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112 (2008) 2104–2117www.elsevier.com/locate/rse

Remote Sensing of Environment

Spatial and temporal patterns of gap dominance by low-canopy lianasdetected using EO-1 Hyperion and Landsat Thematic Mapper

Jane R. Foster a,⁎, Philip A. Townsend a, Chris E. Zganjar b

a University of Wisconsin–Madison, Department of Forest Ecology and Management, Madison, WI 53706, United Statesb The Nature Conservancy, Climate Change Science Initiative, Arlington, VA 22203, United States

Received 2 November 2006; received in revised form 27 June 2007; accepted 15 July 2007

Abstract

Woody lianas are critical to tropical forest dynamics because of their strong influence on forest regeneration, disturbance ecology, andbiodiversity. Recent studies synthesizing plot data from the tropics indicate that lianas are increasing in both abundance and importance in tropicalforests. Moreover, lianas exhibit competitive advantages over trees in elevated CO2 environments and under strong seasonal droughts, suggestingthat lianas may be poised to increase not only in abundance but also in spatial distribution in response to changing climate. We used a combinationof high-resolution color-infrared videography and hyperspectral imagery from EO-1 Hyperion to map low-lying lianas in Noel Kempff MercadoNational Park (NKMNP) in the Bolivian Amazon. Evergreen liana forests comprise as much as 14% of the NKMNP landscape, and low-statureliana patches occupy 1.5% of these forests. We used change vector analysis (CVA) of dry season Landsat TM and ETM+ imagery from 1986 and2000 to determine changes in liana-dominated patches over time and to assess whether those patches were regenerating to canopy forest. Thespatial distribution of liana patches showed that patches were spatially aggregated and were preferentially located in proximity to waterways. TheCVA results showed that most of the dense liana patches increased in brightness and greenness and decreased in wetness over the 14 years of thechange analysis, while non-liana forest patches changed less and in more random directions. Persistent liana patches increased in area by anaverage of 59% over the time period. In comparison, large burned areas appeared to recover completely to canopy forest in the same time period.This suggests that the dense liana patches of NKMNP represent an alternative successional pathway characterized not by tree regeneration butrather by a stalled state of low-canopy liana dominance. This research supports hypotheses that liana forests can be a persistent rather thantransitional component of tropical forests, and may remain so due to competitive advantages that lianas enjoy under changing climatic conditions.© 2008 Elsevier Inc. All rights reserved.

Keywords: Lianas; Woody vines; Change detection; Change vector analysis; Tropical forests, Forest dynamics; Landsat; EO-1 Hyperion; Hyperspectral; Canopygaps; Succession; Bolivia; Noel Kempff Mercado National Park

1. Introduction

Woody vines, commonly called lianas, play an important rolein tropical forest dynamics, often acting as early colonizers ofdisturbed sites (Schnitzer and Bongers 2002; Schnitzer et al.,2000; Tabanez and Viana 2000). Lianas also affect the size andrates of gap formation and recovery. They are known to causestructural stress to host trees, increasing the probability of

⁎ Corresponding author. Department of Forest Ecology and Management,University of Wisconsin–Madison, 120 Russell Labs, 1630 Linden Drive,Madison, WI 53706 USA. Tel.: +1 608 265 6321; fax: +1 608 262 9922.

E-mail address: [email protected] (J.R. Foster).

0034-4257/$ - see front matter © 2008 Elsevier Inc. All rights reserved.doi:10.1016/j.rse.2007.07.027

damage and mortality. By binding multiple trees together as theygrow to the canopy, lianas increase gap sizes because their hoststend to topple over in groups (Clark and Clark 1990). Followingdisturbance, lianas are thought to temporarily dominate forestgaps only until overtopped by taller trees, yet recent research hasshown that lianas are capable of excluding non-pioneeroverstory species for decades or longer through suppression ofregeneration. Dense, low-stature liana tangles have beenobserved to delay tropical forest succession to taller canopytree species for up to 13 years (Schnitzer et al., 2000). Schnitzeret al. (2000) concluded that gaps dominated by low-stature lianaforest represented a stalled successional state that may be morecommon in tropical forests than had previously been thought.

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2105J.R. Foster et al. / Remote Sensing of Environment 112 (2008) 2104–2117

Low-stature liana forests were also found to be common inforests of the eastern Brazilian Amazon where they occupied31% of a 130-ha study area (Gerwing and Farias 2000) and to bevery common within isolated forest fragments and patches insoutheastern Brazil (Tabanez and Viana 2000). Moreover,relatively large gaps (0.45–2 ha or larger) occupied between16 and 30% of montane forests in Bwindi National Park inUganda (Babaasa et al., 2004). Some gaps observed in this studyalso appeared to be in a state of arrested succession, with anabundance of woody lianas and little regeneration of either pi-oneer or late successional overstory species. Therefore, in addi-tion to affecting rates of gap formation, lianas appear to have theability to affect the duration of gap persistence in tropical forests.

By affecting rates of succession and canopy turnover, lianaproliferation has a direct effect on the carbon cycle. Lianassequester far less carbon than the large overstory tree speciesthat typically dominate tropical rainforests (Gerwing and Farias2000; Nascimento and Laurance 2004). Gerwing and Farias(2000) found low-stature liana forests had as little as one-thirdthe aboveground biomass found in taller forests. Similar dif-ferences were found between liana rich forests and their lianapoor counterparts in French Guiana (Chave et al., 2001). Incases where low liana tangles dominate a site and overstorytrees are rare or absent, these differences in biomass and C se-questration are likely to be magnified. Therefore, an abun-dance of forest gaps dominated by low liana tangles for multipledecades may reduce forest productivity and C sequestration inaddition to changing successional dynamics.

Recent pan-tropical analysis of permanent plot data hassuggested that lianas have become more abundant and dominantin tropical forests over the past 20 years (Phillips et al., 2002).Phillips and Gentry (1994) hypothesized that liana expansionresults from increased forest productivity that in turn acceleratesthe dynamics of growth, mortality, and gap formation as tropicalclimates warm. It has also been suggested that lianas can re-spond to increased CO2 in the atmosphere with higher growthrates than other tropical tree species (Zotz et al., 2006). Thusif atmospheric CO2 levels increase, lianas may become evenstronger competitors in tropical forests (Mayle et al., 2004).

Changes in the competitive abilities of lianas relative to treespecies may be most evident in tropical forests that lie close toclimatic and ecoregional boundaries, which may shift in re-sponse to changes in temperature and climate (Mayle et al.,2004). Forests near climatic boundaries typically experiencestrong seasonal droughts, a characteristic of forests where lianasare most abundant and dominant (Schnitzer, 2005). A numberof physiological mechanisms may explain this observed domi-nance. Lianas do not have to dedicate as many resources tostructural support and canopy access as trees do, allowing themto invest instead in deeper roots and larger, more efficient vesselelements (Schnitzer, 2005). This may allow lianas to grow moreduring the dry season, giving them a competitive advantagewhere seasonal droughts are significant (Schnitzer, 2005). Bo-livian forests that lie along the western and southern edges ofthe Amazon basin occupy an important ecotone between tro-pical rain forest and deciduous dry forest or savanna. They areknown for strong seasonality and an unusually high abundance

of lianas (Killeen, 1998; Mayle et al., 2004; Perez-Salicrupet al., 2001). As a consequence, the vegetation cover in thissame region has historically been very variable. Pollen researchshows that the vegetation in Noel Kempff Mercado NationalPark in northeastern Bolivia has only been dominated by tro-pical rainforest for the past 2000–3000 years, before whichdrier forests dominated (Burbridge et al., 2004; Mayle et al.,2000). This expansion and contraction of the Amazonian rainforests in response to past climate change highlights the sen-sitivity of these transitional forest areas to current and futureclimate trends.

Increasing dominance and extent of liana-dominated forestsmay be both a consequence and an indicator of anthropogeni-cally-driven climate change that needs to be better understood.Previous studies quantifying the abundance of woody lianas intropical forests have generally been field and plot based andcovered limited spatial extent (Perez-Salicrup et al., 2001;Schnitzer et al., 2000; Tabanez and Viana, 2000). Yet there is aclear need to better understand the spatial extent and distributionof low-stature liana forests and how those distributions arechanging in tropical forest landscapes.

The objective of this study was to use a multi-temporal,multi-platform approach to map low-stature liana forests in theBolivian Amazon and to quantify changes in liana extent over a14-year period from 1986 to 2000. First, we determined whetherlow-stature liana forest patches were spectrally distinct inmultispectral and hyperspectral imagery so that they could bemapped accurately. Second, we tested whether mapped lianapatches changed spectrally over a 14-year period. Significantdirectional changes in reflectance would indicate that lianaforests are either succeeding to non-pioneer overstory treespecies or becoming increasingly dominated by lianas. Lianapatches exhibiting no changes in spectral reflectance wouldprovide support for the hypothesis that in many areas lianasrepresent a stalled successional state. Finally, we tested whethermapped liana patches increased in spatial extent between 1986and 2000 by migrating into adjacent forests.

2. Methods

2.1. Study area

Noel Kempff Mercado National Park (NKMNP) is located inthe eastern Bolivian Amazon near the border with Brazil at61.11 W and 13.93 S (Fig. 1). It was originally established in1988 under the USAID Parks in Peril Program and more thandoubled in size in 1997 to 1.5 Mha when The Nature Conser-vancy (TNC) and partners developed one of largest climateaction projects of its time.

NKMNP is located within the southernmost reach of theAmazonian rainforest, on the border of two climatic zones, wettropical and tropical wet–dry (Mayle et al., 2000). Its climate isstrongly seasonal, driven by fluctuations in the IntertropicalConvergence Zone (Mayle et al., 2000). Mean annual precip-itation is between 1400 and 1500 mm and mean annual tem-perature falls between 25 and 26 °C based on interpolation fromthree of the closest meteorological stations (Hanagarth 1993;

Fig. 1. Location of Noel Kempff Mercado National Park in northeastern Bolivia. The study area for change detection analysis is in the northern half of the park.Evergreen forests with a high abundance of lianas appear in white, lakes appear in black, seasonally flooded savannas or cerrados in dark grey, other types of lowlandforest in grey, the Huanchaca Plateau appears in light grey (adapted from Killeen, 1998).

2106 J.R. Foster et al. / Remote Sensing of Environment 112 (2008) 2104–2117

Killeen, 1990; Montes de Oca, 1982). The location of NKMNPwithin a climatic transition zone places it in an area of sig-nificant regional climate variability subject to changes in globalclimate. Specifically, climate variability is expected to result inwarmer winters, increased stress on forests during the dryseason, more frequent forest fires and gradual replacement bydrier adapted forest species (Mayle et al., 2004).

The current vegetation of NKMNP is characterized by theconvergence of three major ecoregions: the tropical evergreenforest, the tropical dry-deciduous forest and cerrado (SouthAmerican tropical savannas). Five distinct ecosystem unitshave been described within the park: upland evergreen forest,deciduous forest, upland cerrado savanna, savanna wetlands,and forest wetlands (Killeen, 1998). This environmental di-versity provides habitat for such unique species as jaguar, puma,maned wolf, fresh water dolphins, and over nine species ofmacaw. The eastern half of the park is dominated by theHuanchaca Plateau, which rises abruptly to between 600 and700 m above sea level (A.S.L.) and is characterized by unique,dry adapted vegetation cover types as well as gallery forests.To the west of the plateau, some 400 m below, lie humidlowland forests and cerrados that are characteristic of the restof the park. At the center of the rolling uplands on this lowerplain is an evergreen liana forest detailed in a Landsat-derivedvegetation map created for this area (white areas, Fig. 1)(Killeen, 1998). Killeen (1998) describes this forest as havingan unusual dominance of liana species, a relatively dense

canopy and relatively low stature. Killeen also describes homo-genous liana areas in the same forest with a typical canopyheight of about 4 m. Our study area includes three subwater-sheds of the main drainage basin that forms the heart ofNKMNP (Fig. 1). The subwatersheds were delineated in ageographic information system (GIS) using 90-m Shuttle RadarTopography Mission (SRTM) digital elevation model data.Although lianas are prominent in mountainous areas to thenorthwest and upland forests to the south, we constrained ouranalyses to the central portion of the park to minimize vari-ability caused by differences in topography, disturbance history,and vegetation cover. The study area covers 770.9 km2 and hasa median elevation of 226 m A.S.L.

2.2. Image data

2.2.1. High-resolution videographyAcquisition of extensive ground truth data from the field in

NKMNP is logistically challenging due to the remoteness andinaccessibility of the park. To support this research, The NatureConservancy (TNC) contracted for the acquisition of high-resolution color-infrared aerial videography (CIR-AV) (50 cmpixels, 0.5-km extent along-track, and 1-km swath width)(similar to Brown et al., 2005). These data were acquired duringa field campaign from the 14th to the 18th of April in 2003.Areas within the aerial videography that corresponded to EO-1Hyperion hyperspectral imagery and Landsat data were used to


identify a sample of low-stature liana tangles and forests thatguided mapping of liana forest patches. Low liana tangles wereclearly distinguishable as homogenous areas of bright greenvegetation among the more varied canopies of larger trees in thehigh-resolution CIR-AV images (Fig. 2, a and c).

2.2.2. Landsat TMTwo “anniversary date” Landsat images were used for change

analysis, a Thematic Mapper (TM) image from 18 July 1986 andan Enhanced Thematic Mapper (ETM+) image from 1 August,2000. The time of the image acquisitions coincided with the

Fig. 2. Truecolor videography stills of two liana patch areas are shown on the left (a abrighter, greener color than surrounding forests. These same patches are shown in theTM 2000 tasseled-cap image (b and d). Low liana patches appear bright yellow in thisin the videography data.

middle of the dry season at NKMNP. Dry season images arepreferable because they are more likely to be free of clouds andless likely than wet season images to be influenced by thespectral properties of floodwaters beneath the forest canopy. The30-m pixel resolution Landsat images were georeferenced toUTM coordinates using an existing base image, co-registered(b0.5 pixel root mean square error (RMSE)), and for thepurposes of change vector analysis the 1986 image wasradiometrically normalized to the 2000 image following therecommendations of Collins and Woodcock (1996). Landsatimage values were first converted from digital number (DN) to

nd c). Low-stature liana forests have very low, uniform canopies in general and aboxes on the right in an RGB display of the B, G and W bands from the Landsatdisplay combination. Patch boundaries are black in the Landsat images and light


exoatmospheric reflectance measured at the top of the atmo-sphere (TOA) using the appropriate calibration coefficients foreach sensor (Irish, 2000). The Landsat TM image from 1986 wasnormalized to have the same mean and variance as the ETMimage from 2000 using a standard deviation method over for-ested areas (Radke et al., 2005). This normalization procedurereduces the effects of sensor and atmospheric differences be-tween the two images and sensors. The tasseled-cap index (Cristand Cicone, 1984) was computed for each image to conduct thechange detection analysis using coefficients derived for exo-atmospheric reflectance (Huang et al., 2002). The tasseled capreduced the six multispectral Landsat bands to three bands –image brightness (B), vegetation greenness (G) and wetness(W) – which convey over 95% of the variance in the originalbands. The three tasseled-cap bands are commonly used toconduct spectral change detection analyses because they arescene invariant and represent biophysical characteristics that arebothmeaningful and easy to interpret in the context of vegetationcondition and change (Guild et al., 2004; Healey et al., 2005;Townsend et al., 2004).

2.2.3. EO-1 HyperionHyperion imagery from NASA's EO-1 satellite includes 220

10-nm (0.1 μm) bands covering 0.4–2.5 μm, including thevisible (VIS), near (NIR) and shortwave infrared (SWIR)portions of the electromagnetic spectrum. Hyperion imageshave a 30-m instantaneous field of view (IFOV) (comparable toLandsat), as well as 180-km swath lengths (north–south) similarto Landsat. However, due to constraints of data storage andtransmission on the satellite, swath width is only 7.5-km,meaning only narrow strips of NKMNP can be imaged duringone satellite overpass. We acquired one dry season Hyperionimage of NKMNP (5 September 2001) that overlapped with thestudy area and co-registered it to the 2000 Landsat ETM scene.The Hyperion image was corrected for atmospheric effectsusing ACORN™ (Imspec-LLC, 2002) and sensor effects suchas destriping following methods reported in Townsend et al.(2003). The Hyperion imagery was used to summarize thespectral characteristics of low liana patches and to compare ordistinguish them from the spectra of tree-dominated forests. Aminimum noise fraction transform (MNF) was also applied tothe Hyperion image to reduce data dimensionality to a smallerset of bands that capture the majority of image variation whilereducing the influence of noise (Boardman and Kruse, 1994;Green et al., 1988). Mean values for reflectance, 1st derivativereflectance, and the first ten bands of the minimum noisefraction were compared between low liana forests and highforests.

2.3. Statistical analyses

2.3.1. Patch analysisLiana patches identified in the videography data were used

as training areas to determine reflectance thresholds in thetasseled-cap transformed Landsat image from 2000. Based onthese observations, low liana patches were delineated as areaswith image brightness N4500 and greenness N2300 (all

reflectance values were multiplied by 10,000 for integer datastorage). These thresholds were used to map liana distributionfrom the 2000 Landsat image. Because slight mis-registra-tion errors between images would make tracking of very smallpatches difficult, we limited our change detection analysis topatches 0.45 ha or larger (5 pixels minimum size, delineatedusing image segmentation with an 8-neighbor connectivityrule). The resulting polygons of low liana patches werecompared with additional patches in the videography data forgeneral agreement in size and shape. Edges in the brightnessand greenness bands were also mapped using a Sobel filter(Kanopoulos et al., 1988) and compared visually with patchboundaries from the thresholding and segmentation results.Patch delineation using the same thresholds and segmentationwas also applied to the normalized 1986 image.

Two approaches were used to examine changes in liana patchsize. First, for those patches that were persistent from 1986 to2000 and overlapped on both dates, the area of patches wascompared. Patch areas were log-transformed to approximatenormality and compared using paired T-tests. Second, a 45-mbuffer was delineated from the edges of the liana patch polygonsin the 2000 imagery to characterize reflectance characteristicsalong the edges of the liana patches. We hypothesized that ifthe liana patches were expanding outward, then reflectancewithin buffer areas should also change over the 14 years of theanalysis.

We also compared spectral changes within the liana patchesto changes throughout the adjacent forests at large. Spectralcharacteristics of the forest matrix were derived from a randomsample of forest patches centered further than 240 m from lianapatch boundaries. A buffer length of 240 m was selected be-cause it represented the radius of the largest liana patch mea-sured in 2000. This ensured that randomly selected forestpatches would be non-overlapping, and would avoid existinglow liana patches. The number of center points generated ap-proximated the number of liana patches delineated in 2000.Each center point was then buffered by a randomly assignedradius to produce a sample of circular forest patches with thesame patch size distribution as the liana patches from 2000.

2.3.2. Change vector analysisChange vector analysis (CVA) uses the trigonometric re-

lationships between two sets of vectors to identify the over-all magnitude and direction of change between images from twodifferent dates (Lambin and Strahler, 1994). Using the tasseled-cap bands of brightness, greenness and wetness (collectivelyBGW), differences between each band and each date can becomputed to determine the overall amount of change in allbands (BGW) as well as the direction of change (e.g., increas-ing B, decreasing G, decreasing Wetc.). Typically, brightness isassociated with soil or bare ground, and increasing B indicatesdegradation of vegetation. Note that if lianas are considerab-ly brighter spectrally than dense forest vegetation, increasingbrightness can indicate increasing abundance of lianas in vege-tated areas. Greenness is directly related to chlorophyll abun-dance and photosynthetic activity and as such changes in Gcan be interpreted rather readily. Wetness is a good indicator of


thick, healthy vegetation correlated with canopy and soil mois-ture. Decreases in wetness are generally considered indicatorsof declining vegetation density or vigor, or senescence. How-ever, because the presence of water (especially in floodedforests) also adds wetness to a scene, increases in Wmay also berelated to actual increased wetness. As such, changes in thewetness index must be examined carefully in places whereforests are periodically flooded. We avoided most of this con-fusion by focusing on dry season imagery over upland forests.The chief outputs of CVA include the total magnitude of changefor each pixel, which is computed using the Pythagorean

Fig. 3. Mean reflectance (a), 1st derivative reflectance (b), and minimum noise fractioforest patches (black). Green reflectance is only slightly lower for random forest pa

Theorem as the total change in all bands, regardless of directionof change. Individual changes in each BGW band representdirection cosines and are used to calculate the angular directionof change. More details on the computation and application ofCVA are found in Townsend et al. (2004).

Change vector variables produced by the analysis wereanalyzed for differences among the patch type groups. Resultantvectors, mean resultant vectors, and mean directions of changewere calculated for each contiguous low liana patch, liana patchbuffer, and random forest patch following Townsend et al.(2004) (see also Allen and Kupfer, 2000; Fisher et al., 1987).

n (c) from EO-1 Hyperion image pixels for low liana patches (grey) and randomtches but NIR reflectance is clearly lower than within low liana patches.

Fig. 4. Minimum noise fraction (MNF) band 4 of EO-1 Hyperion image overstudy area. Strong contrast is evident between low liana patches (black) andtaller surrounding forests (grey to white).


This created distributions of mean change vector statisticsfor each patch of each patch type group that were treated asindependent random variables for further comparison amonggroups. In addition, total change vector results were calculatedusing the same vector formulae for each patch type as a whole.Mean resultant vectors vary from 0 to 1 and describe the con-centration of change direction among all the change vectors(for each pixel) within a patch or group. In such an analysis, 0represents complete dispersion, i.e., within a patch or group allof the pixels change in many different ways. A mean resultantvector equal to 1 represents complete concentration of changedirection, meaning that all of the pixels change in the same way.Mean resultant vectors are more appropriate when comparinggroups with differing numbers of pixels, as sample size canaffect resultant vector length. The distributions of mean changevector variables summarized for each patch and buffer typewere compared statistically using both parametric and non-parametric tests when necessary, controlling for an experiment-wide error rate of α=0.05. When distributions of the changevector variables for all the patches were approximately normal,data were compared using ANOVA F-tests and Tukey's test fordifference in means. When the distributions could not meet theassumption of normality, the change vector patch level datawere rank ordered and Tukey's test was used to compare rank-ordered data.

Overall, we tested the following hypotheses:

(1) Reflectance of liana patches changed between 1986 and2000. Changes toward “brighter” and “greener” indicateincreasing dominance by lianas, whereas decreasingbrightness and greenness indicate succession to taller,overstory species. No changes in reflectance suggest thatliana patches were a stable vegetation cover type over the14 years of analysis.

(2) Change vector results within liana patches, buffers aroundliana patches, and random forest patches differedsignificantly from each other, with liana patches changingmore than the forest at large.

(3) Patch areas of persistent patches differed over the timeperiod. Increasing patch areas indicated liana expansion;decreasing patch areas indicated encroachment orregrowth of overstory tree species.

3. Results

3.1. Low liana patch characteristics

Examination of high-resolution videography and Landsatimagery indicated that liana patches generally have much higherreflectance in the green-VIS and NIR than tall overstory forests(Fig. 2, a and c). In the Landsat-derived tasseled-cap bands,liana patches are characterized by much higher image bright-ness and greenness than the surrounding high-canopy forests,resulting in bright yellow patches of lianas surrounded by bluer(wetter) canopy forests in an RGB display of the BGW tasseled-cap bands (Fig. 2, b and d). This is consistent with other studiesthat investigated liana reflectance using leaf-level spectro-

radiometry and found that lianas had higher reflectance in thegreen visible range of the spectrum than canopy tree species(Avalos et al., 1999; Castro-Esau et al., 2004).

Liana patch hyperspectral reflectance was characterized byaveraging spectra from 40 pixels for each of low liana andrandom forest patches that overlapped with the Hyperionimagery (Fig. 3). Mean reflectance spectra showed that lowliana patches were slightly brighter in the green portion of the

Table 1Average change vector results by patch (θ̂—wetness–greenness, φ̂— brightness–greenness)

Patch type Averagepatch meandirection

Average patchresultantvector

Average meanresultantvector

Average patchtotal changemagnitude

θ̂ φ̂ R R̄PMag

Low lianapatches

112.42 55.21 7.23 0.54 455.18

Liana patchbuffers

125.74 89.58 13.44 0.24 359.36

Randomforestpatches

99.24 133.90 4.32 0.36 273.54

Tukey's test and paired T-tests, all comparisons different at α=0.05.


visible spectrum and considerably brighter in the NIR andSWIR portions of the spectrum (Fig. 3a). These spectra showthat low-stature liana forest is generally brighter and greenerthan taller, gallery forests typical of the same humid areas.Differences in 1st derivative spectra (Fig. 3b) are less apparent,though there are differences between 0.50–0.55 μm and 0.70–0.72 μm. The plot of mean MNF band values shows that MNFband 4 displayed the greatest discrimination between low lianaforests and tall, tree-dominated forests in MNF image space(Fig. 3c). A subset of the low liana patches are clearly visible asdark areas in MNF band 4 where the Hyperion image intersectswith the study area (Fig. 4).

A total of 885 patches of low-stature liana forest weredelineated in the Landsat image from 2000 using thresholdingand image segmentation (Fig. 5). Low liana patches are clus-tered primarily near 1st and 2nd order streams. Mean liana patchsize in 2000 was 1.33 ha (+/−0.122 ha, 95% CI) and the largestpatch delineated was 24.3 ha in size (no patches b0.45 ha areaddressed in the change analysis). 34% of the liana patchesmapped were larger than 1 ha in area and 3.7% or 32 lianapatches were larger than 5 ha in size. The distribution of patchsizes was strongly right-skewed, following a negative expo-nential shape. The total area covered by low liana patches was1152 ha or about 1.5% of the study area. If we had set aminimum patch size of one pixel or 0.09 ha, the area covered bylow liana forest would have nearly doubled to 2335 ha or 3%of the study area. Patch number and size distribution werestatistically identical for the generated sample of random forestpatches, as intended. The total area covered by liana patch

Fig. 5. Low liana patches delineated in 2000 are shown in black. Circular random

buffers was larger, however, covering 4574 ha. Buffer area waslarger because the majority of the liana patches were small witha high edge to area ratio. The smallest patches (five pixels)tended to have buffers that were four times as large, necessi-tating special care in the analyses of statistics that are sensitiveto sample size.

3.2. Changes in reflectance (CVA): liana patches, buffers, randompatches

Change vector analysis (CVA) results were calculated foreach pixel, then combined using vector math for each patch, andthen averaged for each of the three groups (low liana patches,

forest patches are visible in light grey. Dark lines represent stream systems.

Fig. 6. Polar plots of mean resultant vector length for individual patches using θ̂(change in wetness). Liana patches (a) and buffers (b) generally show decreasingwetness, while random forest patches (c) show a wider scatter with many patchesincreasing in wetness as well.


liana patch buffers, and random forest patches) (Table 1). Allcomparisons among patch type between 1986 and 2000 for eachchange vector variable were significant at the experiment-wideerror rate of α=0.05. The total change magnitude, regardless ofdirection, was calculated for each pixel and averaged by patch.Group averages are shown in the table. Average total changemagnitude was highest for low liana patches and lowest forrandom forest patches with liana patch buffers falling in be-tween. Average resultant vectors (R) and mean resultant vectors(R̄) showed a slightly different pattern. For R̄, liana patchesshowed the most concentration in change direction with a valueof 0.54. Average R̄ for buffers and random patches was smallerand closer to zero, indicating that the direction of change forpixels within these patch types was more dispersed than lianapatches.

Fig. 7. φ̂ vs. mean resultant vector lengths for individual patches and three patchtypes: liana patches (a), liana patch buffers (b), random forest patches (c). Themajority of liana patches are increasing in both brightness and greenness. Anglesof change in brightness and greenness are more scattered for random patchesthan liana patches or their buffers, with many more patches showing concen-trated change in the direction of decreasing brightness and greenness.


Mean direction for each patch represents the direction inthree dimensional change space of the resultant vector and isdefined by the angles, colatitude (θ̂) and longitude (φ̂). Meandirections help explain how the patches are changing inbrightness–greenness (φ̂) and wetness–greenness (θ̂) imagespace. First, θ̂ shows the direction of change in the wetness–greenness plane and primarily describes changes in wetnessranging from 0 to 180°. Polar plots of mean resultant vectorlength vs. θ̂ for each patch show the distribution of changedirections, with resultant vector lengths closer to one (close toouter edge of the circle) indicating patches with more concen-trated directions of change and points closer to zero representingpatches with dispersed directions of change (Fig. 6). Angularposition on the polar plot indicates the overall type of change,e.g. toward decreased wetness and increased greenness for lianapatches (Fig. 6a). Liana patch buffers (Fig. 6b) have also de-creased in W and increased in G, yet the density of points isclearly clustered at smaller values of R̄, indicating much higherdispersion in change direction within individual buffers thanwithin liana patches. In contrast, the plot of θ̂ for randompatches shows a larger spread with both increasing and decreas-ing wetness. Again, R̄ values are clustered closer to zero forrandom patches, indicating dispersed directions of change.Average θ̂ was statistically different for these three groups ofpatches and is summarized in Table 1. Average θ̂ for randomforest patches is closest to 90°, which would represent thesmallest angular decrease in wetness.

Fig. 8. Spherical plot of mean resultant vector length for individual patches in brightbuffers in blue (+), random forest patches in green (x).

φ̂ records the mean direction of change in the brightness–greenness plane and ranges from 0 to 360° (Fig. 7). For lowliana patches (Fig. 7a), most of the patches fall near the 45-degree line in the sector indicating positive changes in bothbrightness and greenness. Fig. 7b shows the circular distributionof φ̂ for liana patch buffers which has a large number of patchesincreasing in brightness and greenness, but also has a largenumber decreasing in brightness and/or both brightness andgreenness. φ̂ for random patches (Fig. 7c) is again the mostscattered with more changes in almost every sector.

Spherical plots were used to view mean resultant vectors inthree dimensional change space using θ̂ and φ̂ simultaneously(Fig. 8). In Fig. 8 bright colors indicate points closer to the pointof reference, which is the increasing brightness axis, and fadedcolors signify points that are further away. This plot highlightsthe overall patterns of change among patch types. Liana patchesare clumped with higher values of R̄ in the direction of in-creasing brightness and greenness and decreasing wetness.Liana patch buffers are clumped in a similar direction, but aremore dispersed, meaning that they are centered at lower valuesof R̄, near the center of the sphere. Random forest patches aremore scattered throughout the sphere, with a large number de-creasing in brightness and greenness and increasing in wetness,the opposite of most liana patches.

Table 2 summarizes the change vector analysis results cal-culated by group. The results differ from Table 1 in a number ofways. When all the change vectors for every pixel in the low

ness, greenness, and wetness change space. Liana patches are shown in red (o),

Table 3Average change vector results for new and pre-existing patches

Patchpersistence

Averagepatch meandirection

Averagepatch resultantvector

Averagemean resultantvector

Average patchtotal changemagnitude

θ̂ φ̂ R R̄PMag

Existing 115.23 59.81 12.82 0.47 333.42New 108.31 45.59 5.02 0.65 465.03

ANOVA F-tests significant for all comparisons at α=0.0001.


liana forest are combined to create one long resultant vector, themean direction θ̂ becomes smaller and almost identical to themean direction θ̂ for random forest patches. Yet all three patchtypes still differ in mean direction φ̂. Considering resultantvector lengths for all three groups, low liana forest has a largerresultant vector length than random forests, and liana patchbuffers have the largest resultant vector length. This indicatesthat low liana forests are changing more as a group than randomforest patches and that liana patch buffers are changing themost. However, when considering R̄, which reduces the effect ofthe larger area in liana patch buffers, it is apparent that low lianaforests have the most concentrated change, followed by lianapatch buffers and random forest patches. These results for R̄differ from the comparison of average values in Table 1. Mostnotably, random forest patches have a much smaller value of R̄when calculated for the group. This is because the summation ofvectors of equal length that point in opposite directions pro-duces R̄ values of zero, indicating that the changes are com-pletely dispersed (e.g., Fig. 7c). In this case, random forestpatches tend to change in a more concentrated direction thanliana patch buffers, yet when they are combined in three di-mensions, random forest patches with opposite directions ofchange cancel each other out. For this reason it is valuable to usemultiple approaches to evaluate the change vector analysisresults.

3.3. Changes in patch area: persistent patches

672 of the 885 liana patches delineated in 2000 (76%) werepersistent over the 14 years and overlapped with liana patchesdelineated using the same thresholds in the 1986 Landsat image.Average area for low liana patches in 1986 was 1.64 ha and theaverage area for the same set of patches in 2000 was 2.61 ha.Mean patch area for persistent patches increased from 1986to 2000 by an average of 0.970 ha +/−0.33 ha (T=−8.46,Pb0.0001). In addition, one obvious difference between new andpersistent patches is that the new ones are much smaller in size,averaging 0.70 ha compared to 2.66 ha for persistent patches.

Table 3 summarizes change vector results in patches thatwere persistent from 1986 to 2000 and those patches that werenot evident in 1986 but were detected in 2000. In mean changedirection, persistent patches became drier (θ̂) over the 14-yeartime period compared to new patches and new patches in-creased more in brightness than pre-existing ones (φ̂). Meanresultant vectors were higher at 0.65 for new patches than forpersistent ones (0.47), though this may be related to the sig-nificant difference in area between the two groups. Averagetotal change magnitude was higher for new low liana patches

Table 2Change vector results by group (θ̂— wetness, φ̂— brightness–greenness)

Patch type group Mean directions Resultant vector Mean resultant vector

θ̂ φ̂ R R̄

Low liana forest 106.07 49.16 8345.92 0.65Liana patch buffers 113.54 71.74 15458.39 0.30Random forest 107.66 117.67 2229.27 0.17

than for existing ones. If we compare these results to Table 1, wesee that the average total change magnitude for persistent lianapatches is actually lower than that measured for liana patchbuffers.

4. Discussion and summary

Low-stature liana forests are a significant component of theevergreen liana forests of Noel Kempff Mercado National Park.Low liana patches greater than 0.45 ha covered 1.5% of thestudy area, but because gap size distribution in forests oftenfollows a reverse exponential form (Dahir and Lorimer, 1996),the actual area covered by low-stature lianas is likely largerwhen smaller patches are considered.

The large liana patches observed in our study area are con-siderably larger than forest gaps described in other studies ofgap dynamics in tropical forests. This may simply be a functionof gap definition, which often varies depending on researchscale and questions being addressed. What is notable about ourresults when compared to other remote sensing studies of tro-pical gap dynamics is the relatively small size of gaps measuredby other studies (typically created by logging) and the relative-ly quick closure of those gaps. For example, Broadbent et al.(2006) used Aster imagery to map “reduced-impact” logging inthe Bolivian Amazon and found that gaps larger than 0.04 hawere detectable for up to 6 months following logging, whilesmaller gaps were only detectable for three months. This is avery short temporal window compared to our observation ofliana-dominated gaps that persist for over 14 years. Asner et al.(2004) measured individual tree gaps associated with both con-ventional and reduced-impact logging in the Brazilian Amazonover 3 1/2 years and concluded that gap sizes for conventionallogging decreased from 3.14 ha to 0.50 ha over the study period,while reduced-impact logging gaps decreased from 0.79 ha to0.03 ha. Again, our research shows that liana gaps, regardless oftheir origin, can be much more persistent than this in both timeand space. The literature is inconclusive on whether lianasexhibit dominance in small, ephemeral gaps created by logging,but it is clear from our work that larger gaps provide goodconditions for liana persistence. Another similar example can befound in a field based study in the Brazilian Amazon looking atlogging impacts, where Gerwing and Uhl (2002) found thatlianas were more common in multiple tree gaps (larger gaps)than in single tree gaps (smaller gaps). So, although low-statureliana gaps smaller than 0.45 ha certainly exist in our study areaand add to the estimate of the area covered by lianas, thesepatches are more likely to be ephemeral and less likely to be


detectable using imagery through time. Although the largestlow liana patches in our study are considerably larger than tra-ditionally measured forest canopy gaps, the mean size of allliana patches (1.33 ha) and persistent liana patches (2.61 ha) in2000 falls within the range of forest gap sizes reported by bothAsner et al. (2004) and Babaasa et al. (2004).

Low liana forests were consistently brighter and greener thantall tree-dominated forests in the CIR-AV, EO-1 Hyperionimagery and Landsat TM and ETM+ data. Average spectra fromlow liana patches at NKMNP indicate that low liana canopieshave higher reflectance than tree-dominated forest canopies inthe green visible portion of the spectrum as well as in the NIR(Fig. 3a). Castro-Esau et al. (2004) also found that liana leaveshad higher green reflectance than tree leaves when measured atthe leaf level, but observed lower reflectance in the NIR, whichour data does not show. This difference from our results is likelya consequence of the difference in scale between our canopy-level reflectance (900 m2 Landsat pixels) and the leaf-levelreflectance measured by Castro-Esau et al. (2004). Specifical-ly, a leaf-level study avoids the influence of differing canopystructures on spectral reflectance, which is significant whencomparing low liana forests to taller, tree-dominated forests.Avalos et al. (1999) found that liana leaves had significantlylower transmittance than tree leaves, and explained this dif-ference in part by the tendency of liana architecture to form amonolayer, which removes any benefit that canopy trees gainfrom transmitting light to lower layers of leaves. A monolayerof nearly constant height, such as those observed in the video-graphy data (Fig. 2), also creates fewer shadows compared tothe more varied canopy heights in the high forest. This mono-layer structure and relative absence of dark shadows explainsthe observed higher than expected NIR reflectance for low lianapatches relative to tall mature forests.

Low liana forest patches at NKMNP have changed in reflec-tance characteristics and spatial extent over 14 years between1986 and 2000. They have generally increased in brightness andgreenness and decreased in wetness, none of which supports thehypothesis that they are all succeeding to taller, canopy treespecies. Low liana patches have also increased in size by closeto 1 ha per patch on average. This does not support a hypothesisthat non-pioneer tree species are encroaching from the sur-rounding forest matrix into patches. The forests surroundinglow liana patches represented by liana patch buffers are alsochanging in reflectance, generally becoming brighter, greenerand drier as well, but with greater variability and less directionalconcentration than the actual liana patches. The surroundinghigh forest has changed the least, and changes that have oc-curred have been in all change vector directions, includingdirections opposite of the liana patches and their surroundingbuffer areas. The differences observed here between low lianaand random high forest in both reflectance and rates of changemay actually be overly conservative, because lianas are likelyalso present in variable amounts in the high-canopy forests thatare used for comparison. Thus pixels within random forestpatches with increasing brightness and greenness may be relatedto liana dominance in these areas as well, since this forest isknown in general for liana abundance (Killeen, 1998). We have

avoided much of the confusion encountered by other studiestrying to separate liana and tree reflectance (Castro-Esau et al.,2004; Sanchez-Azofeifa and Castro-Esau 2006), some of whichstemmed from differences in phenology, multiple liana species,and mixed canopies, by focusing only on relatively large gapsalmost exclusively covered by spreading liana species. Thisgroundwork should be useful for future advances towardsmapping of liana dominance in high forests as well.

Over the 1986 to 2000 period, a large burned area (data notshown), that shared the brightness and greenness characteristicsof low liana patches in 1986, became less bright, less green, andmuch wetter as it appeared to succeed to a taller forest structureand became nearly indistinguishable in reflectance from sur-rounding forests. This suggests that some disturbed forest areasin NKMNP are following traditional successional trajectoriesthat are detectable in reflectance signatures over the 14-yeartime period. This contrasts with the change vector results forlow liana forests, which shows them persisting and even ex-panding over the same amount of time.

A key question unanswered by the analyses presented here isthe origin of the observed low liana patches at NKMNP andwhether human disturbance was a factor in their creation orpersistence. Parts of the forest were subject to selective loggingand rubber extraction for many years prior to park establish-ment. The proximity of most of the low liana patches to loworder waterways, which could provide human access to uplandforests for resource extraction, suggests that persistent lianapatches may have resulted from human activity. The prevalenceof desired timber species such as mahogany (Swieteniamacrophylla) adjacent to waterways and seepage areas (Groganet al., 2003), may also support speculation that selective loggingplayed a role. In contrast, liana patch distribution and persis-tence may also result from soil and geomorphic or topographicprocesses in the park. Low liana forests may occur preferentially(and possibly naturally) on particularly well-drained or nutrientpoor sites, where they can be superior competitors. None ofthese explanations accounts for the observed increases in lianapatch size over such a short time period, though they are plau-sible hypotheses that merit further exploration.

Change vector analysis (CVA) proved to be an effective toolfor analyzing changes in liana patch reflectance over time. Inparticular, one advantage of CVA with tasseled-cap data is thatcontinuous levels of change in three dimensions can be ob-served simultaneously (Fig. 8). Although changes in indi-vidual bands are straightforward to interpret, vectors in threedimensions are conceptually useful for summarizing the con-centration of change and summarizing the variability in dif-ferent change directions. Interpretation of changes in tasseled-cap image space was straightforward and relevant to thesuccessional processes being explored. The methods used hererepresent a cost-effective approach that may be appropriate andapplicable to similar forests around the rim of the Amazonbasin. Continued, widespread monitoring of low-stature lianaforests will improve our understanding of tropical forest dyna-mics and help us understand where and under what conditionslianas can establish and interfere with succession for long pe-riods of time.


Ultimately, the change analyses presented here provide fur-ther evidence that low liana patches are a common feature intropical forests and can represent a stalled successional state thatcurrently appears to be increasing in liana dominance and ex-panding in area, contrary to expectations. These results supporta general hypothesis that transitional forests lying alongunstable climatic boundaries may be particularly sensitive tohuman disturbance, changing successional rates, and enhancedliana proliferation. If slowly rising temperatures, CO2 levels, orlengthening seasonal droughts are contributing to thesechanges, low liana forests may become more persistent andmore common in these forests than they have been in the recentpast.

Acknowledgements

This research was funded in part by NASA EO-1 ScienceValidation Team grant NCC5-493 and The Nature Conservancy.This research was conducted in part while J. R. Foster and P. A.Townsend were at the University of Maryland Center forEnvironmental Science — Appalachian Laboratory. Criticallogistical support was provided by Gonzolo Peña, former ParkDirector of NKMNP, Servicio Nacional de Areas Protegidas(SERNAP), Fundacion Amigos de la Naturaleza (FAN), andWinrock International. Earl Saxon, Lupita Sanchez Torke, andBen Torke participated in field work and pilot project planningand activities. Important base data, advice and guidance wereprovided by Tim Killeen and the Museo de Historia NaturalNoel Kempff Mercado.

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