Activity
Time:
M.S. Defense: Ahmadreza Pourghodrat
Date:
11:00 am –
12:00 pm
Schorr Center
Room: 211
1100 T St
Lincoln NE 68588
Lincoln NE 68588
Additional Info: SHOR
Virtual Location:
Zoom
Target Audiences:
“Enhancing Remote Sensing Imagery Temporal Resolution using STARFM Data Fusion Approach for Improved Land Surface Monitoring”
High-resolution remote sensing imagery plays an important role in effective farm-level agricultural operations. While high spatial resolution data are available, they often come with low temporal resolution and vice versa. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size, but their main limitation is low temporal resolution, with a 16-day revisit cycle for each. Conversely, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial detail. Additionally, there is a lack of an intuitive open-source tool, which has been noted by researchers and farmers who require high-resolution images in their work. To address these issues, we have developed an open-source GUI tool that leverages the STARFM algorithm and extends its capabilities to integrate high spatial resolution imagery of Landsat 8 and 9 with high temporal resolution imagery of VIIRS to generate Landsat-like images at a 30-meter resolution for any specified date. While the STARFM code is publicly available, its practical use requires pre-processing of input data and post-processing of output images, which are not included in the publicly available version of the STARFM code. We enhanced STARFM by offering a user-friendly interface to automate both pre-processing and post-processing steps in the background. Our tool allows users to specify parameters (e.g., desired dates, paths, rows, and regions) and automates the downloading of required imagery, its preparation for the STARFM algorithm, and post-processing to generate high-quality Landsat-like images. We have evaluated our approach on agricultural fields and validated its performance in regions of Nebraska and Kansas, which have dense agricultural activity. The results demonstrate the effectiveness of our approach over existing methods in generating high-resolution imagery, making it a valuable resource for various users.
Committee members:
Dr. Hongfeng Yu, Advisor
Dr. Christopher Neale
Dr. Ashok Samal
High-resolution remote sensing imagery plays an important role in effective farm-level agricultural operations. While high spatial resolution data are available, they often come with low temporal resolution and vice versa. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size, but their main limitation is low temporal resolution, with a 16-day revisit cycle for each. Conversely, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial detail. Additionally, there is a lack of an intuitive open-source tool, which has been noted by researchers and farmers who require high-resolution images in their work. To address these issues, we have developed an open-source GUI tool that leverages the STARFM algorithm and extends its capabilities to integrate high spatial resolution imagery of Landsat 8 and 9 with high temporal resolution imagery of VIIRS to generate Landsat-like images at a 30-meter resolution for any specified date. While the STARFM code is publicly available, its practical use requires pre-processing of input data and post-processing of output images, which are not included in the publicly available version of the STARFM code. We enhanced STARFM by offering a user-friendly interface to automate both pre-processing and post-processing steps in the background. Our tool allows users to specify parameters (e.g., desired dates, paths, rows, and regions) and automates the downloading of required imagery, its preparation for the STARFM algorithm, and post-processing to generate high-quality Landsat-like images. We have evaluated our approach on agricultural fields and validated its performance in regions of Nebraska and Kansas, which have dense agricultural activity. The results demonstrate the effectiveness of our approach over existing methods in generating high-resolution imagery, making it a valuable resource for various users.
Committee members:
Dr. Hongfeng Yu, Advisor
Dr. Christopher Neale
Dr. Ashok Samal
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This event originated in School of Computing.