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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B3-2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-109-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-109-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
28 Jun 2021
| 28 Jun 2021
TIME SERIES ANALYSIS FOR MONITORING SEAGRASS HABITAT AND ENVIRONMENT IN BUSUANGA, PHILIPPINES USING GOOGLE EARTH ENGINE
A. Tamondong
Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan
Department of Geodetic Engineering, University of the Philippines, Quezon City, Philippines
T. Nakamura
Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan
T. E. A. Quiros
Field Science Center for Northern Hemisphere, Hokkaido University, Akkeshi, Hokkaido, Japan
K. Nadaoka
Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan
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Short summary
This study investigates the sea-air CO2 exchange variabilities across the Tropical Maritime Continent between 2010–2019 using numerical model experiment. Model results suggested some periods with higher-than-usual CO2 degassing to the atmosphere related to Indo-Pacific climatic events over the 2010–2019 course. Study conducted here may give insights about sea-air CO2 exchange dynamics in the undersampled area that poorly represented in many climate models and global reconstruction product.
Masaya Yoshikai, Takashi Nakamura, Rempei Suwa, Sahadev Sharma, Rene Rollon, Jun Yasuoka, Ryohei Egawa, and Kazuo Nadaoka
Biogeosciences, 19, 1813–1832, https://doi.org/10.5194/bg-19-1813-2022, https://doi.org/10.5194/bg-19-1813-2022, 2022
Short summary
Short summary
This study presents a new individual-based vegetation model to investigate salinity control on mangrove productivity. The model incorporates plant hydraulics and tree competition and predicts unique and complex patterns of mangrove forest structures that vary across soil salinity gradients. The presented model does not hold an empirical expression of salinity influence on productivity and thus may provide a better understanding of mangrove forest dynamics in future climate change.
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