[A-CG39] Biogeochemical cycles in Land Ecosystem

Tue. May 28, 2019 1:45 PM - 3:15 PM Poster Hall (International Exhibition Hall8, Makuhari Messe)

convener:Tomomichi Kato(Research Faculty of Agriculture, Hokkaido University), Kazuhito Ichii(Chiba University), Takeshi Ise(FSERC, Kyoto University), Munemasa Teramoto(National Institute for Environmental Studies)

Terrestrial ecosystem influences global climate through cycles of water, carbon, and nitrogen between land surface and atmosphere. For better understanding of those behaviors, a great effort has been paid for developing varieties of approaches and techniques such as biometric survey, eddy and chamber methods, near and satellite remote sensing, biosphere modeling and so on.
In particular, the JapanFlux, founded in 2006 as a researchers network of CO2, H2O and other trace gas flux measurement, has promoted the multi-disciplinal studies not only for flux measurement community, but also for remote sensing and biosphere modeling communities. Moreover, the Research-Group-on-Integrated-Land-Processes, which was founded in 2006, also has contributed to build networks between Japanese researchers to better understanding of physical and biological processes on interactions between terrestrial surface and atmosphere.
This session unites those multi-disciplinal activities, and promotes the oral and poster presentations on the role of terrestrial ecosystem in material circulations of water, carbon, nitrogen, energy and other substances by any approaches and technics. This session takes over the former session in last year: A-CG40.

[ACG39-P01] Long-term warming effect on heterotrophic respiration in a beech forest on Mount Naeba

*Munemasa Teramoto¹, Naishen Liang¹, Masaaki Naramoto², Jiye Zeng¹, Zhao Xin¹, Hajime Tomimastu¹ (1.Center for Global Environmental Research, National Institute for Environmental Studies, 2.Faculty of Agriculture, Shizuoka University)

[ACG39-P02] Simple approach to predict flowering and full blooming dates by bi-directional Self Organizing Maps

*Shin Nagai¹, Hiroshi Morimoto², Taku M Saitoh³ (1.Research and Development Center for Global Change, Japan Agency for Marine-Earth Science and Technology, 2.Nagoya University, 3.River Basin Research Center, Gifu University)

[ACG39-P03] Effects of undergrowth mowing on understory carbon balance in a deciduous broadleaf forest of an urban park in central Japan

Guochao Wang², *Tomotsugu Yazaki¹, Toshio Kume¹, Takamitsu Kai³ (1.School of Agriculture, Meiji University, 2.Graduate School of Agriculture, Meiji University, 3.Kurokawa Field Science Center, Meiji University)

[ACG39-P04] Solar-induced chlorophyll fluorescence for detecting ecosystem photosynthetic activity by high-resolution spectrum measurement in a paddy field in Japan

*KANOKRAT BUAREAL¹, Tomomichi Kato², Keisuke Ono³ (1.Graduate School of Global Food Resources, Hokkaido University, Sapporo, Japan, 2.Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan, 3.National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki, Japan)

[ACG39-P05] Environmental controls on anomalous variation in solar-induced fluorescence during the last 10 years

*Tomomichi Kato¹, Kazuki Mizuno² (1.Research Faculty of Agriculture, Hokkaido University, 2.Graduate School of Agriculture, Hokkaido University)

[ACG39-P06] Estimation of terrestrial carbon cycle in Asia using a machine learning and satellite data

*Riku Kawase¹, Kazuhito Ichii², Yusuke hayashi¹, Zhiyan Liu¹, Masahito Ueyama³, Yuji Kominami⁴ (1.Chiba University, 2.Center for Environmental Remote Sensing, Chiba University, 3.Osaka Prefecture University, 4.Forest Research and Management Organization )

[ACG39-P07] Estimation of global solar-induced chlorophyll fluorescence (SIF) by terrestrial ecosystem model VISIT and comparison with SIF observed by GOSAT

*Tatsuya Miyauchi¹, Makoto Saito¹, Hibiki M Noda¹, Yukio Yoshida¹, Haruki Oshio¹, Akihiko Ito¹ (1.National Institute for Environmental Studies)

[ACG39-P08] Automatic vegetation identification in the satellite image using a convolutional neural network

*Watanabe Shuntaro¹, Kazuaki Sumi², Takeshi Ise^1,3 (1.Field Science Education and Research Center, Kyoto University, 2.Graduate School of Agriculture, Kyoto University, 3.PRESTO, Japan Science and Technology Agency)

Session information