Authors / CoAuthors
Lymburner, L. | McIntyre, A. | Li, F. | Ip, A. | Thankappan, M.
Abstract
Vegetation index time series generated from coarse resolution sensors such as MODIS, MERIS and AVHRR have long been used to characterize land cover, land cover change and vegetation dynamics. However such techniques have not typically been applied to moderate resolution data i.e. Landsat data due to challenges associated with automated atmospheric correction, view angle normalization, BRDF correction and automated cloud/cloud shadow/pixel saturation. However operational approaches that address these challenges [1][2] and [3] have recently been implemented on national archives of Landsat imagery. These two developments make it possible to generate a vegetation index time series at Landsat scales. Furthermore, the conversion of digital numbers to surface reflectance makes it possible to use surface reflectance measurements made by Landsat-5 TM and Landsat-7 ETM+ [4] to create a time series that consists of measurements made by both sensors. The capacity to generate 25 metre resolution vegetation index time series represents an important development in our capacity to monitor food production systems and native vegetation communities from both a productivity and vegetation condition perspective. The capacity to create time series from multiple sensors is a critical step in enabling systematic analysis of deep archives of satellite imagery so that we can compare the changes taking place now with those that have occurred in the past.
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nonGeographicDataset
eCat Id
75173
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- GA Publication
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
Publication Date
2013-01-01T00:00:00
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geoscientificInformation
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