Development of time series stacks of Landsat images for reconstructing forest disturbance history

International Journal of Digital Earth
By: , and 

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Abstract

Forest dynamics is highly relevant to a broad range of earth science studies, many of which have geographic coverage ranging from regional to global scales. While the temporally dense Landsat acquisitions available in many regions provide a unique opportunity for understanding forest disturbance history dating back to 1972, large quantities of Landsat images will need to be analysed for studies at regional to global scales. This will not only require effective change detection algorithms, but also highly automated, high level preprocessing capabilities to produce images with subpixel geolocation accuracies and best achievable radiometric consistency, a status called imagery-ready-to-use (IRU). This paper describes a streamlined approach for producing IRU quality Landsat time series stacks (LTSS). This approach consists of an image selection protocol, high level preprocessing algorithms and IRU quality verification procedures. The high level preprocessing algorithms include updated radiometric calibration and atmospheric correction for calculating surface reflectance and precision registration and orthorectification routines for improving geolocation accuracy. These automated routines have been implemented in the Landsat Ecosystem Disturbance Adaptive System (LEDAPS) designed for processing large quantities of Landsat images. Some characteristics of the LTSS developed using this approach are discussed.

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Publication type Article
Publication Subtype Journal Article
Title Development of time series stacks of Landsat images for reconstructing forest disturbance history
Series title International Journal of Digital Earth
DOI 10.1080/17538940902801614
Volume 2
Issue 3
Year Published 2009
Language English
Publisher Taylor & Francis
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 24 p.
First page 195
Last page 218
Country United States
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