Estimating the coverage of coral reef benthic communities from airborne hyperspectral remote sensing data: multiple discriminant function analysis and linear spectral unmixing

Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. Comparison of the remote sensing results with geological investigations in the study area demonstrate invaluable implications of the remote sensing approach for mapping poorly exposed lithological units, detecting possible zones of listvenite occurrences and discriminating subpixel abundance of alteration mineral assemblages in the damage zones of the Wilson-Bowers and Bowers-Robertson Bay terrane boundaries and in intra-Bowers and Wilson terranes fault zones with high fluid flow. The satellite remote sensing approach developed in this research is explicitly pertinent to detecting key alteration mineral indicators for prospecting hydrothermal/metasomatic ore minerals in remote and inaccessible zones situated in other orogenic systems around the world.

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SPECTRAL UNMIXING BASED ON JOINT SPARSITY AND TOTAL VARIATION USING REMOTE SENSING DATA

International Journal of Recent Trends in Engineering and Research ◽

10.23883/ijrter.conf.20190304.005.rclxe ◽

2019 ◽

Vol 05 (Special Issue 07) ◽

pp. 27-32

Author(s):

J.M. Andiria Joslin ◽

R. .

Keyword(s):

Remote Sensing ◽

Total Variation ◽

Remote Sensing Data ◽

Spectral Unmixing ◽

Sensing Data ◽

Joint Sparsity

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Spectral Characteristics of the Antarctic Vegetation: A Case Study of Barton Peninsula

Remote Sensing ◽

10.3390/rs13132470 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2470

Author(s):

Junhwa Chi ◽

Hyoungseok Lee ◽

Soon Gyu Hong ◽

Hyun-Cheol Kim

Keyword(s):

Remote Sensing ◽

Spectral Characteristics ◽

Remote Sensing Data ◽

Ground Truth ◽

Spectral Unmixing ◽

Polar Regions ◽

Spectral Library ◽

Sensing Data ◽

Barton Peninsula ◽

Shortwave Infrared

Spectral information is a proxy for understanding the characteristics of ground targets without a potentially disruptive contact. A spectral library is a collection of this information and serves as reference data in remote sensing analyses. Although widely used, data of this type for most ground objects in polar regions are notably absent. Remote sensing data are widely used in polar research because they can provide helpful information for difficult-to-access or extensive areas. However, a lack of ground truth hinders remote sensing efforts. Accordingly, a spectral library was developed for 16 common vegetation species and decayed moss in the ice-free areas of Antarctica using a field spectrometer. In particular, the relative importance of shortwave infrared wavelengths in identifying Antarctic vegetation using spectral similarity comparisons was demonstrated. Due to the lack of available remote sensing images of the study area, simulated images were generated using the developed spectral library. Then, these images were used to evaluate the potential performance of the classification and spectral unmixing according to spectral resolution. We believe that the developed library will enhance our understanding of Antarctic vegetation and will assist in the analysis of various remote sensing data.

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Spectral unmixing approach in hyperspectral remote sensing: a tool for oil palm mapping

TecnoLógicas ◽

10.22430/22565337.1228 ◽

2019 ◽

Vol 22 (45) ◽

pp. 129-143

Author(s):

Hector Vargas ◽

Ariolfo Camacho Velasco ◽

Henry Arguello

Keyword(s):

Remote Sensing ◽

Oil Palm ◽

Hyperspectral Image ◽

Remote Sensing Data ◽

Hyperspectral Remote Sensing ◽

Spectral Unmixing ◽

Spectral Variability ◽

Computational Framework ◽

Sensing Data ◽

Spectral Bands

Oil palm plantations typically span large areas; therefore, remote sensing has become a useful tool for advanced oil palm monitoring. This work reviews and evaluates two approaches to analyze oil palm plantations based on hyperspectral remote sensing data: linear spectral unmixing and spectral variability. Moreover, a computational framework based on spectral unmixing for the estimation of fractional abundances of oil palm plantations is proposed in this study. Such approach also considers the spectral variability of hyperspectral image signatures. More specifically, the proposed computational framework modifies the linear mixing model by introducing a weighting vector, so that the spectral bands that contribute the least to the estimation of erroneous fractional abundances can be identified. This approach improves palm detection as it allows to differentiate them from other materials in terms of fractional abundances. Experimental results obtained from hyperspectral remote sensing data in the range 410-990 nm show improvements of 8.18 % in User Accuracy (Uacc) in the identification of oil palms by the proposed framework with respect to traditional unmixing methods. Thus, the proposed method achieved a 95% Uacc. This confirms the capabilities of the proposed computational framework and facilitates the management and monitoring of large areas of oil palm plantations.

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