Title

8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network

Authors

Shin Nagai^1,2, Tomoko Akitsu³, Taku M Saitoh⁴, Robert C Busey⁵, Karibu Fukuzawa⁶, Yoshiaki Honda⁷, Tomoaki Ichie⁸, Reiko Ide⁹, Hiroki Ikawa¹⁰, Akira Iwasaki¹¹, Koki Iwao¹², Koji Kajiwara⁷, Sinkyu Kang¹³, Yongwon Kim⁵, Kho Lip Khoon¹⁴, Alexander V Kononov¹⁵, Yoshiko Kosugi¹⁶, Takahisa Maeda¹⁷, Wataru Mamiya¹⁸, Masayuki Matsuoka⁸, Trofim C Maximov¹⁵, Annette Menzel^19,20, Tomoaki Miura²¹, Toshie Mizunuma²², Tomoki Morozumi²³, Takeshi Motohka²⁴, Hiroyuki Muraoka⁴, Hirohiko Nagano⁵, Taro Nakai²⁵, Tatsuro Nakaji²⁶, Hiroyuki Oguma⁹, Takeshi Ohta²⁷, Keisuke Ono¹⁰, Runi Anak Sylvester Pungga²⁸, Roman E Petrov¹⁵, Rei Sakai¹⁸, Christian Schunk²⁰, Seikoh Sekikawa²⁹, Ruslan Shakhmatov²³, Yowhan Son³⁰, Atsuko Sugimoto³¹, Rikie Suzuki², Kentaro Takagi³², Satoru Takanashi³³, Shunsuke Tei³¹, Satoshi Tsuchida¹², Hirokazu Yamamoto¹², Eri Yamasaki³⁴, Megumi Yamashita³⁵, Tae Kyung Yoon³⁶, Toshiya Yoshida¹⁸, Mitsunori Yoshimura³⁷, Shinpei Yoshitake⁴, Matthew Wilkinson³⁸, Lisa Wingate³⁹, Kenlo Nishida Nasahara³

¹Research and Development Center for Global Change, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan, nagais@jamstec.go.jp

² Institute of Arctic Climate and Environment Research, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan

³ Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan, tomo.akki878@gmail.com, 24dakenlo@gmail.com

⁴ River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan, taku@green.gifu-u.ac.jp, syoshi@green.gifu-u.ac.jp, muraoka@green.gifu-u.ac.jp

⁵ International Arctic Research Center, University of Alaska Fairbanks, 930 Koyukuk Dr., PO Box 757320, Fairbanks, Alaska 99775-7320 USA, kimywjp@gmail.com, rcbusey@alaska.edu, nagano.hirohiko@jaea.go.jp

⁶ Nakagawa Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, 483 Otoineppu, Otoineppu, Hokkaido 098-2502, Japan, caribu@fsc.hokudai.ac.jp

⁷ Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan, kkaji@faculty.chiba-u.jp, yhonda@mtf.biglobe.ne.jp

⁸Faculty of Agriculture and Marine Science, Kochi University , 200 Otsu, Monobe, Nankoku, Kochi 783-8502 Japan, ichie@kochi-u.ac.jp, msykmtok@kochi-u.ac.jp

⁹ National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan, ide.reiko@nies.go.jp, oguma@nies.go.jp

¹⁰ Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan, hikawa.biomet@gmail.com, onok@affrc.go.jp

¹¹ Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan, aiwasaki@sal.rcast.u-tokyo.ac.jp

¹² Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 7, Higashi 1-1-1 Tsukuba, Ibaraki 305-8567, Japan, s.tsuchida@aist.go.jp, iwao@aist.go.jp, hirokazu.yamamoto@aist.go.jp

¹³ Department of Environmental Science, Kangwon National University, Chunchon 200-701, Korea, kangsk@kangwon.ac.kr

¹⁴ Tropical Peat Research Institute, Malaysian Palm Oil Board, No.6, Persiaran Institusi, Bandar Baru Bangi, 43000 Selangor, Malaysia, lip.khoon@mpob.gov.my

¹⁵ Institute for Biological Problems of Cryolithozone, Siberian Division of Russian Academy of Sciences, 41, Lenin Ave., Yakutsk, The Republic of Sakha, 678891, planteco@mail.ru; t.c.maximov@ibpc.ysn.ru, pre2003@mail.ru

¹⁶ Graduate School of Agriculture, Kyoto University, Kitashirakawa, Oiwake-cho, Sakyo-ku, Kyoto, 602-8502, Japan, ykosugi@kais.kyoto-u.ac.jp

¹⁷ Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan, takahisa.maeda@aist.go.jp

¹⁸ Uryu Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Moshiri, Horokanai, Hokkaido, 074-0741, Japan, mamiya-w@fsc.hokudai.ac.jp, reisakai@fsc.hokudai.ac.jp, yoto@fsc.hokudai.ac.jp

¹⁹ Ecoclimatology, Technische Universität München, Hans-Carl-von-Carlowitz Platz 2, 85354 Freising, Germany, amenzel@wzw.tum.de

²⁰ Institute for Advanced Study, Technische Universität München, Lichtenbergstraße 2a, 85748 Garching, Germany, schunk@wzw.tum.de

²¹ Department of Natural Resources and Environmental Management, College of Tropical Agriculture and Human Resources, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA, tomoakim@hawaii.edu

²² Department of Botany, National Museum of Nature and Science, 4-1-1 Amakubo, Tsukuba, Ibaraki 305-0005, Japan, toshie.mizunuma@gmail.com

²³ Graduate School of Environmental Science, Hokkaido University, N10W5, Sapporo, Hokkaido, 060-0810 Japan, both-horns@ees.hokudai.ac.jp, shakhmatovr@gmail.com

²⁴ Earth Observation Research Center, Japan Aerospace Exploration Agency, 2-1-1 Sengen, Tsukuba 305-8505, Japan, motooka.takeshi@jaxa.jp

²⁵ Institute for Space-Earth Environmental Research, Nagoya University, Furo-cho Chikusa-ku, Nagoya 464-8601 Japan, taro.nakai@gmail.com

²⁶ Tomakomai Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Takaoka, Tomakomai, Hokkaido, 053-0035, Japan, nakaji@fsc.hokudai.ac.jp

²⁷ Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho Chikusa-ku, Nagoya 464-8601 Japan, takeshi@agr.nagoya-u.ac.jp

²⁸ Forest Department Sarawak, Tingkat 14, Wisma Sumber Alam Jalan Stadium, Petra Jaya, 93050 Kuching, Sarawak, Malaysia, runisp@sarawak.gov.my

²⁹ Department of Agriculture, Tamagawa University, 6-1-1 Tamagawa-gakuen, Machida, Tokyo 194-8610, Japan, sekisei@agr.tamagawa.ac.jp

³⁰ Division of Environmental Science and Ecological Engineering, Korea University, 145 Anamro, Seungbukgu, Seoul, Korea, yson@korea.ac.kr

³¹ Arctic Research Center, Hokkaido University, N21W11, Kita-ku, Sapporo 001-0021, Japan, atsukos@arc.hokudai.ac.jp, stei@arc.hokudai.ac.jp

³² Teshio Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Toikanbetsu, Horonobe, Hokkaido 098-2943, Japan, kentt@fsc.hokudai.ac.jp

³³ Kansai Research Center, Forestry and Forest Products Research Institute, 68 Nagaikyutaroh, Momoyama, Fushimi, Kyoto 612-0855, Japan, tnsatoru@ffpri.affrc.go.jp

³⁴ Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurertrasse 190, 8057 Zurich, Switzerland, eri.yamasaki@uzh.ch

³⁵ Faculty of Agriculture, Tokyo University of Agriculture and Technology, 3-8-1 Harumi-cho, Fuchu, Tokyo 183-8538, Japan, meguyama@cc.tuat.ac.jp

³⁶ Division of Environmental Strategy, Korea Environment Institute, 370 Sicheong-daero, Sejong, 30147, Republic of Korea, yoon.ecology@gmail.com

³⁷ PASCO Corporation, Research and Development Center, 2-8-11 Higashiyama, Meguro-ku, Tokyo 153-0043, Japan, gr4m-ysmr@asahi-net.or.jp

³⁸ Centre for Sustainable Forestry and Climate Change, Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK, Matthew.Wilkinson@forestry.gsi.gov.uk

³⁹ INRA, UMR Interaction Sol Plante Atmosphère 1391, 33140 Villenave d’Ornon, France, lisa.wingate@inra.fr

*Corresponding author: Dr. Shin NAGAI, Research and Development Center for Global Change, 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

Abstruct

We report long-term continuous phenological and sky images taken by time-lapse cameras through the Phenological Eyes Network (http://www.pheno-eye.org) in various ecosystems from the Arctic to the tropics. Phenological images are useful in recording the year-to-year variability in the timing of flowering, leaf-flush, leaf-coloring, and leaf-fall and detecting the characteristics of phenological patterns and timing sensitivity among species and ecosystems. They can also help interpret variations in carbon, water, and heat cycling in terrestrial ecosystems, and be used to obtain ground-truth data for the validation of satellite-observed products. Sky images are useful in continuously recording atmospheric conditions and obtaining ground-truth data for the validation of cloud contamination and atmospheric noise present in satellite remote-sensing data. We have taken sky, forest canopy, forest floor, and shoot images of a range of tree species and landscapes, using time-lapse cameras installed on forest floors, towers, and rooftops. In total, 84 time-lapse cameras at 29 sites have taken 8 million images since 1999. Our images provide (1) long-term, continuous detailed records of plant phenology that are more quantitative than in situ visual phenological observations of index trees; (2) basic information to explain the responsiveness, vulnerability, and resilience of ecosystem canopies and their functions and services to changes in climate; and (3) ground-truthing for the validation of satellite remote-sensing observations.

Keywords

• Boreal forest
• Decadal data set
• Digital camera
• Grassland
• Ground-truth
• Phenological Eyes Network
• Plant phenology
• Sky image
• Temperate forest
• Tropical forest

Introduction

Monitoring of spatio-temporal characteristics of plant phenology such as the timing and patterns of flowering, leaf-flush, leaf-coloring, and leaf-fall among species and ecosystem types is an extremely important task in evaluating the spatio-temporal variability of ecosystem functions (photosynthesis and evapotranspiration) and cultural services modulated by climate change (Richardson et al. 2013; Nagai et al. 2016a). In the Northern Hemisphere mid to high latitudes, the timing of flowering and leaf-flush has advanced and the timing of leaf-fall has delayed under global warming (Linderholm 2006; Menzel et al. 2006; Doi and Takahashi 2008; Matsumoto 2010; Ogawa-Onishi and Berry 2013). Shifts in the timing of flowering and leaf-flush may cause a mismatch between plant phenology and the life cycles of pollinators and predators, increasing the risk of biodiversity loss (SCBD 2010; Polgar and Primack 2011; Kudo 2014). Variations in the timing of leaf-flush and leaf-fall (which define growing period) also change the period of photosynthetic activity, and the annual carbon budget (sink or source) can be strongly affected (Richardson et al. 2013). Characteristics of growing period among species and ecosystem types (e.g., leaf longevity) correlate well with leaf traits and climate (Wright et al. 2004; Onoda et al. 2011; Kikuzawa et al. 2013). These findings support the need for accurate evaluation of the responsiveness of plant phenology among species and ecosystems to climate change to characterize the vulnerability and resilience of ecosystem functions and services. The geographical distribution of year-to-year variability in the timing of flowering, leaf-flush, leaf-coloring, and leaf-fall is a useful proxy for climate and its change. For instance, the geographical distribution of cherry flowering dates can be used to evaluate the local climate in and around cities (Matsumoto et al. 2006; Ohashi et al. 2012). In this case, long-term (>1200 years) year-to-year variability of air temperature in spring and autumn can be used to examine climate variability and improve the phenological modeling of cherry blooming and peak leaf-coloring (Aono and Kazui 2008; Aono and Tani 2014).

Long-term phenological observations are traditionally made by experts who visually inspect index trees (Linderholm 2006; Menzel et al. 2006; Doi and Takahashi 2008; Matsumoto 2010; Ogawa-Onishi and Berry 2013). This method has the merit of recording detailed temporal changes in plant phenology, but suffers from a number of drawbacks. It is labor intensive, making it difficult to obtain simultaneous observations at multiple points. In addition, it requires a qualitative criterion for each phenology stage (Richardson et al. 2007). To resolve these issues, remote-sensing observations using digital cameras and spectral radiometers mounted on towers, unmanned aerial vehicles, aircraft, and satellites are useful (Morisette et al. 2009). Digital photographs also have the benefit of accurately recording the characteristics of plant phenology among multiple species and ecosystems at the levels of shoot, individual, canopy, and landscape. In addition, depending on the camera quality and spectral characterization, it is possible to detect changes in red, green, and blue strength, although digital cameras are not radiometrically calibrated (Ahrends et al. 2008; Nagai et al. 2011a; Saitoh et al. 2012a; Inoue et al. 2014; Wingate et al. 2015). However, compared with spectral radiometers, digital cameras are very inexpensive and allow flexible installation at multiple sites (Saitoh et al. 2012a). The technology has improved to the point where phenology can now be recorded at unmanned fixed-point observatories at multiple sites across the world using time-lapse digital cameras. Consequently, an international network is now archiving time-lapse imagery data sets at an extremely high temporal resolution equal to or better than satellite products. The camera networks include the Phenological Eyes Network (PEN; mainly in Asia; Nasahara and Nagai 2015; http://www.pheno-eye.org), the PhenoCam network (mainly in North and South America and Europe; Brown et al. 2016; http://phenocam.sr.unh.edu/webcam/), the European Phenology Camera Network (Wingate et al. 2015; http://european-webcam-network.net/), the Australian Phenocam Network (mainly in Australia; Moore et al. 2016; https://phenocam.org.au/), and the e-phenology network (mainly in Brazil; http://www.recod.ic.unicamp.br/ephenology/client/index.html#/).

The use of time-lapse digital cameras has been useful for obtaining ground-truth data for daily satellite remote-sensing products obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensors onboard the Terra and Aqua satellites, and the VEGETATION sensor onboard the SPOT (Satellite Pour l’Observation de la Terre) satellite (Nagai et al. 2010a, 2014a, 2014b; Kobayashi et al. 2016). Because vegetation signals observed by these remote optical sensors can be contaminated by cloud and atmospheric noise, validation with near-proxy data sets can help establish the sky conditions during the satellites’ passage (Motohka et al. 2011; Nagai et al. 2011b, 2014b). Thus, efforts to monitor plant phenology and sky conditions at the same point and time are beneficial. In addition, the long-term continuous monitoring of sky conditions is an important task for revealing the interaction between the atmosphere and terrestrial ecosystem productivity (Mercado et al. 2009; Wilson and Jetz 2016).

Here, we report long-term, continuous daily phenological and sky images taken at 29 sites from boreal forests in Alaska and eastern Siberia to tropical rain forest in Borneo. A total of 84 time-lapse cameras installed on forest floors, towers, and rooftops have been taking forest canopy, forest floor vegetation, tree shoot, landscape, and sky images every day since 1999, so far collecting 8 million images. Our image database is now ready to provide (1) long-term, continuous detailed records of plant phenology that are more quantitative than historical, in situ phenological observations based on fixed-point visual inspection of index trees; (2) basic information for explaining the responsiveness, vulnerability, and resilience of ecosystem canopies and their functions and services to changes in climate; and (3) ground-truth data for the validation of satellite remote-sensing products.

Metadata

1. TITLE

8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network

2. IDENTIFIER

ERDP-2018-05

3. CONTRIBUTOR

Data set owners and contact persons

Data set owners and contact persons are listed in Table 1. To obtain detailed site information and support for the use of the images, we recommend contacting the principal investigator at each site.

Table 1 Summary of data set owners and contact persons at the 29 PEN sites

1: Centre for Sustainable Forestry and Climate Change, Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK

2: Department of Botany, National Museum of Nature and Science, 4-1-1 Amakubo, Tsukuba, Ibaraki 305-0005, Japan

3: Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan

4: National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan

5: Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 7, Higashi 1-1-1 Tsukuba, Ibaraki 305-8567, Japan

6: Kansai Research Center, Forestry and Forest Products Research Institute, 68 Nagaikyutaroh, Momoyama, Fushimi, Kyoto 612-0855 Japan

7: Department of Environmental Science, Kangwon National University, Chunchon 200-701, Korea

8: Department of Natural Resources and Environmental Management, College of Tropical Agriculture and Human Resources, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, USA

9: Technische Universität München, Hans-Carl-von-Carlowitz Platz 2, 85354 Freising, Germany

10: Institute for Advanced Study, Technische Universität München, Lichtenbergstraße 2a, 85748 Garching, Germany

11: Graduate School of Agriculture, Kyoto University, Kitashirakawa, Oiwake-cho, Sakyo-ku, Kyoto 602-8502, Japan

12: Tropical Peat Research Institute, Malaysian Palm Oil Board, No.6, Persiaran Institusi, Bandar Baru Bangi, 43000 Selangor, Malaysia

13: Research and Development Center for Global Change, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan

14: Institute of Arctic Climate and Environment Research, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan

15: Faculty of Agriculture and Marine Science, Kochi University, 200 Otsu, Monobe, Nankoku, Kochi 783-8502, Japan

16: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurertrasse 190, 8057 Zurich, Switzerland

17: Forest Department Sarawak, Tingkat 14, Wisma Sumber Alam Jalan Stadium, Petra Jaya, 93050 Kuching, Sarawak, Malaysia

18: Nakagawa Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, 483 Otoineppu, Otoineppu, Hokkaido 098-2502, Japan

19: Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki 305-8604, Japan

20: Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan

21: Environmental Management Research Institute, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan

22: Earth Observation Research Center, Japan Aerospace Exploration Agency, 2-1-1 Sengen, Tsukuba 305-8505, Japan

23: International Arctic Research Center, University of Alaska Fairbanks, 930 Koyukuk Dr., PO Box 757320, Fairbanks, AK 99775-7320, USA

24: Institute for Space-Earth Environmental Research, Nagoya University, Furo-cho Chikusa-ku, Nagoya 464-8601, Japan

25: PASCO Corporation, Research and Development Center, 2-8-11 Higashiyama, Meguro-ku, Tokyo 153-0043, Japan

26: Faculty of Agriculture, Tokyo University of Agriculture and Technology, 3-8-1 Harumi-cho, Fuchu, Tokyo 183-8538, Japan

27: Department of Agriculture, Tamagawa University, 6-1-1 Tamagawa-gakuen, Machida, Tokyo 194-8610, Japan

28: Division of Environmental Science and Ecological Engineering, Korea University, 145 Anamro, Seungbukgu, Seoul, Korea

29: Division of Environmental Strategy, Korea Environment Institute, 370 Sicheong-daero, Sejong, 30147, Republic of Korea

30: Institute for Biological Problems of Cryolithozone, Siberian Division of Russian Academy of Sciences, 41, Lenin Ave., Yakutsk, The Republic of Sakha (Yakutia), 678891, Russia

31: Arctic Research Center, Hokkaido University, N21W11, Kita-ku, Sapporo 001-0021, Japan

32: Graduate School of Environmental Science, Hokkaido University, N10W5, Sapporo, Hokkaido, 060-0810, Japan

33: Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho Chikusa-ku, Nagoya, 464-8601 Japan

34: River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan

35: Tomakomai Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Takaoka, Tomakomai, Hokkaido 053-0035, Japan

36: Teshio Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Toikanbetsu, Horonobe, Hokkaido 098-2943, Japan

37: Uryu Experimental Forest, Field Science Center for Northern Biosphere, Hokkaido University, Moshiri, Horokanai, Hokkaido 074-0741, Japan

38: Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

4. PROJECTS

Projects associated with each site are listed in Table 2.

Table 2 Summary of projects that provided funding for the 29 PEN sites

5. GEOGRAPHIC COVERAGE

Geographic descriptions and positions

Geographic coordinates and descriptions of the 29 PEN sites are listed in Table 3 and Figure 1. The locations, direction of view, and type of camera system are listed in Table 4. The date of changes in view of phenological photographs is listed in Table 5. In addition to the 29 PEN sites, we have initiated another 5 sites in Japan (2 sites), China (1), France (1), and Egypt (1) (Nasahara and Nagai 2015, http://pen.agbi.tsukuba.ac.jp/), but their data are not publicly accessible yet.

Table 3 Summary of location, plant functional type, and dominant species at the 29 PEN sites

DBF: deciduous broad-leaved forest; DNF: deciduous needle-leaved forest; EBF: evergreen broad-leaved forest; ENF: evergreen needle-leaved forest; URL 1: http://www.asiaflux.net/; URL 2: http://ameriflux.lbl.gov/. Camera IDs are explained in Table 4.

Figure 1 Maps of the Phenological Eyes Network sites (a) around the world and (b) in Japan. Site IDs are explained in Table 3. Click to download full size image(.tif).

Table 4 Summary of locations of the 84 time-lapse digital cameras at the 29 PEN sites

ADFC = automatic digital fish-eye camera.

1: Film camera (not digital). 2: “f02dr” and “f02ur” refer to the same time-lapse digital camera. Camera was rotated upwards and downwards every 60 min. 3: “h01_u”, “l01_u”, and “n01_u” refer to the same time-lapse digital camera used to take images at different exposures. 4: “h28_d”, “l28_d”, and “n28_d” refer to the same time-lapse digital camera used to take images at different exposures. 5: “y02rd” and “y02ru” were the same time-lapse digital camera until DOY 81, 2013. Camera was rotated upwards and downwards every 10 min. After this date, two ADFCs have been running. 6: Height of location was changed from 2 m to 3 m in 2006. 7: “y02_d” and “y02_u” refer to the same time-lapse digital camera. Camera was rotated upwards and downwards every 10 min. Details of camera system are described in “METHODS”.

Table 5 Summary of date of changes in view of phenological photographs of the 73 time-lapse digital cameras at the 27 PEN sites

*: The camera system was pointed downwards in winter.

6. TEMPORAL COVERAGE

The temporal coverage of the images is listed in Table 6. The longest running site is TGF, with continuous phenological image archives dating back to 2003.

Table 6 Summary of observation period, frequency, missing periods >10 days, and major errors of the 84 time-lapse digital cameras at the 29 PEN sites

1: Including images in wrong directions (opposite and oblique). 2: Site manager permitted to use data until DOY 183 in 2010. 3: We evaluated the time stamp of images because the digital camera clock was reset owing to flat battery of hardware clock (2015: DOY 263–365; 2016: 1–185). 4: The time stamp of images included time lags of 1 hour because of daylight saving time setting. 5: Five images were taken at different exposures each time since DOY 120, 2007. 6: Images were out of focus (2014: DOY 102–150, 315–351; 2015: 109–148; 2016: 61–172, 328–366; 2017: 1–107). 7: Images were black owing to sensor errors (2011: DOY 355, 358–365; 2012: 1–195, 198). 8: Including images taken at three exposures. 9: Including images taken at five exposures. 10: Including images taken at seven exposures. 11: Images were black owing to sensor errors (2012: DOY 358–366; 2013: 1–86). 12: Images were black owing to sensor errors (2013: DOY 205–228). 13: Images were black owing to sensor errors (2013: DOY 206–228). 14: Images without fish-eye lens (2012: DOY 178–193, 295). 15: Including images without fish-eye lens (2011: DOY 349; 2012: 212, 296, 320). 16: Observations were suspended in winter.

7. METHODS

Time-lapse camera system

We used seven types of time-lapse cameras, described below.

A. ADFC

An automatic digital fish-eye camera (ADFC) is the main time-lapse camera system of PEN. This system consists of a digital camera (CoolPix 3400 or 4500, Nikon, Tokyo, Japan) and fish-eye lens (FC-E8, Nikon). It was installed at 25 sites (except EGT, LAM, TOE, and TOS; Table 4). An ADFC was installed in a waterproof box with control cables and a circuit board (SPC31A, Hayasaka Rikoh Co. Ltd., Sapporo, Japan). A PC running Linux or Windows controlled the ADFC. Shooting period and frequency were set to different values among ADFCs (Table 6). At KBF, KEW, LBR, and SHA, where it was not possible to use PCs, we used remote release cords (MC-EU1, Nikon, Japan; Table 4). Some ADFCs had a standard lens instead of a fish-eye lens (Table 4). Images were saved to the PEN database in JPEG format at 2272 × 1704 pixels. Exposure was set to automatic except at FJY (h01_u, l01_u, n01_u, h28_d, l28_d, n28_d), SGD (g00_u), TKC (f02_u), and TKY (f02_d, f02_u, L12_u, miz_u, y02_d, and y02_u) (Table 6). White balance was set to different values among ADFCs (see EXIF data of each image). As for the effect of different setting of white balance for analysis of red, green and blue digital numbers extracted from digital images, see Nagai et al. (2013a).

B. AIST Pheno-mon

This system consists of a main camera (EOS Kiss X4 digital SLR, Canon, Tokyo, Japan) equipped with a standard zoom lens (EF-S18–55 mm, f/3.5–5.6 IS, Canon), a supplemental USB camera), and a PC running Linux that controlled the cameras. It was installed at MSE and TGF (Table 4). Images were taken every 30 min during daytime (Table 6). The settings of the main camera were “Program” for exposure, “Auto” for white balance, and 18 mm for focal length (equivalent to 28 mm on 35-mm camera). Images were saved in JPEG format at 5184 × 3456 pixels.

C. Canon EOS

This system consists of a digital SLR camera (EOS Kiss X3, Canon) and an automated shutter timer unit (Garage Shop, Nara, Japan). It was installed at TOE and TOS (Table 4). Images were taken once a day at 11:40 local time (Table 6). Images were saved in JPEG format at 4752 × 3168 pixels. Exposure was set to automatic at f/11. White balance was set to daylight mode.

D. Minolta

This system consists of a film camera (Minolta Alpha 707si, Konika Minolta, Tokyo, Japan) with a semi-fisheye lens, driven by an internal battery. It was installed at EGT (Table 4). Using the internal timer, it operated once a day at approximately noon local time. Exposure was set to automatic. The 35-mm negative film was scanned and digitized, and images were saved in JPEG format at 939 × 680 pixels.

E. Nikon D3300

This system consists of a digital SLR camera (D3300, Nikon) and an automated shutter timer unit (E-6317 N3, Etsumi). It was installed at KEW from 2016. Images were taken at 1 hour or 3 hour interval. Images were saved in JPEG format at 6000 × 4000 pixels. Exposure was set to automatic at f/5.6. White balance was set to daylight.

F. Raspberry Pi

This system consists of a Raspberry Pi model B+ (Raspberry Pi Foundation, UK; https://www.raspberrypi.org/), a hand-held computer about the size of a credit card, with an added camera module (Raspberry Pi Camera V2). It was installed at LAM, PFA, and TKY (Table 4). Under the control of the “crontab” command, the “raspistill” command took images at times and intervals specific to each device (Table 6). Images were saved in JPEG format at 2592 × 1944 pixels. Exposure was set to automatic. White balance was set to different values among devices (see EXIF data of each image).

G. TLC200

The TLC200 is a time-lapse digital camera that runs on AA batteries (Brinno, USA; http://brinno.com/time-lapse-camera/TLC200). This system was installed at KEW and MMF. Images were taken every 1 h (Table 4). Images were saved in AVI format (frame-by-frame recording) at 1280 × 720 pixels. Exposure and white balance settings are not available in this system. We extracted still images in the free AVI2JPG v. 6.10 video editing software.

Tasks of contributors

The administrative tasks and managers are summarized in Table 7.

Table 7 Summary of tasks and managers of the time-lapse digital camera systems

1:Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 7, Higashi 1-1-1 Tsukuba, Ibaraki 305-8569, Japan

2:Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan

3:National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan

4:Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan

5:National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan

6:Research and Development Center for Global Change, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan

8. PUBLICATIONS

All published papers based on the image database are summarized in Table 8.

Table 8 Summary of published papers based on long-term, continuous time-lapse images taken at the 29 PEN sites

9. DATA STRUCTURE

A. File format

The data files are saved in JPEG format.

B. Naming rules of the data files

The data files are named “dc_YEAR_DOY_TIMEUTC_SITE__CAMERA±EXPOSURE.jpg”, where YEAR is the year, DOY is the day of year, TIMEUTC is the time expressed in Universal Coordinated Time, SITE is the site ID, CAMERA is the camera ID, and EXPOSURE is the setting of exposure. “EXPOSURE” is an option (–1, –2, –3, –4, –5, –10, –20, +5, +10 and +20). Eight cameras (SGD: g00_u; TKC: f02_u; TKY: f02_d, f02_u, L12_u, miz_u, y02_d, and y02_u) use this option. As an exception, the data files of t27_w and t27_n in KEW and t30_d in MMF are named “dc_YEAR_DOY_+0900 _SITE__CAMERA_ ORDER.jpg”, where YEAR is the year, DOY is the day of year, SITE is the site ID, CAMERA is the camera ID, and ORDER is the order of photograph (from 1 to 21).

10. DATA ACCESS

The catalog page for the data files is here (http://pen.jamstec.go.jp).

The URL format for the data files is http://pen.jamstec.go.jp/SITE_/public_html/original/dc/dc_YEAR/​dc_YEAR_DOY/, where SITE is the site ID, YEAR is the year, and DOY is the day of year. You can use “wget” command for downloading data.

11. ACCESSIBILITY

License

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

12. ACKNOWLEDGMENTS

This research was supported and conducted by many projects with funding provided by many ministries, institutes, and non-governmental organization (Table 2), notably the Global Change Observation Mission (PI #102, 116, and 117) of the Japan Aerospace Exploration Agency (JAXA). The authors thank K. Kurumado and Y. Miyamoto (River Basin Research Center, Gifu University), Dr. T. Hara (Hokkaido University), technical staff of Uryu and Teshio experimental forests (Hokkaido University), A. Iku (Kyoto University), Dr. U. Shimizu-kaya (Shimane University), and M. Takeuchi (JAMSTEC) for their assistance in the field and laboratory. This data paper is dedicated to the late Dr. Rikie Suzuki of JAMSTEC, our esteemed colleague.

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