Details
| Original language | English |
|---|---|
| Pages (from-to) | 2-24 |
| Number of pages | 23 |
| Journal | IEEE Geoscience and Remote Sensing Magazine |
| Publication status | Published - 12 Apr 2024 |
Abstract
Climate change triggers a wide range of hydrometeorological, glaciological, and geophysical processes that span across vast spatiotemporal scales. With the advances in technology and analytics, a multitude of remote sensing (RS), geodetic, and in situ instruments have been developed to effectively monitor and help comprehend Earth’s system, including its climate variability and the recent anomalies associated with global warming. A huge volume of data is generated by recording these observations, resulting in the need for novel methods to handle and interpret such big datasets. Managing this enormous amount of data extends beyond current computer storage considerations; it also encompasses the complexities of processing, modeling, and analyzing. Big datasets present unique characteristics that set them apart from smaller datasets, thereby posing challenges to traditional approaches. Moreover, computational time plays a crucial role, especially in the context of geohazard warning and response systems, which necessitate low latency requirements.
Keywords
- Big Data, Climate change, Complexity theory, Computational modeling, Data models, Environmental monitoring, Geodesy, Glaciology, Global warming, Hydroelectric power generation, Low latency communication, Meteorological factors, Remote sensing, Risk management, Spatiotemporal phenomena, Storage management
ASJC Scopus subject areas
- Computer Science(all)
- Physics and Astronomy(all)
- Instrumentation
- Earth and Planetary Sciences(all)
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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In: IEEE Geoscience and Remote Sensing Magazine, 12.04.2024, p. 2-24.
Research output: Contribution to journal › Book/Film/Article review in journal › Research › peer review
}
TY - JOUR
T1 - How Big Data Can Help to Monitor the Environment and to Mitigate Risks due to Climate Change
T2 - A review
AU - Montillet, J. P.
AU - Kermarrec, G.
AU - Forootan, E.
AU - Haberreiter, M.
AU - He, X.
AU - Finsterle, W.
AU - Fernandes, R.
AU - Shum, C. K.
N1 - Funding Information: J.-P. Montillet, W. Finsterle, and M. Haberreiter gratefully acknowledge support from the Karbacher-Fonds. G. Kermarrec is supported by the Deutsche Forschungsgemeinschaft under Project KE2453/2-1 STUBA_TLS. E. Forootan is supported by the Danmarks Frie Forskningsfond [10.46540/2035-00247B]. C. K. Shum is partially supported by the U.S. Agency of International Development (USAID) Program’s India forest sustainability project (CA 72038621CA00002), and by the National Science Foundation (NSF) Partnerships for Innovation Program Grant (2044704). We thank Jie Wang for producing Figure 8. X. He acknowledges the National Natural Science Foundation of China (Grant 42364002) and the Major Discipline Academic and Technical Leaders Training Program of Jiangxi Province (Grant 20225BCJ23014).
PY - 2024/4/12
Y1 - 2024/4/12
N2 - Climate change triggers a wide range of hydrometeorological, glaciological, and geophysical processes that span across vast spatiotemporal scales. With the advances in technology and analytics, a multitude of remote sensing (RS), geodetic, and in situ instruments have been developed to effectively monitor and help comprehend Earth’s system, including its climate variability and the recent anomalies associated with global warming. A huge volume of data is generated by recording these observations, resulting in the need for novel methods to handle and interpret such big datasets. Managing this enormous amount of data extends beyond current computer storage considerations; it also encompasses the complexities of processing, modeling, and analyzing. Big datasets present unique characteristics that set them apart from smaller datasets, thereby posing challenges to traditional approaches. Moreover, computational time plays a crucial role, especially in the context of geohazard warning and response systems, which necessitate low latency requirements.
AB - Climate change triggers a wide range of hydrometeorological, glaciological, and geophysical processes that span across vast spatiotemporal scales. With the advances in technology and analytics, a multitude of remote sensing (RS), geodetic, and in situ instruments have been developed to effectively monitor and help comprehend Earth’s system, including its climate variability and the recent anomalies associated with global warming. A huge volume of data is generated by recording these observations, resulting in the need for novel methods to handle and interpret such big datasets. Managing this enormous amount of data extends beyond current computer storage considerations; it also encompasses the complexities of processing, modeling, and analyzing. Big datasets present unique characteristics that set them apart from smaller datasets, thereby posing challenges to traditional approaches. Moreover, computational time plays a crucial role, especially in the context of geohazard warning and response systems, which necessitate low latency requirements.
KW - Big Data
KW - Climate change
KW - Complexity theory
KW - Computational modeling
KW - Data models
KW - Environmental monitoring
KW - Geodesy
KW - Glaciology
KW - Global warming
KW - Hydroelectric power generation
KW - Low latency communication
KW - Meteorological factors
KW - Remote sensing
KW - Risk management
KW - Spatiotemporal phenomena
KW - Storage management
UR - http://www.scopus.com/inward/record.url?scp=85190345767&partnerID=8YFLogxK
U2 - 10.1109/MGRS.2024.3379108
DO - 10.1109/MGRS.2024.3379108
M3 - Book/Film/Article review in journal
AN - SCOPUS:85190345767
SP - 2
EP - 24
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
SN - 2473-2397
ER -