Landslide Detection with Himawari-8 Geostationary Satellite Data: A Case Study of a Torrential Rain Event in Kyushu, Japan

[1]  Fumitoshi Imaizumi,et al.  Effects of forest harvesting on the occurrence of landslides and debris flows in steep terrain of central Japan , 2008 .

[2]  Q. Moteki.  Role of Typhoon Prapiroon (Typhoon No. 7) on the Formation Process of the Baiu Front Inducing Heavy Rain in July 2018 in Western Japan , 2019, SOLA.

[3]  Yu-Pin Lin,et al.  Effects of land cover changes induced by large physical disturbances on hydrological responses in Central Taiwan , 2010, Environmental monitoring and assessment.

[4]  Ken-ichi Shimose,et al.  Predictability of Precipitation Caused by Linear Precipitation Systems During the July 2017 Northern Kyushu Heavy Rainfall Event Using a Cloud-Resolving Numerical Weather Prediction Model , 2018, Journal of Disaster Research.

[5]  Cheng-Chien Liu,et al.  Rapid Response to a Typhoon-Induced Flood with an SAR-Derived Map of Inundated Areas: Case Study and Validation , 2015, Remote. Sens..

[6]  Takashi Kimura,et al.  Spatial Analysis of the Landslide Characteristics Caused by Heavy Rainfall in the Northern Kyushu District in July, 2017 Using Topography, Geology, and Rainfall Levels , 2018, Journal of Disaster Research.

[7]  D. Normile.  Deadly typhoon forces Japan to face its vulnerability to increasingly powerful storms , 2019, Science.

[8]  Yuzo Suga,et al.  Landslide detection using COSMO-SkyMed images: a case study of a landslide event on Kii Peninsula, Japan , 2018 .

[9]  Geoffrey G. Parker,et al.  Effects of hurricane disturbance on a tropical dry forest canopy in western Mexico , 2017, Forest Ecology and Management.

[10]  Izuru Takayabu,et al.  Meteorological overview and mesoscale characteristics of the Heavy Rain Event of July 2018 in Japan , 2018, Landslides.

[11]  A. Okuyama,et al.  An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites , 2016 .

[12]  Timothy J. Schmit,et al.  Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI). , 2018, Journal of Operational Meteorology.

[13]  Ming Wang,et al.  Using MODIS NDVI Time Series to Identify Geographic Patterns of Landslides in Vegetated Regions , 2013, IEEE Geoscience and Remote Sensing Letters.

[14]  Francesco Marinello,et al.  Extreme Weather Events in Agriculture: A Systematic Review , 2019, Sustainability.

[15]  Shi Qiu,et al.  Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data , 2018, Remote. Sens..

[16]  Steven M. de Jong,et al.  Semi-automated detection of landslide timing using harmonic modelling of satellite imagery, Buckinghorse River, Canada , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Guang Xu,et al.  Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8 , 2017 .

[18]  Kazuhito Ichii,et al.  Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data , 2019, Scientific Reports.

[19]  Teng-Chiu Lin,et al.  Remote sensing assessment of forest damage in relation to the 1996 strong typhoon Herb at Lienhuachi Experimental Forest, Taiwan , 2008 .

[20]  Nicholas A. J. Graham,et al.  Climatic and local stressor interactions threaten tropical forests and coral reefs , 2020, Philosophical Transactions of the Royal Society B.

[21]  Lorenzo Busetto,et al.  Rapid Assessment of Crop Status: An Application of MODIS and SAR Data to Rice Areas in Leyte, Philippines Affected by Typhoon Haiyan , 2015, Remote. Sens..

[22]  Eleanor C. Stokes,et al.  Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria , 2019, PloS one.

[23]  Yu Meng,et al.  Landslide Monitoring Using Change Detection in Multitemporal Optical Imagery , 2020, IEEE Geoscience and Remote Sensing Letters.

[24]  R. Sidle,et al.  Long‐term modelling of landslides for different forest management practices , 2003 .

[25]  Thomas Blaschke,et al.  A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India , 2019, Remote. Sens..

[26]  Fausto Guzzetti,et al.  Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images , 2011 .

[27]  Naoto Yokoya,et al.  A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images , 2020, Remote. Sens..

[28]  Margaret Kosmala,et al.  Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery , 2018, Scientific Data.

[29]  Rasmus Fensholt,et al.  Comparisons of compositing period length for vegetation index data from polar-orbiting and geostationary satellites for the cloud-prone region of West Africa , 2007 .

[30]  I. Reda,et al.  Solar position algorithm for solar radiation applications , 2004 .

[31]  A. Belward,et al.  The Best Index Slope Extraction ( BISE): A method for reducing noise in NDVI time-series , 1992 .

[32]  Lina Tang,et al.  Rapid Assessment of a Typhoon Disaster Based on NPP-VIIRS DNB Daily Data: The Case of an Urban Agglomeration along Western Taiwan Straits, China , 2019, Remote. Sens..

[33]  Clemens Eisank,et al.  An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan , 2015, Earth Science Informatics.

[34]  Dong Yan,et al.  Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[35]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .