B
Bin Chen
Researcher at Beijing Normal University
Publications - 460
Citations - 19940
Bin Chen is an academic researcher from Beijing Normal University. The author has contributed to research in topics: Computer science & Emergy. The author has an hindex of 56, co-authored 338 publications receiving 13955 citations. Previous affiliations of Bin Chen include The Chinese University of Hong Kong & Ecologic Brands, Inc..
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Soil organic carbon content and storage of raised field wetlands in different functional zones of a typical shallow freshwater lake, China
TL;DR: In this paper, soil organic carbon (SOC) content, density, and storage, and carbon pool index (CPI) were calculated for each typical zone, and spatial distribution of SOC storage in the region was estimated using the ordinary kriging, interpolated value method.
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Changes of wetland landscape patterns in Dadu River catchment from 1985 to 2000, China
TL;DR: Based on the interpretation and vector processing of remote sensing images in 1985 and 2000, the spatial changes of wetland landscape patterns in Dadu River catchment in the last two decades were studied using spatial analysis method as mentioned in this paper.
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LCA-based Carbon Footprint of a Typical Wind Farm in China☆
TL;DR: In this paper, a method combining the Life Cycle Assessment and Input-Output analyses was introduced to calculate the overall carbon footprint in the construction, operating and dismantling phases of a typical wind farm in China on the basis of the latest acquirable input-output table of province level and province energy statistic.
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Emergy-based Ecological Economic Evaluation of Beijing Urban Ecosystem
TL;DR: In this article, a series of ratios and indices arising from emergy analysis, including emergy intensity, environmental load ratio and environmental sustainability, were used to analyze economic development in Beijing during the years of 1999 to 2006 and the heavy pressure it has put on the environment.
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Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive
Bin Chen,Jing Li,Yufang Jin +2 more
TL;DR: In this paper, a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, was developed to leverage the overlapping Landsat and Sentinel-2 observations during 2016-2019, and reconstruct 10 m Sentinel2 like imagery from 30 m historical Landsat archives.