Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Jie-Lun Chiang"'
Publikováno v:
Environments, Vol 10, Iss 10, p 170 (2023)
This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research
Externí odkaz:
https://doaj.org/article/8d40afdcdbe442ff91a5c4a2e4f7f4a2
Publikováno v:
Water, Vol 14, Iss 20, p 3320 (2022)
The complex and extensive mechanism of landslides and their direct connection to climate change have turned these hazards into critical events on a global scale, which can have significant negative influences on the long-term sustainable development
Externí odkaz:
https://doaj.org/article/5d748aefe80e4cc69bc845d83728d86a
Publikováno v:
Water, Vol 14, Iss 18, p 2935 (2022)
Water temperature is an important indicator of water quality for surface water resources because it impacts solubility of dissolved gases in water, affects metabolic rates of aquatic inhabitants, such as fish and harmful algal blooms (HABs), and dete
Externí odkaz:
https://doaj.org/article/f4e46b65d84f40209661cc2a743b3383
Autor:
Jie-Lun Chiang, 江介倫
94
Environmental data can be classified into three categories: spatial data, temporal data, and spatio-temporal data. For example, a remote sensing image can be considered as spatial data; daily temperature observations of a weather station are
Environmental data can be classified into three categories: spatial data, temporal data, and spatio-temporal data. For example, a remote sensing image can be considered as spatial data; daily temperature observations of a weather station are
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/96136059889724920869
Publikováno v:
Journal of Geoscience and Environment Protection. 11:47-59
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bb0f1013167ce3df0c45291b5187a4a
https://doi.org/10.4236/gep.2023.115003
https://doi.org/10.4236/gep.2023.115003
Publikováno v:
Irrigation and Drainage. 72:554-568
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2b42ea781ee54b2c7af5224f61d0398
https://doi.org/10.1002/ird.2774
https://doi.org/10.1002/ird.2774
Autor:
Sabastian S. Mukonza, Jie-Lun Chiang
Publikováno v:
2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9ee4359173e0883c65580369301a0c6
https://doi.org/10.1109/m2garss52314.2022.9840135
https://doi.org/10.1109/m2garss52314.2022.9840135
Autor:
Jie-Lun Chiang, Chia-Ming Kuo
Taiwan is located in the Pacific volcanic seismic zone and frequently suffers from landslides and debris flow caused by typhoons. On average, there are four typhoons which may cause tremendous disasters such as massive landslides in Taiwan mainly fro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66b57be2df7313ee49483151a5040bfa
https://doi.org/10.5194/egusphere-egu2020-6397
https://doi.org/10.5194/egusphere-egu2020-6397
Publikováno v:
Water Emerging Contaminants & Nanoplastics. 1:17
Plastic pollution in aquatic ecosystems has been identified as a growing global water pollution threat that is negatively impacting water quality and, as a result, affecting the health of humans, aquatic animals, and wildlife. Therefore, it presents
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc08c2b9c33470ec1e31af42aa73444b
https://doi.org/10.20517/wecn.2022.12
https://doi.org/10.20517/wecn.2022.12
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 52:4046-4055
An indicator kriging (IK) approach for remote sensing image classification is proposed. By introducing indicator variables for categorical data, the work of image classification is transformed into estimation of class-dependent probabilities in featu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::542eb9606dea0e4803ef1dfdb7e1718d
https://doi.org/10.1109/tgrs.2013.2279118
https://doi.org/10.1109/tgrs.2013.2279118