Advances in Remote Sensing-based Disaster Monitoring and Assessment
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 d...
Gardado en:
| Formato: | Online |
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| Idioma: | inglés |
| Publicado: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Subjects: | |
| Acceso en liña: | ONIX_20210501_9783039433223_1028 |
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| _version_ | 1869519972213981184 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-69282 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-692822024-03-27T16:34:31Z Advances in Remote Sensing-based Disaster Monitoring and Assessment Im, Jungho Park, Haemi Takeuchi, Wataru 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general 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. 2021-05-01T15:45:45Z 2021-05-01T15:45:45Z 2020 book ONIX_20210501_9783039433223_1028 9783039433223 9783039433230 https://directory.doabooks.org/handle/20.500.12854/69282 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/3072 https://mdpi.com/books/pdfview/book/3072 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03943-323-0 10.3390/books978-3-03943-323-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039433223 9783039433230 232 Basel, Switzerland open access |
| spellingShingle | 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Advances in Remote Sensing-based Disaster Monitoring and Assessment |
| title | Advances in Remote Sensing-based Disaster Monitoring and Assessment |
| title_full | Advances in Remote Sensing-based Disaster Monitoring and Assessment |
| title_fullStr | Advances in Remote Sensing-based Disaster Monitoring and Assessment |
| title_full_unstemmed | Advances in Remote Sensing-based Disaster Monitoring and Assessment |
| title_short | Advances in Remote Sensing-based Disaster Monitoring and Assessment |
| title_sort | advances in remote sensing based disaster monitoring and assessment |
| topic | 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | 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 n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20210501_9783039433223_1028 |