Title

Seasonal leaf phenology data for 12 tree species in a cool-temperate deciduous broadleaved forest in Japan from 2005 to 2014

Authors

Shin Nagai ¹*, Kenlo Nishida Nasahara ²

1. Department of Environmental Geochemical Cycle Research,
   Japan Agency for Marine-Earth Science and Technology,
   3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
2. Faculty of Life and Environmental Sciences, University of Tsukuba,
   1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan

*Corresponding author: Dr. Shin NAGAI,
Department of Environmental Geochemical Cycle Research,
Japan Agency for Marine-Earth Science and Technology,
3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.
Tel.: +81(45)778-5594
Fax: +81(45)778-5706
E-mail: nagais@jamstec.go.jp

Abstract

This paper reports seasonal data regarding leaf number, leaf length and width, leaf area, leaf angle, and SPAD (leaf chlorophyll content index) for 11 genera (12 species) in a cool-temperate deciduous broadleaved forest in Japan. Knowing the leaf phenology of tree species is important for accurately evaluating the temporal variability of ecosystem functions (e.g., photosynthesis and evapotranspiration) under rapid climate change. However, there is a lack of freely available long-term observation data regarding leaf phenological characteristics for many tree species. We collected leaf phenological data from tagged shoots every 1 to 4 weeks from April or May to October or November each year from 2005 to 2014 in Takayama, Japan (36°08′46″N, 137°25′23″E, 1420 m a.s.l.). We targeted typical dominant, codominant, and understory tree species at the site. To evaluate differences among individuals and between sunlit and shaded leaves, we measured one to four shoots of some species and individuals. Our data provide input, calibration, and validation parameters for a terrestrial ecosystem and for radiative-transfer models and remote-sensing observations.

Key words

• broadleaf
• ground-truth
• Japan
• leaf seasonality
• leaf angle
• leaf area
• leaf length and width
• leaf number
• SPAD
• 10-year data set

Introduction

Collection of phenological data is a key task to support accurate evaluation of the spatio-temporal variability of ecosystem functions (e.g., photosynthesis and evapotranspiration), ecosystem services (e.g., regulating and cultural services), and biodiversity and their responses under rapid climate change (Richardson et al. 2013). Timing and patterns of leaf flush and leaf fall differ among tree species in deciduous broadleaved forests (Koike 1990; Nasahara et al. 2008; Nagai et al. 2014). Leaf longevity, determined by dates of leaf flush and leaf fall, is associated with maximum photosynthetic productivity ( Kikuzawa 1991, 1995).

Previous phenological studies have reported long-term, continuous visual observations of index trees (Menzel et al. 2006; Ogawa-Onishi and Berry 2013). These studies provide records of some phenological stages, such as flowering, leaf flush, and leaf fall, but do not provide sufficient data for the calibration and validation of ecological and radiative-transfer models and of remote-sensing observations, which require time-series phenological data for ground-truthing.

Terrestrial ecosystem models of gross primary productivity require seasonal data of leaf area and leaf chlorophyll content for input and validation (Muraoka and Koizumi 2005; Muraoka et al. 2010). Radiative-transfer models used to evaluate ecosystem structure and spectral characteristics similarly require seasonal data of leaf area and leaf angle (Kobayashi and Iwabuchi 2008; Ryu et al. 2010). On the other hand, remote-sensing observations require seasonal data of leaf area and leaf chlorophyll content for the ecological interpretation of vegetation indices derived from image analysis and spectral measurements (Nagai et al. 2011; Ryu et al. 2014). In addition, the validation of leaf area index (LAI) derived from indirect estimation based on the relationship between forest structure and light conditions (Hirose 2005) requires seasonal data of LAI based on in situ observations (Nasahara et al. 2008).

In this paper, we report seasonal data of leaf number, leaf length and width, leaf area, leaf angle, and SPAD ( leaf chlorophyll content index) for 12 tree species in 11 genera in a cool-temperate deciduous broad-leaved forest in Japan from 2005 to 2014. These data will be useful for the validation of phenological observations by satellite remote-sensing. In addition, integrative analysis of our leaf seasonal data with other litterfall data will allow the evaluation of LAI time-series for each species ( Nasahara et al. 2008).

Metadata

1. TITLE

Seasonal leaf phenology data for 12 tree species in a cool-temperate deciduous broadleaved forest in Japan from 2005 to 2014

2. IDENTIFIER

ERDP-2017-01

3. CONTRIBUTOR

A. Data Set Owner

Shin NAGAI
Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology
Address: 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Tel.: +81(45)778-5594
Fax: +81(45)778-5706
E-mail: nagais@jamstec.go.jp

Kenlo Nishida Nasahara
Faculty of Life and Environmental Sciences, University of Tsukuba
Address: 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
Tel./Fax: +81(29)853-4897
E-mail: 24dakenlo@gmail.com

B. Contact Person

Shin NAGAI
Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology
Address: 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Tel.: +81(45)778-5594
Fax: +81(45)778-5706
E-mail: nagais@jamstec.go.jp

4. PROJECTS

A. Titles

1. Environment Research and Technology Development Fund (S-1; Integrated Study for Terrestrial Carbon Management of Asia in the 21st Century Based on Scientific Advancement)
2. 21st Century COE Program (Satellite Ecology, Gifu University)
3. JSPS-NRF-NSFC A3 Foresight Program
4. Development of integrative information of the terrestrial ecosystem
5. Development of evaluation of ecosystem functioning in deciduous forests by satellite remote sensing

B. Personal

1 and 4. Kenlo Nishida Nasahara
Faculty of Life and Environmental Sciences, University of Tsukuba
Address: 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
Tel./Fax: +81(29)853-4897
E-mail: 24dakenlo@gmail.com

2 and 3. Hiroyuki Muraoka
River Basin Research Center, Gifu University
Address: 1-1 Yanagido, Gifu 501-1193, Japan
E-mail: muraoka@green.gifu-u.ac.jp

5. Shin Nagai
Department of Environmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology
Address: 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Tel.: +81(45)778-5594
Fax: +81(45)778-5706
E-mail: nagais@jamstec.go.jp

C. Funding

1. Ministry of the Environment of Japan
2. Japan Society for the Promotion of Science (JSPS)
3. JSPS
4. Global Change Observation Mission (GCOM) of the Japan Aerospace Exploration Agency (JAXA)
5. JSPS

5. GEOGRAPHIC COVERAGE

A. Geographic Description

Takayama, Gifu, Japan

B. Geographic Position

36°08′46″N, 137°25′23″E (WGS84)

6. TEMPORAL COVERAGE

A. Begin

4 May 2005

B. End

17 October 2014

7. TAXONOMIC COVERAGE

Data were obtained from 12 species in 11 genera (Table 1).

8. METHODS

A. Study sites

We recorded leaf phenology in a 60-year-old cool-temperate deciduous secondary forest in Takayama, central Japan (36°08′46″N, 137°25′23″E, 1420 m a.s.l.). This site belongs to the AsiaFlux network (http://asiaflux.net), the Japan Long-Term Ecological Research network (http://www.jalter.org), and the Phenological Eyes Network (http://www.pheno-eye.org; Nasahara and Nagai 2015). The dominant canopy species are Betula ermanii Cham., Betula platyphylla Sukachev var. japonica Hara, and Quercus crispula Blume. Within the 1-ha permanent plot, 40 deciduous broadleaved species and 3 evergreen coniferous species grow. The forest floor is fully covered by the evergreen dwarf bamboo Sasa senanensis Rehder, which reaches 1 to 2 m in height. The basal area was 32.34 m² ha⁻¹, mean diameter at breast height was 11.4 cm, number of stems was 1907 ha⁻¹, and above-ground biomass including foliage was 134.9 Mg ha^–1 ( Ohtsuka et al. 2005). The height of the forest canopy is 13 to 18 m. The annual maximum LAI, which included dominant, codominant, and understory deciduous tree groups and evergreen dwarf bamboo, was 7 on 30 July 2006 (Nasahara et al. 2008). From 1996 to 2009, the annual mean air temperature was 6.5 °C and the annual precipitation was 2089 mm. The snow season was early December to mid-April (Saitoh et al. 2015). An 18-m-tall canopy access tower was located just outside the southeastern corner of the plot. This study site was described in detail by Ohtsuka et al. (2005), Nagai et al. (2014), and Saitoh et al. (2015).

B. Measurement methods

We recorded leaf number, leaf size (length and width), leaf angle, and SPAD value every 1 to 4 weeks from April or May to October or November each year. We sampled 6 species (A. rufinerve, B. ermanii, E. sciadophylloides, M. obovata, P. maximowiczii, and Q. crispula) from the canopy access tower and 6 species (A. distylum, A. alnifolia, F. crenata, H. paniculata, T. japonica, and V. furcatum) from the forest floor. Sample shoots were labeled by attaching a colored tape at the shoot, and assigning an alphanumeric label to the shoot, as described later in this section. If we were unable to measure all shoots in 1 day, we measured the rest within the next 1 or 2 days. The observation dates are listed in Table 2. To evaluate individual differences, we measured 2 to 4 individuals of A. distylum, A. alnifolia, B. ermanii, H. paniculata, Q. crispula, and V. furcatum. To evaluate differences between sunlit and shaded leaves of B. ermanii and Q. crispula, we measured one to four shoots in the sunlit canopy and in the shaded lower canopy. We observed each species for 1 to 10 years. If a label was lost or a sample shoot was broken, we chose a new sample shoot nearby. In this case, the IDs of new sample shoots gained an extra zero; for instance, Be_A1, Be_A10, Be_A100, and Be_A1000. For the forest we studied, the spectral reflectance and transmittance data from leaves of Q. crispula, B. ermanii, and H. paniculata were available (Noda et al. 2014). Sample shoots are listed in Table 3.

Note: Be_A, B. ermanii_2; Be_B, B. ermanii_1; Qc_A, Q. crispula_1; Qc_D, Q. crispula_2; Pm_B, P. maximowiczii_1 on daily canopy surface images (Fig. 2 in Nagai et al. 2015), which were taken by a downward-looking time-lapse digital camera installed on the top of the canopy access tower.

B-1. Number of leaves

We counted all leaves from the top of a sample shoot to the label.

B-2. Leaf size

We measured leaf length and width with a tape measure. We measured 10 to 20 leaves from the top of the shoot to the label. In some cases such as during bad weather, leaf flush, or leaf fall, we measured fewer than 10 leaves or omitted the width. Leaf area was approximated as an ellipse using length and width. We assumed that the measured length and width represented the longest and shortest axes of the ellipse, respectively.

B-3. Leaf angle

We measured leaf angle from the horizontal (= 0°) with a protractor. We measured 20 to 40 leaves from the top of the shoot to the label and from near the shoot. We measured the angle for the leaf lamina and ignored the petiole angle.

B-4. SPAD

We measured SPAD values of leaves with a SPAD-502 meter (Minolta, Tokyo, Japan). We measured 10 to 20 leaves from the top of the shoot to the label. In some cases, when insufficient leaves were available for measurement (e.g., during leaf flush, when there were insufficient fully expanded leaves available to measure), we measured fewer than 10 leaves. Both before and after measuring the leaves of each shoot, we measured the SPAD meter’s calibration board 5 times and recorded the average. We multiplied the ratio of this average to the reference value of the SPAD meter’s calibration board and the SPAD value of each leaf.

9. DATA STRUCTURE

A. File format

The data files are saved in plain text.

B. Naming rules of the data files

The data files are named "2005_2014_TKY_shoot_PARAM_corrected.txt", TKY is Takayama and PARAM is the parameter name:

• angle: leaf angle
• lnmbr: number of leaves
• lsize: leaf length, width, and area
• SPAD: SPAD

Note: "Corrected" in the file names means that the files include processed rather than digitized raw data (YYYY_TKY_shoot_PARAM_original.txt, where YYYY is the year).

C. Header information

Lines 1 to 7–11 describe the data set (labeled ## at the start of each line).

D. Data sequence

Starting at line 8–12, the columns in each file list the following data:

Note: NA, not available.

10. ACCESSIBILITY

A. License

This dataset is provided under a Creative Commons Attribution 4.0 International license (CC-BY 4.0) (https://creativecommons.org/licenses/by/4.0/).

11. ACKNOWLEDGMENTS

We are grateful to K. Kurumado, Y. Miyamoto, and H. Muraoka (River Basin Research Center, Gifu University), H. Mikami (University of Tsukuba), T. Motohka (Japan Aerospace Exploration Agency), T. Inoue (Japan Agency for Marine-Earth Science and Technology), and all Takayama community members for their assistance in the field. We thank the editor and the two anonymous reviewers for their constructive comments and suggestions. This study was supported by a KAKENHI Grant-in-Aid for Scientific Research (B) from the Japan Society for the Promotion of Science (#15H04512).

12. LITERATURE CITED

Hirose T (2005) Development of the Monsi-Saeki theory on canopy structure and function. Annals of Botany 95:483–494.

Kikuzawa K (1991) A cost-benefit analysis of leaf habit and leaf longevity of trees and their geographical pattern. The American Naturalist 138:1250–1263.

Kikuzawa K (1995) The basis for variation in leaf longevity of plants. Vegetatio 121:89–100.

Kobayashi H, Iwabuchi H (2008) A coupled 1-D atmosphere and 3-D canopy radiative transfer model for canopy reflectance, light environment, and photosynthesis simulation in a heterogeneous landscape. Remote Sens Environ 112:173–185.

Koike T (1990) Autumn coloring, photosynthetic performance and leaf development of deciduous broad-leaved trees in relation to forest succession. Tree Physiol 7:21–32.

Menzel A, Sparks TH, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kubler K, Bissolli P, Braslavska O, Briede A, Chmielewski FM, Crepinsek Z, Curnel Y, Dahl A, Defila C, Donnelly A, Filella Y, Jatcza K, Mage F, Mestre A, Nordli O, Penuelas J, Pirinen P, Remisova V, Scheifinger H, Striz M, Susnik A, Van Vliet AJH, Wielgolaski FE, Zach S, Zust A (2006) European phenological response to climate change matches the warming pattern. Global Change Biol 12:1969–1976.

Muraoka H, Koizumi H (2005) Photosynthetic and structural characteristics of canopy and shrub trees in a cool-temperate deciduous broadleaved forest: implication to the ecosystem carbon gain. Agric For Meteorol 134:39–59.

Muraoka H, Saigusa N, Nasahara KN, Noda H, Yoshino J, Saitoh TM, Nagai S, Murayama S, Koizumi H (2010) Effects of seasonal and interannual variations in leaf photosynthesis and canopy leaf area index on gross primary production of a cool-temperate deciduous broadleaf forest in Takayama, Japan. J Plant Res 123:563–576.

Nagai S, Maeda T, Muraoka H, Suzuki R, Nasahara KN (2011) Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecol Diversity 4:78–88.

Nagai S, Inoue T, Ohtsuka T, Kobayashi H, Kurumado K, Muraoka H, Nasahara KN (2014) Relationship between spatio-temporal characteristics of leaf-fall phenology and seasonal variations in near surface- and satellite-observed vegetation indices in a cool-temperate deciduous broad-leaved forest in Japan. Int J Remote Sens 35:3520–3536.

Nagai S, Inoue T, Ohtsuka T, Yoshitake S, Nasahara KN, Saitoh TM (2015) Uncertainties involved in leaf fall phenology detected by digital camera. Ecol Inform 30:124–132.

Nasahara KN, Muraoka H, Nagai S, Mikami H (2008) Vertical integration of leaf area index in a Japanese deciduous broad leaved forest. Agric For Meteorol 148:1136–1146.

Nasahara KN, Nagai S (2015) Development of an in-situ observation network for terrestrial ecological remote sensing — the Phenological Eyes Network (PEN). Ecol Res 30:211–223.

Noda HM, Motohka T, Murakami K, Muraoka H, Nasahara KN (2014) Reflectance and transmittance spectra of leaves and shoots of 22 vascular plant species and reflectance spectra of trunks and branches of 12 tree species in Japan. Ecol Res 29:111.

Ogawa-Onishi Y, Berry PM (2013) Ecological impacts of climate change in Japan: the importance of integrating local and international publications. Biol Conserv 157:361–371.

Ohtsuka T, Akiyama T, Hashimoto Y, Inatomi M, Sakai T, Jia S, Mo W, Tsuda S, Koizumi H (2005) Biometric based estimates of net primary production (NPP) in a cool-temperate deciduous forest stand beneath a flux tower. Agric For Meteorol 134:27–38.

Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M. (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173.

Ryu Y, Sonnentag O, Nilson T, Vargas R, Kobayashi H, Wenk R, Baldocchi DD (2010) How to quantify tree leaf area index in an open savanna ecosystem: A multi-instrument and multi-model approach. Agric For Meteorol 150:63–76.

Ryu Y, Lee G, Jeon S, Song Y, Kimm H (2014) Monitoring multi-layer canopy spring phenology of temperate deciduous and evergreen forests using low-cost spectral sensors. Remote Sens Environ 149:227–238.

Saitoh TM, Nagai S, Yoshino J, Kondo H, Tamagawa I, Muraoka H (2015) Effects of canopy phenology on deciduous overstory and evergreen understory carbon budgets in a cool-temperate forest ecosystem under ongoing climate change. Ecol Res 30 2:267–277.