Ecological Research

, Volume 30, Issue 2, pp 211–223 | Cite as

Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN)

Special Feature Long-term and interdisciplinary research on forest ecosystem functions: Challenges at Takayama site since 1993

Abstract

The Phenological Eyes Network (PEN), which was established in 2003, is a network of long-term ground observation sites. The aim of the PEN is to validate terrestrial ecological remote sensing, with a particular focus on seasonal changes (phenology) in vegetation. There are three types of core sensors at PEN sites: an Automatic Digital Fish-eye Camera, a HemiSpherical SpectroRadiometer, and a Sun Photometer. As of 2014, there are approximately 30 PEN sites, among which many are also FluxNet and/or International Long Term Ecological Research sites. The PEN is now part of a biodiversity observation framework. Collaborations between remote sensing scientists and ecologists working on PEN data have produced various outcomes about remote sensing and long-term in situ monitoring of ecosystem features, such as phenology, gross primary production, and leaf area index. This article reviews the design concept and the outcomes of the PEN, and discusses its future strategy.

Keywords

Remote sensing Phenology Ground truth Biodiversity Vegetation index 

References

  1. Akitsu T, Nasahara KN, Noda H, Motohka T, Murakami K, Tsuchida S, Nagai S (2011) Long-term observation of seasonal and yearly variation of grassland by an automatic digital camera. Bulletin of the Terrestrial Environment Research Cente, University of Tsukuba, vol 12, pp 5–12Google Scholar
  2. Choi J, Kang S, Choi G, Nasahara KN, Motohka T, Lim J-H (2011) Monitoring canopy phenology in a deciduous broadleaf forest using the Phenological Eyes Network (PEN). J Ecol Field Biol 34:149–156CrossRefGoogle Scholar
  3. Gamon JA, Penuelas J, Field C (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44CrossRefGoogle Scholar
  4. Gamon JA, Rahman AF, Dungan JL, Schildhauer M, Huemmrich KF (2006) Spectral Network (SpecNet)––what is it and why do we need it? Remote Sens Environ 103:227–235CrossRefGoogle Scholar
  5. Graham H, Riordan EC, Yuen EM, Estrin D, Rundell PW (2010) Public internet-connected cameras used as a cross-continental ground-based plant phenology monitoring system. Glob Chang Biol 16:3014–3023. doi:10.1111/j.1365-2486.2010.02164.x Google Scholar
  6. Hadano M, Nasahara KN, Motohka T, Noda HM, Murakami K, Hosaka M (2013) High-resolution prediction of leaf onset date in Japan in the 21st century under the IPCC A1B scenario. Ecol Evol 3:1798–1807CrossRefPubMedCentralPubMedGoogle Scholar
  7. Higuchi A, Nishida K, Iida S, Nimura N, Kondoh A (2000) Preliminary Global Imager Experiment at Environmental Research Center, University of Tsukuba (PGLIERC): its overview. J Jpn Assoc Hydrol Sci 30:81–91 (in Japanese with English summary)Google Scholar
  8. Horning N, Robinson JA, Sterling EJ, Turner W, Spector A (2010) Remote sensing for ecology and conservation. Oxford University Press, OxfordGoogle Scholar
  9. Ide R, Oguma H (2010) Use of digital cameras for phenological observations. Ecol Inform 5:339–347. doi:10.1016/j.ecoinf.2010.07.002 CrossRefGoogle Scholar
  10. Ide R, Oguma H (2013) A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems. Ecol Inform 16:25–34. doi:10.1016/j.ecoinf.2013.04.003 CrossRefGoogle Scholar
  11. Ide R, Nakaji T, Motohka T, Oguma H (2011) Advantages of visible-band spectral remote sensing at both satellite and near-surface scales for monitoring the seasonal dynamics of GPP in a Japanese larch forest. J Agric Meteorol 67:75–84CrossRefGoogle Scholar
  12. Inoue T, Nagai S, Saitoh TM, Muraoka H, Nasahara KN, Koizumi H (2014) Detection of the different characteristics of year-to-year variation in foliage phenology among deciduous broad-leaved tree species by using daily continuous canopy surface images. Ecol Inform 22:58–68. doi:10.1016/j.ecoinf.2014.05.009 CrossRefGoogle Scholar
  13. Inoue T, Nagai S, Kobayashi H, Koizumi H (2015) Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem. Ecol Inform 25:1–9. doi:10.1016/j.ecoinf.2014.09.013 CrossRefGoogle Scholar
  14. Ishihara MI, Hiura T (2011) Modeling leaf area index from litter collection and tree data in a deciduous broadleaf forest. Agric For Meteorol 151:1016–1022CrossRefGoogle Scholar
  15. Ishihara M, Matsunaga T, Tsuchida S, Nishida K, Oguma H, Tamura M (2006) A study on alternatives of photochemical reflectance index based on spectral characteristics of MODIS bands. J Remote Sens Soc Jpn 26:125–137 (in Japanese with English summary)Google Scholar
  16. Ishihara M, Inoue Y, Ono K, Akitsu T, Nasahara K (2014) Comparative analysis on the consistency of measurements for a rice paddy obtained by different optical satellite sensors. J Remote Sens Soc Jpn 34:22–32 (in Japanese with English summary)Google Scholar
  17. Ito A (2010) Evaluation of the impacts of defoliation by tropical cyclones on a Japanese forest’s carbon budget using flux data and a process-based model. J Geophys Res 115:G04013Google Scholar
  18. Jones HG, Vaughan RA (2010) Remote sensing of vegetation. Oxford University Press, OxfordGoogle Scholar
  19. 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. doi:10.1016/j.rse.2007.04.010 CrossRefGoogle Scholar
  20. Kume A, Nasahara KN, Nagai S, Muraoka H (2011) The ratio of transmitted near-infrared radiation to photosynthetically active radiation (PAR) increases in proportion to the adsorbed PAR in the canopy. J Plant Res 124:99–106CrossRefPubMedGoogle Scholar
  21. Maeda T, Gamo M (2004) Japan Patent Number 2004-4280823, 24 Feb 2004Google Scholar
  22. Maki M, Goto S, Ishihara M, Nishida K, Kojima T, Akiyama T (2008) Mapping the potential distribution of dwarf bamboo using satellite imagery and DEM. J Remote Sens Soc Jpn 28:28–35 (in Japanese with English summary)Google Scholar
  23. Mikami H, Nishida K, Muraoka H (2006) Automatic detection of forest canopy gaps and estimation of leaf area index (LAI) using the digital fish-eye camera’s images. J Jpn Soc Photogram Remote Sens 45:13–22 (in Japanese with English summary)CrossRefGoogle Scholar
  24. Mizunuma T, Koyanagi T, Mencuccini M, Nasahara KN, Wingate L, Grace J (2011) The comparison of several colour indices for the photographic recording of canopy phenology of Fagus crenata Blume in eastern Japan. Plant Ecol Divers 4:67–77CrossRefGoogle Scholar
  25. Mizunuma T, Wilkinson M, Eaton EL, Mencuccini M, Morison JIL, Grace J (2013) The relationship between carbon dioxide uptake and canopy colour from two camera systems in a deciduous forest in southern England. Funct Ecol 27:196–207. doi:10.1111/1365-2435.12026 CrossRefGoogle Scholar
  26. Motohka T, Nasahara KN, Miyata A, Mano M, Tsuchida S (2009) Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset. Int J Remote Sens 30:4343–4357. doi:10.1080/01431160802549369 CrossRefGoogle Scholar
  27. Motohka T, Nasahara KN, Oguma H, Tsuchida S (2010) Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens 2:2369–2387CrossRefGoogle Scholar
  28. Motohka T, Nasahara KN, Murakami K, Nagai S (2011) Evaluation of sub-pixel cloud noises on MODIS daily spectral indices based on in situ measurements. Remote Sens 3:1644–1662CrossRefGoogle Scholar
  29. Murakami K, Nasahara KN, Akitsu T, Motohka T, Nagai S (2011) Changes of vegetation indices due to spectral specifications of satellite sensors in a grassland. Bulletin of the Terrestrial Environment Research Center, University of Tsukuba, vol 12, pp 13–20Google Scholar
  30. Muraoka H, Koizumi H (2009) “Satellite Ecology” for linking ecology, remote sensing and micrometeorology from plot to regional scales for ecosystem structure and function study. J Plant Res 122:3–20. doi:10.1007/s10265-008-0188-2 CrossRefPubMedGoogle Scholar
  31. 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. doi:10.1007/s10265-009-0270-4 CrossRefPubMedGoogle Scholar
  32. Muraoka H, Ishii R, Nagai S, Suzuki R, Motohka T, Noda H, Hirota M, Nasahara KN, Oguma H, Muramatsu K (2013a) Linking remote sensing and in situ ecosystem/biodiversity observations by “Satellite Ecology”. In: Nakano S et al (eds) The biodiversity observation network in the Asia-Pacific region: toward further development of monitoring. Ecological Research Monographs. Springer, Japan, pp 277–308. doi:10.1007/978-4-431-54032-8_21
  33. Muraoka H, Noda HM, Nagai S, Motohka T, Saitoh TM, Nasahara KN, Saigusa N (2013b) Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest. J Plant Ecol 6:393–407. doi:10.1093/jpe/rts037 CrossRefGoogle Scholar
  34. Myneni R, Keeling C, Tucker C (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:698–702CrossRefGoogle Scholar
  35. Nagai S, Nasahara KN, Ishihara M, Muraoka H (2008) Ecophysiological consideration on vegetation indices observed by satellites. J Jpn Agric Syst Soc 24:183–190 (in Japanese)Google Scholar
  36. Nagai S, Nasahara KN, Muraoka H, Akiyama T, Tsuchida S (2010a) Field experiments to test the use of the normalized difference vegetation index for phenology detection. Agric For Meteorol 150:152–160CrossRefGoogle Scholar
  37. Nagai S, Saigusa N, Muraoka H, Nasahara KN (2010b) What makes the satellite-based EVI–GPP relationship unclear in a deciduous broad-leaved forest? Ecol Res 25:359–365CrossRefGoogle Scholar
  38. Nagai S, Maeda T, Muraoka H, Suzuki R, Nasahara KN (2011a) Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecol Divers 4:78–88CrossRefGoogle Scholar
  39. Nagai S, Saitoh TM, Suzuki R, Nasahara KN, Lee W-K, Son Y, Muraoka H (2011b) The necessity and availability of noise-free daily satellite-observed NDVI during rapid phenological changes in terrestrial ecosystems in East Asia. For Sci Technol 7:174–183Google Scholar
  40. Nagai S, Saitoh TM, Kobayashi H, Ishihara M, Motohka T, Suzuki R, Nasahara KN, Muraoka H (2012) In situ examination for the relationship between various vegetation indices and tree phenology in an evergreen coniferous forest, Japan. Int J Remote Sens 33:6202–6214CrossRefGoogle Scholar
  41. Nagai S, Saitoh TM, Noh NJ, Yoon TK, Kobayashi H, Suzuki R, Nasahara KN, Son Y, Muraoka H (2013a) Utility of information in photographs taken upwards from the floor of closed-canopy deciduous broadleaved and closed-canopy evergreen coniferous forests for continuous observation of canopy phenology. Ecol Inform 18:10–19CrossRefGoogle Scholar
  42. Nagai S, Nakai T, Saitoh TM, Busey RC, Kobayashi H, Suzuki R, Muraoka H, Kim Y (2013b) Seasonal changes in camera-based indices from an open canopy black spruce forest in Alaska, and comparison with indices from a closed canopy evergreen coniferous forest in Japan. Polar Sci 7:125–135CrossRefGoogle Scholar
  43. Nagai S, Saitoh TM, Kurumado K, Tamagawa I, Kobayashi K, Inoue T, Suzuki R, Gamo M, Muraoka H, Nasahara KN (2013c) Detection of bio-meteorological year-to-year variation by using digital canopy surface images of a deciduous broad-leaved forest. SOLA 9:106–110CrossRefGoogle Scholar
  44. Nagai S, Inoue T, Ohtsuka T, Kobayashi H, Kurumado K, Muraoka H, Nasahara KN (2014a) 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 35:3520–3536CrossRefGoogle Scholar
  45. Nagai S, Ishii R, Suhaili A, Kobayashi H, Matsuoka M, Ichie T, Motohka T, Kendawang J, Suzuki R (2014b) Usability of noise-free daily satellite-observed green–red vegetation index values for monitoring ecosystem changes in Borneo. Int J Remote Sens 35:7910–7926. doi:10.1080/01431161.2014.978039 CrossRefGoogle Scholar
  46. Nagai S, Saitoh TM, Nasahara KN, Suzuki R (2015) Spatio-temporal distribution of the timing of start and end of growing season along vertical and horizontal gradients in Japan. Int J Biometeorol. doi:10.1007/s00484-014-0822-8 PubMedGoogle Scholar
  47. Nakaji T, Ide R, Takagi K, Kosugi Y, Ohkubo S, Nasahara KN, Saigusa N, Oguma H (2008) Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agric For Meteorol 148:776–787CrossRefGoogle Scholar
  48. Nakaji T, Kosugi Y, Takanashi S, Niiyama K, Noguchi S, Tani M, Oguma H, Rahim Nik A, Rahman Kassim A (2014) Estimation of light-use efficiency through a combinational use of the photochemical reflectance index and vapor pressure deficit in an evergreen tropical rainforest at Pasoh, Peninsular Malaysia. Remote Sens Environ 150:82CrossRefGoogle Scholar
  49. Nakanishi R, Kosugi Y, Ohkubo S, Nishida K, Oguma H, Takanashi S, Tani M (2006) Seasonal changes of a spectral reflectance index, PRI (photochemical reflectance index) in a temperate Japanese cypress forest. J Jpn Soc Hydrol Water Resour 19:475–482 (in Japanese with English summary)CrossRefGoogle Scholar
  50. Nasahara KN (2009) Simple algorithm for estimation of photosynthetically active radiation (PAR) using satellite data. SOLA 5:037–040. doi:10.2151/sola.2009-010 CrossRefGoogle Scholar
  51. 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 148:1136–1146CrossRefGoogle Scholar
  52. Nishida K (2007) Phenological Eyes Network (PEN): a validation network for remote sensing of the terrestrial ecosystems. AsiaFlux Newslett 21:9–13. http://www.asiaflux.net/
  53. Nishida K, Higuchi A, Iida S, Niimura N, Kondoh A (2001) PGLIERC: a test of remote sensing of hydrology in a grassland. IAHS Publ 267:223–224Google Scholar
  54. Noda HM, Motohka T, Murakami K, Muraoka H, Nasahara KN (2013) Accurate measurement of optical properties of narrow leaves and conifer needles with a typical integrating sphere and spectroradiometer. Plant Cell Environ. doi:10.1111/pce.12100 PubMedGoogle Scholar
  55. 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. doi:10.1007/s11284-013-1096-z Google Scholar
  56. Oikawa T, Yamamoto S (2013) Carbon dynamics in terrestrial ecosystems: the system approach to Earth environment. Kyoto University Press, Kyoto (in Japanese)Google Scholar
  57. Potithep S, Nagai S, Nasahara KN, Muraoka H, Suzuki R (2013) Two separate periods of the LAI–VIs relationships using in situ measurements in a deciduous broadleaf forest. Agric For Meteorol 169:148–155CrossRefGoogle Scholar
  58. Richardson AD, Jenkins JP, Braswell BH, Hollinger DY, Ollinger SV, Smith M-L (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323–334CrossRefPubMedGoogle Scholar
  59. Saigusa N, Ichii K, Murakami H, Hirata R, Asanuma J, Den H, Han S-J, Ide R, Li S-G, Ohta T, Sasai T, Wang S-Q, Yu G-R (2010) Impact of meteorological anomalies in the 2003 summer on Gross Primary Productivity in East Asia. Biogeosciences 7:641–655. doi:10.5194/bg-7-641-2010 CrossRefGoogle Scholar
  60. Saitoh TM, Nagai S, Noda HM, Muraoka H, Nasahara KN (2012a) Examination of the extinction coefficient in the Beer–Lambert law for an accurate estimation of the forest canopy leaf area index. For Sci Tech 8:67–76Google Scholar
  61. Saitoh TM, Nagai S, Yoshino J, Muraoka H, Saigusa N, Tamagawa I (2012b) Functional consequences of differences in canopy phenology for the carbon budgets of two cool-temperate forest types: simulations using the NCAR/LSM model and validation using tower flux and biometric data. Eurasian J For Res 15:19–30Google Scholar
  62. Saitoh TM, Nagai S, Saigusa N, Kobayashi H, Suzuki R, Nasahara KN, Muraoka H (2012c) Assessing the use of camera-based indices for characterizing canopy phenology in relation to gross primary production in a deciduous broad-leaved and an evergreen coniferous forest in Japan. Ecol Inform 11:45–54. doi:10.1016/j.ecoinf.2012.05.001 CrossRefGoogle Scholar
  63. Sasai T, Ichii K, Yamaguchi Y, Nemani R (2005) Simulating terrestrial carbon fluxes using the new biosphere model “biosphere model integrating eco-physiological and mechanistic approaches using satellite data” (BEAMS). J Geophys Res 110(G2):G02014CrossRefGoogle Scholar
  64. Secades C, O’Connor B, Brown C, Walpole M (2014) Earth observation for biodiversity monitoring: a review of current approaches and future opportunities for tracking progress towards the Aichi Biodiversity Targets. Secretariat of the Convention on Biological Diversity, Montreal, Canada. Technical Series 72Google Scholar
  65. Thanyapraneedkul J, Muramatsu K, Daigo M, Furumi S, Soyama N, Nasahara KN, Muraoka H, Noda HM, Nagai S, Maeda T, Mano M, Mizoguchi Y (2012) A vegetation index to estimate terrestrial gross primary production capacity for the GCOM-C/SGLI satellite sensor. Remote Sens 4:3689–3720. doi:10.3390/rs4123689 CrossRefGoogle Scholar
  66. Toda M, Nishida K, Ohte N, Tani M, Musiake K (2002) Observations of energy fluxes and evapotranspiration over terrestrial complex land covers in the tropical Monsoon environment. J Meteorol Soc Japan 80:465–484CrossRefGoogle Scholar
  67. Tsuchida S, Nishida K, Iwao K, Kawato W, Oguma H, Iwasaki A (2005) Phenological Eyes Network for validation of remote sensing data. J Remote Sens Soc Jpn 25:282–288 (in Japanese with English summary)Google Scholar
  68. Turner DP, Ritts WD, Zhao M, Kurc SA, Dunn AL, Wofsy SC, Small EE, Running SW (2006) Assessing interannual variation in MODIS-based estimates of gross primary production. IEEE Trans Geosci Remote Sens 44:1899–1907CrossRefGoogle Scholar
  69. White MA, Thornton PE, Running SW (1997) A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob Biogeochem Cycles 11:217–234CrossRefGoogle Scholar
  70. Wingate L, Richardson AD, Weltzin JF, Nasahara KN, Grace J (2008) Keeping an eye on the carbon balance: linking canopy development and net ecosystem exchange using a webcam. Flux Lett 1:14–17Google Scholar
  71. Yamamoto H, Kamei A, Nakamura R, Yamamoto N, Iwao K, Tsuchida S, Tanaka Y, Sekiguchi S (2010) Field sensor virtual organization integrated with satellite data on a GEO grid. Data Sci J 8:IGY21–IGY31CrossRefGoogle Scholar
  72. Yamashita M, Yoshimura M (2008) Development of sky conditions observation method using whole sky camera. J Jpn Soc Photogram Remote Sens 47:50–59 (in Japanese with English summary)CrossRefGoogle Scholar
  73. Yamashita M, Yoshimura M (2010) Ground based sky conditions observation by whole-sky cameras for incident PAR estimation. J Remote Sens Soc Jpn 30:157–165 (in Japanese with English summary)Google Scholar
  74. Zukemura C, Motohka T, Nasahara KN (2011) Detection of abandoned rice paddies with satellite remote sensing. J Remote Sens Soc Jpn 31:55–62 (in Japanese with English summary)Google Scholar

Copyright information

© The Ecological Society of Japan 2015

Authors and Affiliations

  1. 1.Faculty of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan
  2. 2.Department of Environmental Geochemical Cycle ResearchJapan Agency for Marine-Earth Science and Technology (JAMSTEC)YokohamaJapan

Personalised recommendations