Vai al contenuto principale della pagina

Advances in Remote Sensing-based Disaster Monitoring and Assessment



(Visualizza in formato marc)    (Visualizza in BIBFRAME)

Autore: Im Jungho Visualizza persona
Titolo: Advances in Remote Sensing-based Disaster Monitoring and Assessment Visualizza cluster
Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica: 1 electronic resource (232 p.)
Soggetto topico: Research & information: general
Soggetto non controllato: wildfire
satellite vegetation indices
live fuel moisture
empirical model function
Southern California
chaparral ecosystem
forest fire
forest recovery
satellite remote sensing
vegetation index
burn index
gross primary production
South Korea
land subsidence
PS-InSAR
uneven settlement
building construction
Beijing urban area
floodplain delineation
inaccessible region
machine learning
flash flood
risk
LSSVM
China
Himawari-8
threshold-based algorithm
remote sensing
dryness monitoring
soil moisture
NIR-Red spectral space
Landsat-8
MODIS
Xinjiang province of China
SDE
PE
groundwater level
compressible sediment layer
tropical cyclone formation
WindSat
disaster monitoring
wireless sensor network
debris flow
anomaly detection
deep learning
accelerometer sensor
total precipitable water
Himawari-8 AHI
random forest
deep neural network
XGBoost
Persona (resp. second.): ParkHaemi
TakeuchiWataru
ImJungho
Sommario/riassunto: Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones.
Titolo autorizzato: Advances in Remote Sensing-based Disaster Monitoring and Assessment  Visualizza cluster
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910557765003321
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui