Assessment of Imaging Spectroscopy for rock identification in the Karkonosze Mountains, Poland

Mierczyk, Monika; Zagajewski, Bogdan; Jarocińska, Anna; Knapik, Roksana

doi:10.1515/mgrsd-2015-0016

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…The field data were acquired during an in situ measurement campaign between 4 and 7 August 2015 using an ASD FieldSpec 3 spectroradiometer (ASD Inc., Longmont, CO, USA, Table 1) fit with an ASD PlantProbe [36]. The contact probe, which uses an artificial light and closed chamber for data Data were acquired from seven sites on dry to moist tundra communities dominated by the vascular species mentioned above ( Figure 1).…”

Section: In-situ Data Collection and Processingmentioning

confidence: 99%

Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants

et al. 2017

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Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, which could easily be identified in the field as well as on RapidEye satellite imagery. Spectral measurements of individual species were acquired, and heavy metal contamination stress factors were measured contemporaneously. As a result, a unique spectral library of dominant plant species, heavy metal concentrations and damage ratios were achieved with an indication that species-specific changes due to environmental conditions can best be differentiated in the 1401-2400 nm spectral region. Two key arctic tundra species, Cassiope tetragona and Dryas octopetala, exhibited significant differences in this spectral region that were linked to a changing health status. Relationships between field and satellite measurements were comparable, e.g., the Red Edge Normalized Difference Vegetation Index (RENDVI) showed a strong and significant relationship (R 2 = 0.82; p = 0.036) for the species Dryas octopetala. Cadmium and Lead were below detection levels while manganese, copper and zinc acquired near Longyearbyen were at concentrations comparable to other places in Svalbard. There were high levels of nickel near Longyearbyen (0.014 mg/g), while it was low (0.004 mg/g) elsewhere.

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Section: In-situ Data Collection and Processingmentioning

confidence: 99%

Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants

et al. 2017

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“…e spectral variability of similar rocks types was examined by Mierczyk et al [31] which found an overall accuracy of 63.5% using SAM. Schneider et al [48] compared laboratory and field spectral images using several classification methods and found differences in the performance due to illumination conditions, calibration approach, and the presence of dust deposits.…”

Section: E Feasibility Of Classifying the Rocks Samplesmentioning

confidence: 99%

“…e identification and classification of rocks and minerals have been the focus in many studies either by using their spectral characteristics [30] or by various spectral processing methods such as spectral angle mapper (SAM) [10,[31][32][33], support vector machines (SVM) [32,34], and principal component analysis (PCA) [35]. Recent studies found an overall classification accuracy of 66% based on the spectral data of various rocks [36] and an accuracy of 67.4% and 69.7% based on SAM and spectral information divergence (SID), respectively [37].…”

Section: Introductionmentioning

confidence: 99%

Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach

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Denk

Gläßer

et al. 2021

Journal of Spectroscopy

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Reflectance spectroscopy is a nondestructive, rapid, and easy-to-use technique which can be used to assess the composition of rocks qualitatively or quantitatively. Although it is a powerful tool, it has its limitations especially when it comes to measurements of rocks with a phaneritic texture. The external variability is reflected only in spectroscopy and not in the chemical-mineralogical measurements that are performed on crushed rock in certified laboratories. Hence, the spectral variability of the surface of an uncrushed rock will, in most cases, be higher than the internal chemical-mineralogical variability, which may impair statistical models built on field measurements. For this reason, studying ore-bearing rocks and evaluating their spectral variability in different scales is an important procedure to better understand the factors that may influence the qualitative and quantitative analysis of the rocks. The objectives are to quantify the spectral variability of three types of altered granodiorite using well-established statistical methods with an upscaling approach. With this approach, the samples were measured in the laboratory under supervised ambient conditions and in the field under semisupervised conditions. This study further aims to conclude which statistical method provides the best practical and accurate classification for use in future studies. Our results showed that all statistical methods enable the separation of the rock types, although two types of rocks have exhibited almost identical spectra. Furthermore, the statistical methods that supplied the most significant results for classification purposes were principal component analysis combined with k-nearest neighbor with a classification accuracy for laboratory and field measurements of 68.1% and 100%, respectively.

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“…The wealth of spectral information available from advanced hyperspectral imaging instruments has opened new perspectives in many application domains. For example, monitoring of natural grasslands and the synanthropic patterns of invasive species [1], mapping of tree species in a complex mixed forest ecosystem [2] and analyzing the dominant minerals and rocks in a mountain region [3], mapping and estimation of the amounts of hazardous materials to human health [4] and urban land-cover mapping with high resolution images [5] are among others. Besides, retrieving the atmospheric condition parameters aid to the atmospheric correction and the atmosphere monitoring processes.…”

Section: Introductionmentioning

confidence: 99%

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

et al. 2018

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Abstract:A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (L rs,t (λ)). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (ρ t,pre ) where effects associated with L rs,t (λ) are less influential. The method identifies pixels comprising pure materials from ρ t,pre . AOD values at the pure pixels are iteratively estimated using l 2 -norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06-0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5-10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

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Assessment of Imaging Spectroscopy for rock identification in the Karkonosze Mountains, Poland

Cited by 5 publications

References 11 publications

Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants

Intraspecific Differences in Spectral Reflectance Curves as Indicators of Reduced Vitality in High-Arctic Plants

Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

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