14:45 〜 15:00
[AHW19-15] Flood impact assessment with high-quality nighttime light remote sensing data
キーワード：flood impact, nighttime light, remote sensing, resilience
Flood impact assessment is very important and needed by society. When flooding happens, many places will face power outages due to the damage caused by the strong wind, heavy rain or water inundation. Light intensity will probably decrease compared to normal status. This decrease makes it possible for nighttime light data (NTL) to detect flood impact. Compared with other satellite remote sensing data such as SAR data and day-time optical data such as MODIS which are well used for flooding detection, NTL has the advantage of reflecting flood impact on human behavior with accurate spatial information and high temporal resolution.
NASA released the new VNP46A2 NTL product in 2019. This first, high-quality, daily NTL product with a 500-m spatial resolution is suitable for monitoring rapid light intensity variation and thus can reflect flood impact on human. However, two errors remain uncorrected: the spatial observational coverage mismatch and the angle effect, leading to unexpected daily variation of NTL intensity.
In this case, we firstly proposed a Self-adjusting method with Filtering and Angle Coefficient (SFAC) method for correcting the remained errors and generating the high-quality NTL data. Then we explored the detectability of NTL on flood impact and compared the flood impact information derived from NTL, MODIS and DFO database to confirm the uniqueness of NTL.
The detection of intensity decrease for chosen flood events proved the NTL data has the ability of detecting flood impact and the signal has been strengthened after calibration with our method. For temporal scale, both the durations derived from MODIS and NTL data are longer compared with DFO database’s given properties for most cases in 2013. The inundation period from MODIS is much longer than the flood impact time on light intensity, especially for near river area. For spatial scale, compared with MODIS result of flood impact which focus on inundation area, NTL’s affected areas most locate on human settlement. This makes it possible for NTL to estimate economic loss or fatalities which are related to human. Meanwhile, NTL data may also have huge potential on estimating the power outage amount which is meaningful for indirect economic loss. More information corresponding flood impact on human could be digested with NTL data.
NASA released the new VNP46A2 NTL product in 2019. This first, high-quality, daily NTL product with a 500-m spatial resolution is suitable for monitoring rapid light intensity variation and thus can reflect flood impact on human. However, two errors remain uncorrected: the spatial observational coverage mismatch and the angle effect, leading to unexpected daily variation of NTL intensity.
In this case, we firstly proposed a Self-adjusting method with Filtering and Angle Coefficient (SFAC) method for correcting the remained errors and generating the high-quality NTL data. Then we explored the detectability of NTL on flood impact and compared the flood impact information derived from NTL, MODIS and DFO database to confirm the uniqueness of NTL.
The detection of intensity decrease for chosen flood events proved the NTL data has the ability of detecting flood impact and the signal has been strengthened after calibration with our method. For temporal scale, both the durations derived from MODIS and NTL data are longer compared with DFO database’s given properties for most cases in 2013. The inundation period from MODIS is much longer than the flood impact time on light intensity, especially for near river area. For spatial scale, compared with MODIS result of flood impact which focus on inundation area, NTL’s affected areas most locate on human settlement. This makes it possible for NTL to estimate economic loss or fatalities which are related to human. Meanwhile, NTL data may also have huge potential on estimating the power outage amount which is meaningful for indirect economic loss. More information corresponding flood impact on human could be digested with NTL data.