Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Xiongyi Zhang"'
Autor:
Wei Luo, Guoqing Zhang, Quanqin Shao, Yongxiang Zhao, Dongliang Wang, Xiongyi Zhang, Ke Liu, Xiaoliang Li, Jiandong Liu, Penggang Wang, Lin Li, Guanwu Wang, Fulong Wang, Zhongde Yu
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract It is a challenging and meaningful task to carry out UAV-based livestock monitoring in high-altitude (more than 4500 m on average) and cold regions (annual average – 4 °C) on the Qinghai Tibet Plateau. The purpose of artificial intelligen
Externí odkaz:
https://doaj.org/article/ff618efac4454e2e9367ab74b60bdce0
Autor:
Xiongyi Zhang, Jia Ning
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
Coastal regions stand as pivotal zones of interaction between the marine and terrestrial environments, holding substantial significance for both ecosystems and human societies. Since the 1980s, China’s coastal zones (CCZ) have undergone rapid econo
Externí odkaz:
https://doaj.org/article/8eb57ad7a4f74dcb9284721f05b163d1
Autor:
Xiongyi Zhang, Quanqin Shao, Bing Wang, Xiang Niu, Jia Ning, Meiqi Chen, Tingjing Zhang, Guobo Liu, Shuchao Liu, Linan Niu, Haibo Huang
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4641 (2023)
The National Key Ecological Function Zones (NKEFZ) serve as crucial ecological security barriers in China, playing a vital role in enhancing ecosystem services. This study employed the theoretical framework of ecological benefits assessment in major
Externí odkaz:
https://doaj.org/article/74fb464cc367483b91dd5babc0901074
Publikováno v:
Ecological Indicators, Vol 141, Iss , Pp 109103- (2022)
An ecosystem service assessment can directly reflect the effectiveness of ecological restoration, reveal the process and driving mechanism of ecosystem change, and guide the future management. However, most studies have focused on analyzing the histo
Externí odkaz:
https://doaj.org/article/b8e6af50b6ae42ab9c60bd10b6293f08
Autor:
Xiongyi Zhang, Jia Ning
Publikováno v:
Land, Vol 12, Iss 6, p 1127 (2023)
The Three Rivers Headwaters Region (TRHR), situated in the high-altitude region of the Tibetan Plateau, represents a critical ecological security barrier for both China and Southeast Asia. In addition to providing a vital freshwater supply for China,
Externí odkaz:
https://doaj.org/article/49ad08d6c5f143c4a8ea6465149ab229
Autor:
Guobo Liu, Quanqin Shao, Jiangwen Fan, Jia Ning, Haibo Huang, Shuchao Liu, Xiongyi Zhang, Linan Niu, Jiyuan Liu
Publikováno v:
Remote Sensing, Vol 14, Iss 21, p 5349 (2022)
The Three-River Headwaters Region (TRHR) is an important part of the ecological barrier of the Qinghai–Tibet Plateau. Understanding the TRHR’s major ecosystem service trade-offs and synergies is important for scientifically integrating and optimi
Externí odkaz:
https://doaj.org/article/f8084563966d4098b7c2de0e923ddd34
Publikováno v:
Remote Sensing, Vol 14, Iss 15, p 3550 (2022)
The Upper Yellow River is the most important area for water retention and flow production in the Yellow River basin, and the statuses of the ecosystems in this region are related to the ecological stability of the whole Yellow River basin. In this pa
Externí odkaz:
https://doaj.org/article/074f6c913f904493a1d913bab711ed9a
Autor:
Guobo Liu, Quanqin Shao, Jiangwen Fan, Jia Ning, Kai Rong, Haibo Huang, Shuchao Liu, Xiongyi Zhang, Linan Niu, Jiyuan Liu
Publikováno v:
Remote Sensing, Vol 14, Iss 7, p 1634 (2022)
As an important vegetation parameter and ecological index, vegetation net primary productivity (NPP) can intuitively reflect changes in the ecological environment and the level of the carbon budget. However, the change trend of NPP and its recovery p
Externí odkaz:
https://doaj.org/article/09b38f1309e24053afda7a583190d461
Publikováno v:
Ecological Engineering. 186:106831
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd9692fea0c13ad9be3cc89de1737b93
https://doi.org/10.1016/j.ecoleng.2022.106831
https://doi.org/10.1016/j.ecoleng.2022.106831
Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a multitude of downstream tasks. In this paper we explore whether it is possible to learn disent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de9a3f9e611c9e48ec8dc7fd014dadcb
http://arxiv.org/abs/2104.07155
http://arxiv.org/abs/2104.07155
