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Urban Forestry & Urban Greening 14 (2015) 1059–1067

Contents lists available at ScienceDirect

Urban Forestry & Urban Greening

journa l h om epage: www.elsev ier .com/ locate /u fug

easonal variations in photosynthetic parameters and leaf area indexn an urban park

yungsuk Kimma, Youngryel Ryua,b,c,d,∗

Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South KoreaInterdisciplinary Program in Agricultural and Forest Meteorology, Seoul National University, Seoul, South KoreaInterdisciplinary Program in Landscape Architecture, Seoul National University, Seoul, South KoreaResearch Institute for Agriculture and Life Sciences, Seoul National University, Seoul, South Korea

r t i c l e i n f o

rticle history:eceived 8 October 2014eceived in revised form 3 October 2015ccepted 7 October 2015vailable online 13 October 2015

eywords:eaf area indexeaf nitrogenaximum rate of carboxylationaximum rate of electron transport

hotosynthesisrban park

a b s t r a c t

Parks account for a large proportion of green spaces in urban regions, but most previous studies havefocused on the values of recreational services in urban parks-carbon uptake by plants in urban parks hasbeen studied less extensively. Urban parks typically form complex landscapes in space and time by inte-grating multiple species with open canopies. Thus, to better understand canopy photosynthesis in urbanpark, measuring spatial and temporal variations in photosynthetic parameters and canopy structuralvariables is essential. Here, we report seasonal and spatial variations in two key photosynthetic parame-ters (Vcmax and Jmax which are the maximum rates of carboxylation and electron transport, respectively)and leaf area index (LAI) in Seoul Forest Park. During the peak growing season, we found an eightfolddifference (20 to 149 �mol m−2 s−1) and fourfold difference (38 to 141 �mol m−2 s−1) in Vcmax and Jmax,respectively, across 10 species. Over the seasons, two woody species (Zelkova serrata (Thunb.) Makino andPrunus yedoensis Matsum), respectively showed three- to fivefold differences in Vcmax and two- to fivefolddifferences in Jmax. We evaluated whether leaf nitrogen contents could predict Vcmax and Jmax, and foundsignificant correlations among the three variables during the peak growing season across 10 species,

but no significant correlations among them over the seasons in the two woody species. LAI computedusing in- situ observations and satellite remote-sensing imagery showed a high spatial heterogeneityduring the growing season. These results highlight the considerable spatial and temporal variability inphotosynthetic parameters and LAI, which implies that individual tree-based three-dimensional canopymodeling will be essential for accurate estimation of canopy photosynthesis in urban parks.

© 2015 Elsevier GmbH. All rights reserved.

ntroduction

The number of urban parks is expected to increase in manyrban regions due to rapid urbanization (Angel et al., 2011; Setond Fragkias, 2005; Seto et al., 2012) and citizens’ desire for a bet-er quality of life (Chiesura, 2004; Jim and Chen, 2006; Thompson,002). As urban parks account for a large proportion of green spaces

n urban regions, they might partially offset carbon dioxide (CO2)missions in urban regions which are responsible for more than 60%

f global CO2 emissions (Birol et al., 2008; IEA, 2013). Although aew studies have attempted to quantify carbon (C) stocks in veg-tation and soils in urban parks (Bae and Ryu, 2015; Hutyra et al.,

∗ Corresponding author at: Department of Landscape Architecture and Ruralystems Engineering, Seoul National University, Seoul 151-921, South Korea.el.: +82 2 880 4871; fax: +82 2 873 5113.

E-mail address: yryu@snu.ac.kr (Y. Ryu).

ttp://dx.doi.org/10.1016/j.ufug.2015.10.003618-8667/© 2015 Elsevier GmbH. All rights reserved.

2011), to our knowledge, none has quantified canopy photosyn-thesis, the main driver of the C cycle (Beer et al., 2010; Ryu et al.,2011).

To quantify canopy photosynthesis, an understandingof leaf-level photosynthesis is essential. The Farquhar–vonCaemmerer–Berry (FvCB) model proposed a mechanistic photo-synthesis paradigm (Farquhar et al., 1980), which has been widelyused in predicting photosynthesis from the leaf, to global scales(dePury and Farquhar, 1997; Ryu et al., 2011; Sellers et al., 1997).The FvCB model adopts a biochemical approach based on themechanism of the Calvin cycle, which is limited by the carboxyl-ation rate [i.e., the amount of activated photosynthesis enzymeribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco)], orelectron transport rate [i.e., regeneration rate of the substrate

of Rubisco, ribulose bisphosphate (RuBP)]. The model computesphotosynthesis separately under the assumptions that the Calvincycle is limited by the rate of either carboxylation or electrontransport. Finally, the model provides the minimum value of the

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060 H. Kimm, Y. Ryu / Urban Forestry &

wo as a photosynthesis rate. Thus, the maximum carboxylationate (Vcmax) and the maximum electron transport rate (Jmax) arehe key parameters in the FvCB model and are estimated based onO2 assimilation to leaf internal CO2 concentration curve derived

rom leaf gas exchange measurements (Sharkey et al., 2007). Fieldbservations revealed that both Vcmax and Jmax varied with thenvironment as well as season (Muraoka et al., 2010; Wang et al.,008; Wilson et al., 2000).

Indirect estimation of the photosynthetic parameters throughositive correlations between leaf nitrogen (N) contents andcmax (Amthor, 1994; Friend, 1995), and between Vcmax and Jmax

Wullschleger, 1993), would require less time and effort than directstimation from leaf gas exchange measurements. Specifically, pre-ious studies found a positive correlation between Vcmax and Nontent per leaf area (Evans, 1989; Kattge et al., 2009; Medlynt al., 1999). This correlation is supported by the fact that Vcmax

s affected by the amount of the N-rich enzyme Rubisco. Also,cmax and Jmax were found to be positively correlated. Thompsont al. (1992) suggested that plants maintain a balance betweencmax and Jmax by optimizing the allocation of resources (i.e.,) between Rubisco and chlorophyll to maximize photosynthesis

Niinemets and Tenhunen, 1997; Wilson et al., 2000; Wullschleger,993). Moreover, advanced studies investigated other relationshipsmong various leaf traits, which included leaf N concentration,eaf dry mass per leaf area (LMA), leaf N content per area, Vcmax,max, and the maximum rate of photosynthesis (Osnas et al.,013; Reich et al., 1991; Wilson et al., 2000; Wright et al., 2004).hese relationships, however, have not been tested in urbanarks.

The leaf area index (LAI), defined as the hemi-surface leafrea per unit ground area, is the key variable to scale up photo-ynthesis from leaves to canopies (Baldocchi, 1997; dePury andarquhar, 1997; Leuning et al., 1995; Ryu et al., 2011). The LAIetermines mainly light interception by leaves in a canopy, which

nitiates canopy photosynthesis. The LAI has been measured ineveral forest, grassland, and cropland ecosystems (Chen, 1996;ower and Norman, 1991; Ryu et al., 2010c). Additionally, globalatellite observation systems such as the NASA Terra and Aquaatellites, have provided LAI maps over the globe at a 1-km res-lution, 8-day interval (Myneni et al., 2002). However, there haveeen few studies that measured LAI in urban parks. Furthermore,lobal satellite LAI products provide 1–5-km-resolution maps, andhus the LAI in most parks, with the exception of very largearks such as Central Park in Manhattan, are unlikely to be cap-ured by global satellite LAI products. Thus, our understandingf spatial and temporal variations in the LAI in urban parks isimited.

Quantifying the photosynthetic parameters Vcmax and Jmax, andhe LAI in urban park, is challenging due to the heterogeneity ofanopy structures. To meet citizens’ diverse needs, urban parks areomposed of multiple species and are spatially heterogeneous with

complex composition of land cover, such as forests, ponds, play-rounds, and lawns (Bae and Ryu, 2015; Cao et al., 2010; Jim andhen, 2006). Heterogeneous land cover result in an uneven dis-ribution of vegetation and makes measuring the LAI of an urbanark difficult (Millward and Sabir, 2010). Moreover, a complexpecies composition would impose difficulties in measuring pho-osynthetic parameters because of species-specific differences inhotosynthetic parameters (Kattge et al., 2009; Medlyn et al., 1999).oreover, urban park vegetation is also temporally heterogeneous.

he phenological patterns of factors such as the timing of leaf-outnd leaf-off might vary among species and crowns (Reich et al.,

991; Wilson et al., 2000).

This study aimed to quantify the spatiotemporal heterogeneityf photosynthetic parameters and the LAI in an urban park, Seoulorest Park. We estimated Vcmax and Jmax for 10 representative

n Greening 14 (2015) 1059–1067

species during a peak growing season. For two common speciesin the park, Zelkova serrata (Thunb.) Makino and Prunus yedoen-sis Matsum., we estimated Vcmax and Jmax across the seasons. Wealso measured the LAI in 10 plots over the seasons, which werecombined with Landsat satellite imagery to produce spatial andtemporal maps of LAI in the park. Our scientific questions wereas follows: (1) To what extent do photosynthetic parameters varyspatially during the peak growing season and temporally across theseasons? (2) Can Vcmax and Jmax be estimated indirectly from leaftraits data? (3) To what extent does the LAI vary across differentland cover types over the seasons? Finally, we discuss opportuni-ties associated with modeling of canopy photosynthesis in urbanparks.

Materials and methods

Study site

The study site is an urban park, Seoul Forest Park, in Seoul,South Korea (37.544N, 127.038E). The park is located beside theHan River and covers an area of 1 km2. The site experiences atemperate monsoon climate. The mean annual temperature is12.5 ◦C, and the mean annual precipitation is 1450 mm year−1

(Korea Meteorological Administration). In 2013, the park containedover 415,000 woody plants of 95 species (Seoul Metropolitan Gov-ernment).

Photosynthetic parameters and leaf traits measurement

To quantify the spatial and temporal variations in leaf traits,including Vcmax, Jmax, LMA, and C and N concentrations, we selected10 dominant species. Two species (Z. serrata and Pr. yedoensis)were used to estimate seasonal variations in leaf traits and eightspecies (Ulmus parvifolia Jacq., Quercus acutissima Carruth, Quer-cus palustris Muenchh., Betula platyphylla var. japonica Hara., Ginkgobiloba L., Celtis sinensis Pers., Pinus densiflora Siebold et Zucc., andEuonymus alatus (Thunb.) Siebold) were selected to quantify spa-tial variations of the leaf traits during the peak growing season(day of year, DOY 219–255). As the sunlit leaf traits are the keyinput parameters in canopy photosynthesis models, we sampledonly sunlit leaves located at the top of crowns which were acces-sible from a pedestrian bridge in the park, or the outer side of thesouth face of isolated crowns (dePury and Farquhar, 1997; Kattgeet al., 2009; Ryu et al., 2011). To sample sunlit leaves, we used 3-m-long pruning scissors. We chose one tree per one species andcollected three branches each of which held at least three leaves.One leaf of each branch was used for leaf gas exchange measure-ments and all three leaves of the branch were transported to thelaboratory for measurement of LMA, and leaf N and C concentra-tions.

We estimated the key photosynthetic parameters, Vcmax andJmax, from leaf gas exchange measurements using a portable pho-tosynthesis system (Li-6400; Li-Cor Inc., Lincoln, NE, USA). First,we measured the photosynthesis rate (A) responding to leaf inter-nal CO2 concentrations (Ci) to obtain A/Ci curves. For each leaf,we used the automated program mode in Li-6400 to create anA/Ci curve by changing leaf external CO2 concentrations (400,200, 50, 100, 200 . . . 1400 ppm). Second, we estimated Vcmax

and Jmax from the obtained A/Ci curves using a least-squarescurve-fitting method to fit the measured A/Ci curve to the FvCB

model. We estimated Vcmax and Jmax using the Microsoft ExcelTM

spreadsheet provided by Sharkey et al. (2007). We report Vcmax

and Jmax values that were corrected to the leaf temperature at25 ◦C.

H. Kimm, Y. Ryu / Urban Forestry & Urban Greening 14 (2015) 1059–1067 1061

Table 1Species composition in each plot.

Plots Stem density (stem/m2) Tree height (m) Species

DBF1 0.09 8 Acer palmatum Thunb., Quercus mongolica Fisch.DBF2 0.13 9 Quercus acutissima Carruth, Acer Buegerianum Miq.DBF3 0.07 15 Platanus occidentalis L.DBF4 0.12 11 Quercus acutissima Carruth, Ginkgo biloba L., Acer buegerianum Miq., Aesculus turbinata BlumeDBF5 0.12 8 Quercus acutissima Carruth, Prunus yedoensis Matsum., Zelkova serrata (Thunb.) Makino, Ulmus Parvifolia Jacq.DBF6 0.04 8 Zelkova serrata (Thunb.) Makino, Carpinus laxiflora Siebold & Zucc., Acer palmatum Thunb.DBF7 0.04 9 Zelkova serrata (Thunb.) MakinoDNF 0.39 11 Ginkgo biloba L.MF 0.05 11 Pinus strobus L., Ulmus parvifolia Jacq., Metasequoia glyptostroboides Hu et Cheng, Liriodendron tulipifera L.

ld et Z

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to account for foliar clumping effects in computing LAI (Ryu et al.,2010b). In the case of needleleaf species (P. densiflora and Pinusstrobus), we measured the needle-to-shoot area ratio to accountfor foliar clumping effects appearing at the shoot scale as proposed

ENF 0.08 14 Pinus densiflora Siebo

To quantify LMA and foliar C and N concentrations, we scannedll sampled leaves and computed leaf areas using the MATLABmage-processing toolbox (MathWorks Inc., Woburn, MA, USA).hen we measured the dry mass of each leaf using a high-precisioncale (accuracy: 0.001 g, CUX220H; CAS Corp., Seoul, South Korea)fter 48 h of oven-dry at 80.0 ◦C (C-DH; Chang Shin Scientific Co.,usan, South Korea). Dried leaves were ground into powder to esti-ate C and N concentrations using an Elemental Carbon Analyzer

Flash EA 1112; Thermo Electron Co., Waltham, MA, USA) at theational Instrumentation Center for Environmental Management

NICEM) of Seoul National University. Lastly, we used t-test to checkelationships among leaf traits.

eaf area index measurement

lot designWe randomly established 10 20 × 20-m plots in the planted

reas in the park. Ten plots represented one evergreen needleleaforest (ENF), one mixed forest (MF), one deciduous needleleaf for-st (DNF), and seven deciduous broadleaf forests (DBFs). The plotshowed a tenfold difference in stem density and twofold differ-nce in tree height. The stem density, mean tree height, and speciesomposition of each plot are shown in Table 1.

igital cover photography (DCP)We used DCP to estimate the LAI (Macfarlane et al., 2007; Ryu

t al., 2012, 2014). Photographs were obtained at 1-m height underanopies using a digital single-lens reflex camera (Nikon 600D;ikon, Tokyo, Japan). We obtained three to seven photographs perlot viewing toward 57◦ from the zenith direction. G-function is therojection coefficient of unit foliage area on a plane perpendicularo the view direction and at a 57◦-view zenith angle; the G-functions 0.5 regardless of leaf inclination angle distributions (Nilson, 1971;

ilson, 1960). We chose the 57◦-view zenith angle because mea-uring leaf inclination angles from the canopy top to bottom forarious species was difficult. The lens had focal length of 28.8 mm35-mm equiv.). We obtained photographs under the aperture pri-rity mode and low aperture value (>f/1/5.6) to broaden the depthf field within photographs (Pentland, 1987). We also set the shut-er speed to shorter than 1/15 s to avoid blurred photographs. Wehose the proper exposure level manually by checking the his-ogram of the blue channel using a built-in camera liquid–crystalisplay (LCD) to minimize overexposed canopy pixels for accuratestimation of the LAI. Each time we visited the field, we obtainedhotographs at the same positions and in the same directions toinimize uncertainties caused by inconsistent sampling footprints.e obtained photographs every 2 weeks on average from DOY 121

o DOY 333.To estimate the LAI from photographs, we first extracted the

lue channel, which shows the most marked contrast betweenhe sky and vegetation. In the blue channel histogram, we

ucc.

separated pixels into sky pixels or vegetation pixels using atwo-corner method to set the thresholds for pixel classification(Macfarlane, 2011). The obtained binary images enabled us to cal-culate the fraction of crown cover (CC) and gap fraction withincrowns; i.e., crown porosity (CP; Macfarlane et al., 2007). Thus, wefinally calculated the LAI using the modified Beer’s law (Ryu et al.,2012):

LAI = −CC × log(CP)k

� (2)

where k is the light extinction coefficient [G(57◦)/cos(57◦)] and � isthe needle-to-shoot area ratio. We averaged the logarithm of gapfractions instead of the logarithm over the mean of gap fractions

Fig. 1. (a) Vcmax and (b) Jmax in 10 species measured during the peak growing season.Error bars indicates 95% CI.

1062 H. Kimm, Y. Ryu / Urban Forestry & Urban Greening 14 (2015) 1059–1067

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Jmax with excessive light intensity compared to previous studies(Muraoka et al., 2010; Wilson et al., 2000).

The photosynthetic parameters of Z. serrata and Pr. yedoensisshowed large seasonal variations (Fig. 2). Over the seasons, Vcmax

ig. 2. Seasonal variation in (a) Vcmax and (b) Jmax for Zelkova serrata (Thunb.) Makinond Prunus yedoensis Matsum. Error bars indicate 95% CI.

y Ryu et al. (2014). The � value for P. densiflora was 2.11 ± 0.28.he image analysis was conducted with a technical computing lan-uage, MATLAB (MathWorks Inc.).

atellite remote-sensing data processing

We used Landsat 8 imagery (30-m resolution). We selectedatellite data for 5 days (DOY 86, 131, 179, 259, and 355) thatad less than 10% cloud cover per image. To remove path radianceffects in the atmosphere, we adopted the dark object subtractionethod (Song et al., 2001). To obtain corrected digital numbers

fter the atmospheric correction, we converted the digital num-ers to spectral reflectance using conversion coefficients providedy the Landsat 8 metafile. We computed the normalized differenceegetation index (NDVI) using red and near-infrared reflectanceTucker, 1979):

DVI = �NIR − �RED�NIR + �RED

(3)

where �NIR and �RED are the spectral reflectance of near-nfrared and red, respectively. We extracted NDVI values withinach LAI measurement plot. To infer the LAI from the NDVI (Ryut al., 2010a), we developed an exponential regression model

LAI = 3.44 exp (NDVI)—3.38; R2 = 0.79, P < 0.001]. Using this equa-ion, we generated LAI maps from Landsat NDVI imagery for 5 daysDOY 86, 131, 179, 259, and 355).

Fig. 3. Comparison of Vcmax and Jmax for 10 species measured during the peak grow-ing season.

Results and discussion

To what extent do photosynthetic parameters vary spatiallyduring the peak growing season and temporally across theseasons?

Two photosynthetic parameters, Vcmax and Jmax, of the10 species in the park presented a wide range of variationduring the peak growing season [7.5-fold variation (from 20to 149 �mol m−2 s−1) in Vcmax and 3.7-fold variation (from 38to 141 �mol m−2 s−1) in Jmax; Fig. 1]. U. parvifolia Jacq. and C.sinensis showed the highest and lowest Vcmax values (149 and20 �mol m−2 s−1, respectively), E. alatus and C. sinensis displayedthe highest and lowest Jmax values (141 and 38 �mol m−2 s−1,respectively), and U. parvifolia and C. sinensis had the highest andlowest values in the photosynthetic parameters. In a meta-analysisstudy, Kattge et al. (2009) reported 57.7 ± 21.2 �mol m−2 s−1

[mean ± 1 standard deviation (S.D.), n = 404] Vcmax in temperatebroad-leaved deciduous trees globally, while our results indi-cated 82.9 ± 37.3 �mol m−2 s−1 in Vcmax (n = 7) for the same plantfunctional type trees, indicating greater variation in Vcmax. Weacknowledge the possibility of erroneous values in Vcmax and

Fig. 4. Comparison of the leaf nitrogen content per unit area and Vcmax for 10 speciesmeasured during the peak growing season.

H. Kimm, Y. Ryu / Urban Forestry & Urba

Fig. 5. Seasonal trend in leaf nitrogen content per unit area for Zelkova serrata(Thunb.) Makino and Prunus yedoensis Matsum. Error bars indicate 95% CI. The shadedarea represents the monsoon period.

Fig. 6. Seasonal variation in the

n Greening 14 (2015) 1059–1067 1063

varied from 20 to 100 �mol m−2 s−1 and 40 to 120 �mol m−2 s−1

for Z. serrata and Pr. yedoensis, respectively. The trends depictedleaf ontogeny and senescence, as reported for deciduous broad-leaved trees in previous studies (Muraoka et al., 2010; Wang et al.,2008; Wilson et al., 2000). Our results showed prolonged peak Vcmax

values until DOY 250, followed by a reduction, which is similar tothe findings of a study conducted in a temperate deciduous forestin Japan (Muraoka et al., 2010).

The heterogeneous composition of plant species in urban parks,which caused marked variations in photosynthetic parameters,requires a different approach to canopy photosynthesis modeling.In natural ecosystems, high biodiversity of woody plants under anopen canopy, which is the case in urban parks, is uncommon. Forexample, savanna is a typical case of open canopy but includeslower woody plant biodiversity, which are distributed sparsely(Baldocchi et al., 2004). In contrast, tropical forests have high woodyplant biodiversity, which represent a closed canopy (Asner andMartin, 2009). The large variation in Vcmax during the peak grow-ing season in the urban park (20–149 �mol m−2 s−1, nine species ofwoody plants) was greater than that reported previously in an Ama-

zon tropical forest (30–80 �mol m−2 s−1, 12 species; Domingueset al., 2012), a temperate deciduous forest (40–60 �mol m−2 s−1,4 species; Wilson et al., 2000), and an woody savanna forest(130 �mol m−2 s−1, 1 species; Baldocchi et al., 2004). Therefore,

leaf area index in 10 plots.

1064 H. Kimm, Y. Ryu / Urban Forestry & Urban Greening 14 (2015) 1059–1067

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ig. 7. (a) Leaf area index (LAI) map for day of year (DOY) 259 derived from Landsa59, and 355. The curves indicate histograms fit to a normal distribution.

e assume that an individual-tree-based three-dimensional (3D)anopy photosynthesis model (Kobayashi and Iwabuchi, 2008;

idlowski et al., 2006) is more appropriate for estimating canopyhotosynthesis in urban parks. Conventional photosynthesis mod-ls – such as a big-leaf model (Sellers 1985) or two-leaf modelsdePury and Farquhar, 1997; Ryu et al., 2011) – which simplifycosystem structure and functions into one or two big leaves, arenlikely to simulate accurately canopy photosynthesis in urbanarks.

an Vcmax and Jmax be estimated indirectly from leaf traits data?

We confirmed the possibility of estimating the photosyntheticarameters from leaf N content per unit leaf area for data measured

ges. (b) Histograms of Landsat-derived LAI values in the park for DOY 86, 131, 179,

during the summer (DOY 207–255) across the species. These datashowed significant correlations between Vcmax and Jmax (R = 0.87,P < 0.001; Fig. 3), and between Vcmax and N content (R = 0.64,P < 0.001; Fig. 4). The slope of the linear relationship between Vcmax

and Jmax (0.85) was lower than those reported previously; i.e.,1.2–3.33 (Grassi et al., 2005; Wilson et al., 2000; Wullschleger,1993).

However, we found no significant correlation between Vcmax

and N content for the data on Z. serrata and Pr. yedoensis mea-sured over the seasons (R = 0.07, P = 0.16). We speculate that the

summer monsoon decouples the Vcmax and N relationship. Thestudy year (2013) experienced a remarkably long monsoon period(DOY 168–216). We assume that both the low-light environmentcaused by frequent clouds and rainfall would decrease the leaf N

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H. Kimm, Y. Ryu / Urban Forestry &

ontent during the monsoon period (34 days of precipitation with0 mm day−1 on average according to a weather station located

km from the study site; Korea Meteorological Administration).ontinuous rainfall might have reduced leaf N contents because N

eaching could account for ∼15% of the total amount of N returnedrom the leaves to soils (Chapin and Moilanen, 1991; Lamberst al., 1998). Indeed, our results showed that leaf N contentsn the two species decreased significantly during the monsooneriod (shaded area in Fig. 5), whereas the Vcmax values of bothpecies were fairly stable during that period (Fig. 2). Furthermore,uring autumn, leaf N contents increased (Fig. 5), which is con-rary to the nutrient resorption pattern (i.e., reduction of leaf Nontents due to leaf nutrient withdrawal by plants before leafbscission; Reich et al., 1991; Wilson et al., 2000). We do notave strong evidence for the uncommon pattern of leaf N season-lity at this study site. We speculate that a prolonged monsooneriod and sufficient N input to the urban park through atmo-pheric N deposition and fertilization might be related to the leaf Neasonality.

We found a significant correlation between Vcmax and Jmax for. serrata and Pr. yedoensis across the seasons (R = 0.85, P < 0.01).hus, the tight correlations between Vcmax and Jmax were validoth spatially during the peak growing season and temporallycross seasons. Thus, finding correlations between leaf N contentsnd Vcmax is the essential step. Leaf N contents were predictive ofcmax across 10 species during the peak growing season (R = 0.64,

< 0.001), but not for the two species across the seasons. Identi-cation of correlations between Vcmax and other variables – suchs the LAI (Fig. 6) – over the seasons might offer an alternativeethod of inferring seasonality in Vcmax (Houborg et al., 2009;

yu et al., 2011), although we were unable to test this hypothesisecause the crowns from which the Z. serrata and Pr. yedoensis leafamples were collected were not included in our LAI observationlots.

o what extent does the LAI vary across different land cover typesver the seasons?

The peak LAI values derived from DCP varied from 3.1 to 4.4cross the plots in the park during the peak growing season (Fig. 6).ll plots showed a clear seasonality in the LAI. We obtained thepatial distribution of the LAI for the entire park by merging in situAI observations using DCP and Landsat NDVI images (Fig. 7a). TheAI clearly showed considerable spatial variation from 1.03 to 3.76t DOY 259.

We investigated the histograms of the LAI in the park for DOY6, 131, 179, 259, and 355 (Fig. 7b). During winter, the histogram ofhe LAI showed the lowest mean ± S.D. values (DOY 86: 0.97 ± 0.15,OY 355: 0.60 ± 0.13). The skewness and kurtosis were close to 0nd 3, respectively, indicating that the histograms followed a nor-al distribution. In the peak growing season (e.g., DOY 259), the

AI values showed the largest spread in distribution (0.55 S.D.) and non-normal distribution caused by higher kurtosis (4.49), whichas skewed left (−1.20). The non-normal, considerable spread in

he LAI distribution reflects the heterogeneous canopy structuren the urban park, which cannot be represented by a single meanalue.

Given the large spatial and temporal variations in photosyn-hetic parameters and LAI, we argue that individual tree-basedD canopy photosynthesis models such as FLiES (Kobayashi andwabuchi, 2008) and MAESTRA (Wang and Jarvis, 1990) will be

ssential for predicting canopy photosynthesis in urban parks. Wecknowledge that the 30-m resolution in Landsat images mighte insufficient to capture spatial heterogeneity in urban parks.ecent advances in light detection and ranging (LiDAR) offers new

n Greening 14 (2015) 1059–1067 1065

opportunities to extract 3D canopy structure information, whichwill facilitate quantification of the complex canopy structures inurban parks.

Broader implications for future urban park design

Urban planners and designers have paid less attention to thepotential role in carbon uptake by plants. Mixture of open spaceand clumped canopy with diverse species, which promotes diverseactivities by visitors, pervades in many urban parks. As shown inthe results, the study site included a range of different specieswith large spatial and temporal variability in canopy structure andphotosynthetic parameters (Figs. 1 and 7). Canopy photosynthe-sis generally increases with light when solar irradiance is low,and it becomes saturated when solar irradiance is high. Trees thathave high Vcmax (i.e. a proxy of photosynthetic capacity) couldincrease canopy photosynthesis even in high solar radiation con-ditions. Thus, archiving Vcmax values in typical urban park treespecies will be useful in selecting tree species in planting design.For example, to enhance canopy photosynthesis, it would be desir-able to use trees with high Vcmax in isolated, sparsely distributed,or row-planted canopies which could intercept more solar beamradiation. We do not argue carbon capture is the most impor-tant function in urban parks; rather, we hope carbon sequestrationcould be harmonized in the ecosystem services that urban parksprovide. Owing to 3D canopy photosynthesis models, it will bepossible to simulate carbon fluxes with a range of different scenar-ios that include different plant distributions, plant species, canopyheights and multi-layered canopy structures. We expect applying3D canopy photosynthesis model into urban parks will be war-ranted in the future studies to better understand carbon cycles inurban regions as well as to help simulation-informed park planningand design.

Summary and conclusions

In this report, we described the spatial and temporal patterns inphotosynthetic parameters (Vcmax and Jmax) and the LAI in an urbanpark in Seoul. The answers to the three scientific questions are asfollows: (1) To what extent do photosynthetic parameters vary spa-tially during the peak growing season and temporally across theseasons? During the peak growing season, we found an eightfolddifference in Vcmax and fourfold difference in Jmax across 10 species.Over the seasons, two woody species (Z. serrata and Pr. yedoensis)showed three- to fivefold differences in Vcmax and two- to five-fold differences in Jmax, respectively. (2) Can one estimate Vcmax

and Jmax indirectly from leaf traits data? We found that the leaf Ncontent was predictive of Vcmax across 10 species during the peakgrowing season. Also, a strong positive correlation existed betweenVcmax and Jmax. However, across the seasons, the leaf N contentwas not significantly correlated with Vcmax, probably because ofthe prolonged summer monsoon. Thus, we did not find a univer-sal relationship among leaf N, Vcmax, and Jmax. (3) To what extentdoes the LAI vary across land cover types over the seasons? LAImaps derived from the Landsat NDVI and in situ LAI observationsrevealed a normal distribution with a small spread in LAI values inwinter, whereas a non-normal distribution with a large spread inLAI estimates was observed during the peak growing season. Ourfindings highlight the large spatial and temporal variation in pho-

tosynthetic parameters and LAI in urban parks. Thus, we concludethat the use of an individual-tree-based, 3D canopy photosynthesismodel is essential for accurately predicting canopy photosynthesisin urban parks.

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cknowledgements

This research was supported by Basic Science Research Pro-ram through the National Research Foundation of Korea (NRF)unded by the Ministry of Science, ICT and Future PlanningNRF-2012R1A1A1004065). English proofread was supported byesearch Institute of Agricultural and Life Sciences in SNU.e thank Eunyoung, Jeehwan, Seungjun, Youngkeun, and Jong-in who helped data collection and analysis, and the staffs in

eoul Forest Park who allowed us to collect samples in theark.

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