Paper
5 November 2008 Sensitivity of Landsat MSS and TM to land cover change in the Golden Horseshoe, Ontario, Canada
Jamie FitzGibbon, Dongmei Chen
Author Affiliations +
Proceedings Volume 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics; 71440V (2008) https://doi.org/10.1117/12.812725
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
An ideal situation for conducting change detection is to use multi-temporal images acquired from the same sensor. However, many conditions (such as the discontinuity of sensors, weather conditions) would bring an end to the ideal temporal change detection. Imagery availability issues will force change detection studies in the future to increasingly incorporate multiple sensors. This study conducted change detection between Landsat TM (TM) and Landsat MSS (MSS) images from July 30, 1995 to June 2, 2003. The study area was centered on the Greater Toronto Area (GTA) in south-central Ontario, Canada. Post-classification change detection was used to determine the type of change between the images. Results demonstrated that despite the different spatial resolution of the MSS and TM data, the change detection using both MSS and TM was similar in results to that of TM alone. A change detection where MSS is resampled to 30 meters was most effective in capturing the amount and type of change in the TM change study.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jamie FitzGibbon and Dongmei Chen "Sensitivity of Landsat MSS and TM to land cover change in the Golden Horseshoe, Ontario, Canada", Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 71440V (5 November 2008); https://doi.org/10.1117/12.812725
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KEYWORDS
Sensors

Image classification

Agriculture

Earth observing sensors

Landsat

Spatial resolution

Vegetation

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