Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Abstract

Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes.

Modulating spectral reflectance and its impact on thematic mapping: an analysis on glacier facies using very high-resolution satellite imagery

“…Nevertheless, we present the results of MHD here to illustrate its performance. For further details of this experiment and more extensive analysis the reader is referred to our previous work 4,5,15 .…”

Spatiotemporal Variations of Glacier Surface Facies (GSFs) in Svalbard: An Example of Midtre Lovénbreen

“…Although Landsat data can be pansharpened, we have observed, in previous experiments, that pansharpening distorts spectral information within the pixel [7][8][9]. Thus, while enhancing spatial resolution may improve the identification of facies, the distorted spectral information may misrepresent the facies, causing misclassification.…”

“…The Midtre Lovénbreen (ML) glacier (Figure 1) is well monitored, with a long mass balance record [10]. ML has been utilized for mapping facies by several studies [9]. This provides a working repository of literature for this current study.…”

“…However, radar facies and optical facies cannot be directly compared on account of the inherent differences in both the mechanism and nomenclature used to generate the maps. Although spatiotemporal variations using optical data have not been tested in Ny-Ålesund, snapshot classifications of facies have been performed for selected glaciers from the same region [5][6][7][8][9]. The current literature for mapping facies does not address the challenges and requirements for performing a long-term spatiotemporal analysis for larger study sites.…”

Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

“…Consequently, developing an improved algorithm or a combination of algorithms for mineral mapping becomes important. Despite some researchers dedicating efforts to evaluating various spectral mapping algorithms, particularly the eight algorithms featured in ENVI software, most of these studies have not been devoted exclusively to mineral spectral mapping [55][56][57][58][59][60][61]. Furthermore, some researchers did not validate the results of their geological and mineral mapping outcomes using ground-truth information [12,35,36].…”