Shin NAGAI

Peer-reviewed paper

• 69. Shin N, Maruya Y, Saitoh TM and Tsutsumida N. 2021. Usefulness of social sensing using text mining of tweets for detection of autumn phenology. Front. For. Glob. Change 4: 659910. doi: 10.3389/ffgc.2021.659910.
• 68. Kotani A, Shin N, Tei S, Makarov A and Gavrilyeva T. 2021. Seasonality in human interest in berry plants detection by Google Trends. Front. For. Glob. Change 4: 688835. doi: 10.3389/ffgc.2021.688835.
• 67. Tei S, Kotani A, Sugimoto A and Shin N. 2021. Geographical, climatological, and biological characteristics of tree radial growth response to autumn climate change. Front. For. Glob. Change 4: 687749. doi: 10.3389/ffgc.2021.687749.
• 66. Shin N, Saitoh TM, Nasahara KN. 2021. How did the characteristics of the growing season change during the past 100 years at a steep river basin in Japan? PLoS ONE 16(7): e0255078. https://doi.org/10.1371/journal.pone.0255078.
• 65. Nagai S, Saitoh TM, Morimoto H. 2020. Does global warming decrease the correlation between cherry blossom flowering date and latitude in Japan?. International Journal of Biometeorology 64: 2205-2210. https://doi.org/10.1007/s00484-020-02004-w.
• 64. Shin N, Kotani A, Sato T, Sugimoto A, Maximov TC, Nogovitcyn A, Miyamoto Y, Kobayashi H, Tei S. 2020. Direct measurement of leaf area index in a deciduous needle-leaf forest, eastern Siberia. Polar Science, https://doi.org/10.1016/j.polar.2020.100550.
• 63. Nagai S, Kotani A, Morozumi T, Kononov AV, Petrov RE, Shakhmatov R, Ohta T, Sugimoto A, Maximov TC, Suzuki R, Tei S. 2020. Detection of year-to-year spring and autumn bio-meteorological variations in siberian ecosystems. Polar Science, https://doi.org/10.1016/j.polar.2020.100534.
• 62. Miura T, Nagai S. 2020. Landslide detection with Himawari-8 geostationary satellite data: A case study of a torrential rain event in Kyushu, Japan. Remote Sensing 12: 1734, doi:10.3390/rs12111734.
• 61. Nakano T, Nagai S, Yamatogi T, Kurihara T,Okamura K. 2020. Use of sea surface discoloration to monitor and discriminate the causative genera of harmful algal blooms (HABs): Practical use of digital repeat photography. Ecological Informatics 59: https://doi.org/10.1016/j.ecoinf.2020.101114.
• 60. Alles GR, Comba JLD, Vincent J-M, Nagai S, Schnorr LM. Measuring phenology uncertainty with large scale image processing. Ecological Informatics 59: https://doi.org/10.1016/j.ecoinf.2020.101109.
• 59. Nagai S, Saitoh TM, Miura T. Peak autumn leaf colouring along latitudinal and elevational gradients in Japan evaluated with online phenological data. International Journal of Biometeorology, 64: 1743-1754, https://doi.org/10.1007/s00484-020-01953-6.
• 58. Shin N, Shibata H, Osawa T, Yamakita T, Nakamura M, Kenta T. 2020. Toward more data publication of long-term ecological observations. Ecological Research, in press.
• 57. Morozumi T, Sugimoto A, Rikie Suzuki R, Nagai S, Kobayashi H, Tei S, Takano S, Shakhmatov R, Maximov T. 2020. Photographic records of plant phenology and spring river flush timings in river lowland ecosystem of taiga-tundra boundary, north-eastern Siberia. Ecological Research, https://doi.org/10.1111/1440-1703.12107.
• 56. Nagai S, Morimoto H, Saitoh TM. 2020. A simpler way to predict flowering and full bloom dates of cherry blossoms by self-organizing maps. Ecological Informatics, 56: 101040, https://doi.org/10.1016/j.ecoinf.2019.101040.
• 55. Miura T, Nagai S, Takeuchi M, Ichii K, Yoshioka H. Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data. Scientific Reports, 9: 15692, https://doi.org/10.1038/s41598-019-52076-x.
• 54. Tei S, Morozumi T, Nagai S, Takano S, Sugimoto A, Shingubara R, Fan R, Fedorov A, Gavrilyeva T, Tananaev N,Maximov T. An extreme flood caused by a heavy snowfall over the Indigirka River basin in Northeastern Siberia. Hydrological Processes, 34(3): 522-537, DOI: 10.1002/hyp.13601.
• 53. Nagai S, Saitoh TM, Yoshitake S. (2019) Cultural ecosystem services provided by flowering of cherry trees under climate change: a case study of the relationship between the periods of flowering and festivals. International Journal of Biometeorology, 63(8): 1051-1058.
• 52. Tsutsumida N. Nagai S, Rodríguez-Veiga P, Katagi J, Nasahara K, Tadono T. (2019) Mapping spatial accuracy of the forest type classification in JAXA’s high-resolution land use and land cover map. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-3/W1: 57-63.
• 51. Morozumi T, Shingubara R, Murase J, Nagai S, Kobayashi H, Takano S, Tei S, Fan R, Maximov TC, Sugimoto A. Usability of water surface reflectance for the determination of riverine dissolved methane during extreme flooding in northeastern Siberia. Polar Science, 21: 186–194, https://doi.org/10.1016/j.polar.2019.01.005
• 50. Tei S, Nagai S, Sugimoto A. Effects of climate dataset type on tree-ring analysis: A case study for Siberian forests. Polar Science, 21: 136–145, https://doi.org/10.1016/j.polar.2018.10.008
• 49. Nagai S, Ikeda K, Kobayashi H. (2018) Simple method to detect year-to-year variability of blooming phenology of Cerasus × yedoensis by digital camera. International Journal of Biometeorology 62(12) 2183-2188
• 48. Kobayashi H, Nagai S, Kim Y, Yang W, Ikeda K, Ikawa H, Nagano H, Suzuki R. (2018) In situ observations reveal how spectral reflectance responds to growing season phenology of an open evergreen forest in Alaska. Remote Sensing, 10:1071; doi:10.3390/rs10071071.
• 47. Nagai S, Akitsu T, Saitoh TM, et al. (2018) 8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological Eyes Network. Ecological Research 33, 1091–1092, DOI 10.1007/s11284-018-1633-x (data paper).
• 46. Nagai S, Saitoh TM, Kajiwara K, Yoshitake S, Honda Y. (2018) Investigation of the potential of drone observations for detection of forest disturbance caused by heavy snow damage in a Japanese cedar (Cryptomeria japonica) forest. Journal of Agricultural Meteorology, 74(3):123-127 (Research Note).
• 45. Nagai S, Nasahara KN, Yoshitake S, Saitoh TM (2017) Seasonality of leaf litter and leaf area index data for various tree species in a cool-temperate deciduous broad-leaved forest, Japan, 2005–2014. Ecological Research, 32:297–297.
• 44. Nagai S, Nasahara KN (2017) Seasonal leaf phenology data for 12 tree species in a cool-temperate deciduous broadleaved forest in Japan from 2005 to 2014. Ecological Research, 32:107–107.
• 43. Anderson HB, Nilsen L, Tømmervik H, Karlsen SR, Nagai S, Cooper EJ. (2016) Using ordinary digital cameras in place of near-infrared sensors to derive vegetation indices for phenology studies of high Arctic vegetation. Remote Sensing, 8:847, doi:10.3390/rs8100847
• 42. Kobayashi H, Yunus AP, Nagai S, Sugiura K, Kim Y, Van Dam B, Nagano H, Zona D, Harazono Y, Bret-Harte MS, Ichii K, Ikawa H, Iwata H, Oechel WC, Ueyama M, Suzuki R (2016) Latitudinal gradient of spruce forest understory and tundra phenology in Alaska as observed from satellite and ground-based data. Remote Sensing Environment, 177:160–170.
• 41. Nagai S, Ichie T, Yoneyama A, Kobayashi H, Inoue T, Ishii R, Suzuki R, Itioka T (2016) Usability of time-lapse digital camera images to detect characteristics of tree phenology in a tropical rainforest. Ecological Informatics, 32:91–106.
• 40. Brown T, Hultine K, Steltzer H, Denny E, Denslow M, Granados J, Henderson S, Moore D, Nagai S, SanClements M, Sánchez-Azofeifa GA, Sonnentag O, Tazic D, Richardson A. Using phenocams to monitor our changing Earth: towards a global phenocam network. Frontiers in Ecology and the Environment 14(2), 84–93
• 39. Nagai S, Inoue T, Ohtsuka T, Yoshitake S, Nasahara KN, Saitoh TM. 2015. Uncertainties involved in leaf fall phenology detected by digital camera. Ecological Informatics, 30:124-132.
• 38. Nagai S, Nasahara KN, Inoue T, Saitoh TM, Suzuki R. (2016) Review: Advances in in situ and satellite phenological observations in Japan. Int J Biometeorol, 60:615-627
• 37. Ikawa H, Nakai T, Busey RC, Kim Y, Kobayashi H, Nagai S, Ueyama M, Saito K, Nagano H, Suzuki R, 2015. Hinzman L. Understory CO2, sensible heat, and latent heat fluxes in a black spruce forest in interior Alaska. Agric For Meteorol, 214–215:80–90
• 36. Inoue T, Nagai S. (2015) Influence of temperature change on plant tourism in Japan: a case study of the flowering of Lycoris radiata (red spider lily). Jpn J Biometeorol, 52(4): 175-184.
• 35. Nagai S, Inoue T, Suzuki R (2015) Leaf-coloring information published on web sites and its utility in the ground-truthing of satellite remote-sensing data for mapping autumn leaf phenology. Jpn J Biometeorol 52(2):119–129 (in Japanese with English abstract)
• 34. Nasahara KN., Nagai S. 2015. Development of an in-situ observation network for terrestrial ecological remote sensing — the Phenological Eyes Network (PEN). Ecological Research 30(2):211–223.
• 33. 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. Ecological Research, 30(2):267-277.
• 32. 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. Ecological Informatics, 25:1–9.
• 31. Inoue T., Nagai S., Yamashita S., Fadaei H., Ishii R., Okabe K., Taki H., Honda Y., Kajiwara K., Suzuki R. 2014. Unmanned aerial survey of fallen trees in a deciduous broadleaved forest in eastern Japan. PLOS ONE 9(10): e109881.
• 30. Nagai S., Ishii R., Suhaili A.B., Kobayashi H., Matsuoka M., Ichie T., Motohka T., Kendawang J.J., Suzuki R. 2014. Usability of noise-free daily satellite-observed green–red vegetation index values for monitoring ecosystem changes in Borneo. International Journal of Remote Sensing, 35(23): 7910–7926.
• 29. Nagai S., Yoshitake S., Inoue T., Suzuki R., Muraoka H., Nasahara KN., Saitoh TM. Year-to-year blooming phenology observation by using time-lapse digital camera images. Journal of Agriculture and Meteorology, 70 (3): 163-170.
• 28. 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. Ecological Informatics, 22:58–68.
• 27. 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 59:47–54
• 26. 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. International Journal of Remote Sensing, 35(10): 3520–3536.
• 25. Kobayashi H., Suzuki R., Nagai S., Nakai T., Kim Y. 2014. Spatial scale and landscape heterogeneity effects on FAPAR in an open canopy black spruce forest in interior Alaska. Geoscience and Remote Sensing Letters 35(10): 3520–3536.
• 24. Nagai S., Saitoh T.M., Kurumado K., Tamagawa I., Kobayashi H., Inoue T., Suzuki R., Gamo M., Muraoka H., Nasahara K.N. 2013. Detection of bio-meteorological year-to-year variation by using digital canopy surface images of a deciduous broad-leaved forest. SOLA 9: 106-110.
• 23. Nagai S., Saitoh T.M., Noh N.J., Yoon T.K., Kobayashi H., Suzuki R., Nasahara K.N., Yowhan Son Y., Muraoka H. 2013. 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. Ecological Informatics 18: 10-19
• 22. Nakai T., Kim Y., Busey R.C., Suzuki R., Nagai S., Kobayashi H., Park H., Sugiura K., Ito A. 2013. Characteristics of evapotranspiration from a permafrost black spruce forest in interior Alaska. Polar Science 7: 136-148
• 21. Sugiura K., Nagai S., Nakai T., Suzuki R. 2013. Applicability of time-lapse digital cameras for ground-truth measurements of satellite indices in the boreal forests of Alaska. Polar Science 7: 149-161
• 20. Nagai S., Nakai T., Saitoh T.M., Busey R.C., Kobayashi H., Suzuki R., Muraoka H., Kim Y. 2013. 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 Science 7: 125-135
• 19. Thanyapraneedkul J., Muramatsu K., Daigo M., Furumi S., Soyama N., Nasahara K.N., Muraoka H., Noda H.M., Nagai S., Maeda T., Mano M., Mizoguchi Y. 2012.
  A Vegetation Index to Estimate Terrestrial Gross Primary Production Capacity for the Global Change Observation Mission-Climate (GCOM-C)/Second-Generation Global Imager (SGLI) Satellite Sensor. Remote Sensing 4: 3689-3712
• 18. Muraoka H., Noda H.M., Nagai S., Motohka T., Saitoh T.M., Nasahara K.N., Saigusa N. 2012. Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest. Journal of Plant Ecology doi: 10.1093/jpe/rts037.
• 17. Potithep S., Nagai S., Nasahara K.N., Muraoka H., Suzuki R. 2013. Two separate periods of the LAI-VIs relationships using in situ measurements in a deciduous broadleaf forest. Agricultural and Forest Meteorology. 169:148-155
• 16. Saitoh T.M., Nagai S, Saigusa N, Kobayashi H., Suzuki R., Nasahara K.N., Muraoka H. 2012. 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. Ecological Informatics 11:45-54
• 15. Saitoh T.M., Nagai S., Noda H.M., Muraoka H. and Nasahara K.N. 2012. Examination of the extinction coefficient in the Beer-Lambert law for an accurate estimation of the forest canopy leaf area index. Forest Science and Technology8(2):67-76
• 14. Saitoh, T.M., Nagai, S., Yoshino, J., Muraoka, H., Saigusa, N., Tamagawa, I. 2012. 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 Journal of Forest Research 15-1:19-30
• 13. Inoue, T., Nagai, S., Inoue, S., Ozaki, M., Sakai, S., Muraoka, H., Koizumi, H. 2012. Seasonal variability of soil respiration in multiple ecosystems under the same physical-geographical environmental conditions in central Japan. Forest Science and Technology 8(2):52-60
• 12. Nagai, S., Saitoh, T.M., Kobayashi, H., Ishihara, M., Motohka, T., Suzuki, R., Nasahara, K.N., Muraoka, H. 2012. In situ examination for the relationship between various vegetation indices and tree phenology in an evergreen coniferous forest, Japan. International Journal of Remote Sensing 33(19):6202-6214
• 11. Nagai, S., Saitoh, T.M., Suzuki, R., Nasahara, K.N., Lee, W.-K., Son, T., Muraoka, H. 2011. The necessity and availability of noise-free daily satellite-observed NDVI during rapid phenological changes in terrestrial ecosystems in East Asia. Forest Science and Technology 7(4):174-183
• 10. Motohka, T., Nasahara, K.N., Murakami, K., Nagai, S. 2011. Evaluation of cloud noises on MODIS daily spectral indices based on in situ measurements. Remote Sensing 2:2369-2387
• 9. Nagai, S., Maeda, T., Gamo, M., Muraoka, H., Suzuki, R., Nasahara, K.N. 2011. Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecology and Diversity 4:78-88
• 8. Sugiura, K. et al. (8th author) 2011. Supersite as a common platform for multi-observations in Alaska for a collaborative framework between JAMSTEC and IARC. Jamstec Report of Research and Development 12:61-69
• 7. Kume, A., Nasahara, K.N., 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. Journal of Plant Research 124(1):99-106
• 6. Muraoka, H., Saigusa, N., Nasahara, K.N., Noda, H., Yoshino, J., Saitoh, T.M., 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, Journal of Plant Research 123(4):563-576
• 5. Nagai, S., Nasahara, K.N., Muraoka, H., Akiyama, T., Tsuchida, S. 2010. Field experiments to test the use of the normalized difference vegetation index for phenology detection. Agricultural and Forest Meteorology 150:152-160
• 4. Nagai, S., Saigusa, N., Muraoka, H., Nasahara, K.N. 2010. What makes the satellite-based EVI-GPP relationship unclear in a deciduous broad-leaved forest? Ecological Research 25:359-365
• 3. Nasahara, K.N., Muraoka, H., Nagai, S., Mikami, H. 2008. Vertical integration of leaf area index in a Japanese deciduous broad-leaved forest. Agricultural and Forest Meteorology 148:1136-1146
• 2. Nagai, S., Ichii, K., Morimoto, H. 2007. Interannual variations in vegetation activities and climate variability caused by ENSO in tropical rainforests. International Journal of Remote Sensing 28(6):1285–1297
• 1. Nagai, S., Ichii, K., Morimoto, H. 2005. Relationship between interannual variations in satellite-based vegetation index and ENSO phase over tropical forests. Journal of Agricultural Meteorology 60(6):1211-1214

Book

• 6. Nagai S, Kobayashi H, Suzuki R. (2019) Remote sensing of vegetation. In Water-Carbon Dynamics in Eastern Siberia, Eds. Ohta T, Hiyama T, Iijima Y, Kotani A, Maximov TC, Springer, pp.231–252.
• 5. Nagai S, Nasahara KN, Akitsu TK, Saitoh TM, Muraoka H. (2020) Importance of the collection of abundant ground-truth data for accurate detection of spatial and temporal variability of vegetation by satellite remote sensing, in biogeochemical cycles: ecological drivers and environmental impact. American Geophysical Union.
  https://doi.org/10.1002/9781119413332.ch11.
• 4. Nagai S, Kobayashi H, Suzuki R.
  Remote sensing of vegetation. Water-Carbon Dynamics in Eastern Siberia, Eds. T. Ohta, T. Hiyama, Y. Iijima, A. Kotani, TC. Maximov, Springer, in press
• 3. Nagai, S. 2012. Phenological observations by using daily satellite-observed vegetation index, in Pastoralism and ecosystem network in Mongolia (eds. Batjargal, Z., Fujita, N., Yamamura, N.), Admon Mongolia, 576p (in Mongolia).
• 2. Muraoka, H., Ishii, R., Nagai, S. et al. 2012. Linking remote sensing and in situ ecosystem / biodiversity observations by "Satellite Ecology", in Biodiversity Observation Network in Asia-Pacific Region (Ecological Research Monographs) (eds. Nakano, S., Nakashizuka, T., and Yahara, T.), Springer Verlag Japan, 277-308
• 1. Maeda, T., Gamo, M., Kondo, H., Panuthai, S., Ishida, A., Nagai, S., Okamoto, S. 2008. Leaf phenology detected by fixed view camera images in a tropical seasonal forest at Mae Klong, Thailand, Tropical forestry change in a changing world volume 3: GIS/GPS/RS: Applications in natural resources and environmental management. FORTROP II international conference, Kasetsart University, Bangkok, Thailand, 167-181