High spatial and temporal resolutions of satellite imagery are necessary for improving the ability to monitor rapid environment changes at finer scales. However, no single satellite can produce images with both high spatial and temporal resolutions. To address this issue, spatio-temporal fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was proposed to synthesize high spatial and temporal resolution images. On the other hand, water level monitoring is important to support natural hazard management, such as floods and tsunamis. However, continuously monitoring these hazards are challenging for a remote sensing satellite due to either its low spatial resolution or low temporal resolution. For example, Operational Land Imager (OLI) onboard Landsat 8 with a spatial resolution of 30 m has been applied on water level detection, but it cannot capture dynamic events due to its low temporal resolution. On the other hand, The Advanced Himawari Imager (AHI) 8 only needs 10 minutes to watch the hemisphere once, but its coarse resolution hampers the accurate mapping of sea level change. This study, therefore, aims to blend Landsat OLI imagery with Himawari-8 imagery to monitor the dynamic and local behavior of sea level changes. To be specific, we first calculate the modified Normalized Difference Water Index (mNDWI) using Landsat and Himawari-8 images and then fuse the index images using the STARFM algorithm. Finally, the water coverage is delineated by setting a threshold on the mNDWI index. By comparing the retrieved water coverage percentage with in-situ water level observations, we have seen a promising result.
|出版狀態||已出版 - 2017|
|事件||38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017 - New Delhi, India|
持續時間: 23 10月 2017 → 27 10月 2017
|???event.eventtypes.event.conference???||38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017|
|期間||23/10/17 → 27/10/17|