Original Reflectance Data Research Articles

Evaluating ASTER data and field spectrometry for lithological discrimination in semi-arid region, Northeast Kohat Plateau, Pakistan

This study employs data of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate and map the distribution of sedimentary lithologies in the semi-arid region of Kohat Plateau, Pakistan. False color composites (FCC) and various image transformation and enhancement techniques including the principal component analysis (PCA), minimum noise fraction (MNF), and band rationing (BR) were used successfully to differentiate four lithological classes. These lithologies include chemically/biochemically formed beds of the marine environment and detrital sequences of marginal marine to the riverine environment. FCC from original reflectance data, PCA, and BR techniques displayed more prominent lithological variation. To map the lithology and show the potential of ASTER data, field spectrometry over the barren lithologies was carried out. The end-member spectra from field spectrometry shows strong agreement with the pixels spectra from ASTER scene. The Spectral Angle Mapper (SAM) mapping method was then used to produce a classified lithological map, where the image pixels spectra proved more suitable reference, in comparison to the end-member spectra. The accuracy of the classified lithological map was evaluated based on field-based point data, which resulted an overall accuracy of 70% and a Kappa coefficient value of 0.679. Carbonates and evaporites showed relatively higher user and producer accuracies which are attributed to their topographic behavior and weathered scree over the adjacent rock unit. The final lithological map provided a clearer picture of surface geology where the existing geological maps lacked lithological continuity.

Estimating Chemical Oxygen Demand in estuarine urban rivers using unmanned aerial vehicle hyperspectral images

In this study, we combined ground-based hyperspectral data, unmanned aerial vehicles (UAVs) remotely sensed hyperspectral images, and 1D-CNN algorithms to quantitatively characterize and estimate the Chemical Oxygen Demand (COD) of estuarine urban rivers. The spectral response mechanism of COD is imprecise due to its complex composition; however, we found that hyperspectral remote sensing data could be used for COD monitoring because of the data's rich spectral information. The potential of hyperspectral sensors installed on UAVs to estimate and map the COD of urban rivers has not been thoroughly explored. We used in situ above-water hyperspectral data from 498 sites and synchronous water samples in band ratio, SVM, and 1D-CNN algorithms to build retrieval models. We found that the 1D-CNN model performed the best with an R2 of 0.78 and an RMSE of 5.22 when using the original reflectance data as input. The 1D-CNN model may also have a better ability to identify water samples with abnormally high concentrations. Our results revealed that transferring the ground-based derived 1D-CNN retrieval model for COD to the high-resolution hyperspectral images is a reliable method for determining COD from the images. We concluded that UAV remotely sensed hyperspectral images are valuable for COD concentration monitoring and mapping, critical to urban water quality management decision-making.

Study of the Relationship Between the Oil Content of Oil Sands and Spectral Reflectance Based on Spectral Derivatives

The oil content of oil sands is an important factor in oil sand resource evaluation. In this study, we use spectral reflectance to study the oil content parameter. The spectrograph can be used to determine the target spectral reflectance directly, using a fiber-optic probe, without destroying samples or using chemical reagents, which has the advantage of quick measurement and analysis. This paper documents the acquisition of hyperspectral data in the range of 350–2500 nm through spectrometry of oil sand samples. We process the original reflectivity data using the first-order and second-order differentials and then calculate and analyze the correlation coefficient with reflectivity of oil sands and the first and second derivatives of reflectance. Based on the characteristics of the correlation coefficient graphs, we determine the wave bands sensitive to oil content and verify that using hyperspectral data in the study of oil content is feasible.

Visualization, interpretation, and cognitive cybernetics

Interpretation of 3D seismic data involves the analysis and integration of many forms and derivatives of the original reflectivity data. This can lead to the generation of an overwhelming amount of data that can be difficult to use effectively when relying on conventional interpretation techniques. Our natural cognitive processes have evolved so that we can absorb and understand large amounts of complex data extremely quickly and effectively. However, these cognitive processes are heavily influenced by context and color perception. Seismic interpretation can benefit greatly through better exploiting the positive aspects of visual cognition and through techniques designed to minimize the pitfalls inherent in the cognitive process. The interpretation of data also requires the ability to combine data analysis with knowledge and expertise that is held by the interpreter. It is this combination of visual perception techniques to see the information, combined with interpreter guidance to understand what is seen, that makes interpretation of seismic data effective. Geological Expression workflows that are data driven and interpreter guided enable us to see and effectively interpret the geology that is present in the seismic data. In effect this gives us a Cognitive Interpretation of the data.

Synchrotron X-Ray Reflectivity Study of Ni-Oxide-Passive-Layer-and-Water Interface and Yttrium-Stabilized-Zirconia-and-Water Interface

The hydration and water interaction at Ni-oxide-water interface and yttrium-stabilized-zirconia (YSZ)-water interface, respectively, have been investigated with synchrotron X-ray reflectivity. For the nickel-oxide-water interface, a thin passive oxide layer was grown on a single crystal Ni(110) substrate in ultra-high vacuum chamber by blowing in pure O2 gas at room temperature after several cycles of sputtering-and-annealing treatment. The high-resolution specular X-ray reflectivity from the passive oxide layer as grown on the single crystal metal substrate was measured in He environment at room temperature to characterize the thin film thickness and the atomic correlations within the oxide thin film. The detailed interfacial structure and the substrate relaxation with a subatomic resolution could be derived based on the measured X-ray reflectivity in terms of the laterally averaged electron density profile in surface normal direction. The virgin passive oxide layer then exposed to pure water, the effect of hydration was observed with the subsequent reflectivity measurement at the same diffractometer geometry. The resultant X-ray reflectivity data showed drastic changes from the original reflectivity data, which indicates a substantial influence of water interaction on the surface termination of the passive layer. On the other hand, single crystal YSZ-water interfaces were studied as representing the most stable ZrO2 phase and water interface system. The three major crystallographic orientations, (111), (110), and (100), were measured and analyzed for the interfacial hydration structures. The studies eventually aim at fundamental understanding of the water interaction on passive oxide layer of metal alloy surface in nuclear power plant environment. Before directly approaching to the simulated reaction in hot pressurized water, the current study provides the reference data to be compared to.

Spectroscopic analysis of soil metal contamination around a derelict mine site in the Blue Mountains, Australia

Abstract. Abandoned mine sites pose the potential threat of the heavy metal pollution spread through streams and via runoff leading to contamination of soil and water in their surrounding areas. Regular monitoring of these areas is critical to minimise impacts on water resources, flora and fauna. Conventional ground based monitoring is expensive and sometimes impractical; spectroscopic methods have been emerged as a reliable alternative for this purpose. In this study, the capabilities of the spectroscopy method were examined for modelling soil contamination from around the abandoned silver-zinc mine located at Yerranderie, NSW Australia. The diagnostic characteristics of the original reflectance data were compared with models derived from first and second derivatives of the reflectance data. The results indicate that the models derived from the first derivative of the reflectance data estimate heavy metals significantly more accurately than model derived from the original reflectance. It was also found in this study that there is no need to use second derivative for modelling heavy metal soil contamination. Finally, the results indicate that estimates were of greater accuracy for arsenic and lead compared to other heavy metals, while the estimation for silver was found to be the most erroneous.

Identification of invasive vegetation using hyperspectral imagery in the shore of the Kinu River, Japan

Weeping love grass (Eragrostis curvula) has become a well-established invasive species along the Kinu River, Japan and is now considered a problematic invasive weed species. The aim of this study was to map the probability of the establishment of this invasive grass in the shore of the Kinu River using airborne hyperspectral imagery. Binary logistic regression analysis was used to model the probable presence/absence of weeping love grass. This study tried entering two types of input variables, original reflectance bands and MNF (Minimum Noise Fraction) transformed bands, into the regression model. No available variable of original reflectance data was selected, but two bands of MNF were selected in the regression analysis. The final classification, using the selected MNF bands, has distinguished weeping love grass from pseudo-absence pixels with user's and producer's accuracies of 100% and 66.7% respectively. The kappa coefficient was 0.74. These results indicate that the MNF transformed hyperspectral bands are more suitable than the original reflectance data to estimate the distribution of invasive weeping love grass in the shore of the Kinu River.

Using canopy reflectance and partial least squares regression to calculate within-field statistical variation in crop growth and nitrogen status of rice

For the site-specific prescription of fertilizer topdressing in rice cultivation, a non-destructive diagnosis of the rice growth and nutrition status is necessary. Three experiments were done to develop and test a model using canopy reflectance for the non-destructive diagnosis of plant growth and N status in rice. Two experiments for model development were conducted, one in 2000 and another in 2003 in Suwon, Korea, including two rice varieties and four nitrogen (N) rates in 2000 and four rice varieties and 10 N treatments in 2003. Hyperspectral canopy reflectance (300–1,100 nm) data recorded at various growth stages before heading were used to develop a partial least squares regression (PLS) model to calculate plant biomass and N nutrition status. The 342 observations were split for model calibration (75%) and validation (25%). The PLS model was then tested to calculate within-field statistical variation of four crop variables: shoot dry weight (SDW), shoot N concentration (SN), shoot N density (SND) and N nutrition index (NNI) using measured canopy reflectance data from a field of 6,500 m2 in 2004. Results showed that PLS regression using logarithm reflectance had better performance than both the PLS and multiple stepwise linear regression (MSLR) models using original reflectance data to calculate the four plant variables in year 2000 and 2003. It produced values with an acceptable model coefficient of determination (R2) and relative error of calculation (REC). The model R2 and REC ranged from .83 to .89 and 13.4% to 22.8% for calibration, and .76 to .87 and 14.0% to 24.4% for validation, respectively. The PLS regression model R2 was reduced in the test data of year 2004 but the root mean square error of calculation (RMSEC) was smaller, suggesting that the PLS regression model using canopy reflectance data could be a promising method to calculate within-field spatial variation of rice crop growth and N status.

Improved Parametric Representation of Water Dielectric Data for Lifshitz Theory Calculations

Calculations of van der Waals interactions using Lifshitz theory require the availability and convenient mathematical representation of dielectric data for the materials involved, especially in the ultraviolet region of the spectrum. The UV representation of water is especially frequently needed in Lifshitz theory calculations in view of the ubiquitous appearance of aqueous media in the intervening space between solid surfaces, both macroscopic and colloidal. We present here a modified parameter set to describe the UV dielectric behavior of water. It fits the original reflectivity data much more closely than the set usually used, and leads to significant changes in calculated Hamaker constants—for materials interacting across water, a reduction of as much as 40% is seen. These results illustrate the importance of accurate spectral information, or conversely, the potential uncertainties in calculated properties in the absence of extensive and accurate data.