| dc.description.abstract | ABSTRACT
Recent improvements of remote sensing largely increase the applicability of satellite images, which has been utilized in many applications such as urban planning, precision agriculture, air pollution monitoring, and forestry management. In the meantime, higher spatial and temporal resolutions of satellite imagery are desired to further strengthen its monitoring capability for changes at finer scales. However, no single satellite can produce images with both high spatial and temporal resolutions. This type of problem will be an enormous issue while sensors are applied in applications that needs these both high resolutions, among which is the surface water coverage monitoring.
The surface water coverage dynamically changes as it can shrink or expand due to natural or human factors. Various remote sensors had been applied in detecting and monitoring surface water since the 1970s, such as Moderate Resolution Imaging Spectroradiometer (MODIS), and Suomi National Polar-orbiting Partnership - Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). However, no single satellite can provide imagery with both high spatial and temporal resolutions. To overcome this well-known issue in remote sensing, there are two possible strategies, image simulation and image fusion. As the image simulation becomes one of the solutions to solve the lack of remotely-sensed data by generating multitemporal images, the image fusion is a remote sensing technique that has been applied to obtain more information by integrating images from different sensors.
With recent advancement, we have (1) more satellite images to understand the relationship between water height and surface water coverage and (2) geostationary satellites such as Himawari-8 to provide ultra-high temporal resolution images to monitor the dynamics of water coverage. Therefore, this research tries to examine how recent remote sensing advancement can help water coverage monitoring.
To be specific, this research aims to derive spatial and temporal modified Normalized Difference Water Index (mNDWI) images by combining imagery from Landsat Operational Land Imager (OLI) and Advance Himawari Imager (AHI). For image simulation, we generate an image of a certain water height via linear interpolation with two images that have nearest water heights. For the image fusion, an existing model called Spatial and Temporal Adaptive Fusion Model (STARFM) is applied to generate an image of a certain water height.
This research proposes six approaches, which are A1, A2, A3, A4, A5, and A6, where A1 and A2 are image simulation approaches, A3 and A4 are image fusion approaches with simulated reference data, and A5 and A6 are image fusion approaches with reference images that have closest water heights. The A1, A3, and A5 estimate water heights from a tide model while A2, A4, and A6 estimate water heights based on AHI imagery.
We examine these approaches with normal cases and anomaly cases. The difference between these two types of cases is based on whether water heights follow the tide model or not. The experimental result shows that A2, A4, and A6 can achieve better results for normal case with correlation coefficient generally greater than 0.90 and overall accuracy more than 0.95 for anomaly case. In summary, this research has the following conclusions. With the current advance in remote sensing technologies, both the image simulation and image fusion can solve the problem of data lacking. The ultra-high temporal resolution AHI imagery can help capture the dynamics of water coverage changes in the intertidal area, with which higher spatial and temporal resolution water coverage monitoring capability can be achieved.
Keywords: Water coverage monitoring; Image simulation; Spatial and temporal image fusion | en_US |