Yang Zhou

Dr. Yang Zhou


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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26 October 2022 in Journal of Mountain Science
Verified
Journal Article
Closed access
PDF file
YZYang Zhou
Yang Zhou
YYYujun Yi
Yujun Yi
HLHongxi Liu
Hongxi Liu
CTCAIHONG TANG
CAIHONG TANG
YZYulong Zhu
Yulong Zhu
SZShanghong Zhang
Shanghong Zhang

1 June 2024 in Journal of Hydrodynamics
Verified
Journal Article
Closed access
PDF file
YZYang Zhou
Yang Zhou
YLYi-Ling Leng
Yi-Ling Leng
PWPeng-Yu Wang
Peng-Yu Wang
SZShang-Hong Zhang
Shang-Hong Zhang
YZYulong Zhu
Yulong Zhu
YYYu-Jun Yi
Yu-Jun Yi

11 August 2025 in Journal of Hydrology
Verified
Journal Article
Closed access
JSJianying Song
Jianying Song
CZChengxiang Zhang
Chengxiang Zhang
YZYang Zhou
Yang Zhou
YYYujun Yi
Yujun Yi

1 February 2024 in Journal of Hydrodynamics
Verified
Journal Article
Closed access
PDF file
SZShanghong Zhang
Shanghong Zhang
RZRong-Qi Zhang
Rong-Qi Zhang
WLWen-Da Li
Wen-Da Li
XYXi-Yan Yang
Xi-Yan Yang
YZYang Zhou
Yang Zhou

6 July 2026 in Remote Sensing
Verified
Journal Article
Open access
PDF file
YZYang Zhou
Yang Zhou
SFSHENGJUN FU
SHENGJUN FU
RLRenhe Liu
Renhe Liu
杨杨杨柳 杨
杨柳 杨
YZYinjun Zhou
Yinjun Zhou
SZShanghong Zhang
Shanghong Zhang
Conventional remote sensing faces challenges in regions characterized by complex terrain, frequent cloud and fog cover, image scarcity, and mixed pixels. This study develops an integrated framework that sequentially combines spatiotemporal fusion, water masking, feature engineering, and machine learning to enable long-term SSC retrieval. Key results include: (1) RASTFM achieved the highest spectral fidelity among four fusion algorithms; (2) development of an adaptive machine learning model for independent hydrological stations and a water body masking strategy; (3) mainstream SSC decreased by 21.2% temporally and a 93.8% longitudinally from 2006 to 2020, generating 15-year along-channel mainstream SSC data for the Three Gorges Reservoir area.

25 April 2024 in Forests
Verified
Journal Article
Open access
PDF file
LSLian Sun
Lian Sun
WDWangke Ding
Wangke Ding
YZYang Zhou
Yang Zhou
JWJiejun Wang
Jiejun Wang
XOXingyue Ouyang
Xingyue Ouyang
CZChonghong Zhang
Chonghong Zhang
Southwestern China is a critical biodiversity hotspot area, and many large hydroelectric projects have been established in the valleys in the region. Tree growth in the valleys will be affected by both regional climate and reservoir impoundment. However, it remains unknown whether the radial growth of trees in the valleys has a common response pattern to the regional climate, and it is also unclear whether the response of radial growth to reservoir impoundment can be detected. In this study, we developed tree-ring width chronologies of Pinus yunnanensis Franch. collected at 11 sites with vertical and horizontal gradients to three hydroelectric reservoirs in three riverine valleys in southwestern China. We analyzed the radial growth responses to the regional climate from 1986 to 2017 by correlation with instrumental meteorological data. Tree growth responses to reservoir impoundment were investigated through spatial and temporal comparisons using the change in the Euclidean distance and difference test. We also distinguished their responses at tree-ring sites without influenced by reservoir impoundment including two sites in the valleys and seven sites at high elevations. The results showed that the climate conditions in May and the dry season before the growth season significantly limit the radial growth in the valleys, which is different to that at high-elevation areas in southwestern China. Growth variations in the valleys are related to elevations and the trees in similar slopes positions exhibit similar responses. For trees in the low slope positions, both variance and mean values of radial growth are affected by reservoir impoundment. Trees at relatively low sites (i.e., sites M2, R2, L2), rather than the trees close to the reservoirs (i.e., sites M1, R1, L1), respond more sensitively to reservoir impoundment.