Japan Geoscience Union Meeting 2023

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[E] Oral

M (Multidisciplinary and Interdisciplinary) » M-IS Intersection

[M-IS01] Environmental, Socio-Economic and Climatic Changes in Northern Eurasia

Thu. May 25, 2023 10:45 AM - 12:00 PM 103 (International Conference Hall, Makuhari Messe)

convener:Pavel Groisman(NC State University Research Scholar at NOAA National Centers for Environmental Information, Asheville, North Carolina, USA), Shamil Maksyutov(National Institute for Environmental Studies), Elena Kukavskaya(V.N. Sukachev Institute of Forest of the Siberian Branch of the Russian Academy of Sciences - separate subdivision of the FRC KSC SB RAS), Vera Kuklina(George Washington University), Chairperson:Alexander Olchev(Lomonosov Moscow State University, Moscow, Russia), Pavel Groisman(NC State University Research Scholar at NOAA National Centers for Environmental Information, Asheville, North Carolina, USA), Shamil Maksyutov(National Institute for Environmental Studies)

10:45 AM - 11:00 AM

[MIS01-06] Annual maps of surface water bodies and wetlands in Northeast China during 2015-2020

*Xiangming Xiao1, Chenchen Zhang1, Xinxin Wang2, Yuanwei Qin1 (1.University of Oklahoma, Norman, Oklahoma 73019, USA, 2.Fudan University, Shanghai, China)

Keywords:land use and land cover change, surface water bodies, paddy rice, wetlands, remote sensing

Annual maps of surface water bodies and wetlands in Northeast China are important for the studies of biodiversity, climate change, food security, and zoonotic infectious diseases. However, the current maps of surface water bodies and wetlands in Northeast China have moderate accuracy and large uncertainty. In this presentation, we report the results from our recent study that integrated optical images (e.g., Landsat, Sentinel-2), microwave images (Sentinel-1, ALOS PALSAR-2), and thermal images (MODIS). We used time series Landsat, Sentinel-2, and Sentinel-1 images to identify and generate annual maps of year-long, seasonal, and ephemeral surface water bodies. We used time series Landsat, Sentinel-2, and Sentinel-1 images to identify and generate annual maps of paddy rice, open-canopy and closed-canopy marshes. We use PALSAR-2, and time series Landsat and Sentinel-2 images to identify and generate annual maps of forest, evergreen forest, deciduous forest, and deciduous forest swamp. We assessed the resultant annual map of surface water bodies, paddy rice, and natural wetlands in 2020 at 10-m spatial resolution with in-situ reference data, and the accuracy assessment shows a high overall accuracy of 95%. According to the annual map in 2020, the Northeast China had total of 21,019 km2 year-long surface water bodies, 83,365 km2 paddy rice, 37,852 km2 seasonal open-canopy marsh, 63,606 km2 yearlong closed-canopy marsh, and 37,400 km2 deciduous forest swamp. Our study has demonstrated the potential of integrated image datasets and knowledge-based algorithms for identifying and generating annual maps of surface water bodies and wetlands in Northeast China. The algorithms developed and evaluated over the Northeast China could be readily adapted to other regions in the Northeastern Asia.