Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 67-89 |
| Seitenumfang | 23 |
| Fachzeitschrift | IEEE Geoscience and Remote Sensing Magazine |
| Jahrgang | 12 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - Juni 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Physik und Astronomie (insg.)
- Instrumentierung
- Erdkunde und Planetologie (insg.)
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: IEEE Geoscience and Remote Sensing Magazine, Jahrgang 12, Nr. 2, 06.2024, S. 67-89.
Publikation: Beitrag in Fachzeitschrift › Rezension in Fachzeitschrift › Forschung › 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 - Publisher Copyright: IEEE
PY - 2024/6
Y1 - 2024/6
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
VL - 12
SP - 67
EP - 89
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
SN - 2473-2397
IS - 2
ER -