永井 信

プロフィール

Shin NAGAI

物質循環・人間圏研究グループ
主任研究員

連絡先

国立研究開発法人 海洋研究開発機構　
地球表層システム研究センター　横浜研究所
〒236-0001 神奈川県横浜市金沢区昭和町3173-25

nagais [at] jamstec.go.jp

履歴

生年月日

1975年12月10日

学歴

1999年 名古屋大学情報文化学部社会システム情報学科卒業
2001年 名古屋大学大学院人間情報学研究科社会情報学専攻修了
2007年 名古屋大学大学院環境学研究科地球環境科学専攻修了（博士（環境学））

専攻分野

リモートセンシング、生態学、地上検証、Phenological Eyes Network、気候変動

職歴

2019年4月〜現在 海洋研究開発機構　地球環境部門地球表層システム研究センター/北極環境変動総合研究センター　主任研究員
2017年7月～2019年3月 海洋研究開発機構　地球環境観測研究開発センター　主任研究員
2014年4月～2017年6月 海洋研究開発機構　地球表層物質循環研究分野　主任研究員
2009年4月～2014年3月 海洋研究開発機構　地球環境変動領域　技術研究副主任
2007年4月～2009年3月 岐阜大学21世紀COEプログラム「衛星生態学創生拠点」ポスドク研究員
2003～2005年度 名古屋大学21世紀COEプログラム「太陽・地球・生命圏相互作用系の変動学」DC研究員等

受賞

  • 第18回 Ecological Research 論文賞

    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.

出版物

査読付き論文

  • 80. 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.
  • 79. 丸谷靖幸・小林知朋・永井 信・宮本昇平・矢野真一郎. 2021. 気象官署の降水量を用いた気候変動に伴う日本全国の長期的気候変化傾向の解明. 土木学会論文集B1（水工学）特集号・水工学論文集. 印刷中.
  • 78. 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.
  • 77. 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.
  • 76. 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.
  • 75. 永井 信・丸谷靖幸・斎藤 琢. 2021. 中山間地域の流域における人・森林・気象災害の現状と関わり: 高山市大八賀川流域における豪雨・豪雪を事例として. 流域圏学会誌. 8(1): 10−24（査読付き解説）.
  • 74. 永井 信・関川清広・小林秀樹・友常満利. 2020. 神奈川県におけるクヌギ・コナラのフェノロジー ― 海洋研究開発機構横浜研究所と玉川大学キャンパスの比較―. 玉川大学農学部研究教育紀要. 5: 15―22.
  • 73. 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.
  • 72. 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.
  • 71. 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.
  • 70. 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.
  • 69. 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.
  • 68. 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.
  • 67. 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.
  • 66. 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.
  • 65. 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.
  • 64. 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.
  • 63. 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.
  • 62. 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.
  • 61. 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.
  • 60. 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.
  • 59. 村岡裕由・丸谷靖幸・永井 信（2019）山地森林の炭素循環と生態系機能の環境応答に関する長期・複合的研究の展望. 地学雑誌. 128(1):129-146.
  • 58. 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
  • 57. 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
  • 56. 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
  • 55. 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.
  • 54. 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).
  • 53. 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).
  • 52. 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.
  • 51. 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.
  • 50. 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
  • 49. 奥崎　穣, 持田浩治, 永井　信, 中路達郎, 小熊宏之（2017）生態学者のための分光計測. 日本生態学会誌, 67:41-56.
  • 48. 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.
  • 47. 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.
  • 46. 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 (2016) Using phenocams to monitor our changing Earth: towards a global phenocam network. Frontiers in Ecology and the Environment 14(2), 84–93
  • 45. 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.
  • 44. 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
  • 43. 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
  • 42. 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.
  • 41. 永井　信, 井上智晴, 鈴木力英 (2015) 秋の衛星季節学におけるウェブサイト上で公開されている紅葉情報の有用性. 日生気誌 52(2):119–129
  • 40. 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.
  • 39. 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.
  • 38. 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.
  • 37. 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.
  • 36. 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.
  • 35. Nagai S., Yoshitake S., Inoue T., Suzuki R., Muraoka H., Nasahara KN., Saitoh TM. 2014. Year-to-year blooming phenology observation by using time-lapse digital camera images. Journal of Agriculture and Meteorology, 70 (3): 163-170.
  • 34. 斎藤琢・永井信・村岡裕由 陸域生態系の炭素収支の現状診断と将来予測−リモートセンシングの利用−. 日本生態学会誌. 2014　64, 243-252
  • 33. 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.
  • 32. 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
  • 31. 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.
  • 30. 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.
  • 29. 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.
  • 28. 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
  • 27. 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
  • 26. 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
  • 25. 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
  • 24. 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

  • 23. 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.

  • 22. 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
  • 21. 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
  • 20. 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
  • 19. 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
  • 18. 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
  • 17. 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
  • 16. 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
  • 15. 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
  • 14. 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
  • 13. 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
  • 12. 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
  • 11. 村上和隆・奈佐原顕郎・秋津朋子・本岡毅・永井信 2011. 衛星センサの分光仕様が草原の植生指数観測に与える影響. 筑波大学陸域環境研究センター報告. 12:13-19
  • 10. 秋津朋子・奈佐原顕郎・野田響・本岡毅・村上和隆・土田聡・永井信 2011. 草原の季節変動と年々変動に関するデジタルカメラを用いた長期連続自動観測. 筑波大学陸域環境研究センター報告. 12:5-12
  • 9. 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
  • 8. 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
  • 7. 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
  • 6. 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
  • 5. 永井信・奈佐原（西田）顕郎・石原光則・村岡裕由 2008. 衛星観測で得られた植生指数に対する生理生態学的な考察. システム農学 24(3):183-190
  • 4. 玉川一郎・吉野純・加野利生・安田孝志・村岡裕由・児島利治・石原光則・永井信・斎藤琢・李美善・牧雅康・秋山侃・小泉博 2008. 生態プロセスとリモートセンシングを結ぶモデルの開発. システム農学 24(2):129-136
  • 3. 吉野純・村岡裕由・永井信・石原光則・斎藤琢・児島利治・玉川一郎・安田孝志 2008. 流域圏を支える森林環境保全のための森林健康診断手法. 環境システム講演論文集 36:277-286
  • 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

著書

  • 9. 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.
  • 8. 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.
  • 7. 第2章 地球規模での森林環境の現状把握 -リモートセンシングによるアプローチ- 森林科学シリーズ第6巻「森林と地球環境変動」(三枝信子、柴田英昭編集). 共立出版. 242頁. 2019.
  • 6. 永井信. 生態系の変動を測る－衛星リモートセンシング(終章 草原と遊牧の未来). 環境人間学と地域 モンゴル 草原生態系ネットワークの崩壊と再生(藤田昇・加藤聡史・草野栄一・幸田良介編集). 京都大学学術出版会. 2013.
  • 5. 岩波生物学辞典第5版（分担執筆）. 2013.
  • 4. 村岡裕由,野田響,斎藤琢,永井信,奈佐原顕郎. 2012. 森林生態系の光合成:生理生態学と衛星観測の融合による長期・広域評価. 植物科学の最前線（BSJ-Review）3:30-45 (http://bsj.or.jp/saizensen/).
  • 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

解説

  • 1. 永井 信・斎藤琢・奈佐原(西田)顕郎, 2018. リモートセンシングとオープンアクセスデータの統合的解析による植物季節観測と土地利用土地被覆分類の高精度化. 日本リモートセンシング学会誌 28(2), 99–104.

その他

  • 2. 永井 信・遠藤拓洋・下田彰子. 2021. SENTINEL-2A/B衛星による自然教育園のナラ枯れ観測. 自然教育園報告. 53: 35-38.
  • 1. 永井 信・遠藤拓洋・奈佐原顕郎. 2020. 高頻度・高空間分解能:SENTINEL-2A/B衛星による自然教育園の植物季節観測. 自然教育園報告. 52: 19-24.

研究費・プロジェクト

  • 学術変革領域研究(A)（計画研究）「水共生学の創生に向けた水とその周辺環境情報の創出と展開」（代表：渡部哲史）（R3-R7）研究分担者
  • NHK番組アーカイブス学術利用トライアル2021年度後期「映像データレスキューによる1980年代の東シベリアの人々の暮らしと景観情報のマイニング」研究代表者（R3）
  • 令和3年度戦略的国際共同研究推進委託事業のうち二国間国際共同研究事業（農水省）「地球規模の諸課題に対するシベリア北方林の適応的森林管理戦略」（代表：鷹尾 元）（R3-R5）研究分担者
  • 基盤研究(B)（一般）「アブラヤシ農園拡大の環境リスク評価」（代表：熊谷朝臣）（R3-R5）研究分担者
  • 科学研究費補助金、基盤研究（B）「森林光合成とフェノロジーへの気候変動ストレス影響の生理生態学的解明と将来変動予測」（R1-R4、研究分担者）
  • 宇宙航空研究開発機構 第2回地球観測研究公募共同研究 「GCOM-C衛星による陸域生態系観測の高精度化を目的とした観測空白地帯における統合的地上真値の取得」（R1-R3、研究代表者）
  • 宇宙航空研究開発機構 地球環境変動観測ミッション(GCOM) 「森林物理量の衛星による高精度マッピングのための地上真値の取得」（H28-30、研究代表者（H29途中より））
  • 科研費、基盤研究（C）「リモートセンシング観測による里山林の代表的な樹種の判別と分布域の地図化手法の開発」（H29-R3、研究代表者）
  • 科学技術振興機構、ベルモント•フォーラム 2014年CRA（国際共同研究）
    「東部ロシア北極域永 久凍土上の生態系と都市と村落の炭素収支」 (H27-H31、研究分担者）
  • 文部科学省、北極域研究推進プロジェクト（ArCS）「国際共同研究推進:テーマ3 北極気候に関わる大気物質」 (H27-H31、研究分担者)
  • 科学研究費補助金、基盤研究（A）「アブラヤシ農園の拡大が東南アジア熱帯林の水・炭素循環に与えるインパクト」（H27-H30、研究分担者）
  • 科学研究費補助金、基盤研究（B）「植生遷移に伴う落葉広葉樹林生態系機能の環境応答特性の変遷とその変動機構の解明」（H27-H30、研究分担者）
  • 科学研究費補助金、基盤研究（B）「3次元森林構造に蛍光分布情報を付加した新しい光環境―光合成モジュールの開発」（H25-H28、研究分担者）
  • 科学研究費補助金、若手研究（B）「広葉樹林における衛星観測による生態系機能評価の高精度化」（H24-H27、研究代表者）
  • 環境研究総合推進費「アジア規模での生物多様性観測・評価・予測に関する総合的研究（S-9）」（H23-H27、研究分担者）

国際交流事業

  • 日本学術振興会：平成30年度「外国人招へい研究者（長期）」
  • Tomoaki Miura教授：「多センサ・リモートセンシングによるアジア太平洋地域の植生変動解析」

発表資料

Test the use of NDVI for phenology detection

Test the use of NDVI for phenology detection
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Detection of canopy condition by digital images

Detection of canopy condition by digital images
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Necessity and availability of noise-free daily satellite-observed NDVI during rapid phenological changes in terrestrial ecosystems in East Asia

Necessity and availability of noise-free daily satellite-observed NDVI during rapid phenological changes in terrestrial ecosystems in East Asia
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公開データ

Phenological Eyes Network (PEN)

Phenological Eyes Network (PEN)
北極域から熱帯に至る29生態系サイトにおいて取得した800万枚の植物季節と天空画像