Shan Sang

Mrs. Shan Sang

Hohai Univeristy, Nanjing, China
orcid

Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

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

Representative Publications

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7 March 2023 in National Science Review
Verified
Journal Article
Open access
PDF file
YLYan Li
Yan Li
SSShan Sang
Shan Sang
SMSafa Mote
Safa Mote
JRJorge Rivas
Jorge Rivas
EKEugenia Kalnay
Eugenia Kalnay
With the growing recognition of coupled human and natural systems (CHANS), modeling CHANS with two-way feedbacks has become a frontier research area and a critical tool to achieve sustainability. The challenges in CHANS modeling and opportunities to advance its science and application to promote the sustainability of CHANS are discussed in this paper.

22 May 2024 in Earth System Science Data
Verified
Journal Article
Open access
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CHChengcheng Hou
Chengcheng Hou
YLYan Li
Yan Li
SSShan Sang
Shan Sang
XZXu Zhao
Xu Zhao
LYLiu Yanxu
Liu Yanxu
FZFang Zhao
Fang Zhao
High-quality gridded data on industrial water use are vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, which is called the China Industrial Water Withdrawal (CIWW) dataset; this dataset spans a 56-year period from 1965 to 2020 at spatial resolutions of 0.1 and 0.25°. We utilized > 400 000 records of industrial enterprises, monthly industrial product output data, and continuous statistical IWW records from 1965 to 2020 to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in developing the dataset. Our CIWW dataset is a significant improvement in comparison to previous data for the characterization of the spatial and seasonal patterns of the IWW dynamics in China and achieves better consistency with statistical records at the local scale. The CIWW dataset, together with its methodology and auxiliary data, will be useful for water resource management and hydrological models. This new dataset is now available at https://doi.org/10.6084/m9.figshare.21901074 (Hou and Li, 2023).

1 October 2021 in Remote Sensing
Verified
Journal Article
Open access
PDF file
SSShan Sang
Shan Sang
Taixia WuTaixia Wu
Taixia Wu
Ying ying YangYing ying Yang
Ying ying Yang
YYYang Yingying
Yang Yingying
YLYiyao Liu
Yiyao Liu
YZYuting Zhao
Yuting Zhao
Terrestrial ecosystems provide a variety of benefits for human life and production, and are a key link for achieving sustainable development goals (SDGs). The basin ecosystem is one type of terrestrial ecosystem. Ecological security (ES) assessments are an important component of the overall strategy to achieve regional sustainable development. The Huaihe River Basin (HRB) has the common characteristics of most basins, such as high population density, a rapidly developing economy, and many environmental problems. This study constructed an ES evaluation system by applying a pressure-state-response framework as an assessment method for the sustainable development of basins. Taking the HRB as an example, this study determined the ES status of the region from 2001 to 2019 and analyzed crucial factors for any variation observed by combining remote sensing and climate data, relevant policies, and spatial information technology. The results highlight the importance of reserves and the negative impact of urban expansion on ES. Additionally, the enactment of policies had a positive impact on ES, whereas precipitation had a negative effect on ES in most areas of the HRB. Based on these results, the government should strengthen the protection of forests, grasslands, and wetlands and improve water conservation facilities. This study provides guidance for the subsequent economic development, environmental protection, and the achievements of SDG 15 in the HRB.

16 April 2025 in Agricultural Water Management
Verified
Journal Article
Open access
SZShuangshuang Zi
Shuangshuang Zi
YLYan Li
Yan Li
Jingwen ZhangJingwen Zhang
Jingwen Zhang
CHChengcheng Hou
Chengcheng Hou
HLHuiqing Lin
Huiqing Lin
BFBojie Fu
Bojie Fu

9 April 2025 in Geography and Sustainability
Verified
Journal Article
Open access
SSShan Sang
Shan Sang
YLYan Li
Yan Li
SZShuang Zong
Shuang Zong
LYLu Yu
Lu Yu
SWShuai Wang
Shuai Wang
BFBojie Fu
Bojie Fu

21 September 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Verified
Journal Article
Open access
Taixia WuTaixia Wu
Taixia Wu
MLMengyao Li
Mengyao Li
Ying ying YangYing ying Yang
Ying ying Yang
YYYang Yingying
Yang Yingying
SSShan Sang
Shan Sang
DJDongzhen Jia
Dongzhen Jia
Urban black-odor water is a serious water environmental problem worldwide that seriously affects the living environment and the health of residents. The detection of black-odor water is a primary problem of centralized governance. Remote sensing is an effective means for water-quality monitoring. However, in most urban areas, with their complex underlying conditions, black-odor water always shows an uncertain distribution and small area. Furthermore, because of similar spectral characteristics, it is easily confused with dark objects such as asphalt roads, dark roofs, bridges, river levees, and building shadows. In this article, we propose a new black-odor water detection method, without shadow misjudgment, for complex urban conditions. A remote sensing image from the Chinese high-resolution satellite Gaofen-2 (GF-2) was used for detection. Using in situ water-quality measurement data for verification, the results showed that the proposed method achieved an accuracy of 85.7% while reducing the false alarm rate to 3.5%. Compared to four other existing methods, the proposed method proved to be more practical because it can reduce the false extraction of nonblack-odor water to the greatest extent possible while ensuring accuracy The method proposed in this article has a good effect on the extraction of black-odor water in Nanjing City. Therefore, our method can be applied widely to screening and management of large-scale urban black-odor water and can provide reliable data for centralized treatment.