scholarly journals Analysis of glacier facies using satellite techniques

1991 ◽  
Vol 37 (125) ◽  
pp. 120-128 ◽  
Author(s):  
Richard S. Williams ◽  
Dorothy K. Hall ◽  
Carl S. Benson
Keyword(s):  
Near Infrared ◽  
Infrared Image ◽  
Image Data ◽  
Landsat Data ◽  
Near Infrared Reflectance ◽  
Outlet Glacier ◽  
Northern Margin ◽  
Ice Cap ◽  
Snow Line ◽  
Wet Snow

AbstractThe different snow and ice types on a glacier may be subdivided according to the glacier-facies concept. The surficial expression of some facies may be detected at the end of the balance year by the use of visible and near-infrared image data from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Ice and snow can be distinguished by reflectivity differences in individual or ratioed TM bands on Brúarjökull, an outlet glacier on the northern margin of the Vatnajökull ice cap, Iceland. The Landsat scene shows the upper limit of wet snow on 24 August 1986. Landsat-derived reflectance is lowest for exposed ice and increases markedly at the transient snow line. Above the slush zone is a gradual increase in near-infrared reflectance as a result of decreasing grain-size of the snow, which characterizes drier snow. Landsat data are useful in measuring the areal extent of the ice facies, the slush zone within the wet-snow facies, the snow facies (combined wet-snow, percolation and dry-snow facies), and the respective positions of the transient snow line and the slush limit. In addition, fresh snowfall and/or airborne contaminants, such as soot and tcphra, can limit the utility of Landsat data for delineation of the glacier facies in some cases.

scholarly journals Analysis of glacier facies using satellite techniques

1991 ◽  
Vol 37 (125) ◽  
pp. 120-128 ◽  
Author(s):  
Richard S. Williams ◽  
Dorothy K. Hall ◽  
Carl S. Benson
Keyword(s):  
Near Infrared ◽  
Infrared Image ◽  
Image Data ◽  
Landsat Data ◽  
Near Infrared Reflectance ◽  
Outlet Glacier ◽  
Northern Margin ◽  
Ice Cap ◽  
Snow Line ◽  
Wet Snow

Abstract The different snow and ice types on a glacier may be subdivided according to the glacier-facies concept. The surficial expression of some facies may be detected at the end of the balance year by the use of visible and near-infrared image data from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Ice and snow can be distinguished by reflectivity differences in individual or ratioed TM bands on Brúarjökull, an outlet glacier on the northern margin of the Vatnajökull ice cap, Iceland. The Landsat scene shows the upper limit of wet snow on 24 August 1986. Landsat-derived reflectance is lowest for exposed ice and increases markedly at the transient snow line. Above the slush zone is a gradual increase in near-infrared reflectance as a result of decreasing grain-size of the snow, which characterizes drier snow. Landsat data are useful in measuring the areal extent of the ice facies, the slush zone within the wet-snow facies, the snow facies (combined wet-snow, percolation and dry-snow facies), and the respective positions of the transient snow line and the slush limit. In addition, fresh snowfall and/or airborne contaminants, such as soot and tcphra, can limit the utility of Landsat data for delineation of the glacier facies in some cases.

Fusion on Citrus Image Data from Cold Mirror Acquisition System

2012 ◽  
Vol 2 (4) ◽  
pp. 11-24 ◽  
Author(s):  
Peilin Li ◽  
Sang-Heon Lee ◽  
Hung-Yao Hsu
Keyword(s):  
Near Infrared ◽  
Color Image ◽  
Infrared Image ◽  
Image Data ◽  
Acquisition System ◽  
Support Vector ◽  
Self Organizing Map ◽  
Visible Image ◽  
Low Pass ◽  
Low Dimensional

In this paper, an image fusion is presented to improve the citrus identification by filtering the incoming data from two cameras. The citrus image data has been photographed by using a portable bi-camera cold mirror acquisition system. The prototype of the customized fixture has been manufactured to position and align a classical cold mirror with two CCD cameras in relative kinematic position. The algorithmic registration on the pairwise images has been bypassed by both the spatial alignment of two cameras with recourse of software calibration and the triggering synchronization in temporal during the photographing. The pairwise frames have been fused by using the Daubechies wavelets decomposition filters. The pixel level fusion index rule is proposed to combine the low pass coefficients of the visible image and the low pass coefficients of the near-infrared image convoluted by the complementary of entropy filter from the visible low pass coefficients. In the study, the fused artifact color image and the non-fused color image have been processed and compared by some classification methods such as low dimensional projection, self-organizing map and the support vector machine.

scholarly journals Texture-based classification of cloud and ice-cap surface features

1993 ◽  
Vol 17 ◽  
pp. 250-254
Author(s):  
W.G. Rees ◽  
I-I Lin
Keyword(s):  
Cloud Cover ◽  
Near Infrared ◽  
Landsat Imagery ◽  
Computation Time ◽  
Image Texture ◽  
Good Prospect ◽  
Ice Cap ◽  
Wet Snow ◽  
Occurrence Matrix

It is well known that the interpretation of high resolution (<100 m) visible and near infrared (e.g. Landsat) imagery of large ice masses is hindered by the uniform reflectivity of snow, ice and cloud surfaces. Such interpretation is at present largely performed manually, but there is a good prospect that it could be automated by the incorporation of image texture. This paper describes preliminary work towards the identification of the most appropriate texture technique, or combination of techniques, and assesses the likely performance of such methods.Different textures are identified with different types of surface cover, and the use of these differences to classify images is investigated. Specifically, we compare a traditional texture measure, the Grey Level Co-occurrence Matrix (GLCM), with a modification of a relatively new technique, fractional Brownian motion (FBM). These two methods are applied to three Landsat MSS images of the Nordaustlandet ice cap, Svalbard. The classification accuracy, computation time and memory required, advantages and limitations of the two methods are compared. The GLCM technique appears to be able to distinguish three groups of image classes, namely dry snow, wet snow, and melt features, ablation areas or cloud cover. The FBM technique is computationally more efficient, and though it performs in general less well than the GLCM technique it gives better discrimination of cloud cover.

scholarly journals Satellite Remote Sensing of Vatnajökull, Iceland

1987 ◽  
Vol 9 ◽  
pp. 127-135 ◽  
Author(s):  
Richard S. Williams
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Image Data ◽  
Late Summer ◽  
Test Site ◽  
Landsat Image ◽  
Synthetic Aperture ◽  
Ice Cap ◽  
Wet Snow ◽  
Surface Ice

Iceland’s largest ice cap, Vatnajökull, has been the test site for a series of airborne and satellite remote-sensing studies since 1966. Various types of image data acquired by the Landsat Multispectral Scanner (MSS) and the Seasat Synthetic Aperture Radar (SAR) are assessed for their value to glaciological studies of Vatnajökull. A low Sun angle winter 1973 MSS band 7 Landsat image of Vatnajökull provides information about the distribution and size of subglacial volcanic calderas, cauldron subsidence caused by subglacial geothermal and (or) intrusive volcanic activity, and delineation of the probable position of surface ice divides. Two types of multi-spectral digital enhancements were applied to a late summer 1973 MSS image of Vatnajökull. The first type was used to prepare a planimetric base map showing the location of the principal surface features and an inventory of 38 named outlet glaciers, one internal ice cap (Öraefajökull), and two detached glaciers which comprise this complex ice cap, and to measure its area (8300 km2). The second type provides information about the position of the snow line at the approximate end of the 1973 melt season, the areas encompassed by the ice fades of the ablation area and the slush zone and wet-snow facies/percolation facies of the accumulation area. More information about the surface morphology of Vatnajökull was available from the Sow Sun angle winter and the digitally enhanced summer Landsat image of the ice cap than from the Seasat SAR image.

scholarly journals Satellite Remote Sensing of Vatnajökull, Iceland

1987 ◽  
Vol 9 ◽  
pp. 127-135 ◽  
Author(s):  
Richard S. Williams
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Image Data ◽  
Late Summer ◽  
Test Site ◽  
Landsat Image ◽  
Synthetic Aperture ◽  
Ice Cap ◽  
Wet Snow ◽  
Surface Ice

Iceland’s largest ice cap, Vatnajökull, has been the test site for a series of airborne and satellite remote-sensing studies since 1966. Various types of image data acquired by the Landsat Multispectral Scanner (MSS) and the Seasat Synthetic Aperture Radar (SAR) are assessed for their value to glaciological studies of Vatnajökull. A low Sun angle winter 1973 MSS band 7 Landsat image of Vatnajökull provides information about the distribution and size of subglacial volcanic calderas, cauldron subsidence caused by subglacial geothermal and (or) intrusive volcanic activity, and delineation of the probable position of surface ice divides. Two types of multi-spectral digital enhancements were applied to a late summer 1973 MSS image of Vatnajökull. The first type was used to prepare a planimetric base map showing the location of the principal surface features and an inventory of 38 named outlet glaciers, one internal ice cap (Öraefajökull), and two detached glaciers which comprise this complex ice cap, and to measure its area (8300 km2). The second type provides information about the position of the snow line at the approximate end of the 1973 melt season, the areas encompassed by the ice fades of the ablation area and the slush zone and wet-snow facies/percolation facies of the accumulation area. More information about the surface morphology of Vatnajökull was available from the Sow Sun angle winter and the digitally enhanced summer Landsat image of the ice cap than from the Seasat SAR image.

scholarly journals Texture-based classification of cloud and ice-cap surface features

1993 ◽  
Vol 17 ◽  
pp. 250-254 ◽  
Author(s):  
W.G. Rees ◽  
I-I Lin
Keyword(s):  
Cloud Cover ◽  
Near Infrared ◽  
Landsat Imagery ◽  
Computation Time ◽  
Image Texture ◽  
Good Prospect ◽  
Ice Cap ◽  
Wet Snow ◽  
Occurrence Matrix

It is well known that the interpretation of high resolution (&lt;100 m) visible and near infrared (e.g. Landsat) imagery of large ice masses is hindered by the uniform reflectivity of snow, ice and cloud surfaces. Such interpretation is at present largely performed manually, but there is a good prospect that it could be automated by the incorporation of image texture. This paper describes preliminary work towards the identification of the most appropriate texture technique, or combination of techniques, and assesses the likely performance of such methods. Different textures are identified with different types of surface cover, and the use of these differences to classify images is investigated. Specifically, we compare a traditional texture measure, the Grey Level Co-occurrence Matrix (GLCM), with a modification of a relatively new technique, fractional Brownian motion (FBM). These two methods are applied to three Landsat MSS images of the Nordaustlandet ice cap, Svalbard. The classification accuracy, computation time and memory required, advantages and limitations of the two methods are compared. The GLCM technique appears to be able to distinguish three groups of image classes, namely dry snow, wet snow, and melt features, ablation areas or cloud cover. The FBM technique is computationally more efficient, and though it performs in general less well than the GLCM technique it gives better discrimination of cloud cover.

Sign in / Sign up

Export Citation Format

Share Document