Detection of Important Atmospheric and Surface Features by Employing Principal Component Image Transformation of GOES Imagery

Abstract

The detection of dust, fire hot spots, and smoke from the Geostationary Operational Environmental Satellite (GOES) is made easier by employing the principal component image (PCI) technique. PCIs are created by an eigenvector transformation of spectral band images from the five-band GOES Imager. The transformation is a powerful tool that provides a new set of images that are linear combinations of the original spectral band images. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. Whereas this multispectral technique is normally applied to high-spatial-resolution land remote sensing imagery, the application is herein made to lower-spatial-resolution weather satellite imagery for the purpose of feature detection and enhancement. Features used as examples include atmospheric dust as well as forest and range fire hot spots and their resulting smoke plumes. The applications of PCIs to GOES utilized the three infrared window images (bands 2, 4, and 5) in dust situations as well as the visible image (band 1) in smoke situations. Two conclusions of this study are 1) atmospheric and surface features are more easily identified in multiband PCIs than in the enhanced single-band images or even in some two-band difference images and 2) the elimination of certain bands can be made either directly by inspection of the PCIs, discarding bands that do not to contribute to the PCIs showing the desired features, or by including all available bands and letting the transformation process indicate the bands that are useful for detecting the desired features. This technique will be increasingly useful with the introduction of new and increased numbers of spectral bands with current and future satellite instrumentation.