Continuum Removal Research Articles

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Remote sensing is an important method for monitoring marine oil-spill accidents. However, methods for measuring oil-film thickness remain insufficient. Due to the stable differences in the surface emissivity and temperature of oil and water, the oil film can be detected using thermal infrared. This study measured emissivity of seven different oil-film thicknesses and seven different American Petroleum Institute (API) densities, and analyzed the spectral characteristics. Results show an optimal wavelength position for oil-film thickness and fuel API density monitoring is 12.55 μm. Principal component analysis and continuum removal methods were used for data processing. Stepwise multiple linear regression was used to establish relationships between emissivity and oil slick thicknesses and API densities. Oil-film thickness and fuel API density data were analyzed by principal component analysis and continuum removal before regression analysis. The spectral emissivity data was convolved into Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) thermal bands to determine potential of the sensor in oil-film detection. The result shows that neither could be used to estimate thickness. The AVHRR-4 band and band 12 and 13 of the ASTER could be used to separate oils from water and have potential to distinguish different oil types.

Spectral behavior of Persian oak under compound stress of water deficit and dust storm

Persian oak (Quercus Brantii Lindl.) which is the most widely distributed tree in the Zagros Mountain forests is affected by western dust storms, mostly originating in Iraq, and harsh water stress as well. The objective of this research is to analyze the spectral behavior of Persian oak under water and dust stress scenarios, aiming to pave the way for modeling the stresses of drought and dust storms on oak trees using remote sensing images. Experiments were carried out on 54 two-year old oak tree seedlings, using a portable wind tunnel in greenhouse conditions. Water stress was induced on seedlings by means of changes in irrigation practices, i.e. well-watered (100 % field capacity), medium water deficit condition (40 % field capacity), and severe water deficit condition (20 % field capacity) treatments. Dust stress is also investigated by using three different dust particle concentrations, i.e. 350, 750 and 1500 (μg/m³). The spectrometry experiments were carried out at leaf and canopy levels in dark room by Fieldspec-3-ASD spectrometer. Spectral analysis was conducted using four procedures: (i) narrow-band spectral indices analysis, (ii) geometric indicators extraction from absorption features, (iii) Partial Least Squares Regression (PLSR), and SVM classifier. Results show that water stress could be modeled much better using PLSR statistic (R2 = 0.87, RMSE = 0.12), narrow-band indices analysis (R2cv = 0.75, RMSEcv = 0.17), and continuum removal (R2 = 0.71, RMSE = 0.20), respectively. For dust stress, PLSR (R2 = 0.83, RMSE = 0.14) and narrow-band indices (R2cv = 0.7, RMSE cv = 0.30) showed the best results, respectively. SVM could successfully separate stressed and not-stressed samples and also the stress types at both leaf and canopy levels, but it could not distinguish the different levels of stresses.

Determination of Heavy Metal Soil Contaminants Based on Photoacoustic Spectroscopy

Heavy metal (HM) contamination in soil is a threat to human health and environmental safety. This paper presents an infrared photoacoustic spectroscopic non-destructive testing for HM evaluation by a robust customized photoacoustic spectrometric system. Cadmium (Cd) was selected as the target contamination which was blended in soil samples. A two-layer feed-forward artificial neural network (ANN) model was developed for HM concentration estimation. The results show that the standard normal variate and continuum removal methods are the best preprocessing algorithms regarding the maximal correction coefficient (R2 > 0.90) and the minimal root mean square error criteria. The correction coefficient analysis shows a better prediction performance (R2 > 0.95) for a HM concentration estimation with the two preprocessing methods. In conclusion, the prediction accuracy can be significantly improved by implementing specific preprocessing and training methods. The ANN model-based infrared photoacoustic spectroscopic method has a future potential for the featureless HM contamination estimation in soil.

Identifying Degraded Grass Species in Inner Mongolia Based on Measured Hyperspectral Data

Grassland degradation is one of the most serious ecological crises today. Using hyperspectral data to identify grass species on a more fine scale will allow for a more comprehensive exploration of grassland degradation processes from the perspective of ecological changes in grassland structure, which will aid in the development of targeted measures to control grassland degradation. To this purpose, we selected representative dominant grass and degenerative indicator grass species in typical steppe landforms of Inner Mongolia to evaluate their spectral separability. Spectral characteristics of interspecies were determined using the confidence interval mean difference method (CIMDM) developed for this article and principal component analysis based on the original first-order derivative reflectance spectral and continuum removal data, whereas species identification was determined through stepwise discriminant analysis. Results showed a detectable difference between spectral data from both the dominant and degraded indicator grass species, wherein the spectral separability of the latter was generally better than the former, among which Caragana microphylla Lam. was best, whereas Leymus chinensis , Allium ramosum L., and Stipa grandis were generally poor. Based on CIMDM results, the distribution of most band characteristics was between 540 and 750 nm. Furthermore, the recognition accuracy of both methods exceeded 90%, confirming the effectiveness of measured spectral data in identifying these grass species on a more fine scale. This article provides new concepts in identifying plant species based on measured spectra, while laying the foundation for future large-scale grass species identification and degradation monitoring using hyperspectral remote sensing imagery.

Spectral Diversity Successfully Estimates the α-Diversity of Biocrust-Forming Lichens

Biocrusts, topsoil communities formed by mosses, lichens, liverworts, algae, and cyanobacteria, are a key biotic component of dryland ecosystems worldwide. Experiments carried out with lichen- and moss-dominated biocrusts indicate that climate change may dramatically reduce their cover and diversity. Therefore, the development of reproducible methods to monitor changes in biocrust diversity and abundance across multiple spatio-temporal scales is key for evaluating how climate change may impact biocrust communities and the myriad of ecosystem functions and services that rely on them. In this study, we collected lichen-dominated biocrust samples from a semi-arid ecosystem in central Spain. Their α-diversity was then evaluated using very high spatial resolution hyperspectral images (pixel size of 0.091 mm) measured in laboratory under controlled conditions. Support vector machines were used to map the biocrust composition. Traditional α-diversity metrics (i.e., species richness, Shannon’s, Simpson’s, and Pielou’s indices) were calculated using lichen fractional cover data derived from their classifications in the hyperspectral imagery. Spectral diversity was calculated at different wavelength ranges as the coefficient of variation of different regions of the reflectance spectra of lichens and as the standard deviation of the continuum removal algorithm (SD_CR). The accuracy of the classifications of the images obtained was close to 100%. The results showed the best coefficient of determination (r2 = 0.47) between SD_CR calculated at 680 nm and the α-diversity calculated as the Simpson’s index, which includes species richness and their evenness. These findings indicate that this spectral diversity index could be used to track spatio-temporal changes in lichen-dominated biocrust communities. Thus, they are the first step to monitor α-diversity of biocrust-forming lichens at the ecosystem and regional levels, a key task for any program aiming to evaluate changes in biodiversity and associated ecosystem services in drylands.

A novel strategy of near-infrared spectroscopy dimensionality reduction for discrimination of grades, varieties and origins of green tea

Supervised orthogonal locality preserving projection (SOLPP), a supervised manifold learning dimensionality reduction method, was employed to reduce near-infrared (NIR) feature dimension of green tea, and the newly low-dimensional variables were applied to identify green tea with different grades, varieties and origins. First, the continuum removal was applied to highlight the subtle difference of spectral characteristics between two groups from grades, varieties and origins of different green tea. Then, the capability of SOLPP for mining special effective features was assessed in comparison with the unsupervised methods such as principal component analysis (PCA), multidimensional scaling (MDS), and locality preserving projections (LPP). Support vector machine (SVM) was used to build discrimination models based on variables represented by PCA, MDS, LPP and SOLPP. Results indicated the data processed by SOLPP displayed better separability between two classes on the premise of preserving prior level information and local manifold structure in each dataset. The combination of both SOLPP and SVM could identify grades, varieties and origins of green tea with improving performance. The accuracy and Kappa values achieved 100% and 1.00 in all datasets. Satisfactory results indicated that SOLPP as a valuable NIR spectral feature mining and dimensionality reduction method can evaluate quality of agricultural products.

Brightness gradient-corrected hyperspectral image mosaics for fractional vegetation cover mapping in northern California

ABSTRACTWe evaluated the effectiveness of different approaches to compensate for across-track brightness gradients within a hyperspectral image mosaic comprised of multiple flight lines in the San Francisco Bay Area. We calculated the spectral consistency of adjacent flight lines and conducted regression-based unmixing of woody- and non-woody vegetation fractions to assess the comparative benefits of the methods. Results showed that a class-wise empirical approach produced the most spectrally consistent, nearly seamless image mosaics and led to accurate vegetation fraction maps (mean absolute error = 12.6%). Overall, a class-wise empirical approach is recommended as a simple, flexible and transferable technique to compensate for brightness gradients over a global empirical approach, brightness normalization or continuum removal.

DIFFERENT STATISTICAL METHODS FOR THE DISCRIMINATION OF TROPICAL MANGROVE SPECIES USING IN-SITU HYPERSPECTRAL DATA

The study highlights the hyperspectral characteristics of canopies of 14 tropical mangrove species, belonging to nine families found in the tidal forests of the Indian Sundarbans. Hyperspectral observations were recorded using a field spectroradiometer, pre-processed and subjected to derivative analysis and continuum removal. Mann-Whitney U tests were applied on the spectral data in four spectral forms: (i) Reflectance Spectra (ii) First Derivative, (iii) Second Derivative and (iv) Continuum Removal Reflectance Spectra. Factor analysis was applied in each of the spectral forms for feature reduction and identification of the important wavelengths for species discrimination. Stepwise discriminant analysis was used on the feature reduced reflectance spectra to obtain optimal bands for computation of Jeffries–Matusita distance. The Mann-Whitney U test could be satisfactorily used for determining the significant (separable) bands for discriminating the species. In general, the red region, red edge domain, specific near infrared bands (including 759, 919, 934, 940, 948, 1152, 1156, 1159 and 1212 nm) and shortwave infrared region (1503–1766 nm) played major roles in spectral separability. Overall, hyperspectral data showed potential for discriminating between mangrove canopies of different species and the results of the study also indicated the usefulness of the applied statistical tools for discrimination.

Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

Fusarium head blight (FHB) is one of the diseases caused by fungal infection of winter wheat (Triticum aestivum), which is an important cause of wheat yield loss. It produces the deoxynivalenol (DON) toxin, which is harmful to human and animal health. In this paper, a total of 89 samples were collected from FHB endemic areas. The occurrence of FHB is completely natural in experimental fields. Non-imaging hyperspectral data were first processed by spectral standardization. Spectral features of the first-order derivatives, the spectral absorption features of the continuum removal, and vegetation indices were used to evaluate the ability to identify FHB. Then, the spectral feature sets, which were sensitive to FHB and have significant differences between classes, were extracted from the front, side, and erect angles of winter wheat ear, respectively. Finally, Fisher’s linear discriminant analysis (FLDA) for dimensionality reduction and a support vector machine (SVM) based on radical basis function (RBF) were used to construct an effective identification model for disease severity at front, side, and erect angles. Among selected features, the first-order derivative features of SDg/SDb and (SDg-SDb)/(SDg+SDb) were most dominant in the model produced for the three angles. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB; (2) the accuracy of the FLDA model for the side, front, and erect angles was 77.1%, 85.7%, and 62.9%. The overall accuracy of the SVM (80.0%, 82.9%, 65.7%) was slightly better than FLDA, but the effect was not obvious; (3) LDA combined with SVM can effectively improve the overall accuracy, user’s accuracy, and producer’s accuracy of the model for the three angles. The over accuracy of the side (88.6%) was better than the front (85.7%), while the over accuracy of the erect angle was the lowest (68.6%).

Spectral imaging application to discriminate different diets of live rainbow trout (Oncorhynchus mykiss)

The main aim of this study was to evaluate the feasibility of hyperspectral imagery for determining the correlation between fish skin changes and different diets. Rainbow trout (Oncorhynchus mykiss) were fed either a commercial based diet (N = 80) or a 100% plant-based diet (N = 80). Hyperspectral images were made using a push-broom hyperspectral imaging system in the spectral region of 394–1009 nm. All images were calibrated using dark and white reference, and the average spectral data from the region of interest were extracted. Seven spectral pre-treatment methods were used, including Savitzky-Golay (SG), First Derivative (FD), Second Derivative (SD), Standard Normal Variate (SNV), Multiplicative Scatter Correction(MSC) and Continuum removal (CR) then a support vector machine (SVM) with linear kernel was applied to establish the classification models. Overall classification models developed from full wavelengths with different preprocessing methods showed good performance (Correct Classification Rate (CCR) = 0.83, Kappa = 0.66) when coupled with SG and SD or SG and MSC. The overall results indicate that the integration of Vis/NIR hyperspectral imaging system and machine learning algorithms have promise for discriminating different diets based on the live fish skin. These procedures can be used to not only identify the diet used for fish feeding in the case where we are not sure but also monitor different diets impacts on live fish skin for more precise monitoring of fish status during cultivation and ultimately for better implementation of precision fish farming.

Development of soil spectral allocation models considering the effect of soil moisture

Soil spectral allocation or classification is usually conducted on air-dried soils. However, the field soils are not all air-dried, and the change of soil moisture will affect soil reflectance. We introduce a soil allocation model that considers the effect of soil moisture for the purpose of eliminating the effect of soil moisture. The topsoil spectral curves of four typical soils from the Songnen Plain in Northeast China were re-sampled to 10-nm intervals and converted to first-derivative spectral curves and continuum removal curves. The spectral feature parameters were extracted from continuum removal curves in the visible-near infrared (VNIR) range (350–2500 nm), and the range of 430–2400 nm was used to build soil allocation models for reducing the effect of noise. Samples with different soil moisture were mixed into air-dried soils and we calculated the coefficient of variation (CV) of different inputs to assess the effect of soil moisture and to find allocation indices that were not affected by soil moisture. We used allocation indices of Zhang et al. (2018) because of the high accuracy of their DT (Decision Tree) model to allocate mixed-soil samples. We also used allocation indices that were not affected by soil moisture to allocate mixed-soil samples with decision tree (DT), multinomial logistic regression (MLR) and multi-layer perception neural network (MLPNN), and compared the results of the two methods. The results show the following: 1) As SFPs were built with shorter bands, SFP was less sensitive to soil moisture than PCR and PCFD and thus SFP is more suitable to build soil allocation models that consider the effect of soil moisture as input than PCR and PCFD. 2) Differences in soil moisture had little effect on absorption valley shoulders, symmetry and absorption positions, moderate effect on absorption area and depth, and a major effect on the slope of different bands. 3) The effect of soil moisture on continuum removal curves of different soil classes was variable. There was little effect on Arenosols, a moderate effect on Chernozems and Cambisols, and a large effect on Phaeozems. 4) The accuracy of the DT model using allocation indices that were not affected by soil moisture was 91.892% with a Kappa coefficient of 0.888. Our results suggest that it is feasible to build soil spectral allocation models that are not affected by soil moisture, and this improves the universality of soil spectral allocation, especially to field soils, which can be of considerable help in soil classification.

Remote Detection of Hydrocarbon Microseepage in a Loess Covered Area

Hydrocarbon microseepage can result in related near-surface mineral alterations. In this study, we evaluated the potential of detecting these alterations with field measured and satellite acquired hyperspectral data. Fourteen soil samples and reflectance spectra were collected in the Xifeng Oilfield, a loess covered area. Soil samples were analyzed in the laboratory for calcite, dolomite, kaolinite, illite, and mixed-layer illite/smectite content, and we processed reflectance spectra for continuum removal to derive clay and carbonate mineral absorption depth (H). High correlation between absorption depth and mineral content was shown for clay and mineral carbonate with field measured spectra. Based on the result for the field spectra, we proposed and tested a fast index based on the absorption depth of clay and carbonate minerals with a hyperspectral image of the area. The detected hydrocarbon microseepage anomalies matched well with those shown in the geological map.

Random forest-based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data.

Heavy metals in the agricultural soils of reclaimed mining areas can contaminate food and endanger human health. The objective of this study is to effectively estimate the concentrations of heavy metals, such as zinc, chromium, arsenic, and lead, using hyperspectral sensor data and the random forest (RF) algorithm in the study area of Xuzhou, China. The RF's built-in feature selection ability and modeling expressive ability in heavy metal estimation of soil were explored. After the preprocessing of the spectrum obtained by an ASD (analytical spectral device) field spectrometer, the random forest algorithm was carried out to establish the estimation model based on the correlation-selected features and the full-spectrum features respectively. Results of all the different processes were compared with classical approaches, such as partial least squares (PLS) regression and support vector machine (SVM). In all the experimental results, from the perspective of models, the best estimation model for Zn (R2 = 0.9061; RMSE = 6.5008) is based on the full-spectrum data of continuum removal (CR) pretreatment, and the best models for Cr (R2 = 0.9110; RMSE = 4.5683), As (R2 = 0.9912; RMSE = 0.5327), and Pb (R2 = 0.9756; RMSE = 1.1694) are all derived from the correlation-selected features. And these best models of these heavy metals are all established by the RF method. The experiments in this paper show that random forests can make full use of the input spectral data in the estimation of four kinds of heavy metals, and the obtained models are superior to those established by traditional methods.

Hyperspectral indirect inversion of heavy-metal copper in reclaimed soil of iron ore area.

To explore a rapid detection technology for detecting heavy metals in soil based on hyperspectral data, this paper took an iron mine in Daye Country, Hubei Province, as the research area, used a FieldSpec4 portable ground object spectrometer to obtain soil spectral reflectance and combine the measured data, and used three spectral transformation methods: first-order differential, second-order differential, and continuum removal. We studied the indirect hyperspectral inversion of heavy metals in reclaimed soils in the iron mine area by using three models: partial least squares regression, support vector machine, and back propagation (BP) neural network. The results show that spectral transformation can effectively highlight the position of spectral characteristic bands and improve the correlation between spectral curves and iron (Fe) element concentration. The partial least squares regression model based on first-order differential had the highest inversion accuracy for Fe element concentration in the study area, R2 and RMSE were 0.88 and 0.53, respectively. The correlation analysis of soil elements showed that the highest correlation coefficient between Cu and Fe was 0.81. We selected the copper (Cu) element with the largest correlation coefficient with the Fe element as an example and realized the indirect prediction of soil Cu concentration using a linear regression model and BP neural network model. Among them, the model based on BP neural network is better, R2 was 0.82, RMSE was 0.62, compared with the direct method, the model R2 increased by about 0.2, and the root mean square error decreased by about 0.1. The effect of the indirect method was better than that of the direct method. We selected the optimum statistical interpolation method for spatial analysis of Fe and Cu concentrations in the soil of the study area and further demonstrated the feasibility of the indirect inversion method of heavy metals in the soil of iron mine areas based on hyperspectral data. These results provide a theoretical basis and new ideas for the application of near-earth sensing technology in soil and for efficient detection of heavy metals in iron ore areas.

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms’ status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 µg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis

Leaf nitrogen concentration (LNC) is an important indicator for accurate diagnosis and quantitative evaluation of plant growth status. The objective was to apply a discrete wavelet transform (DWT) analysis in winter wheat for the estimation of LNC based on visible and near-infrared (400–1350 nm) canopy reflectance spectra. In this paper, in situ LNC data and ground-based hyperspectral canopy reflectance was measured over three years at different sites during the tillering, jointing, booting and filling stages of winter wheat. The DWT analysis was conducted on canopy original spectrum, log-transformed spectrum, first derivative spectrum and continuum removal spectrum, respectively, to obtain approximation coefficients, detail coefficients and energy values to characterize canopy spectra. The quantitative relationships between LNC and characteristic parameters were investigated and compared with models established by sensitive band reflectance and typical spectral indices. The results showed combining log-transformed spectrum and a sym8 wavelet function with partial least squares regression (PLS) based on the approximation coefficients at decomposition level 4 most accurately predicted LNC. This approach could explain 11% more variability in LNC than the best spectral index mSR705 alone, and was more stable in estimating LNC than models based on random forest regression (RF). The results indicated that narrowband reflectance spectroscopy (450–1350 nm) combined with DWT analysis and PLS regression was a promising method for rapid and nondestructive estimation of LNC for winter wheat across a range in growth stages.

Analysis of reflectance spectra of tropical seagrass species and their value for mapping using multispectral satellite images

ABSTRACT Mapping of the distribution of individual seagrass species is essential for any attempts to manage seagrass ecosystems. It is therefore important to understand how the spectra of different seagrass species vary, in order to establish their unique absorption features and how these can be utilised for mapping by making use of remote-sensing images. This paper presents measurements of the reflectance spectra between 400 and 900 nm for nine tropical species of seagrass. Continuum removal and multispectral resampling procedures were applied to the spectra. Dendrogram analysis was carried out to identify species clustering as the basis for a mapping scheme. Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) approaches were employed for the classification of seagrass species using WorldView-2 images and measured spectra as the input endmember. Classification Tree Analysis (CTA) and an image segmentation approach using CTA (Object-Based Image Analysis – OBIA) were performed as a means of comparison. The results indicate that the absorption features and overall shape of the spectra for all seagrass species are relatively similar, and implied that the major differences are attributable to the absolute reflectance values. Consequently, SAM and SID produced results of low accuracy (<30%), whereas, CTA and OBIA delivered results exhibiting higher accuracy (60–92%). The use of a spectral-based classification algorithm was ineffective for the classification and mapping of seagrass species using multispectral images. The utilisation of absolute reflectance values was beneficial for the classification of seagrass species having similar spectral shape.

Characterization and Retrieval of Snow and Urban Land Cover Parameters using Hyperspectral Imaging

Author(s): Singh, SK; Jain, G; Siddiqui, A; Naik, S; Rathore, BP; Garg, V; Snehmani; Kumar, V; Bahuguna, IM; Sharma, SA; Shekhar, C; Thakur, PK; Mishra, K; Kumar, P; Painter, TH; Dozier, J | Abstract: Snow and urban land cover are important due to their role in hydrological management and utility, climate response, social aspects and economic viability, along with influencing the Earth's environment at local, regional and global scale. Hyperspectral data enable identification, characterization and retrieval of these land-cover features based on physical and chemical properties of compositional materials. AVIRISNG hyperspectral airborne data, with synchronous ground observations using field spectroradiometer and collateral instruments, were collected over two widely varied land-cover types, viz. a relatively homogenous area covered by snow in the extreme cold environment of the Himalaya (Bhaga sub-basin, Himachal Pradesh), and a completely heterogeneous urban area of a metropolitan city (Ahmedabad, Gujarat). AVIRIS-NG airborne data were analysed to understand the effect of terrain parameters such as slope and aspect on snow reflectance. Snow grain index using visible and near-infrared (VNIR) bands and absorption peak in the near-infrared (NIR) were used to retrieve grain size in parts of the Himalayan region. A radiative transfer model was used to understand the grain size variability and its effect on absorption peak in NIR. Continuum removal was performed for snow spectral observations obtained from airborne, modelled and field platforms to estimate band depth at 1030 nm. Grain size was observed to vary with altitude from 100 to 500 μm using AVIRIS-NG image. In the urban area, the data also separated pervious and impervious surface cover using spectral unmixing technique, identified several urban features over multispectral data such as buildings with red tiled roofs, metallic surfaces and tarpaulin sheets using the material spectral profiles. Two single-frame superresolution methods namely sparse regression and natural prior (SRP), and gradient profile prior (GPP) were applied on AVIRIS-NG data for the mixed environment around Kankaria Lake in the city of Ahmedabad, which revealed that SRP method was better than GPP, and affirmed by eight indices. Preliminary analysis of AVIRIS-NG imaging over snow-covered areas and densely populated cities indicated utility of future spaceborne hyperspectral missions, particularly for hydrological and climatological applications in such diverse environments.

A new vegetation heavy metal pollution index for detecting the pollution degree of different varieties of maize under copper stress

ABSTRACTThis study proposed a new vegetation heavy metal pollution index VHMPI to detect the pollution degree of different varieties of maize under copper stress, which provides a new idea for the detection of heavy metal pollution in vegetation. In order to ensure the outdoor growth environment of maize, we put all maize into outdoor greenhouse. The spectral reflectance interval of 450 nm–850 nm of maize leaves was processed by the first order differential (D) and continuum removal (CR), and the DCR spectral curve was obtained. The Pearson correlation coefficient (R) was used to analyze the DCR data and the biochemical data and select characteristic bands that sensitive to heavy metal Cu. The calculated Pearson correlation coefficients suggested that the DCR value at 490 nm–520 nm and 680 nm–700 nm presented a linear positive correlation close to 1 with the Cu2+ contents in soil and leaves, and a linear negative correlation close to −1 was present in the range of 630 nm–650 nm and 710 nm–750 nm. We selected the DCR value of wavelengths 505 nm, 640 nm, 690 nm and 730 nm to establish VHMPI, and compared it with conventional vegetation indices (VIs) by calculating Pearson correlation coefficient between them and Cu contents in soil and leaves, Vegetation indices include WBI (Water Band Index), PSNDa (Pigment Specific Normalized Difference a), PRI (Photochemical Reflectance Index), NDVI (Normalized Difference Vegetation Index). Maize leaf spectral data obtained from experiments in 2017 were used for verification, VHMPI was also compared with WBI, PSNDa, PRI and NDVI.The results suggested that VHMPI showed a significant correlation with Cu2+ stress concentration,and the correlation of VHMPI was much stronger than that of other vegetation indices. The proposed VHMPI detects the pollution degree of maize with different varieties and in different periods under copper stress has advantages of straightforward calculation, robustness, and high effectiveness. This study focused on the laboratory leaf scale, so it is expected that future work extends it to a wide range of field scale and image scale.

Nonlinear unmixing of minerals based on the log and continuum removal model

ABSTRACT Spectral mixing models for minerals can be complex, and choosing the right unmixing model is indispensable to ensure the accuracy of spectral unmixing. Continuum removal (CR) and natural log operation have the potential to eliminate nonlinear effects in spectral mixing, and have already been used in nonlinear spectral unmixing applications. In this study, the newly proposed log and CR (LCR) model and four other spectral unmixing models for mineral analysis (the linear model, CR model, natural log model, and simplified Hapke model) were summarized and applied to both laboratory mineral powder spectra and hyperspectral data of Cuprite, Nevada, USA. This paper summarizes the concept of mixing reflectance reconstruction (MRR), along with a comprehensive method to determine accuracy based on MRR results, which can be performed in different dimensions, such as spatial dimension and spectral dimension. The LCR model performed the best in both laboratory experiments and image data analysis, indicating its strong potential for practical application. The level of atmospheric correction’s influence on unmixing accuracy varied for different spectral unmixing models, among which LCR model also had the best performance.