scholarly journals SEASONAL SPECTRAL SEPARABILITY OF SELECTED GRASSES: CASE STUDY FROM THE KRKONOŠE MTS. TUNDRA ECOSYSTEM

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 371-376
Author(s):  
L. Červená ◽  
L. Kupková ◽  
M. Potůčková ◽  
J. Lysák
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Single Layer ◽  
Image Data ◽  
Nardus Stricta ◽  
Spectral Bands ◽  
Tundra Ecosystem ◽  
Leaf Level ◽  
Spectral Separability ◽  
June July

Abstract. This paper focuses on spectral separability of closed alpine grasslands dominated with Nardus stricta and competitive grasses Calamagrostis villosa and Molinia caerulea in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. The spectral data were acquired and compared at three levels: spectra of a single layer of leaves measured with the ASD FieldSpec4 Wide-Res spectroradiometer coupled with a contact probe in a laboratory (leaf level), canopy spectra measured in a field with the same spectroradiometer using the fiber optic cable with a pistol grip (canopy level), and hyperspectral image data acquired by Nano-Hyperspec® fastened to the DJI Matrice 600 Pro drone (image level). All the measurements were repeated three times during the 2019 vegetation season – in June, July and August. Using the methods of analysis of variance and Welch's (unpaired) t-test, it was proven that there were differences in the results for all three spectra sources. But in general, for each combination of species and each data source a suitable date and intervals of the spectral bands for species separation exist. The most suitable term for data acquisition in order to differentiate all the species is July. At the leaf level, the best species separability was observed in the near-infrared and shortwave infrared spectral ranges. At the canopy and image levels, the visible bands are of higher importance for discriminating the species.

Processes governing the VIS/NIR spectral reflectance behavior of lunar swirls

2020 ◽  
Vol 639 ◽  
pp. A12
Author(s):  
M. Hess ◽  
C. Wöhler ◽  
M. Bhatt ◽  
A. A. Berezhnoy ◽  
A. Grumpe ◽  
...  
Keyword(s):  
Solar Wind ◽  
Absorption Band ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Image Data ◽  
Time Of Day ◽  
Magnetic Shielding ◽  
Solar Wind Interaction ◽  
Infrared Wavelength ◽  
Band Depth

We investigated six bright swirls associated with magnetic anomalies of variable strength using Chandrayaan-1 Moon Mineralogy Mapper (M3) hyperspectral image data. We examined the 3 μm absorption band generally ascribed to solar wind-induced OH/H2O and spectral trends in the near-infrared wavelength range at on-swirl and off-swirl locations. We found that the 3 μm absorption band is weaker at on-swirl than at off-swirl locations and shows only weak variations with time-of-day. This result is consistent with magnetic anomaly shielding that reduces solar wind interaction with the surface. For a small swirl structure in Mare Moscoviense, we found the 3 μm absorption band to be similar to that of its surroundings due to the absence of strong magnetic shielding. Our spectral analysis results at on-swirl and off-swirl locations suggest that the spectral trends at on-swirl and off-swirl locations cannot always be explained by reduced space-weathering alone. We propose that a combination of soil compaction possibly resulting from the interaction between the surface and cometary gas and subsequent magnetic shielding is able to explain all observed on-swirl vs. off-swirl spectral trends including the absorption band depth near 3 μm. Our results suggest that an external mechanism of interaction between a comet and the uppermost regolith layer might play a significant role in lunar swirl formation.

scholarly journals Validation and Comparison of Physical Models for Soil Salinity Mapping over an Arid Landscape Using Spectral Reflectance Measurements and Landsat-OLI Data

2021 ◽  
Vol 13 (3) ◽  
pp. 494
Author(s):  
Z. M. Al-Ali ◽  
A. Bannari ◽  
H. Rhinane ◽  
A. El-Battay ◽  
S. A. Shahid ◽  
...  
Keyword(s):  
Soil Sample ◽  
Soil Salinity ◽  
Near Infrared ◽  
Field Survey ◽  
Statistical Tests ◽  
Spectral Response ◽  
Image Data ◽  
Physical Models ◽  
Coefficient Of Determination ◽  
Spectral Bands

The present study focuses on the validation and comparison of eight different physical models for soil salinity mapping in an arid landscape using two independent Landsat-Operational Land Imager (OLI) datasets: simulated and image data. The examined and compared models were previously developed for different semi-arid and arid geographic regions around the world, i.e., Latino-America, the Middle East, North and East Africa and Asia. These models integrate different spectral bands and unlike mathematical functions in their conceptualization. To achieve the objectives of the study, four main steps were completed. For simulated data, a field survey was organized, and 100 soil samples were collected with various degrees of salinity levels. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an analytical spectral device (ASD) FieldSpec-4 Hi-Res spectroradiometer. These measurements were resampled and convolved in the solar-reflective bands of the Operational Land Imager (OLI) sensor using a radiative transfer code and the relative spectral response profiles characterizing the filters of the OLI sensor. Then, they were converted in terms of the considered models. Moreover, the OLI image acquired simultaneously with the field survey was radiometrically preprocessed, and the models were implemented to derive soil salinity maps. The laboratory analyses were performed to derive electrical conductivity (EC-Lab) from each soil sample for validation and comparison purposes. These steps were undertaken between predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab) in the same way for simulated and image data using regression analysis (p ˂ 0.05), coefficient of determination (R2), and root mean square error (RMSE). Moreover, the derived maps were visually interpreted and validated by comparison with observations from the field visit, ancillary data (soil, geology, geomorphology and water table maps) and soil laboratory analyses. Regardless of data sources (simulated or image) or the validation mode, the results obtained show that the predictive models based on visible- and near-infrared (VNIR) bands and vegetation indices are inadequate for soil salinity prediction in an arid landscape due to serious signals confusion between the salt crust and soil optical properties in these spectral bands. The statistical tests revealed insignificant fits (R2 ≤ 0.41) with very high prediction errors (RMSE ≥ 0.65), while the model based on the second-order polynomial function and integrating the shortwave infrared (SWIR) bands provided the results of best fit, with the field observations (EC-Lab), yielding an R2 of 0.97 and a low overall RMSE of 0.13. These findings were corroborated by visual interpretation of derived maps and their validation by comparison with the ground truthing.

scholarly journals Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

2021 ◽  
Vol 13 (3) ◽  
pp. 536
Author(s):  
Eve Laroche-Pinel ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Véronique Chéret ◽  
Jacques Rousseau ◽  
...  
Keyword(s):  
Near Infrared ◽  
Water Status ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Spatial And Temporal Scales ◽  
Spectral Bands ◽  
Main Challenge ◽  
Spectral Scale ◽  
Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data

scholarly journals Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 316
Author(s):  
Lakkana Pitak ◽  
Kittipong Laloon ◽  
Seree Wongpichet ◽  
Panmanas Sirisomboon ◽  
Jetsada Posom
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Thermal Conversion ◽  
High Energy ◽  
Volatile Matter ◽  
Successive Projections Algorithm ◽  
Color Distribution ◽  
Standard Normal Variate ◽  
Distribution Mapping ◽  
Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

Optimisation of Magnification Levels for near Infrared Chemical Imaging of Blending of Pharmaceutical Powders

2007 ◽  
Vol 15 (3) ◽  
pp. 137-151 ◽  
Author(s):  
Hua Ma ◽  
Carl A. Anderson
Keyword(s):  
Near Infrared ◽  
Image Data ◽  
Principal Component ◽  
Chemical Imaging ◽  
Field Of View ◽  
Pharmaceutical Dosage Forms ◽  
Component Distribution ◽  
Circular Field ◽  
Pharmaceutical Powder ◽  
Magnification Level

A critical parameter in the evaluation of pharmaceutical dosage forms by hyperspectral imaging is the level of magnification. If the magnification (as set by the optical objective) is inadequate to resolve the relevant features, then the value of the imaging is diminished; if the magnification level is greater than is required, then the field of view is unnecessarily reduced. The purpose of this study was to determine an optimum magnification level for the study of powder mixing. Relevant features in this system include distribution of individual components within samples and the overall content of a given sample. In the present study, three magnification levels of near infrared (NIR) chemical imaging objectives were evaluated for their effects on imaging a blend of pharmaceutical materials (powders). High, medium and low objective magnification levels were investigated by comparing the resulting blend surface images of a two-component (salicylic acid and lactose) pharmaceutical powder mixture. Multiple images from high and medium magnification were concatenated so that an equivalent field of view was obtained for all magnification levels. Univariate images, principal component analysis score images, partial least squares predicted images and spectra extracted from different intensity regions in the area images were analysed qualitatively and quantitatively for comparison. A series of images spanning a strip across the centre of the circular field were collected at each magnification level and compared with respect to surface features elucidated and area of blend surface imaged. Analyses of images indicate that the three magnification levels delineate the component distribution for this particular powder system similarly. Images obtained at the low magnification level demonstrated adequate resolution and provided the broadest view of the blend surface. It is concluded that the low optical magnification level was adequate for the system being studied and is the preferred mode for pharmaceutical powder blend image data collection for this system.

scholarly journals Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Elena Goi ◽  
Xi Chen ◽  
Qiming Zhang ◽  
Benjamin P. Cumming ◽  
Steffen Schoenhardt ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Near Infrared ◽  
Single Layer ◽  
Infrared Region ◽  
Optical Devices ◽  
Medical Diagnostics ◽  
Oxide Semiconductor ◽  
Process Data ◽  
Neuron Density

AbstractOptical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors, designed for operation in the near-infrared region, are nanoprinted on complementary metal-oxide–semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm1,2, achieving a neuron density of >500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3, sensing4, medical diagnostics5 and computing6,7.

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