Effects of Different Illumination and Observation Techniques of Cultivated Soils on Their Hyperspectral Bidirectional Measurements Under Field and Laboratory Conditions

Cierniewski, Jerzy; Kaźmierowski, Cezary; Królewicz, Sławomir; Piekarczyk, J.; Wróbel, Marcin; Zagajewski, Bogdan

doi:10.1109/jstars.2014.2298098

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…The state of cell structures, photosynthetically active pigments, water, lignin and cellulose content determines the spectral reflectance curve in different parts of the electromagnetic spectrum [7]. In addition, the relationship between absorbed and scattered photons allows an exploration of the stress resistance mechanism(s) according to species properties [8][9][10]. main source of information, ASD FieldSpec 3/4 hyperspectral measurements were used to analyze interactions between the electromagnetic spectrum and the morphology and physiology of plants.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards

Kycko

Zagajewski

Lavender³

et al. 2018

Remote Sensing

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This research focuses on the effect of trampling on vegetation in high-mountain ecosystems through the electromagnetic spectrum's interaction with plant pigments, cell structure, water content and other substances that have a direct impact on leaf properties. The aim of the study was to confirm with the use of fluorescence methods of variability in the state of high-mountain vegetation previously measured spectrometrically. The most heavily visited part of the High Tatras in Poland was divided into polygons and, after selecting the dominant species within alpine swards, a detailed analysis of trampled and reference patterns was performed. The Analytical Spectral Devices (ASD) FieldSpec 3/4 were used to acquire high-resolution spectral properties of plants, their fluorescence and the leaf chlorophyll content with the difference between the plant surface temperature (ts), and the air temperature (ta) as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR) used as reference data. The results show that, along tourist trails, vegetation adapts to trampling with the impact depending on the species. A lower chlorophyll value was confirmed by a decrease in fluorescence, and the cellular structures were degraded in trampled compared to reference species, with a lower leaf reflectance. In addition, at the extreme, trampling can eliminate certain species such as Luzula alpino-pilosa, for which significant changes were noted due to trampling.

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Section: Introductionmentioning

confidence: 99%

The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards

Kycko

Zagajewski

Lavender³

et al. 2018

Remote Sensing

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“…The CDN in function (9) not only weights the dissimilarity in sparse coefficients between pairwise bands but also enhances the sparsity of coefficient matrix Z. The predefined dissimilarity measurement is independent of formulating the optimization program (9), and interested readers can try other dissimilarity measurements in the program.…”

Section: B Dissimilarity-weighted Regularization Model Of All Band Vmentioning

confidence: 99%

“…The classification map of hyperspectral imagery (HSI) plays significant roles in the environmental monitoring [4], [5], geological explorations [6], [7], precision farming [8], [9], and national defenses [10]. However, numerous bands along with strong intraband correlations also bring about big problems for the classification operations [11].…”

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confidence: 99%

A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification

Sun

Zhang

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A new dissimilarity-weighted sparse selfrepresentation (DWSSR) method has been presented to select a proper band subset for hyperspectral imagery (HSI) classification. The DWSSR assumes that all the bands can be represented by the selected band subset, and it formulates sparse representation of all the bands into a sparse self-representation (SSR) model with row-sparsity constraint in the coefficient matrix. Furthermore, the DWSSR integrates a dissimilarity-weighted regularization term with the SSR model to avoid the issue of too-close bands encountered in the SSR. The regularization term explains the encoding cost of all bands with the representative bands, and a new composite dissimilarity measure which combines spectral information divergence with intraband correlation is implemented to estimate the encoding weight. The DWSSR program is solved by the alternating direction method of multipliers (ADMM) framework, and the representative bands are finally selected according to the norm rankings of nonzero rows in the estimated coefficient matrix. Five groups of experiments on three popular HSI datasets are designed to test the performance of DWSSR in band selection, and five state-of-the-art methods are utilized to make comparisons. The results show that the DWSSR performs almost best among all the six methods, either in computational time or classification accuracies.Index Terms-Band selection, classification, dissimilarityweighted sparse self-representation (DWSSR), hyperspectral imagery (HSI).

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“…The roughness of the cultivated surfaces was investigated as revealed by stereophotographs taken with a Canon 450D digital camera moving along a leveled construction 1.5 m above the ground [15], [17], [18]. PCI Geomatica Orthoengine 10.2 software was employed to construct digital elevation models (DEMs) of the tested soil surfaces by photogrammetric processing of the stereophotographs taken.…”

Section: A Field Measurementsmentioning

confidence: 99%

“…This methodological difference between these field and laboratory measurements can make it difficult to use the results of both methods in one data set with the required correctness. Earlier, an evaluation was made of the goodness-of-fit between the bidirectional reflectance data of soils formed by some farming tools collected in field conditions as illuminated by direct and diffuse solar radiation and their bidirectional reflectance equivalents measured in the laboratory with only a direct radiation component [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effects of Surface Roughness on the Relationship Between Soil BRF Data and Broadband Albedo

Cierniewski

Kaźmierowski

Królewicz

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper reports a preliminary study on correctness of the broadband albedo (αb) of cultivated soils that could be calculated by their narrowband bidirectional reflectance factors (BRF) measured from many directions with a prototype goniometric device in a laboratory on their samples with similar roughness, as these soils revealed in the field. The correctness was tested on examples of Phaeozem, Luvisol, and Albeluvisol with roughness formed by a plow, disk harrow, pulverizing harrow, and smoothing harrow. The αb values of the soils calculated using the equation proposed here were not affected by the root mean square error higher than 0.01 as compared to the data measured in the field if the BRF data were obtained on the soil samples with a sufficiently low roughness. It was found that the roughness of those soil samples should be sufficiently low to ensure that the values of their height standard deviation along the length of the minor axis of a sensor field of view area are greater than 15.Index Terms-Bidirectional reflectance factor (BRF), field and laboratory measurements, hyperspectral directional reflectance, soil surface roughness.

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Effects of Different Illumination and Observation Techniques of Cultivated Soils on Their Hyperspectral Bidirectional Measurements Under Field and Laboratory Conditions

Cited by 12 publications

References 15 publications

The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards

The Impact of Tourist Traffic on the Condition and Cell Structures of Alpine Swards

A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification

Evaluation of the Effects of Surface Roughness on the Relationship Between Soil BRF Data and Broadband Albedo

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