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Applied Vegetation Science 8: 49-56, 2005© IAVS; Opulus Press Uppsala.

AbstractQuestion: How do meteorological variations at seasonal,interannual scales differentially affect the canopy dynamics offour contrasting landscape units within a region?Location: Flooding Pampa, Buenos Aires, Argentina. 5000km2. Central point: 35°15' S, 57°45' W.Methods: We used a 19-year series of the normalized differ-ence vegetation index (NDVI) derived from NOAA-AVHRRPAL (Pathfinder AVHRR Land) images and meteorologicaldata provided by a nearby weather station. The NDVI wasused as surrogate of canopy photosynthetic status. The rela-tionship between annually integrated NDVI and meteorologi-cal conditions was explored by stepwise multiple regressionsfor each defined unit. PCA was performed to compare unitsand growing seasons on a multivariate basis.Results: Mean seasonal NDVI curve was similarly shapedamong landscapes. However, the absolute values differedwidely. There was high interannual variation so that the meanseasonal pattern was seldom observed in any particular year.Annually integrated NDVI of all landscapes was negativelyassociated with summer temperature and positively with pre-vious year precipitation. It was also directly related withcurrent year winter precipitation in two landscapes and withsummer precipitation in the others. NDVI response to Septem-ber and March precipitation accounted for some of the differ-ences in interannual variation among landscapes.Conclusions: Our results revealed a strong intra-regionalvariation of canopy dynamics, closely linked to landscape(vegetation-soil) and water availability (mainly in summerand during the previous year). These links may be used topredict forage production rates for livestock.

Keywords: Forage production; Interannual variation; NOAA-AVHRR; Primary production; Remote sensing; Seasonal vari-ation; Summer water balance.

Abbreviations: ANPP = Above-ground net primary produc-tion; AVHRR = Advanced Very High Resolution Radiometer;ENSO = El Niño southern oscillation; fAPAR = Photosyn-thetically active radiation, fraction absorbed by canopy; LAI =Leaf area index; NDVI: Normalized difference vegetationindex; Integral; PAL = Pathfinder AVHRR land; PP = Precipi-tation; T = Mean temperature.

Landscape, soil and meteorological influences on canopy dynamicsof northern flooding Pampa grasslands, Argentina

Posse, Gabriela1*; Oesterheld, Martín2 & Di Bella, Carlos Marcelo1,3

1Instituto de Clima y Agua, CIRN, Instituto Nacional de Tecnología Agropecuaria (INTA) Las Cabañas y Los Reseros s/n, 1712Castelar, Prov. Buenos Aires, Argentina; 2IFEVA, Departamento de Ecología, Facultad de Agronomía, Universidad de BuenosAires, Av. San Martín 4453, 1417 Buenos Aires, Argentina; E-mail oesterhe@ifeva.edu.ar; 3E-mail cdibella@cnia.inta.gov.ar;

*Corresponding author: Fax: +54 1146215663; E-mail gposse@cnia.inta.gov.ar; http://www.intacya.org/staff/posse.htm

Introduction

Grassland above-ground net primary production(ANPP), a key process that determines the entry ofcarbon and energy to ecosystems and their availabilityfor herbivores (McNaughton et al. 1989), varies atdifferent spatial scales. At a regional scale ANPP isprimarily correlated with mean annual precipitation(Lauenroth 1979; McNaughton 1985; Sala et al. 1988)and secondarily with temperature, seasonal distribu-tion of precipitation, soil fertility, amount of distur-bance, successional status and soil water capacity(Lauenroth 1979; Sala et al. 1988; Nicholson & Farrar1994). At a local scale, within landscapes, ANPP alsovaries according to water availability, which in thiscase is not determined by rainfall variations, but bytopographic features and soil properties, sometimesregulated by disturbances (Knapp et al. 1998;Oesterheld et al. 1999). Intermediate scales of varia-tion in ANPP, between regional and local scales, havebeen less examined (Holm et al. 2003). Thesesubregional variations usually share a common cli-matic regime and differ in landscape features. Theyencompass several landscapes with different types,proportions and spatial arrangements of vegetation,topographic features and soils.

Grassland ANPP also varies temporally at differentscales. Whole year, interannual variability has been asubject for research for many years. Here again, rainfallvariations are frequently responsible for ANPP varia-tions (e.g. Lauenroth & Sala 1992; Oesterheld et al.2001). Understanding within year, seasonal variationsof ANPP are at the core of many issues in ecosystemecology and animal production based on natural grass-lands (e.g. Vallentine 2001). Additionally, seasonalitymay also vary interannually, since the seasonal produc-tion curve may be subjected to year to year variations. Ingeneral, whole year interannual variations of ANPP havereceived much more attention than seasonal variationsand more than the interaction between seasonal andinterannual. This gradient of understanding probably

50 POSSE, G. ET AL.

stems from the difficulty of measuring plant biomassand the subsequent problems in estimating ANPP basedon extensive data (Sala & Austin 2000; Scurlock et al.2002).

Fortunately, the proportion of PAR (Photosynthe-tically active radiation) absorbed by the canopy, a com-ponent of ANPP, may be readily estimated by remotesensing images to describe both seasonal and interannualvariations (Gamon et al. 1995; Running et al. 2000).Canopy cover, expressed by LAI (Leaf area index), isone of the major components of spatial and temporalvariations of both gross and net primary production(Chapin et al. 2002). The Normalized difference vegeta-tion index (NDVI), a parameter derived from red andnear-infrared reflectance, is a good indicator of bothLAI and the fraction of photosynthetic active radiationabsorbed by the canopy (fAPAR) (Gamon et al. 1995;Running et al. 2000). Both parameters (LAI and fAPAR)directly affect ANPP. NDVI is directly related withfAPAR and ANPP is directly related with the fAPAR(ANPP = ε * ∫ fAPAR * PAR). The relationship betweenLAI and NDVI seems to be saturated at high LAI values(Bouman 1992). In the grasslands of our studied area thecharacteristic values of LAI were not greater than 1.2(Sala et al. 1986). Thus, descriptions of subregionalpatterns of canopy dynamics and their interannual andseasonal dynamics are now becoming feasible usingsatellite information and utilising information from pre-vious field work (Jobbágy et al. 2002; Holm et al. 2003;Posse & Cingolani 2004).

The flooding Pampa, in the Province of BuenosAires, Argentina, is the most extensive (90 000 km2)portion of Pampa grasslands (Soriano 1991), and maybe particularly useful for the study of spatial and tempo-

ral patterns of canopy dynamics. Devoted principally tocattle production, with low cultivation development,these grasslands exhibit a remarkable spatial heteroge-neity in terms of community composition and canopystructure (Burkart et al. 1990; Perelman et al. 2001).Plant communities are spatially distributed in such a finemosaic that 50% of the entire regional flora may berepresented within a small area, ca. 10 km2 (Perelman etal. 2001). There is, however, strong subregional levelheterogeneity because of the diverse geomorphology thatdetermines strong contrasts of spatial arrangement ofsoils and vegetation (Burkart et al. 1990). Besides a fewlocalized short-term studies of primary production (re-viewed by McNaughton et al. 1993, see also Paruelo etal. 2000), the interannual and seasonal patterns of canopydynamics and ANPP remain largely unknown. In con-trast to field studies, which are usually restricted toparticular areas and moments, information obtainedthrough remote sensing provides a much higher spatialand temporal coverage.

The objectives of this paper are (1) to describe thecanopy dynamics of the northern portion of the floodingPampa, where an interesting combination of landscapesand soil types provides the framework for studying therelationship between canopy dynamics and structuralfeatures of vegetation, geomorphology and soils and (2)to associate the dynamics with interannual and seasonalmeteorological variations.

Material and Methods

Landscapes, vegetation and soils

The flooding Pampa (Fig. 1) has a subhumid climatewith mean annual rainfall ca. 1000 mm (1971-1991).Mean monthly temperatures range from 6.8 °C in July(winter) to 21.8 °C in January (summer) (Chaneton etal. 1996). Our study area comprised ca. 5000 km2

located between Río Samborombón and Río de LaPlata (Fig. 1).

There are five main landscape units in this subregion(León et al. 1979). Three are continental and are exten-sive enough for a coarse resolution approach as utilizedin the study: rolling, plain and fluvial. The rolling land-scape is composed of a hilly terrain dissected by smallstreams that mostly drain to Río de La Plata. The plainlandscape has been partially affected by marine actionduring its formation, it is flatter and has a relative lowertopographic position than the rolling landscape. Thefluvial landscape unit corresponds to wide flood plainsrelated to Río Samborombón. The other two landscapeunits are coastal (narrow bands parallel to Río de laPlata) and account for a small portion of the region, their

Fig. 1. Study area with the four landscape-soil-vegetationunits: Roll N (northern section of the rolling landscape), Roll S(southern section of the rolling landscape), Plain and Fluvial.

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shape is unsuitable for a coarse resolution study.Each landscape has characteristic soils and vegeta-

tion (Sanchez et al. 1976, Anon. 1991). The rollinglandscape includes two types with different dominantsoils: a northern area (Roll N) and a southern area (RollS). Roll N is dominated by typical argiudolls. Roll S isdominated by vertic argiudolls. Both Roll N and Roll Shave similar vegetation composition (León et al. 1979).They are mostly covered by a single plant communitywith dense cover of tussock grasses, such as Stipacharruana. The plain landscape is dominated bynatralbolls, natraquolls and natraqualfs. Dominant plantcommunities have lower cover than those in the rollinglandscape and are characterized by halophytic species,such as Distichlis spp. (León et al. 1979). Finally, thefluvial landscape is dominated by typic natraquolls anda fine complex of hydromorphic and sometimes natricsoils. There, the vegetation is also highly heterogene-ous, a mixture of very humid communities with speciessuch as Leersia hexandra, Alternanthera philoxeroidesand Ludwigia peploides (León et al. 1979).

Satellite data

We worked with a long-term series (1981-2000) ofNOAA-AVHRR (Advanced Very High ResolutionRadiometer) PAL images (Pathfinder AVHRR Landdata set) compiled by NASA (James & Kallury 1994).This data set originates from NOAA-7, -9, -11 and 14satellite images that were radiometrically and spatiallycorrected (Rao & Chen 1995). Their spatial resolution is8 km × 8 km. To minimize cloud, atmospheric interfer-ence and other degrading effects, we produced monthlymaximum NDVI composite images from the original10-day data (Holben 1986). We analysed these datafrom July 1981 to March 2000. Data from May 1994 toJune 1995 were not available due to satellite receptionproblems. Each study unit was represented by two pixelslocated ca. one pixel from the unit boundaries to mini-mize the chance of overlapping due to co-registrationand geometric correction problems.

Meteorological data

We obtained meteorological data from the ‘La Plata’meteorological station, the nearest station with a com-plete data set. It is situated ca. 12 km from the northernlimit of the study area (34°54' S, 57°57' W, Fig. 1). Wecollected mean monthly temperature and total monthlyprecipitation from January 1982 to March 2000, andcalculated various synthetic descriptors, such as meanannual and mean seasonal rainfall and temperature.

Data analysis

The growing season was considered to begin in July(winter) and end in June of the next calendar year. Tocharacterize the mean canopy dynamics of each studyunit, we calculated mean values for all growing seasons.We analysed the integral of the NDVI seasonal curve(NDVI-I) and its variation across years and units. Therelationship between NDVI-I and meteorological con-ditions was explored by stepwise multiple regressionsfor each defined unit. Candidate variables were: (1)mean annual temperature of previous year; (2) annualrainfall of previous year; and (3) seasonal precipitationand temperature of the current year (autumn: April-June, winter: July-September, spring: October-Decem-ber and summer: January-March).

Principal component analysis (PCA) was performedwith variance-covariance matrix and SPSS 7.5 softwareto compare units and growing seasons on a multivariatebasis, using a matrix of four cases (the units) by 18variables (NDVI-I data of each unit/growing season).PCA allowed the differentiation of the four units takingthe units as samples. In this way, we forced the units todifferentiate themselves (Afifi 1984). The analysis wasperformed to explore the association between landscapedifferences and meteorological variables. Since this wasa more detailed analysis than the multiple regressionjust described, we used monthly instead of seasonalvalues of precipitation and temperature. The associa-tions were evaluated using Spearman rank order corre-lations with the first two PCA axes.

Results

The seasonal pattern of NDVI for the four landscapeunits had both similarities and differences (Fig. 2). Themajor similarity was the general shape, with minimumvalues in winter, maximum in spring (November), and asmall peak in the fall, after a variable summer decrease(Fig. 2a). The major differences were that (1) the Fluvialand the Roll N landscapes had higher means than theother two landscapes, (2) the Fluvial landscape hadhigher NDVI than any other landscape from spring tofall and (3) the Roll N landscape had intermediatevalues during summer. However, this general patternintegrates a high interannual variation (as indicated bythe interannual standard deviations shown in Fig. 2 b-e).As a consequence, the mean seasonal pattern was sel-dom observed in any particular year (Fig. 3).

Integrating monthly NDVI values at the growingseason level also showed considerable interannual vari-ation (Fig. 4). The four landscape units tended to showsynchronous interannual variations at this level. For

52 POSSE, G. ET AL.

Fig. 2. a. Mean seasonal pattern of NDVI for thefour landscape units over 15 growing seasons.Error bars correspond to the spatial standard de-viations (across pixels of the same landscape).The four smaller graphs show the same meanswith their corresponding interannual standard de-viations (across years) over each landscape unit:b. Roll N; c. Roll S; d. Plain; e. Fluvial.

Fig. 3. Monthly NDVI values for the four land-scapes from July 1981 to May 2000. Data fromMay 1994 to June 1995 were not available due tosatellite reception problems. Dotted vertical linescorrespond to July of each year.

example, all units had relatively high NDVI-I values in1983, 1997 and 1998, and low values in 1988 and1995. However, some landscape units changed moreinterannually than others. Particularly, the Roll N land-scape was more stable across years (Fig. 4). As alreadyindicated in Fig. 2a, the fluvial landscape had higherNDVI-I values for most years, followed by the Roll Nlandscape.

Multiple regressions showed that NDVI-I values ofall landscapes were negatively associated with summertemperature and positively with previous year precipita-tion (Table 1). Additionally, NDVI-I was directly re-lated with current year winter precipitation in the Flu-vial and Plain landscapes and with summer precipitationin the Roll N and Roll S landscapes.

The first two axes of the PCA analysis of the tempo-ral variation of NDVI-I accounted for 96.4 % of the totalvariance (Fig. 5). Although we performed the PCA toforce the units to separate from each other, the four unitswere positioned very closely at the same extreme of thefirst axis. So we attributed the large variance accountedfor by Axis I (89.2%) to the temporal variation ofNDVI-I rather than with landscape unit characteristics.January mean temperature (Jan T) was negatively corre-

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lated with Axis I (r = – 0.52, p < 0.05), showing thenegative effect of this variable in all units. Contrarily,February precipitation (Feb PP) was correlated posi-tively with this axis (r = 0.55, p < 0.05), showing itsrelationship with all NDVI-I values. Landscape unitswere partially separated along Axis II, particularlyRoll N. September precipitation (Sep PP) was corre-lated with Axis 2 (r = –0.72, p < 0.01). Thus, thisvariable had some influence on the relative position ofRoll N compared to the other units. In years with lowprecipitation in September, Roll N landscape had ahigher rank position among the NDVI-I values. Oppo-site to this variable, March precipitation (Mar PP) pre-sented a positive relationship with Axis 2 (r = 0.54, p <0.05). This points out that the NDVI-I values of Roll Nwill be greater than the other NDVI-I values in yearswith higher precipitation values in this month.

Fig. 4. Annual NDVI-I values corresponding to the fourlandscape units. Each data point represents a growing seasonstarting in July of the year indicated as x-axis label. Thereare no data for the period 1992-1994 due to missing data.

Table 1. Multiple regression significant coefficients (P < 0.05) betweenNDVI-I values as dependent variable and meteorologicalindependent variables for each landscape unit. In all cases a stepwise analysis was performed. Meteorological variables were: AnnualPrecipitation previous year (PPyr–1), Seasonal Precipitation (PPseason) and Seasonal Temperature (Tseason).

Unit Model r2 adj. F (df) P

Roll N NDVI-I = 0.763 + 8.91E – 5 * PPsummer + 6.30E – 5 * PPyr–1 – 4.44E – 3 * Tsummer 0.76 12.5(3,8) 0.002

Roll S NDVI-I = 0.930 + 6.189E – 5 * PPsummer + 7.027E – 5 * PPyr–1– 7.18E – 3 * Tsummer 0.62 6.9(3,8) 0.013

Plain NDVI-I = 0.936 + 3.038E – 4 * PPwinter + 9.77E – 5 * PPyr–1 – 8.21E – 3 * Tsummer 0.80 15.9(3,8) 0.001

Fluvial NDVI-I = 0.931 + 3E – 4 * PPwinter + 8.51E – 5 * PPyr–1 – 7.52E – 3 * Tsummer 0.73 10.9(3,8) 0.003

Fig. 5. PCA ordination diagram (Axes 1 and 2) of NDVI-Ivalues over the15 growing seasons. Eigenvalues were 3.570and 0.285 for the first and second axes. A. Ordination of thefour unit samples. Arrows represent the correlation coeffi-cients values of the meteorological variables significantlycorrelated with either axis. B. Ordination of the 15 growingseasons.

54 POSSE, G. ET AL.

Discussion

The general seasonal pattern of NDVI describedhere is similar to the pattern of ANPP reported by thefew published estimates available for the region (Sala etal. 1981a; Oesterheld & León 1987). This similarityencourages the use of NDVI dynamics to evaluate theseasonal dynamics of growth (Justice et al. 1985). Gen-erally, there were two production peaks: the first (andmore important) in spring and the second in autumn. Insummer, water deficits are frequent (Sala et al. 1981b;Sierra & Montecinos 1990) and probably account for animportant decrease of mean NDVI (Fig. 2). Summerwater balance seems to be an important regulator ofcanopy dynamics, as evidenced by the strong interannualvariability (Fig. 4) and the relevance of summer meteor-ology explaining variations of NDVI-I (Table 1). Theseasonal pattern of canopy dynamics of these grasslands(Fig. 2) contrasts with typical temperate grasslands de-scribed in the northern hemisphere, which have a shortergrowing season (Goodin & Henebry 1997; Paruelo &Lauenroth 1998; Griffith et al. 2002).

Monthly and annual NDVI values showed that theFluvial landscape usually had the highest NDVI (Figs. 2and 4). The high water availability in this landscapeprobably accounts for this pattern, since the largestdifferences occurred during the summer. This landscapeunit collects and retains water from a large watershed. Asa consequence, it may suffer less intense water deficitduring summer, when temperature and solar radiation areoptimal for growth of this unit’s communities, largelydominated by flood tolerant tropical C4 grasses (León etal. 1979). Interestingly, the two versions of the rollinglandscape, which differ in soil characteristics but havesimilar species composition, showed different canopydynamics. Roll N had higher NDVI values than Roll S(Fig. 2). The same community was more productive ontypical argiudolls (Roll N) than on vertic argiudolls (RollS). Vertic argiudolls have a much finer texture thantypical argiudolls, which make them, among other things,more prone to experience water deficits.

Finally, the lower NDVI values observed in thePlain landscape unit agree with the strong limitations interms of soil salinity and alkalinity. In some cases, thecontrasts at the landscape scale suggested that canopydynamics were controlled by processes different fromthose acting at the local or regional scales: landscape unitsacting as water sinks, which allow them higher canopydevelopment and buffers their seasonal dynamics, orlandscape units with strong similarities in plant commu-nity structure having different canopy dynamics accord-ing to soil type. In other cases, landscape contrasts merelytranslated local variations, as in the strong impact of soilsalinity on canopy dynamics of the Plain landscape.

Linking NDVI-I interannual change with meteoro-logical data opens the possibility of understanding thecontrols of canopy dynamics and primary productionand to make predictions of their behaviour on the basisof meteorological variables. Although some of this vari-ation can be attributed to uncertainties in satellite navi-gation and differences among AVHRR sensors, we foundhigh correlation coefficients between NDVI-I data cal-culated directly from the images and data estimatedfrom the models developed for each unit (Table 1).Nevertheless, the differences among AVHRR sensorsthat compose the PAL series were expected to be mini-mized by the processes made to the series by NASA(James & Kallury 1984; Rao & Chen 1995). Some of theinterannual changes may be related to the El NiñoSouthern Oscillation (ENSO) mode. However, ENSOmode is implicit in the analysis through the inclusion ofseasonal precipitation and temperature values. Specifi-cally, we found two variables consistently associatedwith the dynamics of the four landscape units.1. Precipitation in the previous growing season had apositive influence on current year NDVI-I, and presum-ably on ANPP. This delayed influence has been de-tected for a semi-arid grassland (Oesterheld et al. 2001).2. Summer temperature had a negative influence onNDVI-I values (Table 1), which is probably related towater balance during summer (Sala et al. 1981b; Sierra& Montecinos 1990). In addition, two other variableshad some influence on particular landscapes. Summerprecipitation had a positive relationship with NDVI-I inthe Roll N and Roll S landscapes. Winter precipitationhad a positive influence on NDVI-I in the Plain andFluvial landscapes, with communities dominated bytropical grasses (León et al. 1979). In spite of similarinterannual dynamics of NDVI-I across units, somechanges in the rank order of landscape units were ob-served (Fig. 4). Specifically, Roll N was close to Fluvialin some years and close to the other two landscape unitsin other years. PCA showed that March and Septemberprecipitation had some influence on the relative changeof Roll N-NDVI values. This analysis also reinforcesthe idea of the negative influence of the summer tem-perature (specifically Jan T) and the positive influenceof summer precipitation (specifically Feb PP) over theNDVI-I values and so, over the annual growth.

Conclusions

Our research demonstrated the utility of coarse reso-lution NDVI for identifying patterns in seasonal andinterannual canopy characteristics of different landscapetypes. Some of these patterns were associated withlandscape level processes, such as water redistribution.

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Others seemed to be constrained by similar meteoro-logical variables in general terms, although each land-scape unit responded in a particular way to the climate.We detected some controls of primary productivity thatoperated on the study area: summer water deficit, waterbuffer effect of soil and soil texture differences.

This research has some implications on our under-standing of grassland production and our ability to prop-erly manage grazing systems. There has been signifi-cant progress in understanding inter-biome variation inthe patterns of annual productivity (Knapp & Smith2001). Our study highlights finer-grained controls ofinter-landscape variation within a biome, and detectedassociations between canopy dynamics and meteoro-logical variations occurring in a few months of the year(similar to findings of Jobbágy et al. (2002). Under thereasonable assumption that canopy dynamics reflects alarge proportion of the variation in primary production(Chapin et al. 2002), our results may help rangelandmanagers to make key decisions, such as setting stock-ing rates or assess risks, because they now count with a20-year landscape-specific record of grassland produc-tion, the major input to their agro-ecosystems. The useof NOAA PAL series allows the analysis of this longtime period to provide a historical perspective. Land-scape analysis may be sufficient to determine manage-ment, where livestock are managed over extensive pad-dock areas. Despite the low resolution of PAL data, theywere used with success to describe and characterizedifferent functional vegetation units (Gutman et al. 1998;Seaquist & Olsson 1999; Paruelo et al. 2001).

Acknowledgements. This research was supported by theInstituto Nacional de Tecnología Agropecuaria (INTA),FONCYT PICT 99-080671, University of Buenos Aires(Proyecto Estrategico 5988/01) and the Regional Fund forAgricultural Technology (IICA/BID FTG/RF 0103RG). Wegratefully acknowledge the suggestions of the editor and tworeviewers to improve the manuscript.

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Received 2 February 2004;Accepted 19 February 2005.

Co-ordinating Editor: P. Townsend.