Discrimination of sedimentary lithologies using Hyperion and Landsat Thematic Mapper data: a case study at Melville Island, Canadian High Arctic

Abstract

The use of remote-sensing techniques in the discrimination of rock and soil classes in northern regions can support a diverse range of activities, such as environmental characterization, mineral exploration and the study of Quaternary paleoenvironments. Although images with low spectral resolution can commonly be used in the mapping of classes possessing distinct spectral properties, hyperspectral images offer greater potential for discrimination of materials characterized by more subtle reflectance properties. In an effort to better constrain the utility of broadband and hyperspectral datasets in high-latitude research, this study investigated the effectiveness of Landsat Thematic Mapper (TM) and EO-1 Hyperion data for discrimination of lithological classes at eastern Melville Island, Nunavut, Canada. TM data were classified using a standard neural-network algorithm, and both TM and Hyperion data were linearly unmixed using ground-truth spectra. TM classification results successfully discriminate between classes over much of the study area, although with incomplete separation between clastic and carbonate materials. TM unmixing results are poor, with useful class separation restricted to vegetation and red-weathered sandstone classes. Hyperion results effectively depict the fractional cover of end members, although the abundance images of several classes contain background abundance values that overestimate surface exposure in some areas. For the study area and surface classes involved, noisy hyperspectral data were found to be of greater utility than higher-fidelity broadband multispectral data in the generation of fractional abundance images for an inclusive set of surface-cover classes.

Landsat-TM-Based Discrimination of Lithological Units Associated with the Purtuniq Ophiolite, Quebec, Canada

“…Differences in the reflectance properties of geological classes can be subtle, and hyperspectral images (acquired over dozens to hundreds of individual wavelength ranges) have the potential to provide the constraints necessary for the successful separation of such classes in unmixing exercises (e.g., [53]). Past studies involving ophiolitic units have suggested that multispectral data can be useful in the general discrimination of igneous and metamorphic lithologies (particularly when working with data acquired in the near-and shortwave-infrared), but have also highlighted the need for improved spectral resolution to support the deconvolution of image or reflectance data.…”

“…Neural networks are especially useful for the mapping of geological materials, since individual geological classes are commonly characterized by substantial variation in reflectance properties as a result of spatial inhomogeneities in mineralogy, degree of chemical alteration, and surface exposure [44,53]. Unlike classifiers such as the maximum likelihood algorithm, neural networks do not require parameterization of training data using simple distribution models, allowing irregular (e.g., multimodal) distributions in training databases to be more properly considered during classification.…”

Detection and Mapping of Black Rock Coatings Using Hyperion Images: Sudbury, Ontario, Canada

“…Though Hyperion images are characterized by relatively high levels of noise [59,[62][63][64][65][66][67][68], they represent important alternatives to datasets generated by orbiting multispectral systems. In numerous case studies, Hyperion images have proven useful in the mapping of lithological and mineralogical classes [63,64,67,[69][70][71][72][73][74][75][76]. In the Sudbury region, Hyperion images have previously been used in the study of vegetation growth related to land reclamation near smelters [20,77].…”

The Impact of Vegetation on Lithological Mapping Using Airborne Multispectral Data: A Case Study for the North Troodos Region, Cyprus

“…Nevertheless, it has been shown that it is possible to extract some useful lithological information from the ATM imagery provided that an appropriate technique is employed. Enhanced mapping of the spatial distribution of the lithologies in highly vegetated areas may be possible if geobotanical relationships with the underlying substrates are realized and the vegetation spectra used as proxies [12,14,78]. Indeed, for this same study area, Grebby et al [34] exploited geobotanical relationships in conjunction with an artificial neural network classifier to produce an accurate (65.5%; K = 0.54) lithological map with well-defined contacts and contiguous spatial coverage.…”