Using field survey and remote sensing to assess rainforestcanopy damage following Cyclone Larry

NICOLE J. MOORE* AND DAVID S. GILLIESONSchool of Earth & Environmental Sciences, James Cook University, PO Box 6811, Cairns, QLD 4870,Australia (Email: Nicky.Moore@jcu.edu.au)

Abstract We surveyed canopy damage in upland and lowland rainforests following Cyclone Larry, whichseverely impacted the Innisfail and Atherton Tableland regions in March 2006. An existing damage-classificationwas used as a basis for field assessment of rainforest canopy damage. Our field measurements showed that thedamage categories were not clearly separated. Upland and lowland sites significantly differed in tree fall variablesand measures of forest structure. There was a difference in recruitment of disturbance indicator species owingto varying levels of coarse woody debris at sites. Aspect was not found to be a significant variable in predictingdamage owing to complexity of topography and the cyclone wind field. Analysis of remotely sensed imageryindicated that only high damage levels could be reliably discerned. Areas of very rapid vegetation growth inseverely damaged sites are most easily detected with vegetation indices based on both near infrared and shortwave infrared data. Numbers of fallen trees and their trunk orientations can be reliably quantified using highresolution (sub-metre) colour aerial photography. This permits some estimation of whether the wind field wasunidirectional or locally vortical.

Key words: cyclone, damage, hurricane, rainforest, remote sensing.

INTRODUCTION

On 20 March 2006 severe tropical Cyclone Larrycrossed the north Queensland coast and proceededinland across the Atherton Tablelands (Turton 2008).Larry was a fast moving category 4 cyclone on theAustralian scale. The highest wind gust measuredat South Johnstone was 181 km h-1 (98 knots) onMonday, 20 March, while the lowest reported pressurewas 959.3 hPa (Bureau of Meteorology 2007). Severeforest damage only extended about 35 km from thecentral track of the cyclone. Beyond 75 km, forestdamage was restricted to exposed areas of elevatedterrain and in places exposed to strong leeward gravitywaves from the west (Turton 2008).

Cyclones (also known as hurricanes and typhoons)are important disturbance agents affecting the struc-ture and dynamics of rainforest ecosystems (Elmqvistet al. 1994; Laurance 1997; Ostertag et al. 2005). Thecoastal and foothills vegetation along the wet tropicalcoast of Far North Queensland has been shaped byfrequent cyclonic disturbance to form so-called‘cyclone scrub’ (Webb 1958): rainforest with a lowuneven canopy often dominated by vine towers.Repeated cyclone disturbance is hypothesized toreduce canopy height and basal area and increasedensity in lowland rain forest (De Gouvenain &Silander 2003; Quigley & Platt 2003).

The aim of this paper is to test the efficacy of usingremote sensing to determine the type and extent ofcyclone structural damage to rainforest.This study hastwo components: first, an assessment of the ability ofremotely sensed data to discriminate levels of canopydamage; second, to characterize damage categoriesusing a number of physiognomic and landscapevariables.The results from both these assessment tech-niques are then compared.

Remotely sensed data are widely used at varyingspatial scales to provide surrogate variables for rainfor-est structure, canopy coverage, leaf area index andbiomass (Foody et al. 2001; Gillespie et al. 2006).Rarely is it possible to identify individual tree speciesusing remote sensing. A variety of vegetation indicesare commonly employed as surrogates, validationbeing by correlation and regression of image pixelvalues with colocated field measurements. Mismatchesbetween the resolution of the remotely sensed data andof the field measurements can be obviated by takingmean values surrounding a sampling point usingkernel filters. The resulting datasets can be tested forcorrelation between vegetation structure variables andimage band radiance and derived indices, for example,normalized difference vegetation index (NDVI). Fromthis, the best set of variables for generalized linearmodelling can be chosen.

One of the principal characteristics of cyclonedamaged forest is the large number of trees partially ortotally fallen over. These trees are clearly visible*Corresponding author.

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on high-resolution aerial photography taken post-cyclone. Tree fall orientations can be obtained byscreen digitizing the trunks and then processing theresultant vectors in GIS. Where fallen trunks arealigned, information on the prevailing wind directionin the cyclone can be forthcoming.Where fallen trunksare splayed radially, it suggests that the local windswere vortices akin to tornado effects. These local vor-tices can be induced or enhanced by cyclone strength,topography and forest edge effects.

The fundamental questions we asked were:1. Are the damage-classes of Unwin et al. (1988)

clearly differentiated by our measured variables?2. Is there a difference in damage response between

upland and lowland forests?3. What are the best predictor variables of cyclone

damage-classes? We hypothesized that proximityto cyclone path, tree size, aspect, exposureand elevation would be strongly related todamage-class.

4. Can remotely sensed data identify the samedamage-classes identified on the ground?

5. Can we gain information on cyclone damageand wind vectors from fallen trees in rainforestpatches?

METHODS

The study aimed to test the effectiveness of surrogacymeasures (remotely-sensed imagery as a surrogate forvegetation structure) in determining damage at a land-scape scale. First, we gathered data on three classes offorest structural damage. We then examine pairwisecorrelations between field measurements of foreststructure and remote sensing methods to determinehow closely the two compare in revealing patterns ofstructural damage.

Based on the literature and our observations, wehypothesized that damaged areas will have the follow-ing characteristics:• Reduced canopy cover• Reduced basal area• Increased large woody debris• Increased establishment of disturbance species• Greater damage to sites exposed to predominant

wind direction

Site selection

Sampling locations were chosen across the geographi-cal region of cyclone impact (within 30–50 km of theeye), from south of Gordonvale to Mission Beach inthe coastal lowlands and Millaa Millaa toTinaroo FallsDam in the uplands (Fig. 1). Within the generalizedsampling locations, sites were chosen to establish per-manent transects. Field sites were selected based on

replication across three levels of cyclone damage, asfollows:

Damage category 1 reflects the Unwin et al. (1988)categories of slight canopy disturbance: foliage loss,occasional stem or branch breakage (i.e. Unwin et al.damage category 4).Damage category 2 is moderate canopy disturbance,structural loss mostly branch and foliage, some tree-falls and most stems erect (i.e. Unwin et al. damagecategory 3).Damage category 3 is severe canopy damage, boles orcrowns of most trees broken, smashed or wind-thrown. Contains both windward and multidirectionalimpacts (i.e. Unwin et al. damage categories 1 and 2).

Sites were further stratified by elevation (upland>600 m or lowland <400 m), with care taken to avoidsites falling between these altitudinal break points(Fig. 1). Although we attempted to stratify sites byparent rock type and soil fertility, this was not possibledue to the narrow cyclone track and access problems.

All sites were chosen within continuous blocks ofmesophyll or notophyll rainforest (sensu Tracey 1982)with an area of at least 50 ha. To minimize confound-ing effects related to patchiness in damage level, siteswere chosen within relatively uniform areas compris-ing at least two hectares judged to be mostly of a singledamage category. No sites were located within 300 mof another.

Within each site, two 50 m transects were located atleast 50 m distant from the forest edge.Where possibletransects were located on slopes less than 10%.Variables were recorded within each of three10 m ¥ 10 m plots spaced at 10 m intervals along thetransect (Fig. 2). While two transects were placed atmost sites, the local topography at some sites restrictedsampling to one transect. A total of 141 plots weresampled across 25 sites (13 upland sites and 12lowland sites: Table 1).

Field measurements

Field measurement of vegetation structure was basedon a modified Kanowski and Catterall (2006) habitatproforma. Within each plot a range of physiognomic,edaphic and landscape variables were recorded(Table 2). All living stems (as indicated by the pres-ence of foliage) were counted, as were all fallen stems>10 cm Diameter at breast height (DBH). Damage toindividual trees in classes over 20 cm DBH comprisingmajor broken limbs, windthrown (leaning >45°) orwith a snapped trunk was also recorded. The abun-dance of lianes, vine towers, rattans (Calamus spp.),stinging trees (Dendrocnide muroides), weeds and seed-lings were estimated on an ordinal scale (0–4). Canopycover (%) over the plot was estimated against

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standardized photographic images (Walker & Hopkins1990), while understorey cover was measured by aver-aging four densiometer readings taken around the plotcentre point. Basal area was calculated using a Haglofbasal area wedge. The percentage of coarse and finewoody debris was recorded in a 1 m2 quadrat in thecentre of each plot. The location and altitude of eachplot was determined using a Garmin differentialGlobal Positioning System (GPS) with a four metreexternal antenna, and its general aspect determinedfrom a digital elevation model of the area. Aspect wasthen grouped into the four main quadrants foranalysis.

Data analysis

We tested variables recorded across the three damage-classes using data from the individual plots to increase

the sample size and statistical power. We also testedbetween damage-classes at upland and lowland sitesto determine whether variables were significantlydifferent. As variables were not normally distributed,we used Kruskal Wallace analysis to test betweendamage-classes and Mann–Whitney U-tests tocompare sample medians.

Remote sensing analysis

A road survey of forest damage was carried out on thecentral AthertonTableland. Observations of GPS loca-tion, forest damage category, vegetation type, geology,aspect and slope were made at 110 sites spaced 5 kmapart along roads. These data were overlain on SPOTsatellite images to gain an appreciation of the regionalpattern of tree canopy damage.

System Pour le Observation de laTerre #5 (SPOT5)satellite imagery for 4 June 2006 with 10 m resolutionwas sourced from the Queensland Department ofNatural Resources and Water. This imagery had beenorthorectified in Orthobase using a 25 m digital eleva-tion model (DEM) and ground control points (GCPs)derived from orthophotos. RMS errors for 42 GCPswere 1.5563 m in eastings and 2.3920 m in northings.SPOT imagery for 1 July 2002 was sourced and

Fig. 1. Location of detailed field study sites in the Wet Tropics bioregion of north Queensland.

5m 25m 45m

10m

50m0m

1m2

5

5

Fig. 2. Plot layout along a transect. Two transects weresampled at most sites.

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registered to the 2006 imagery with an RMS errorof 0.590 pixels using ERMapper 7.1. For both imagesraw digital numbers were converted to calibrated radi-ance using gain coefficients derived from individualSPOT scene metadata (Table 3).

This study used remotely sensed data to create veg-etation indices with which relationships to rainforest

canopy damage were developed. The standard NDVIwas created using SPOT imagery (Campbell 2002).Plant–water sensitive vegetation indices (known asstress-related vegetation indices or STVI) include red,near-infrared (NIR) and short-wave infrared (SWIR)regions of the electromagnetic spectrum. They con-trast higher NIR leaf reflectance with chlorophyllabsorption in the red and water absorption in theSWIR, and are designed to respond positively toincreasing vegetation health (Thenkabail et al. 1994).O’Neill (1996) and Chen and Gillieson (in review)applied these indices to shrublands in western NewSouth Wales and suggested that plant-water sensitiveindices can be useful for vegetation mapping andanalysis, with good relationships to total plant coverand basal area.

An index STVI4 was calculated using the SPOT5red band (0.61–0.68 mm), NIR band (0.78–0.89 mm)and the SWIR band (1.58–1.75 mm) with the follow-ing formula:

STVI NIR RED SWIR NIR SWIR4 = − ×( )( ) +( )

This index and NDVI were applied to both 2006and 2002 satellite images and a difference imagecreated in ER Mapper 7.1.This result was re-classifiedinto seven classes corresponding to standard devia-tions above and below the mean difference imagevalue.

A DEM for the study area was sourced fromAdvanced Spacebourne Thermal Emission andReflection Radiometer (ASTER) level IB satellitedata for 6 October 2006. This dataset had a cell reso-lution of 15 m and vertical accuracy of 1 m. TheDEM was used for the extraction of altitude, localterrain relief, slope and aspect variables using the

Table 1. Survey sites by damage category and altitude

Altitude

No. of plots in each damage category

Total by altitude1 (low) 2 (moderate) 3 (severe)

Upland >600 m elevation 27 30 21 78 (13 sites)Lowland <400 m elevation 12 27 24 63 (12 sites)Total by damage category 39 57 45 141 plots (25 sites)

Table 2. Variables recorded at each plot

Variables Basis of measurement

Damage-class of site Damage categories 1:Slight, 2: Moderate,3: Severe

Damage to tree by size class Major limb loss; snappedbole; windthrown

Standing basal area M3/haCanopy height* MetresCanopy cover %Subcanopy cover %Woody debris (fine and coarse) % based on 1 m2

Rattans (Calamus spp) OrdinalVines as lianes and towers OrdinalStinging trees (Dendrocnide

muroides)Ordinal

Notable life-forms (epiphytes,ferns, palms, lilies etc)

Presence/absence

Exotic weeds OrdinalSeedling recruitment OrdinalNumber of fallen trees

DBH classes: 10–20 cm;20–50 cm; 50–100 cm;>100 cm

Count

Number of standing treesDBH classes: <2.5 cm &

>2 m high; 2.5–10 cm;10–20 cm; 20–50 cm;50–100 cm; >100 cm

Count

Distance from the forest edge MetresSlope DegreesAspect Ordinal: 4 quadrantsElevation MetresTopographic position OrdinalSoil/parent rock Basalt, metamorphic,

granite, sand, alluviumDistance to nearest watercourse Metres

*Canopy height records were later discarded as the highlyvariable nature of the remaining canopy at the severelydamaged sites made consistent measurement difficult.

Table 3. SPOT band information for June 2006

Band Bandwidth Gains (division)

Green 0.50–0.59 mm 3.80370Red 0.61–0.68 mm 3.16240NIR 0.78–0.89 mm 2.41152SWIR 1.58–1.75 mm 10.74122

NIR, near-infrared; SPOT, System Pour le Observation dela Terre; SWIR, short-wave infrared.

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Spatial Analyst extensionof ArcGIS 9.1. GPS loca-tions for individual detailed field quadrats were con-verted to ArcGIS shapefiles, buffered to 15 m radiusand used to extract satellite image data and derivedterrain variables using ER Mapper 7.1 software. Anaverage of four cells was sampled at each site and amean value obtained. This permitted direct correla-tion of field variables with satellite and DEM deriveddata.

Digital aerial photography of the study area wasobtained from Aerometrex P/L and had been cap-tured on 19 September 2006. The individual photo-graphs had a ground resolution of 0.45 m and wereorthorectified with an RMS error of less than 1 m. Ata local scale, distinct areas of multiple tree falls werevisible. Using ER Mapper 7.1, tree fall areas (distinctareas of forest where multiple trees had fallen) wereidentified and screen digitized as polygon shapefiles.Within these 87 tree fall area polygons, fallen treetrunks were digitized as line shapefiles. A minimumof 30 trees were digitized for each polygon, giving atotal sample of 1410 fallen trees. These files werethen analysed in ArcGIS 9.1 using the linear direc-tional means routine in ArcToolbox to measure thelength and orientation of each fallen tree in eachpolygon. Individual polygon tree fall data and anaggregated tree fall dataset were tested against anassumption of uniform distribution of tree fall orien-tations using Chi-squared analysis.

RESULTS

Are the damage categories robust?

The number of fallen trees as a percentage of thosepresent before the cyclone was calculated for all stemsat least 10 cm DBH by summing the recently fallenand the standing trees in each size class. Dead standingstems were counted if they were judged to have diedrecently (bark still adhering; obvious cyclone damageetc). When all plots were pooled into damagecategories, there was a significant increase in the per-centage of tree falls across all size-classes (10 cm–100 cm DBH) with increasing damage (P < 0.001)(Tables 4,5). This was supported by a significantdecrease in canopy and subcanopy cover with increas-ing damage-class (P < 0.001).

When pairwise comparisons were made betweenthe three damage-classes, although the differences incanopy and subcanopy cover remained significant, dif-ferences in tree fall data did not. Sites that were classedas slightly or moderately damaged could not be differ-entiated on tree fall variables, whereas they were verysignificantly different from the severely damaged sites(Table 6). Thus the main structural variables we mea-sured did not give unequivocal support to the damagecategories we identified on the ground.The separationbetween less and severely damaged sites was moresignificant in the upland sites (Tables 4,6).

Table 4. Difference in variables with damage category for sites pooled by damage-class (slight, moderate and severe damage)regardless of altitude, and for sites separated into damage-class and grouped by altitude (Kruskal Wallace values)

VariablePooled all sites

n = 141Upland sites

n = 78Lowland sites

n = 63

Fallen 10–20 cm (%) 33.117*** 22.174*** 9.666**Fallen 20–50 cm (%) 26.543*** 19.488*** 9.334**Fallen 50–100 cm (%) 15.189*** 13.717*** NSSubcanopy cover (%) 37.424*** 25.271*** 14.285***Canopy cover (%) 35.229*** 21.289*** 16.948***Basal Area sqm/ha 6.337* 8.831* NS

Significance: *P < 0.05, **P < 0.01, ***P < 0.001.

Table 5. Pairwise comparison between damage categories 1 (slight damage) and 2 (moderate damage) for sites pooledregardless of altitude or grouped by altitude (Kruskal Wallace values)

VariableAll sitesn = 96

Upland sitesn = 57

Lowland sitesn = 39

Fallen 10–20 cm (%) 8.628** 4.058* NSFallen 20–50 cm (%) NS NS NSFallen 50–100 cm (%) NS NS NSSubcanopy cover (%) 11.392*** NS 12.430***Canopy cover (%) 4.303* 4.589* NSBasal Area sqm/ha NS NS NS

Significance: *P < 0.05, **P < 0.01, ***P < 0.001.

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When all plots were pooled, there was a significantdifference between damage-classes for the variablesshown (Table 4). This relationship held for most vari-ables when tested between upland and lowland sites,although basal area and number of larger fallen trees(50–100 cm DBH) did not differ between damagecategories for lowland sites. Unpacking this furtherindicated that not only were there fewer large treespresent in the lowland plots than the upland plots(Mann–Whitney U-test: P = 0.05, Z = -1.964), ahigher percentage of standing stems fell in thelowland slightly and moderately damaged plots thandid in the equivalent upland sites (size-classes50–100 cm c2 = 3.971, P = 0.046*; >100 cm c2 =4.091, P = 0.043*).

Aspect

Plots (n = 141) were pooled and tested for a relation-ship between damage-class and exposure to aspect;

however, no significant relationship was found (Fig.3).When plots were grouped by altitude and tested bydamage-class, there were insufficient data for testing.Inspection of Figures 4 and 5 showed no obviousrelationship between aspect and damage category forupland plots, whereas a predominant eastern to south-ern aspect prevailed on the lowlands.This reflected theprevailing wind direction of the cyclone on the low-lands within a short time of it crossing the coast.

Disturbance indicators

All sites were grouped into the three damage-classesand tested for presence of species or structures indica-tive of disturbance. Significant relationships existedbetween damage-class and presence of rattans(Calamus spp.), vine-towers and stinging plants (D.muroides) (Table 7).

Stinging plants were recorded in only 56% ofseverely damaged and 37% of moderately damaged

Table 6. Pairwise comparison between damage categories 2 (moderate damage) and 3 (severe damage) for sites pooledregardless of altitude or grouped by altitude (Kruskal Wallace values)

VariableAll sitesn = 102

Upland sitesn = 51

Lowland sitesn = 51

Fallen 10–20 cm (%) 12.013*** 9.740** 4.967*Fallen 20–50 cm (%) 21.732*** 14.634*** 4.477*Fallen 50–100 cm (%) 12.614*** 7.405** 4.255*Subcanopy cover (%) 13.088*** 15.058*** NSCanopy cover (%) 21.910*** 13.694*** 8.324**Basal Area sqm ha-1 6.210** 7.200** NS

Significance: *P < 0.05, **P < 0.01, ***P < 0.001.

Damage categorysevere canopymoderate canopyslight canopy

Co

un

t

20

15

10

5

0

271-359 degrees181-270 deg91-180 degrees0-90 degrees

General aspect

Fig. 3. The relationship between aspect and damage category for slightly, moderately and severely damaged plots (n = 118)(Pearson’s c2 = 3.014, P = 0.807, 6 d.f.).

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sites, even though 9 months had elapsed since thecyclone’s passage. Only 5% of slightly damaged sitesrecorded stinging trees, reflecting the lack of canopygaps at these sites.

Vine towers were significantly more common inseverely damaged sites. While towers were commonlypresent in the severely damaged sites on both theuplands (38%) and lowlands (63%), there was nosignificant difference with elevation (Table 7).

While at least 80% of all sites (n = 141) containedCalamus, significantly more Calamus was present onthe uplands than the lowlands, and in the severelydamaged rather than less damaged sites (Table 7).Calamus was recorded significantly more often inslightly damaged upland sites (92.6%) than compa-rable lowland sites (50%). As with vine towers, thepresence of Calamus usually indicated previousdisturbance.

Damage Category:severe canopymoderate canopyslight canopy

Co

un

t

15

10

5

0

271-359 degrees181-270 degrees91-180 degrees0-90 degrees

Aspect

Fig. 4. Effect of general aspect on severity of damage for lowlands plots.Those sites with severe damage show a predominanteastern to southern aspect.

Damage Category:severe canopymoderate canopyslight canopy

Co

un

t

10

8

6

4

2

0

271-359 degrees181-270 degrees91-180 degress0-90 degrees

Aspect

Fig. 5. Effect of general aspect on severity of damage for upland sites.There is no obvious pattern evident, indicating that localtopography masked the general wind direction.

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Soil fertility

We found that there was a significantly higher percent-age of tree falls in the 20 cm–50 cm DBH size class inthe less fertile granite-based sites on the uplands, com-pared with the more fertile basalt-based soils (KruskalWallace c2 = 6.385*, P = 0.012). No relationshipbetween soil fertility and canopy cover, subcanopycover or basal area was found on the uplands.

In contrast to the uplands, there was no significantdifference in the percentage of fallen trees with soilfertility on the lowlands. However, significantly morecanopy cover remained in the less fertile comparedwith more fertile sites on the lowlands (KruskalWallace c2 = 6.967**, P = 0.008).

Results of the road survey

Examination of the frequency of damage categories atthe road survey sites showed that the most commoncategory was 2, then 1, zero and 3; large parts of theTablelands (72% of sites visited) had moderate toslight canopy disturbance, with associated loss ofbranches and/or foliage. About 19% of the field sitessuffered no visible damage from the cyclone. Less than10% of sites surveyed had severe category 3 damage,and this was mostly in the area around Millaa Millaaand immediately southeast of the town, and at twosites near Malanda (Fig. 6). Damage was extremelylocalized; areas of category 3 damage were containedwithin larger areas of category 1 or 2 damage, wherewind vortices had randomly torn through, creatingnarrow corridors of devastation. Similar effects werereported in areas of banana plantations, where somecompletely destroyed areas sat adjacent to seeminglyuntouched plants. South facing sites had the highestnumber of category 3 damage sites (Table 8); however,the low number of category 3 sites makes it difficult todraw any clear conclusion on the role of aspect. Com-bined category 1 and 2 damaged sites, which make up

the majority of damage types, were evenly spread overall aspects, suggesting that sites experienced significantdamage regardless of aspect.

Vegetation indices and canopy damage

The STVI4 vegetation index image had a range from-20.23 to 0.85, while the NDVI image range was from-0.91 to 0.80.The dynamic range of the STVI4 imagegave greater discrimination between varying levels offorest canopy reflectance (Fig. 7). The differenceimage between 2006 and 2002 STVI4 indices (Fig. 8)was used to indicate areas where there had been asignificant change in vegetation canopy reflectance. Ahigh negative value indicated a trend to reducedcanopy cover and/or vegetation vigour. A positive valueconversely indicated image cells where there had beenan increase in canopy cover or vegetation vigour.In some areas, changes in cropping types or fallowshowed changes in vegetation indices; these were easilyidentified owing to their rectangular shapes. In otherforest areas, very significant reductions in vegetationindex values (two or three standard deviations belowthe mean) supported canopy damage by CycloneLarry.

Extracted values of STVI4 and NDVI for thedetailed field study sites were compared with canopydamage categories (Table 9) and measured basal areaof standing live trees. In all cases, values of basal area,STVI4 and NDVI reduced with increasing damagecategory. There was no significant difference betweenbasal area, STVI4 and NDVI for categories 1 and 2.Only damage category 3 sites were significantly differ-ent at the 0.05 level.

Tree basal area has been used as a surrogate vari-able for canopy damage (Franklin et al. 2004) andshowed significant reduction at the highest damagecategory. Generalized linear modelling was used toexplore the relationship between basal area (mea-sured in the field) and the STVI4 vegetation index,

Table 7. Presence of disturbance indictors by damage-class using Pearson’s Chi squared (with continuity correction appliedfor altitudinal testing#)

Disturbance indicator

Pooled upland &lowland sites

n = 141

Between upland & lowland sites#

Slight damagen = 31

Moderate damagen = 57

Severe damagen = 45

Stinging plants c2 = 23.996*** NS c2 = 8.974** NSP = 0.000 P = 0.003

Calamus c2 = 7.798* c2 = 6.846** NS NSP = 0.02 P = 0.009

Vine towers c2 = 20.895*** NS NS NSP = 0.000

Significance: *P < 0.05, **P < 0.01, ***P < 0.001.

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as well as topographic variables (Table 10) derivedfrom satellite imagery and the DEM. The combina-tion of STVI4 with local relief (a measure of surfaceroughness), altitude and aspect explained 56.6% ofthe variance in basal area and was significant at the0.001 level.

Tree fall orientation and vortices

For the entire tree fall dataset, a Chi-squared analysis ofthe number of trees in six orientation categories indi-

cated that there was a strong preferred orientation inthe range 30–60° (48.6% of all trees) with a secondaryorientation in the range 60–90°. The Pearson Chi-square value (Table 11) was 556.51 with 5° of freedom,the probability level (under a null hypothesis of nopreferred orientation) was P < 0.001. At a broaderlevel, 78.6% of tree falls had an orientation between 0°and 90°, while only 3.6% fell in the 90–180° orientationgroup. Thus there was a strong tree fall orientation inthe northeast-southwest direction. Only 17.9% of alltree fall orientations were found to be randomly distrib-uted (P = 0.05) in an individual polygon.This was mostlikely due to the effect of local wind vortices creating aradial pattern of tree fall. Inspection of post-cycloneaerial photography indicated that vortices werecommon, and combined with leeward pressure wavesresulting from topographical complexity (Turton 2008)would have confounded any relationship between thepredominant wind direction and damage, especially onthe dissected uplands.

Examples of the overall dispersion for all tree fallsand polygons 28, 33 and 62 (Fig. 9) showed strongconvergence on the northeast-southwest orientationwith some dispersion owing to local vortices.

Fig. 6. Road survey of damage categories over the Atherton Tableland.

Table 8. Distribution of damage categories and aspectfrom road survey

Damagecategory

Aspect

TotalNorth East South West

3 3 1 4 2 102 14 15 6 7 421 7 8 12 10 370 8 5 6 2 21total 32 29 28 21 110

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Fig. 7. (a) 2006 NDVI image for Lake Eacham, (b) 2006 STVI image for Lake Eacham. NDVI, normalized differencevegetation index; STVI, stress-related vegetation indices.

Fig. 8. STVI difference image for Lakes Eacham and Barrine. Areas in red are -3 standard deviations below mean image value.STVI, stress-related vegetation indices.

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DISCUSSION

How robust are the damage categories?

Both remotely sensed and on-ground datasets indi-cated that it was difficult to distinguish between slightand moderately disturbed sites. It was possible,however, to separate the sites into two categories: lessdisturbed and severely disturbed. The sites were ini-tially assigned to damage categories based on extent ofcanopy cover and structural damage evident. Whilecanopy and subcanopy cover did show a strong rela-tionship with damage-class, tree fall data gave equivo-cal results. Thus our data provide support for twoclearly identifiable damage categories rather than threeor even more.

The period prior to the cyclone was relatively dry,resulting in few windthown stems (Metcalfe et al.2008). However, except for several weeks in July, rainfell almost daily in the 9 months following the cyclone.This delayed both the fieldwork and acquisition ofpost-cyclone ASTER satellite imagery, as the cloudcover did not lift for many months. Webb (1958) andUnwin et al. (1988) noted the importance of docu-menting cyclone damage immediately after the eventwhile indications of impact are apparent. The lack ofimmediate field access meant that substantial cop-picing and regrowth may have confounded precisedamage recording, affecting our ability to more pre-cisely define damage categories. While refoliation andcoppicing may have affected our classification of lesserdamaged sites, severely damaged sites were still readilyidentified by both field inspection and remotely sensedimagery.

What are the best field variables to predictdamage-classes on the ground?

Our dataset suggested that some field variables werebetter predictors of damage than others. Those fieldvariables which proved to be less useful include aspect,basal area, larger size trees and canopy height.We also

found that our method of assessing woody debrisbased on the percentage cover in a single 1 m2 quadratwas not representative of the plot as a whole. Eithersampling more quadrats or a categorical estimatebased on the entire plot may provide a better solution.

Aspect

When CycloneWinifred (category 3) crossed the northQueensland coast south of Innisfail in 1986, bothaspect and exposure to the main wind direction werefound to be a significant determinant of damage(Unwin et al. 1988). This differs markedly from ourresults, where aspect was found to have no significantrelationship to damage. The literature abounds withexamples of both instances (e.g. Putz & Sharitz 1991;Burslem et al. 2000; Metcalfe et al. 2008) indicatingthat aspect is not a consistent predictor of damageowing to unique local and regional characteristics. It isknown that vortices, leeward waves and topographicalcomplexity all contribute to confound the impacts ofwind on the landscape. Inspection of post-cycloneaerial photography indicated that vortices werecommon and, when combined with leeward pressurewaves resulting from topographical complexity(Turton 2008), would have confounded any relation-ship between the predominant wind direction anddamage, especially on the dissected uplands. Theseelements effectively masked any consistent effectowing to site aspect and exposure. Similar topographiceffects have been noted in studies in Puerto Rico byWalker et al. (1991).

Basal area

The remote sensing analysis found that there was agood relationship between a vegetation index and fieldmeasured basal area. From the field data, there was asignificant difference in basal area between severedamage and the other categories. However, field mea-surements of basal area alone did not seem to be aparticularly sensitive measure of relative damage.

Tree fall

Unlike the pattern on the uplands, there was no signifi-cant difference in the larger tree fall data with damage-class on the lowlands. As the coastal lowlandsexperienced a greater frequency of more destructivewinds than the uplands, trees on the lowlands wereeither already fallen or were resistant species which hadbeen selected to withstand the winds better than theirupland counterparts.As tree species were not recorded,this cannot be tested. However, if we compare the

Table 9. Mean values of basal area and vegetation indicesfor damage categories, derived from buffered field surveysites

Damage category 1 2 3

Basal area (m2/ha) 46 33.5 23.0*STVI4 mean -0.7419 -0.9952 -1.1851*NDVI mean 0.5691 0.5402 0.4904*

*Significant at 0.05 level.NDVI, normalized difference vegetation index; STVI,

stress-related vegetation indices.

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number of standing larger trees between upland andlowland sites for each damage-class, the only differ-ences were recorded at the less damaged sites (Table 4).There were significantly more large trees both standingand fallen in the uplands than in the lowlands indicatingthat there were fewer large trees on the coastal sitesprior to the cyclone.This may reflect (i) heavier logging

of lowland sites left fewer large trees to be impacted; or(ii) recurrent disturbance had left fewer larger treesstanding in the lowlands; or (iii) that lowland trees growmore slowly than upland trees.

While most upland sites were located on rich basaltderived soils, many lowland sites were located in lowerfertility soils derived from metamorphics or on sandy

Table 10. Generalized linear regression between basal area (BA; m2/ha), STVI4 vegetation index and terrain variables: localrelief (HTRANGE; m), aspect (ASPECT; degrees) and altitude (ALTITUDE; m)

Response variate: Basal Area (BA; m2/ha)Fitted terms: Constant + STVI4 + HTRANGE + ASPECT + ALTITUDE +

STVI4.HTRANGE + STVI4.ASPECT + HTRANGE.ASPECT +STVI4.ALTITUDE + HTRANGE.ALTITUDE + ASPECT.ALTITUDE +STVI4.HTRANGE.ASPECT + STVI4.HTRANGE.ALTITUDE +STVI4.ASPECT.ALTITUDE + HTRANGE.ASPECT.ALTITUDE +STVI4.HTRANGE.ASPECT.ALTITUDE

Summary of analysisd.f. s.s. MS v.r. F pr.

Regression 15 12037 802.5 6.39 <0.001Residual 47 5903 125.6Total 62 17941 289.4Percentage variance accounted for 56.6Standard error of observations is estimated to be 11.2

Estimates of parametersParameter Estimate SE t t pr.Constant -180.8 53.5 -3.38 0.001ALTITUDE 0.3843 0.0692 5.56 <0.001STVI4 -133.4 33.5 -3.98 <0.001ASPECT 0.919 0.306 3 0.004HTRANGE 20.54 5.57 3.69 <0.001ALTITUDE.STVI4 0.2578 0.0502 5.14 <0.001ALTITUDE.ASPECT -0.00177 0.000387 -4.57 <0.001STVI4.ASPECT 0.627 0.244 2.57 0.013ALTITUDE.HTRANGE -0.04624 0.0082 -5.64 <0.001STVI4.HTRANGE 15.78 4.63 3.41 0.001ASPECT.HTRANGE -0.0922 0.0319 -2.89 0.006ALTITUDE.STVI4.ASPECT -0.00122 0.000332 -3.68 <0.001ALTITUDE.STVI4.HTRANGE -0.03605 0.00778 -4.64 <0.001ALTITUDE.ASPECT.HTRANGE 0.000221 4.08E-05 5.41 <0.001STVI4.ASPECT.HTRANGE -0.0716 0.0308 -2.32 0.025ALTITUDE.STVI4.ASPECT.HTRANGE 0.00017 4.27E-05 3.98 <0.001

STVI, stress-related vegetation indices; d.f., degrees of freedom; s.s., sum of squares; MS, mean square; v.r., variance ratio; Fpr., F probability.

Table 11. Chi-squared analysis of all and selected tree fall orientations

Interval (degrees) All tree falls Polygon 28 Polygon 33 Polygon 62

0–30 311 9 1 2030–60 682 18 35 2060–90 245 13 17 990–120 53 2 2 6120–150 45 5 0 2150–180 74 1 0 1Total 1410 48 55 57Pearson Chi-squared, d.f. = 5 556.51 27.5 110.71 37.25P <0.001 <0.001 <0.001 <0.001

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alluvium. Forests on more fertile soils are characteris-tically resilient to cyclone damage (high tree mortalitybut massive recruitment thus respond to disturbance),while forests on less fertile sites are characterized byresistant species (low tree damage and little increasein recruitment thus withstand disturbance) (Batista &Platt 2003; Tanner & Bellingham 2006).

We found that there were significantly more tree fallsin the 20 cm–50 cm DBH size cohort in the morefertile sites on the uplands. This result is at odds withthe literature which suggests resistant trees, whichwould withstand the wind storm, are more likely tooccur on infertile sites (Herbert et al. 1999). Signifi-cantly more canopy cover remained in the less fertilesites on the lowlands (Kruskal Wallace c2 = 6.967,P = 0.008), but no relationship was found on theuplands. We hypothesize that the occurrence of thistype of very powerful and fast moving cyclone is prob-ably unusual, and that the upland sites are not exposedto such high winds on a regular basis. If this is correct,there may be little selection pressure for developing aresistance strategy on the uplands regardless of soilfertility factors.

In contrast to the uplands, there were significantlymore trees left standing in the 10–20 cm size class on

the lowlands on poorer fertility sites (Kruskal Wallacec2 = 9.432 P = 0.002). This suggests either that thissize cohort of lowland trees was less exposed to thewind, or that they were more resistant. However, thefinding that larger trees suffered similar levels of treefall across the range of soil fertility lends more weightto the former hypothesis. In lowland sites, seasonalwaterlogging may reduce physical fertility while nutri-ent levels are maintained; larger trees may also be morevulnerable to wind throw at waterlogged sites.

Canopy height

Canopy height was difficult to consistently estimate inthe severely damaged sites owing to its very variableand broken nature. Lowland rainforest canopy tendsto be more variable in canopy size and height, whileupland rainforest generally has a less variable,smoother canopy which causes less wind resistance. Asa site is repeatedly disturbed, the canopy heightbecomes progressively reduced (Webb 1958) withmore emergent and persistent vine towers. This hasbeen the case in the coastal lowlands of theWetTropicsbioregion where estimates of return intervals for

Fig. 9. Orientations and cumulative lengths of fallen trees for the entire tree fall dataset and three selected polygons.

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moderate to severe cyclones range from every 40 years(Webb 1958) to every 15 years (Turton 2008).There-fore, the difference between the mean emergent heightand the reduced canopy height is a measure ofrepeated cyclones over a long period, rather than justbeing the result of the last cyclone measured.

Remote sensing analysis

This study found that remote sensing is able to dis-criminate between more damaged and less damagedrainforest at a landscape scale. Bellingham (1991)observed that aerial or satellite imagery may be usefulin detecting differences in canopy texture as a result ofdefoliation; however, it may not reveal smaller scalebut important damage under relatively intact canopies.

This situation may be exacerbated if the imagerycannot be obtained before refoliation is complete,which in many instances occurs within only a fewweeks or months of the cyclone (Brokaw & Walker1991). As cyclone related tree mortality may not beevident for more than 1 year following a cyclone(Tanner & Bellingham 2006), imagery is likely to givean overestimate of forest health if acquired too soon,and an underestimate of immediate damage ifacquired too late. The best approach is therefore toacquire at least two sets, including one as soon aspossible following the event and another set 12 ormore months later, and carry out difference imaging.

The stress-related vegetation index STVI4 per-formed better than NDVI in this study. Further use ofshort wave infrared image data should be explored.There was a useful relationship between the STVI andtree basal area, which may be used for predicting treestand density changes after cyclones.

High-resolution aerial photography provided a valu-able data source for analysis of tree fall orientations.Quantification of both orientation and fallen treelengths was possible, permitting conclusions to bedrawn regarding dominant vectors and the occurrenceof local vortices in the cyclonic wind flow. We dis-cerned a dominant northeast to southwest orientationin fallen trees over a distance of 50 km, and evidencethat there were strong vortices forming in the complexwind flow associated with Cyclone Larry.

Disturbance species with altitude

Wet tropical rainforest in north Queensland have beensubject to recurrent disturbance natural (wind, fire)and anthropogenic disturbance (clearing, logging).Thus the accumulated history of disturbance, in addi-tion to the latest cyclonic disturbance, was what wewere sampling. This has been demonstrated in Africawhere the canopy height of rainforest reflects the

history of recurrent disturbance (De Gouvenain &Silander 2003).The pattern of disturbance indicators,such as longer lived gap species, should also provideevidence of past disturbance.

Stinging plants are short lived pioneers that germi-nate in high light conditions following disturbanceonly to disappear once the canopy closes, whereasrattans, for example, Calamus, are long lived plantsuseful in identifying previous disturbance. Wherelianas respond to high light conditions by climbing upand smothering trees, these are called climber or vinetowers (Webb 1958). Table 7 illustrates this life historydifference with stinging plants showing a much fasterresponse to changed light conditions than eitherrattans or vine towers, which had yet to respond to thecanopy loss owing to Cyclone Larry.

When looking at the altitudinal distribution ofdisturbance species within damage-classes, neitherstinging plants nor vine towers showed a significantaltitudinal pattern of distribution in slightly damagedsites. This was not surprising as stinging plants areshort-lived gap species, but it indicated that there wasno recent history of previous canopy disturbance tothese sites given the lack of older vine towers.

The low percentage of severely damaged sites withstinging plants present may be due to the large amountof coarse woody debris on the ground at these sites,making germination and/or detection of small plantsdifficult. As time progresses, the proportion of severelydamaged sites with stingers is expected to increase asthe debris decomposes allowing light onto the ground.

CONCLUSIONS

Overall it appeared that there was only a reliably sig-nificant difference between severe and the slight tomoderate damage categories, while there was little todistinguish in the measured variables between thetwo lowest categories of damage. There was certainlya difference in the response of upland and lowlandforests to Cyclone Larry; we hypothesize that thisreflected both canopy roughness and topographicfactors. Local vortices owing to chaotic effects in thecyclonic windfield can result in very significant varia-tion in damage levels, unrelated to aspect. Of thefield measured variables, the tree fall classes showedthe most consistent relationships with damage andaltitude. There appears to be potential in the use ofplant-water related vegetation indices for estimationof changes in tree density, as indexed by basal area.Difference imaging can be helpful in discerningregional patterns in cyclone damage. The persistenceof cloud cover for some months after cyclone eventscan reduce the sensitivity of remote sensing analysis;by the time satellite imagery is available, significantregrowth will have occurred, masking actual damage

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levels. Remotely sensed damage is likely to be anunderestimate as it is only sensitive to the higherlevels of canopy damage and fails to detect sub-canopy damage.

The impact of soil fertility on damage was equivocalas other confounding factors intruded. In lowlandsites, seasonal waterlogging may reduce physical fertil-ity while nutrient levels are maintained. This is lesslikely to be an issue on upland sites.

ACKNOWLEDGEMENTS

This research was funded by the JCU/CSIROTropicalLandscapes Joint Venture and the Skyrail RainforestFoundation. Permission to carry out the research wasgiven by the Queensland Parks & Wildlife Service(Permit No. WITK04323507) and the AboriginalRainforest Council. Sincere thanks to Carla Catteralland John Kanowski for use of their modified pro formafor revegetation monitoring. We are grateful for thefield assistance provided by Tina Lawson, AndrewColvill, Bridie McCarthy and Les Moore. Sincerethanks to SusanWalker who carried out the orientationanalysis of fallen trees.

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