpixel, and this difference in scale introduced some errors andbiases into the comparison which are partially addressed below.The results show a good correspondence between MODIS LAIand measurements in northern NSW with both measured andMODIS LAI values varying between 1 and 1.5 on themeasurement dates (Fig. 13a). At the tropical rainforest site,the suite of samples collected near the Cape Tribulation fluxtower site are compared with MODIS LAI estimates from themain algorithm, estimates with some anomalies and estimatesfrom the back-up algorithm (Fig. 13b). In general measuredvalues were lower than the average MODIS LAI of around 5.Measured values ranged mainly from 2 to 5 with some outliersbelow 1 and above 6. The variation in field measurements with

Fig. 11. Comparison of average MODIS LAI (over the 4 years, 2000–2004) with historical measurements (shown in bold or bold dotted lines) of LAI for a selection ofagricultural sites across Australia. Bars indicate the standard deviation across years. Sites were excluded if MODIS data were missing, or profiles were obviouslyshowing a major land use change since the field data were collected.

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hemispherical photography shows the diversity in canopy coveroccurring within a single MODIS pixel.

The other sites formed part of a major analysis involvingforest and woodland assessment with LIDAR (LIght DetectionAnd Ranging) (Fig. 13c, d). At the Victorian sites, there wasrelatively poor correspondence between MODIS LAI and fieldmeasurements (R2 =0.34) and this only improved marginallywhen MODIS data were adjusted to remove poor quality values(R2 =0.36; Fig. 13c). It is possible to provide a more detailed

analysis of the relationships between site characteristics andMODIS LAI for the Victorian sites. Points in Fig. 13c are codedwith a number (adjacent to the land cover class symbol)corresponding to the major points below.

1. The more open or woodland sites (S1, C1, F1) haddifferences between MODIS and photo of 1.5 units or less.Two of these sites were classed as cropland, four as savannaand one as forest by the 6BSLCC. Given the open canopies

Fig. 12. Comparison of mean and standard deviation of MODIS LAI withhistorical measurements of LAI along two transects. (a) North to south NATtransect in the Northern Territory (1999–2000); (b) west to east transectrecorded by McVicar in NSW (1996). In (a) tree LAI and lower stratum LAIwere measured separately and at different locations.

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and potential understorey signal effects, the MODIS valuesare a fair approximation.

2. Two plots were forest corridors (S2—fence line; C2—oneriver) within a matrix of cropping—therefore, the MODISLAI value may reflect the dominant agricultural signal ratherthan the forest signal.

3. The group of forest plots showing a large over-estimation ofLAI by the MODIS product (F3, N3, S3) had a number ofcharacteristics which might provide an explanation:

a. the trees were mature (i.e., not disturbed for at least 10years), and in many cases old growth forests, with meanpredominant height of 27 m, and a mean FPC of 60%;

b. in most cases, plots had multiple height strata, andsignificant understorey component, usually with morescattered overstorey trees;

c. in three cases, plots had significant areas of lush, grassyground cover without overstorey trees, which wouldincrease the average MODIS LAI for a 1 km pixel;

d. two of the plots were located close (within 200 m) to largestreams, therefore the 1 km pixel could be reflecting themore lush growth this water supply would give; and

e. four plots were on steep slopes with mature, multi-layeredvegetation—in this case the understorey could beproviding a significant contribution to the reflectancesignal seen by MODIS through the overstorey andmidstorey vertical leaf habit of the dominant eucalypts.

4. Four of the tall forest sites (F4) had been burnt within the last5 years which may have thinned out the canopy and reducedthe understorey.

In summary, MODIS LAI shows reasonable agreementwith more open canopied woodland plots allowing forsignificant seasonal understorey influence on the reflectancesignals. In some cases, deviations between MODIS LAI andground measurements are due to ground sampling within awider agricultural matrix that provides the dominant signal.It is also likely that large deviations between MODIS andground measurement in the general matrix of forest are duein part to ground measurements not including estimates froma lush grassy understorey. However, the MODIS LAIproduct appears to be significantly over-estimating LAI inthe mature, taller forest, with higher cover and multipleheight strata.

At Injune in Queensland, the correspondence betweenMODIS LAI and field measurements was better (R2 =0.51;Fig. 13d). The plots with the largest differences (1.2–1.4) werethose which had had recent disturbance at the plot or nearby,within the 1km pixel (i.e. clearing across road from plots, orrecovering from clearing or logging in last 5 years, thereforethere is a flush of new growth and grass understorey etc).Otherwise, the mature sites mostly have deviations betweenMODIS LAI and measured LAI of less than 1.

In summary, correspondence between MODIS LAI andconcurrent field measurements was reasonably good allowingfor issues of scale however, the relatively poor correspondencefor the Victorian woodland and forest sites suggest that there arespecific factors that may be causing bias in MODIS LAI forcertain types of Australian forest.

3.5. The six biome structural land cover classification

The MODIS LAI algorithm depends upon the 6BSLCC todefine the “space of canopy realisation” which relates tree andcanopy architecture to spectral leaf albedo for MODIS bands.This land cover classification therefore plays a large role indetermining MODIS LAI estimates particularly around theboundaries between class definition such as Broadleaf Forestvs. Savanna, Savanna vs. Shrubs and Shrubs vs. Grass andCereal Crops. We examined the 6BSLCC in relation to theSCAV (AUSLIG, 1990), the LUC (Stewart et al., 2001) and theindividual sites, both historical and concurrent. The SCAV is apolygon-based map. Therefore, we would expect that landcover type would vary somewhat within a polygon, but that thepredominant land cover would approximately match thedefinition; hence we would also expect that there would bebroad correspondence between appropriate 6BSLCC classesand the SCAV. The proportion of each SCAV polygon allocated

Fig. 13. Comparison of MODIS LAI with concurrent field measurements for (a) Northern NSW woodland; (b) Queensland tropical rainforest; (c) a range of Victorianforest and woodland sites; and d) Queensland woodlands. For (b), field data show 24 local scale sites where LAI was measured with hemispherical photography, alongwith the average MODIS LAI from dates when the main algorithm was used, when the main algorithm was used but there were some quality issues, and when only theback-up algorithm was used. For (c) and (d), MODIS values are the mean of 6 composite dates around the sample data. Quality adjusted values are means of Quality 1data only. Letters indicate 6BSLCC classes: G, GCC; R, SHR; C, BCR; S, SAV; F, BLF; and N, NLF. Points in Fig. 13c are coded with a number (adjacent to the landcover class symbol) corresponding to the major points in the text in Section 3.4. The lines in (c) and (d) indicate the 1:1 correspondence line between measured andMODIS LAI.

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to the 6BSLCC classes is shown in Fig. 14. The analysis showsthat:

(a) wS1yG shrublands, eM3L woodlands, eM3L forest,eT3L/M forest and xM4 forest are consistently mappedto the appropriate 6BSLCC classes—Shrubs, Savanna,Broadleaf Forest, Broadleaf Forest and Broadleaf Forestrespectively;

(b) aG, tH and yG grasslands are consistently mapped to6BSLCC Shrubs class;

(c) xZ3 dense shrubland is mapped partly to Broadleaf Forestand partly to Savanna, while kZ short shrubland ismapped substantially to Shrubs;

(d) eM1yG and eM2S woodlands are predominantly mappedas Shrubs when they would most appropriately fall intothe Savanna category.

The LUC is a raster map, with most probable land use classesassigned for the 1996 year. It must be emphasised that theseclasses were developed by inference between agriculturalstatistics and AVHRR NDVI profiles and therefore represent a“most likely land use” and contain significant uncertainty. TheLUC tends to map cotton and sugar cane well, but the accuracyof cereal mapping is much poorer (L. Randall, personalcommunication). However, it is certain that the assigned landuse is common in the region and that a substantial proportion of

Fig. 14. Allocation of 6BSLCC classes to replicate SCAV classes for the main SCAV vegetation types used in this analysis. Each graph shows the percentage of polygon area mapped to each 6BSLCC class for eachsampled polygon of a single SCAV vegetation class. Polygons are plotted with different symbols where there are significant regional (denoted by State) differences in the proportions. Legends identify sites by State,which broadly relate to differences in climatic systems and land cover mixes. Part (a) shows Mitchell grassland; tussock grassland; Acacia open savanna; hummock grassland, shrubland and Eucalypt open savanna. Part(b) shows eucalypt woodlands, forest, tall forest and tropical forest.

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pixels assigned to a class are in fact that land use type. Therecould be significant variation between land use classes for 2001and 1996 since land use, particularly on cropland could varydue to rotations among cereal and broadleaf crops, and cropsand pastures. However, we would expect a substantialpersistence of land use from 1996 to 2001 such that a majorityof pixels would still be the same. The proportions of SLA-basedLUC classes allocated to the 6BSLCC showed substantialvariation and some unusual class correspondence (Fig. 15).Sugar cane sites mapped predominantly into the 6BSLCCSavanna class with some Broadleaf Forest as well. The lattermight be expected from the edges of the sugar cane with tropicalforest in northern Queensland. Cotton sites show highvariability in 6BSLCC class with sites mapping to Grass and

Fig. 15. Allocation of 6BSLCC classes to replicate LUC classes for sugar cane, cottonpolygon area mapped to each 6BSLCC class for each sampled polygon of a single LUsignificant regional (denoted by State) differences in the proportions. Legends identifcover mixes.

Cereal Crops, Shrubs, Savanna and even to Broadleaf Forest.Cotton is grown on black cracking clay soils and these mayinfluence the spectral information used in the MODIS landcover and LAI/FPAR methods. The behaviour and relativeresponse of red and near infrared reflectance to increasing leafarea changes from both increasing, to red decreasing and NIRincreasing, to both decreasing as background varies from darkto intermediate to very bright (Shabanov et al., 2002).

The cereal crops fall into two groups in relation to the6BSLCC; those predominantly mapping to Grass and CerealCrops; and those with significant components of Shrubs. Thelocations where Shrubs was significant correspond to sites incentral and southern Queensland, and a site on the eastern edgeof the northern wheat belt in WA, close to the edge of the

, cereals and modified pasture land use types. Each graph shows the percentage ofC vegetation class. Polygons are plotted with different symbols where there arey sites by State, which broadly relate to differences in climatic systems and land

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pastoral zone, where significant woodland or shrubland is likelyto be present. In the Queensland cases, significant shrubvegetation would be expected to be side-by-side with croppingsince Acacia shrubs regrow rapidly from lignotubers andremnant material if the clearing process fails, or land isabandoned. The cycle of clearing and abandonment has been along-standing process in this environment.

Modified pastures mapped principally into Grass and CerealCrops and Broadleaf crops classes with a significant componentof Savanna. This spread of 6BSLCC classes is entirelyconsistent with the kind of variation in cover that would beexperienced over the site transect. Annual pastures arepredominantly made up of clovers and broadleaf weeds with avariable grass component, and these predominate in southernMediterranean pasture zone in the south west of WesternAustralia, and southern inland parts of eastern Australia.Perennial pastures may be more grass dominant in coastal andhighland parts of south-eastern Australia. A mixture of mappingbetween Grass and Cereal Crops and Broadleaf crops isappropriate for these locations, modified by the presence of a“parkland” environment with significant tree component. Thereare locations in NSW, Victoria, SA and WA that map over 20%of the site into the Savanna class, and this is quite likely to bereasonably accurate given the open forest canopies found in thedryer environments. For the concurrent field sites, the 6BSLCCwas Broadleaf Forest for the tropical rainforest site, Savanna forthe NSW woodland site, and varied between Savanna andBroadleaf Forest classes for the Victorian sites, and Shrubs andSavanna for the Queensland sites.

In summary, the 6BSLCC does a fair job in coveringAustralian vegetation types considering the limitations of a sixclass system. However, it is notable that much of the aridgrassland maps to the Shrub class and significant amounts of theeM1yG open woodland with <10% canopy cover maps to theShrub class. It is also clear that eucalypt forest with open canopydue to vertical leaf inclination, and highly seasonal LAI pattern,tends to map into the Savanna class while forest and woodlandwith similar canopy density is mapped to Broadleaf Forest.

4. Discussion

The preceding assessment of the Collection 4 MODIS LAIproduct was designed to provide an overview of the geograph-ical variation in patterns of MODIS LAI across a representativeset of vegetation and land use types, and to empirically comparethe MODIS LAI to available historical and concurrent ground-based measurement of LAI. The assessment reveals significantdifferences in LAI profiles between vegetation types in theSCAV, and between the broad land use types in the LUC forAustralia. The MODIS LAI discriminates between geographi-cally distinct seasonal and non-seasonal patterns within majorSCAV vegetation classes such as the eM3L forest/woodlandtype and the eM2S woodland type. These very different profileswithin types identify sub-groups within the SCAV classificationwhere dominant species and foliage cover for the overstorey aresimilar, but the canopy density and understorey shrubs are verydifferent between the more arid and more mesic environments.

The LAI product also clearly distinguishes between the densemixed shrubland xZ3, and the more sparse, arid chenopod andacacia shrubland kZ2 and wZ1. The results suggest that theMODIS product is able to characterise LAI profiles at aconsiderably finer level of land cover description than isrepresented by the 6BSLCC used to define the “space of canopyrealisation” in the algorithm. The LAI product also characteriseddifferences in the main agricultural profiles both between landuse types and across the geographical transects within types. Theresults suggest that the LAI product could be used to supportmodelling at continental scale since definition of LAI profiles forland cover and land use types with discrimination betweenseasonal and geographical responses could provide an appro-priate level of information.

The comparison between historical and concurrent fieldmeasurements and average MODIS LAI values suggests thatthe product provides reasonable estimates of LAI for the aridinteriors, woodlands and agricultural lands. The comparison isinfluenced by two key factors: (a) the disconnection betweencurrent land cover and land use, and land cover and land use atthe time of historical LAI measurements; and (b) the scale ofsampling for concurrent field measurements and representa-tiveness of the whole MODIS pixel of the field-sampledvegetation type. Close examination of the field sites suggestedthat significant agricultural contribution, and significant grassyunderstorey contributions to vegetation signals could beresulting in MODIS LAI values being higher than measure-ments from hemispherical photography at some sites. Thisillustrates a constraint on the effectiveness of this ad hoc type ofproduct assessment, when compared with a dedicated fieldcampaign where sub-pixel heterogeneity is explicitly assessed.However, both circumstantial evidence (e.g. Leuning et al.,2005) and direct field measurements in this study, suggest thatMODIS LAI is significantly overestimating actual LAI for somemature Eucalypt forests.

One factor that may affect the accuracy of MODIS LAIestimation for Australian vegetation may be the misallocation of6BSLCC classes between Broadleaf Forest and Savanna forsouthern forest and dense woodland vegetation types. Inaddition it may also be important to assess the validity of theBroadleaf Forest “space of canopy realisation” for AustralianEucalypt vegetation with highly vertical leaf inclinations. Forthe Victorian forest and woodland sites (Fig. 14c) measured LAIranged from 0.2 to 2.2. Correspondence between MODIS LAIand measurement was best, although still high, for the sitesclassified as Savanna, while correspondence was very poorwhen sites were classified as Broadleaf Forest with MODIS LAI4–5 times the measured values. Other studies in southern NSW,around the flux tower at Tumbarumba, have suggested thatMODIS LAI estimates are too high for the open, dry sclerophyllforest in these areas (Leuning et al., 2005). Problems with thematching between the spectral response “space of canopyrealisation” between SeaWifs data used in algorithm develop-ment and actual spectral response for the 6BSLCC from theMODIS sensor have been documented, and it is proposed thatCollection 5 will be based on the MODIS values (Shabanov etal., 2004). However, further attention may need to be paid to the

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Australian open forests and woodlands to ensure that theparameterisation of the Broadleaf Forest and Savanna 6BSLCCclasses is able to deal with the low foliage projected coverrelative to actual leaf area resulting from the high leaf inclinationangles in Eucalypt dominant stands (Anderson, 1981).

The relationship between the 6BSLCC and the SCAVidentifies some specific issues in terms of correct classificationof Australian vegetation types. The global land cover mappingwith MODIS has been subject to wide ranging calibration(Friedl et al., 2002). However, even the primary IGBPclassification scheme is relatively simplistic at individual regionor biome scales. The 6BSLCC represents a necessarysimplification of this to enable inversion with a manageablenumber of unknowns (Lotsch et al., 2003; Myneni et al., 1997).A pre-launch assessment of the 6BSLCC found that asubstantial amount of disagreement in LAI was attributable toconfusion between Grass and Cereal Crops and Savanna andsome confusion between Broadleaf Forest and NeedleleafForest with Savanna (Lotsch et al., 2003). Importantly,background reflectance properties influenced the variabilityover shrublands—this may be a factor in classification ofvegetation over the large expanses of black, cracking clay soilssupporting Mitchell grasslands (aG) in Australia.

For Australia, the SCAV represents a generalisation of typesbased on species, stratum layers and canopy cover at a muchmore detailed level than 6BSLCC. The comparison of the twoclassifications for the polygons used in this study identified anumber of contrasts. For example, Astrebla and Triodiagrasslands all map to the Shrubs class in 6BSLCC, whilstother tussock grasslands form two groups: one predominantlyShrubs and the other predominantly Savanna. If we accept thatarid grasslands correctly correspond more to the Shrubs thanGrass and Cereal Crops, because ground cover is predominantlyin the 10–70% ranges (Table 1), then these allocations makesense, as some of the northern tussock grasslands havesignificant tree presence and would be regarded as savannas(Savanna). In addition, the differentiation in 6BSLCC betweenallocation of dense mixed shrubland to Broadleaf Forest andeven some Needleleaf Forest (Casuarina and Acacia foliagemimicking needle-leaves), and allocation of short, sparseshrubland to Shrubs is also entirely consistent with the structuralproperties. However, the variation in allocation of open Eucalyptsavanna between Shrubs and Savanna, and in allocation of eM2Swoodland between Shrubs, Savanna and even Broadleaf Forestorganised by geographical groups suggests that the 6BSLCCmay be sensitive to the variation in nature of the “S” (understoreyshrubs and low trees) stratum and its influence on overall canopycover. In this case, these different class allocations may fairlyrepresent the on-ground variability.

Although the evidence provided by this study is notconclusive, and a field validation program is required, wesuspect that the main source of misclassification, and then mis-application of the spectral response “space of canopy realisa-tion” occurs within the forest and dense woodland types eM2S,eM3L and eT3L. First there is an issue as to how these forestsshould be classified i.e. at what density of canopy do theytransform from Savanna to Broadleaf Forest class given the

previously mentioned contrast between projected cover andLAI. Second, there is the issue of whether the classification intothe “wrong” group in the 6BSLCC actually results in a poorretrieval of LAI value by the algorithm. The changes to thealgorithm and specific referencing to MODIS spectral responsesin Collection 5 may address some of these concerns. From thefield studies with LIDAR and hemispherical photography (A.Lee, unpublished data), there is reasonable agreement withcover estimates from LIDAR and the photo LAI, so it's possiblethat LAI could be directly estimated from LIDAR, and thereforelarger scale estimates of LAI (i.e. at least 50% of a MODISpixel) could be undertaken across the landscape, which wouldimprove the calibration in variable landscapes.

Nevertheless, it would be worthwhile to examine theconceptual issues associated with dealing with the peculiarcanopy properties of Australian Eucalypt forests and woodlandswithin the synergistic LAI algorithm. An improved level ofaccuracy and reliability in LAI estimation for Australian forestsand woodlands will be an important attribute for operational useof LAI products from the VIRS sensor on NPOESS.

5. Conclusions

1. The MODIS LAI product produces a wide variety ofgeographically and structurally specific temporal responseprofiles between different classes and even for sub-groupswithin classes of the SCAV.

2. The utility of the MODIS LAI product is severely limited insome coastal and highland forest locations in eastern andwestern Australia through failure of the main algorithm dueto cloud and poor retrieval of reflectances.

3. The 6BSLCC shows significant contrast in class allocationcompared to the SCAV with potential issues in allocation ofgrasslands to Shrubs, geographically grouped allocation ofsavanna woodlands to Shrubs, Savanna and BroadleafForest, and allocation of open forests between Savanna andBroadleaf Forest.

4. Historical and concurrent field measurements indicated thatMODIS LAI was giving reasonable estimates for LAI formost cover types and land use types, but that majoroverestimation of LAI occurs in some eastern Australianopen forests and woodlands.

5. The quantitative, radiative transfer-based algorithm repre-sents a major advance to estimation of LAI over empiricalmethods, and some of the issues presented here may beredressed by improvements for processing in Collection 5.

6. The land cover and LAI products could benefit from someadditional examination of Australian data, and conceptualissues relating to the classification of forests and thestructural representation of Eucalypt forest canopies withinthe “space of canopy realisation” for Savanna and BroadleafForest in the synergistic algorithm.

Acknowledgements

This research was funded by the Cooperative ResearchCentre for Greenhouse Accounting. Udaya Senarath is

517M.J. Hill et al. / Remote Sensing of Environment 101 (2006) 495–518

supported by collaborative funding between the CRCGA,CSIRO Land and Water and the Bureau of Rural Sciences.

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