The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite.
To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat.
Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes.
In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM.
In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM. / Ph. D. / The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat’s role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). With its spatial resolution of 30 m, Landsat facilitates analysis at a local scale. However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. This long revisit cycle limits the utility of Landsat data for such tasks as tracking rapid changes or investigating intra-seasonal variations.
To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat. Landsat-MODIS fusion is a challenging problem due to differences between the two sensors that greatly affect the prediction accuracy over areas having various land cover types. This work presents two Landsat-MODIS fusion models to estimate the unavailable Landsat images from the spatial and temporal perspectives as an attempt to achieve better prediction accuracy especially over heterogeneous areas.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/81092 |
| Date | 07 December 2017 |
| Creators | Ghannam, Sherin Ghannam |
| Contributors | Electrical and Computer Engineering, Abbott, A. Lynn, Wynne, Randolph H., Baker, Joseph B. H., Batra, Dhruv, Thomas, Valerie A., Hussein, Mohamed E. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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