Canadian Forest Service Publications

An efficient protocol to process Landsat images for change detection with Tasselled Cap Transformation. 2007. Han, T.; Wulder, M.A.; White, J.C.; Coops, N.C.; Alvarez, M.F.; Butson, C.R. IEEE Geoscience and Remote Sensing Letters 4(1): 147-151.

Year: 2007

Available from: Pacific Forestry Centre

Catalog ID: 26714

Language: English

CFS Availability: Order paper copy (free), PDF (download)

Abstract

Change detection approaches, such as computing change in spectral indices through time, are a mature and established science, which is increasingly being applied in operational remote sensing programs. The quality and consistency of the changes detected using these approaches are linked however to the processing of the imagery which is required to address issues related to image radiometry, normalization, and computation of the spectral indices. These processing steps are typically undertaken independently providing opportunities for computation errors, increasing disk storage needs, and consuming processing time. In this communication we present an approach for combining these processing steps to facilitate a more streamlined and computationally efficient approach to change detection using Landsat 5 and 7. The individual elements of the algorithm (raw Landsat-5 or -7, to calibrated Landsat-7, to top-of-atmosphere reflectance, to Tasselled Cap components) are described, followed by a description and illustration of the protocol to algebraically combine the elements. Rather than producing intermediate outputs, the sequentially integrated data processing protocol operates in memory and produces only the desired outputs. The proposed approach mitigates opportunities for inappropriate scaling between processing steps, the consistency of which is especially important for threshold based change detection procedures. In addition, savings in both processing time and disk storage are afforded through the combination of processing steps, with processing of the time-1 images reduced from 3 to 2 stages and 5 to 2 stages for the time-2 images, resulting in savings of 50% and 69% in computing times and disk space requirements respectively.

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