Abstract
In this study, we investigated the utility of Himawari-8 Advanced Himawari Imager (AHI), one of third-generation geostationary satellite sensors, for mapping landslides caused by torrential rain that hit the northern Kyushu area in Japan in the summer of 2017. AHI normalized difference vegetation index (NDVI) time series data had distinctive temporal signatures over landslide areas where the NDVI abruptly decreased after the rain event. The observed changes in the NDVI were linearly correlated with the percent landslide area, the percentage of landslide areas within the AHI pixel footprint, obtained with aerial survey (r = 0.78). AHI 10 min resolution data obtained near cloud-free coverage of the landslide region by the 8th day after the disaster event. This was comparable to the amount of time it took to obtain near cloud-free image coverage with aerial survey, and better than those with the polar-orbiting satellite sensors of Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite, Landsat-8 Operational Land Imager, and Sentinel-2A/B MultiSpectral Instrument. These results suggest that third-generation geostationary satellite data can serve as another useful resource for post-event, region-wide initial assessment of landslide areas after a heavy rain event.
Highlights
Extreme weather and climate events, such as heavy precipitation, have increased in frequency and are projected to continue increasing in this century [1,2]
We investigated the utility of Himawari-8 Advanced Himawari Imager (AHI) data for mapping the spatial extent of landslide-affected areas caused by a torrential rain event that hit the southern part of Japan in the summer of 2017
The study results have shown that Himawari-8 AHI normalized difference vegetation index (NDVI), while moderate/low in spatial resolution, successfully detected landslides where the percent impacted areas were 7% or greater
Summary
Extreme weather and climate events, such as heavy precipitation, have increased in frequency and are projected to continue increasing in this century [1,2]. These events can impact humans and ecosystems extremely, which can be conceptualized as disasters or emergencies and include major destruction of assets, loss of human lives, and loss of and impacts on plants, animals, and ecosystem services [3,4,5]. Remote sensing has been shown useful in various phases of disaster response, starting from early situational assessment to long-term recovery monitoring, and even to pre-event monitoring and mitigation planning [7]. Boschetti et al [11] developed a rapid assessment approach based on SAR and Moderate Resolution Imaging Spectroradiometer (MODIS) data that provided pre-event, in-season information on the status of rice and other field crops, and their damage risk posed by tropical storms for food security
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