国際高等研究基幹

山本 雄平

ヤマモト ユウヘイ  (Yuhei Yamamoto)

基本情報

所属
千葉大学 国際高等研究基幹 テニュアトラック助教
学位
博士(理学)(京都大学)

研究者番号
30845102
J-GLOBAL ID
201901007933474275
Researcher ID
AAU-2161-2020
researchmap会員ID
B000355521

外部リンク

論文

 12
  • Munseon Beak, Kazuhito Ichii, Yuhei Yamamoto, Ruci Wang, Beichen Zhang, Ram C. Sharma, Tetsuya Hiyama
    Progress in Earth and Planetary Science 12(1) 2025年1月6日  
    Abstract Understanding the land cover is crucial to comprehending the functioning of the Earth’s system. The land cover of Siberia is characterized by uncertainty because it is wide-ranging and comprises various classification types. However, comparisons among land cover products reveal substantial discrepancies and uncertainties. Therefore, a reliable land cover product for Siberia is necessary. In this study, we generated new land cover data for Siberia using random forest (RF) classifiers with global land cover datasets. To assess their accuracy and characteristics, we individually validated global land cover products in Siberia using multi-source sample datasets. We trained the RF classifiers with multiple land cover products to produce a more precise land cover product for Siberia. The validations showed that: (a) the generated new land cover data achieved the highest overall accuracy (85.04%) and kappa coefficient (82.62%); (b) the classifications of mixed forest (user accuracy: 97.85%) and grasses (user accuracy: 94.85%) demonstrated improvements, showing higher performance compared to most other types; and (c) by comparing the distribution of land cover across climate zones, we discovered that temperature is a critical factor throughout Siberia. However, in warm summer climates, precipitation plays a critical role in vegetation distribution. The more accurate and detailed land cover created in this study enhances the reliability of analyses in Siberia and fosters a deeper understanding of the impact of the carbon cycle.
  • Zhiyan Liu, Kazuhito Ichii, Yuhei Yamamoto, Masahito Ueyama, Hideki Kobayashi, Tetsuya Hiyama, Ayumi Kotani, Trofim Maximov, Ryan C. Sullivan, Sébastien Biraud
    IEEE Geoscience and Remote Sensing Letters 22 1-5 2025年  
  • Beichen Zhang, Kazuhito Ichii, Wei Li, Yuhei Yamamoto, Wei Yang, Ram C. Sharma, Hiroki Yoshioka, Kenta Obata, Masayuki Matsuoka, Tomoaki Miura
    Remote Sensing of Environment 316 114491-114491 2025年1月  
  • Wei LI, Kazuhito ICHII, Beichen ZHANG, Yuhei YAMAMOTO, Wei YANG, Tomoaki MIURA, Hiroki YOSHIOKA, Masayuki MATSUOKA, Kenta OBATA, Ram C. SHARMA, Hirokazu YAMAMOTO, Hitoshi IRIE, Pradeep KHATRI, Ben LILEY, Isamu MORINO, Hideaki TAKENAKA, Atsushi HIGUCHI
    Journal of the Meteorological Society of Japan. Ser. II 2025年  
  • Zhiyan Liu, Kazuhito Ichii, Yuhei Yamamoto, Ruci Wang, Hideki Kobayashi, Masahito Ueyama
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1-13 2024年4月  査読有り

MISC

 8

書籍等出版物

 2
  • Yuhei Yamamoto (担当:分担執筆, 範囲:Chapter 9: Satellite-Based Assessment of Urban Thermal Environments)
    CRC Press 2024年6月 (ISBN: 9781003244561)  Refereed
    Satellite remote sensing provides land surface temperature (LST) data across entire urban areas, allowing for the quantification of the surface urban heat island (SUHI) in terms of both its “overall height” and “elevation distribution”. Such a quantified SUHI offers critical scientific evidence for sustainable urban development and heat mitigation strategies. However, no satellite LST products or SUHI quantification methods are universally applicable across various spatiotemporal scales and diverse urban structures. The available LST products have different spatiotemporal resolutions, time spans, and swath widths, depending on factors such as the satellite's sensor, orbit height, and mission. In addition, the error characteristics of LST differ based on the retrieval algorithm. The quantification methods for SUHI vary based on urban/non-urban configuration, background climate and topographic characteristics, spatiotemporal scales, and availability of land use/land cover data. Therefore, users need to select an appropriate LST product and quantification method that aligns with their specific focus. This chapter begins by introducing the types of satellite LST products, LST retrieval algorithms, and unique biases of urban surfaces. This chapter then introduces various methods for quantifying SUHI and discusses the contexts in which they are most appropriately used.
  • 日本リモートセンシング学会 (担当:分担執筆, 範囲:第5章「陸域」5-2 植生の一次生産、第9章「災害」9-20 熱波)
    丸善出版 2022年12月 (ISBN: 9784621307762)

講演・口頭発表等

 41

共同研究・競争的資金等の研究課題

 10