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• Use of N-Dimensional Spectral Solid Angle (NSSA) for band selection. • Guidelines for the selection of two key parameters for NSSA band selection. • Band selection for the improved discrimination of spectrally similar materials. • Bands capturing absorption feature position, shape, and depth. Selecting a subset of bands from hyperspectral data can improve the discrimination of ground targets because the most distinguishing spectral features are utilized. Targets with similar spectra are particularly challenging for band selection. A band selection method using the N-dimensional Solid Spectral Angle (NSSA) was recently proposed by Tian et al. (2016) to select the most dissimilar spectral regions amongst targets, but no case studies have been conducted using data from natural targets and there are currently no guidelines for the parameter selection in the NSSA band selection method. This study uses two spectral datasets of geologic relevance (clay minerals and ultramafic rocks), each with spectrally similar materials, to establish guidelines for the selection of two parameters (k and threshold) that will enable the use of the method for practical applications. K defines the band interval (relates to feature width) from which NSSA is calculated, and the threshold defines the number of bands selected from a profile of NSSA as a function of wavelength. The first guideline consists in constraining the maximum k value based on the spectral dimensionality of the widest significant spectral feature for the materials under study. The second guideline is to use a profile of the NSSA value as a function of wavelength for each permissible k value to capture the primary wavelength regions of high NSSA values. Finally, the threshold parameter for each k is estimated from a graph of the NSSA value as a function of the number of bands. The guidelines on the parameter definition allow non-expert users to select a subset of bands while capturing both narrow and broad discriminating features. Results show that bands selected from the two datasets are in good agreement with known spectral features. Of significance is that the bands encompass a range of distinguishing and often subtle spectral characteristics that include absorption feature position, and shape (asymmetry) and depth, the same that are recognized by experts, and thus can be used to assist experts in identifying key features. Moreover, the bands selected with NSSA show improved class separation as illustrated for datasets of spectrally similar materials. When tested for the discrimination of clay minerals, a competitive method named Variable Number Variable Band Selection (VNVBS) did not provide adequate information for the selection of bands. There are several valuable band selection methods reported in the literature, but few can be applied to datasets encompassing a relatively small number of spectra and to select bands that enable the discrimination of spectrally similar materials. As demonstrated in this study, the NSSA method should be of great value to studies that require feature identification from spectral libraries either resulting from the collection of field spectra or the extraction of endmembers from imagery. [ABSTRACT FROM AUTHOR]

Abstract Increases in liana abundance in tropical forests are pervasive threats to the current and future forest carbon stocks. Never before has the need been more evident for new approaches to detect the presence of liana in ecosystems, given their significance as fingerprints of global environmental change. In this study, we explore the use of longwave infrared reflectance (LWIR, 8–11 μm) as a wavelength region for the classification of liana and tree leaves and compare classification results with those obtained using visible-near infrared reflectance data (VIS-NIR, 0.45–0.95 μm). Twenty sun leaves were collected from each of 14 liana species and 21 tree species located at the canopy or forest edge (n = 700) in Santa Rosa National Park, Costa Rica. LWIR and VIS-NIR reflectance measurements were performed on these leaves using a portable calibrated Fourier Transform Infrared Spectroscopy (FTIR) Agilent ExoScan 4100 and a UniSpec spectral analysis system, respectively. The VIS-NIR and LWIR data were first resampled. Then these two spectral libraries were pre-processed for noise reduction and spectral feature enhancement resulting in three datasets for each spectral region as follows: filtered only, filtered followed by extraction of the first derivative, and continuous wavelet transformation (CWT). Data reduction was then applied to these data sets using principal components analysis (PCA). The outputs obtained from the PCA were used to conduct the supervised classification of liana and tree leaves. In total, 21 classifiers were applied to datasets of training and testing to extract the classification accuracy and agreement for liana and tree leaves. The results suggest that the classification of leaves based on LWIR data can reach accuracy values between 66 and 96% and agreement values between 32 and 92%, regardless of the type of classifier. In contrast, the classification based on VIS-NIR data shows accuracy values between 50 and 70% and agreement values between 0.01 and 40%. The highest classification rates of liana and tree leaves were obtained from datasets pre-processed using the CWT or from the extraction of the first derivative and classified using either random forest, k -nearest neighbor, or support vector machine with radial kernel. The results using the LWIR reflectance highlight the potential of this spectral region for the accurate detection of liana extent in tropical ecosystems. Future studies should consider this potential and test the regional monitoring of lianas. Highlights • Life form classification using visible-near infrared or longwave infrared spectra. • Four pre-processing approaches and 21 classifiers were evaluated. • Best classification of life forms obtained with longwave infrared reflectance. • Best discrimination by pre-processing with continuous wavelet transformation. • Radial kernel SVM and random forest are of the most promising classifiers. [ABSTRACT FROM AUTHOR]

Natural samples of the rare earth element (REE)-bearing silicate minerals cerite, mosandrite, kainosite, zircon, and eudialyte were studied using reflectance spectroscopy in the visible to short-wave infrared regions (500 to 2500 nm) and further characterized by scanning electron microscopy and electron microprobe analysis. Spectral features of these minerals are driven primarily by 4f-4f intraconfigurational electronic transitions of trivalent lanthanides, as well as 5f-5f electronic transitions of uranium and vibrational overtones and combinations of H2O and OH–. Spectra of eudialyte are also impacted by relative amounts of IVFe2+ and VFe2+. Respective spectra of these REE-bearing silicate minerals are sufficiently distinct to enable spectral classification. Spectral variability (e.g., band depths and locations) of some specific REE-related absorptions, such as an Er3+- and Yb3+-related absorption near 978 nm and Nd3+-related absorptions near 746, 803, and 875 nm, are interpreted to be driven by cation site differences in the crystal structures. This work adds to the growing understanding of REE-bearing mineral reflectance spectroscopy, which facilitates detection, identification, and quantification of REE-bearing silicate minerals in remote sensing applications. This is especially relevant for hyperspectral imaging spectroscopy with high spatial resolutions where the spectral response of a pixel becomes increasingly dominated by mineralogy rather than lithology. [ABSTRACT FROM AUTHOR]

Clay minerals play a crucial role in the processability of oil sands ores and in the management of tailings. An increase in fine content generally leads to a decrease in both bitumen recovery performance and tailings settling rate. It is thus important to identify clay types and their abundance in oil sands ores and tailings. This study made use of oil sands samples characterized for quantitative mineralogy by x-ray diffraction, to gain an understanding of changes in the reflectance spectra of oil sands. The sample suite included bitumen-removed oil sands ore samples and their different fine size fractions. Spectral metrics applicable to the prediction of quartz and clay contents in oil sands were then derived with a focus on metrics correlating with sample content in total 2:1 clays (total of illite and illite-smectite) and kaolinite. Metrics in the shortwave infrared (SWIR) and longwave infrared (LWIR) were found to correlate with mineral contents. The best predictions of clays and quartz were achieved using LWIR metrics (R 2 > 0.89). Results also demonstrated the applicability of LWIR metrics in the prediction of kaolinite and total 2:1 clays. [ABSTRACT FROM AUTHOR]

The paper addresses the challenge of measuring water content and evaporative fluxes from oil sands soft tailings surfaces using hyperspectral observations. Hyperspectral time-series laboratory observations were collected from four different mature fine tailings (MFT) samples displaying variations in swelling potential and bitumen concentration. The samples were allowed to evaporate from an initial state of water saturation to an air-dried state. From these data, several spectral features were evaluated to predict water content and normalized evaporation rate from the optically sensed portion of the tailings surface (less than a few hundred micrometres). For the samples tested, the best estimate of moisture content was achieved using the normalized soil moisture index (NSMI) index (coefficient of determination R2 = 0.97). The absolute reflectance at 1920 nm was found to be the best spectral estimator of normalized evaporation ( R2 = 0.97), with the NSMI index also being valuable ( R2 = 0.95). In both instances, the NSMI index may be of value for estimations attempted in the field. Remote estimation of moisture content and evaporation could help tailings managers assess the drying process to determine when the deposit has stopped drying at the surface and decide when the next lift should be deposited. In future efforts, the models obtained from this laboratory investigation will be assessed for their applicability in field settings and validated using concurrent sampling. [ABSTRACT FROM AUTHOR]

Reflectance spectroscopy in the visible to short-wave infrared regions (500 to 2500 nm) was carried out using natural samples of the rare earth element (REE) phosphate minerals monazite, xenotime, and britholite. Samples were characterized by scanning electron microscopy and electron microprobe analysis. Absorption band positions were recorded with their probable origins, and spectral variability among the samples is discussed. Spectral features of these minerals are driven primarily by 4f-4f intraconfigurational electronic transitions of trivalent lanthanides. The distinct REE distributions of monazite, xenotime, and britholite drive their bulk spectral patterns, which in turn are sufficiently distinct to enable spectral classification. Spectral variability of some specific REE-related absorptions are interpreted to be driven by differences of the coordination polyhedra for the lanthanide cations between the crystal structures. Spectra of these minerals were also compared against carbonatitehosted REE bearing fluorapatite. The work presented here strengthens the growing foundation for the interpretation of reflectance spectra of these REE phosphate minerals and enables exploitation of the observed features by the remote sensing community for detection, identification, and quantification of REE phosphate minerals. This is especially relevant for hyperspectral imaging spectroscopy with high spatial resolution, where the spectral response of a pixel becomes increasingly dominated by mineralogy. [ABSTRACT FROM AUTHOR]

An understanding of the mineralogy and petrogenesis of rare earth element deposits has significant implications for their economic viability. Lanthanide-bearing compounds are known to produce sharp absorption features in the visible to short-wave infrared region (VIS-SWIR), however, a significant knowledge gap exists between the fields of hyperspectral reflectance spectroscopy and rare earth element mineralogy. Reflectance spectra were collected from four bastnäsite samples, two parisite samples, and one synchysite sample from the visible into the short-wave infrared. These REE fluorocarbonate mineral samples were characterized via scanning electron microscopy and electron probe microanalysis. Sharp absorptions of REE-bearing minerals are mostly the result of 4f-4f intraconfigurational electron transitions and for the light REE-enriched fluorocarbonates, the bulk of the features can be ascribed to Nd3+, Pr3+, Sm3+, and Eu3+. The lanthanide-related spectral responses of the REE fluorocarbonates are consistent across the group, supporting the notion that the REE cation site is very similar in each of these minerals. Carbonate-related spectral responses differed between these minerals, supporting the notion that the crystallographic sites for the carbonate radical differ between bastnäsite, synchysite, and parisite. Exploitable spectral differences include a distinct absorption band at 2243 nm that separates bastnäsite from synchysite and parisite. Similarly, for bastnäsite a dominantly Pr3+-related absorption band located is at 1968 nm, while in synchysite and parisite it occurs at 1961 nm. [ABSTRACT FROM AUTHOR]

Rationale: Neuromuscular electrical stimulation (NMES) of the lower limbs is an emerging training strategy in patients with COPD. The efficacy of this technique is related to the intensity of the stimulation that is applied during the training sessions. However, little is known about tolerance to stimulation current intensity and physiological factors that could determine it. Our goal was to find potential physiological predictors of the tolerance to increasing NMES stimulation intensity in patients with mild to severe COPD. Methods: 20 patients with COPD (FEV1 = 54±14% pred.) completed 2 supervised NMES sessions followed by 5 self-directed sessions at home and one final supervised session. NMES was applied simultaneously to both quadriceps for 45 minutes, at a stimulation frequency of 50 Hz. Spirometry, body composition, muscle function and aerobic capacity were assessed at baseline. Cardiorespiratory responses, leg discomfort, muscle fatigue and markers of systemic inflammation were assessed during or after the last NMES session. Tolerance to NMES was quantified as the increase in current intensity from the initial to the final NMES session (ΔInt). Results: Mean ΔInt was 12±10 mA. FEV1, fat-free-mass, quadriceps strength, aerobic capacity and leg discomfort during the last NMES session positively correlated with ΔInt (r = 0.42 to 0.64, all p≤0.06) while post/pre NMES IL-6 ratio negatively correlated with ΔInt (r = −0.57, p = 0.001). FEV1, leg discomfort during last NMES session and post/pre IL-6 ratio to NMES were independent factors of variance in ΔInt (r2 = 0.72, p = 0.001). Conclusion: Lower tolerance to NMES was associated with increasing airflow obstruction, low tolerance to leg discomfort during NMES and the magnitude of the IL-6 response after NMES. Trial Registration: ClinicalTrials.gov NCT00809120 [ABSTRACT FROM AUTHOR]

Abstract: Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA). Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R 2: 0.51–0.82, p <0.0001). The best robustness (R 2 =0.74, RMSE=18.97g/m2 and Bias=0.12g/m2) was obtained using a combination of two low-scale features (1639nm, scale 4) and (2133nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass. [Copyright &y& Elsevier]

Abstract: Phengite is the main potassic dioctahedral mica identified at the Olympic Dam iron oxide–copper–gold (IOCG) deposit, South Australia, where its mineral chemistry is quite variable. These differences can be explained by contrasting degrees of hydrothermal alteration. In the heavily-sericitized, ore-bearing rocks, the phengites display a lower-Si content, a higher-Al content, and a lower Mg-number than the phengites from the weakly-sericitized alteration halo that surrounds the deposit. Variations are also observed in the near- and mid-infrared reflectance spectra collected from phengite-bearing rocks. In the near-infrared, high-Al phengite produces a spectral absorption feature at 2.206μm, and this feature is displaced to 2.213μm for low-Al phengite. In the mid-infrared, high-Al phengite produces a strong reflectance peak at 9.59μm, whereas this peak is observed at 9.57μm in the spectra from low-Al phengite. Additional peaks were also identified at 10.98, 12.22, and 13.33μm. These were most intense in the spectra from high-Al phengite. A drill core profile was produced using the results of the spectral analysis that shows the change in phengite mineral chemistry and phengite abundance as a function of depth. In general, near- and mid-infrared reflectance spectroscopy can be used to characterize the aluminum content of potassic dioctahedral micas like phengite, and this information can be used to infer the degree of sericitic alteration that has occurred as a result of hydrothermal fluid flow. [Copyright &y& Elsevier]

Abstract: Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panamá (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data. The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732–1736nm at scale 4, 1874–1878nm at scale 6, and 1338–1341nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with spectroscopy data. [Copyright &y& Elsevier]

Abstract: This study evaluates the use of waveform LIDAR data for the characterization of secondary forests stages in a Tropical Dry Forest (TDF) area of Guanacaste, Costa Rica. A secondary forest succession is defined here as regrowth of woody vegetation following a complete or heavy forest clearance for pasture, agriculture, or other human activity. We first compare the known spatial distribution of three main TDF successional stages (Early, Intermediate and Late successional stages) as constrained by published field observations of tree height with that obtained from a three-class classification of the LIDAR data. In doing so we explicitly assess the possibility of using LIDAR data to map the distribution of the three main TDFs stages. We demonstrates that changes in the forest vertical structure (such as height) associated with principal successional stages (Early, Intermediate and Late) of TDF secondary growth can be effectively identified from LIDAR data. The successional sequence observed is related to changes in the vertical distribution of woody components that occur when forest patches evolve from an Early Stage which is dominated by sparse trees and grass, to initial stages of intermediate secondary succession characterized by small canopies and a high density of understory regeneration, and then to more complex and developed stages of intermediate and late stage secondary succession which share some attributes of Old growth forests. We then focus on the Intermediate successional Stage since it is of highest interest to policy makers dealing with programs implementing payments for environmental services. We show that an analysis of the LIDAR data can identify successional three subclasses within the Intermediate Stage providing further insights in the development of secondary forest growth. A validation of the three new forest classes is provided using field observations showing a mean tree height and standard deviation of 6.16±0.87, 7.82±0.31 and 8.62±1.22. m. [Copyright &y& Elsevier]

Species identification and characterization in tropical environments is an emerging field in tropical remote sensing. Significant efforts are currently aimed at the detection of tree species, of levels of forest successional stages, and the extent of liana occurrence at the top of canopies. In this paper we describe our use of high resolution imagery from the Quickbird Satellite to estimate the flowering population of Tabebuia guayacan trees at Barro Colorado Island (BCI), in Panama. The imagery was acquired on 29 April 2002 and 21 March 2004. Spectral Angle Mapping via a One-Class Support Vector machine was used to detect the presence of 422 and 557 flowering tress in the April 2002 and March 2004 imagery. Of these, 273 flowering trees are common to both dates. This study presents a new perspective on the effectiveness of high resolution remote sensing for monitoring a phenological response and its use as a tool for potential conservation and management of natural resources in tropical environments. [ABSTRACT FROM AUTHOR]

Fractional abundances predicted for a given pixel using spectral mixture analysis (SMA) are most accurate when only the endmembers that comprise it are used, with larger errors occurring if inappropriate endmembers are included in the unmixing process. This paper presents an iterative implementation of SMA (ISMA) to determine optimal per-pixel endmember sets from the image endmember set using two steps: 1) an iterative unconstrained unmixing, which removes one endmember per iteration based on minimum abundance and 2) analysis of the root-mean-square error as a function of iteration to locate the critical iteration defining the optimal endmember set. The ISMA was tested using simulated data at various signal-to-noise ratios (SNRs), and the results were compared with those of published unmixing methods. The ISMA method correctly selected the optimal endmember set 96% of the time for SNR of 100 : 1. As a result, per-pixel errors in fractional abundances were lower than for unmixing each pixel using the full endmember set. ISMA was also applied to Airborne Visible/Infrared Imaging Spectrometer hyperspectral data of Cuprite, NV. Results show that the ISMA is effective in obtaining abundance fractions that are physically realistic (sum close to one and nonnegative) and is more effective at selecting endmembers that occur within a pixel as opposed to those that are simply used to improve the goodness of fit of the model but not part of the mixture. [ABSTRACT FROM AUTHOR]

Abstract: Hyperspectral remote sensing provides great potential to monitor and study biodiversity of tropical forests through species identification and mapping. In this study, five species were selected to examine crown-level spectral variation within and between species using HYperspectral Digital Collection Experiment (HYDICE) data collected over La Selva, Costa Rica. Spectral angle was used to evaluate the spectral variation in reflectance, first derivative and wavelet-transformed spectral domains. Results indicated that intra-crown spectral variation does not always follow a normal distribution and can vary from crown to crown, therefore presenting challenges to statistically define the spectral variation within species using conventional classification approaches that assume normal distributions. Although derivative analysis has been used extensively in hyperspectral remote sensing of vegetation, our results suggest that it might not be optimal for species identification in tropical forestry using airborne hyperspectral data. The wavelet-transformed spectra, however, were useful for the identification of tree species. The wavelet coefficients at coarse spectral scales and the wavelet energy feature are more capable of reducing variation within crowns/species and capturing spectral differences between species. The implications of this examination of intra- and inter-specific variability at crown-level were: (1) the wavelet transform is a robust tool for the identification of tree species using hyperspectral data because it can provide a systematic view of the spectra at multiple scales; and (2) it may be impractical to identify every species using only hyperspectral data, given that spectral similarity may exist between species and that within-crown/species variability may be influenced by many factors. [Copyright &y& Elsevier]

Abstract: After leaves are clipped their reflectance properties change over time at variable rates. Spectral change can in part be attributed to the changing water content of the leaf, which affects absorption in the VIS, NIR and the SWIR. Maintaining water volume within samples has been the motivation behind many leaf handling techniques. This study has assessed the time constraints between leaf collection and spectral measurement. Specifically the relationship between leaf water content and foliar spectra (350–2500 nm) was examined over time for five tropical trees (common guava (Psidium guajava), purple guava (Psidium littorale), weeping fig (Ficus benjamina), floss silk (Chorisia speciosa), and coffee (Coffea arabica)). This investigation was carried for leaves wrapped with moist gauze around their petiole (treatment leaves) and leaves with no treatment. Spectral measurements and mass measurements were repeated for each leaf once every hour for the first 12 h, then every 4–6 h for 18 h, followed by one measurement after 12 h, and finally once a day until the control samples became air-dry. Foliar reflectance in the visible spectrum was not immediately responsive to water content changes and did not change until wilting of the leaf was observed. The NIR and SWIR wavelength regions were affected immediately by small changes in water content. Thus, by the time wilting was first observed the NIR and SWIR foliar reflectance differed considerably from corresponding fresh leaf reflectance. No common time limit could be observed for leaf clipping and reflectance measurement. Leaves have a variety of water contents and dehydration rates hence measurement time constraints are dependent on the properties of the leaf or species. Rather than using a time limit it is recommended that leaf handling techniques be based upon managing leaf water content and leaf structure. The results of this study indicate that leaves with petioles wrapped in moist paper towel and placed within plastic bags will maintain leaf reflectance longer than equivalent leaves without treatment; samples tested here lasted a minimum of 7 days. θ and D indices (“angle difference” and “root mean square difference”, respectively) revealed a stronger relationship between leaf water content and spectral shape than between leaf water and raw reflectance magnitude. The ratio of 1187/1096 nm, when compared with θ and D indices and individual reflectance bands, showed the highest coefficient of determination with leaf water content (r 2 =0.952). [Copyright &y& Elsevier]

Abstract: In subarctic regions the ubiquitous presence of rock encrusting lichens compromises the ability to map the reflectance signatures of minerals from imaging spectrometer data. The use of lichen as an endmember in spectral mixture analysis (SMA) may overcome these limitations. Because lichens rarely completely occupy the Instantaneous Field of View (IFOV), it is difficult to define a lichen endmember from an image using visual or automated endmember extraction tools. Spectral similarity of various crustose/foliose lichen species in the short wave infrared (SWIR) suggests that spectral unmixing of rock and lichens may be successfully accomplished using a single lichen endmember for this spectral range. We report the use of a spectral normalization method to minimize differences in SWIR reflectance between five lichen species (U. torrefacta, R. bolanderi, R. geminatum, R. geographicum, A. cinerea). When the normalization is applied to reflectance spectra from 2000–2400 nm acquired for a lichen encrusted quartzite rock sample we show that only a single lichen endmember is required to account for the lichen contribution in the observed mixtures. In contrast, two such endmembers are required when the normalization is not applied to the reflectance data. We illustrate this point using examples where endmembers are extracted manually and automatically, and compare the SMA results against abundances estimated from digital photography. For both the reflectance and normalized reflectance data, SMA results correlate well (R 2>0.9) with abundances estimated from digital photography. The use of normalized reflectance implies that any field/laboratory lichen spectrum can be selected as the lichen endmember for SMA of airborne/spaceborne imagery. [Copyright &y& Elsevier]

This study investigates the usefulness of thermal infrared reflectance spectroscopy (TIR) to map rock faces in an underground environment. A laboratory Fourier transform infrared (FTIR) spectrometer was used to measure reflectance spectra (500–5000 cm-1) of freshly broken rock surfaces. A total of 37 samples covering 10 different rock types, including barren silicate rocks and rocks with disseminated sulfides, was collected from eight mines around the Sudbury Basin in Ontario. Freshly broken rock surfaces were measured wet and dry to address environmental conditions encountered underground. Spectral data were processed by the second-derivative approach and the spectral angle mapper algorithm (SAM) for rock-type identification. An average of 77% accuracy was achieved for all rock types. The success of this effort implies that hyperspectral analysis using second-derivative spectra is effective to remove the effect of liquid water, local geometry, and disseminated sulfides while preserving diagnostic rock spectral signatures between 700 and 1300 cm-1. [ABSTRACT FROM AUTHOR]

Spectral mixture analysis (SMA) has been used extensively in the hyperspectral remote sensing community for the subpixel abundance estimation of targets. However, the task of defining every end member can be difficult, as evident from the importance attributed to the topic in the recent literature. The effectiveness of SMA can be compromised when the required spectral endmembers are not well constrained in terms of their spectral magnitude and shape. The spectral magnitude of the endmembers is more difficult to obtain than their spectral shape, in part because the effects of the atmosphere and topography are difficult to constrain. This paper presents a derivative spectral unmixing (DSU) model, which is an extension of the spectral mixture analysis and derivative analysis. Using a DSU approach, it is possible to estimate the fraction of an endmember characterized by one or more diagnostic absorption features despite having only a general knowledge of the spectral shapes of the remaining endmembers. The DSU is assessed using spectral data acquired for a lichen-covered rock sample, and the estimated fractions of lichen and rock are assessed against that obtained from a high spatial resolution digital photograph. The results of the laboratory experiments suggests that the DSU is a promising algorithm for the quantitative analysis of hyperspectral data, but experiments on airborne/spaceborne imagery are now required to assess its value for geological mapping. [ABSTRACT FROM AUTHOR]

Compact Airborne Spectrographic Imager (CASI) hyperspectral data is used to investigate the effects of topography on the selection of spectral end members, and to assess whether the topographic correction improves the discrimination of rock units for lithologic mapping. A publicly available Digital Elevation Model (DEM), at a scale of 1:50,000, is used to model the radiance variation of the scene as a function of topography, assuming a Lambertian surface. Skylight is estimated and removed from the airborne data using a dark object correction. The CASI data is corrected on a pixel-by-pixel basis to normalize the scene to a uniform solar illumination and viewing geometry. The results show that topography has the effect of expanding end member clusters at times resulting in the overlap of clusters and that the correction process can effectively reduce the variation in detected radiance due to changes in local illumination. When topographic effects are embedded in the hyperspectral data, methods typically used for the selection of end members, such as the convex hull method, can miss end members or result in the selection of nonrepresentative pixels as end members. Thus, end members selected by some conventional methods are very likely “incomplete” or “nonrepresentative” if the topographic effect is embedded in the data. As shown in this study, the topographic correction can reveal hidden end members and achieve a better representation of end members via the statistical center of isolated clusters. [Copyright &y& Elsevier]

Reflectance spectra of rock encrusting lichens were acquired to determine the influence that this vegetation type may have on the reflectance properties of rock exposures located in high latitude and subarctic environments. The samples investigated consist of crustose and foliose lichen species collected from exposures of the Gog quartzite formation in Alberta, Canada. Lichen transmittance was estimated to be <3% throughout the 350–2500-nm spectral region, using spectra measured from the foliose lichen, Umbilicaria torrefacta, as a representative sample of a broader class of lichens. These findings suggest that lichen prevents the transmission of light to the underlying rock substrate. Therefore, the subpixel influence of lichen and rock within a scene can be considered linearly weighted. Discrimination of lichen species is made possible using ratios of reflectance at 400/685 and 773/685 nm. An index using the band ratios 2132/2198 and 2232/2198 nm shows the similarity of lichen spectra in the infrared and a distinguishing feature between rocks with OH bearing minerals and lichen. Thus, spectral unmixing of rock and crustose/foliose lichens may be successfully accomplished using a single lichen end-member for this spectral range. [Copyright &y& Elsevier]

This study is the first of two companion papers using hyperspectral data to generate predictive models of oil sand ore and froth characteristics as a potential new means for process control. In Alberta, Canada, shallow oil sands deposits are accessed by surface mining and crushed ore is transported to a processing plant for extraction of bitumen using flotation processes. The ore displays considerable variability in clay, bitumen, and fines which affects their behavior in flotation units. Using oil sand ore spanning a range of bitumen and fines characteristics, flotation experiments were performed to generate froth in a batch extractor to determine ore processability (e.g., separation performance) and froth characteristics (color, bitumen, solids). From hyperspectral observations of ore, models can predict the %bitumen content and %fines (particle passing at 44 and 3.9 µm) of ore but the models with highest r2 (>0.96) predict the solids/bitumen of froth and the processability of ore. Spectral observations collected on ore upstream of the separation vessels could therefore offer a first order assessment of froth quality for an ore blend before the ore enters the plant. These models could also potentially be used to monitor and control the performance of the blending process as another means to control the performance of the flotation process. [ABSTRACT FROM AUTHOR]

This is the second part of a study of predictive models of oil sand ore and froth characteristics using infrared hyperspectral data as a potential new means for process control. In Alberta, Canada, bitumen in shallow oil sands deposits is accessed by surface mining and then extracted from ore using flotation processes. The ore displays variability in the clay, bitumen, and fines content and this variability affects the separability and product quality in flotation units. Flotation experiments were performed on a set of ore samples of different types to generate froth and determine the ore processability (e.g., separation performance) and froth characteristics (bitumen and solids content, fines distribution). We show that point spectra and spectral imagery of good quality can be acquired rapidly (<1 s and <15 s, respectively) and these capture spectral features diagnostic of bitumen and solids. Ensuing models can predict the solids/bitumen (r2 = 0.88) and the %fines and ultrafines (particle passing at 3.9 and 0.5 µm) content of froth (r2 = 0.8 and 0.9, respectively). The latter model could be used to reject froth with a high solids content. Alternately, the strength of the illite-smectite absorption observed in froth could be used to retain all the samples above a pre-defined processability. Given that point spectrometers can currently be acquired for less than half the cost of an imaging system, we recommend the use of the former for future trials in operating environments. [ABSTRACT FROM AUTHOR]

The Highland Valley Copper (HVC) district in British Columbia, Canada, is host to at least four major porphyry Cu systems: Bethlehem (~209 Ma), and Valley, Lornex, and Highmont (~208 to 207 Ma). High spatial resolution (0.2–1.0 mm/pixel) hyperspectral imagery in the shortwave infrared (SWIR) were acquired on 755 rock samples and 400 m of continuous drill core. Spectral metrics are used to measure the relative abundance of 12 minerals and an additional metric is derived to estimate white mica grain size. In the Valley and Lornex deposits, coarse-grained white mica is associated with mineralization and is detectable up to 4 km away from the deposits. Kaolinite is present within 2 km of the mineralized centers but does not necessarily occur within strongly mineralized intervals. Prehnite is ubiquitous from 4 to 8 km from the deposits. In the Bethlehem deposit, tourmaline and epidote are associated with mineralization. We propose a spectral alteration score based on these proximal hyperspectral SWIR mineralogical patterns to assist explorers in targeting porphyry Cu systems when using drill core, surface rock samples and potentially remote sensing imagery. In a production environment, this metric could serve to facilitate ore-sorting. [ABSTRACT FROM AUTHOR]

• Band selection incorporated in the spatial spectral endmember selection. • N-Dimensional Spectral Solid Angle (NSSA) for band selection from imagery. • Produces more clearly separated endmember clusters. • Produces more detailed mapping of spectrally similar targets. The impact of band selection on endmember selection is seldom explored in the analysis of hyperspectral imagery. This study incorporates the N-dimensional Spectral Solid Angle (NSSA) band selection tool into the Spectral-Spatial Endmember Extraction (SSEE) tool to determine a band set that can be used to better define endmembers classes used in spectral mixture analysis. The incorporation aims to define a band set that improves the spectral contrast between endmembers at each step of the spatial-spectral endmember search and ultimately captures key features for discriminating spectrally similar materials. The proposed method (NSSA-SSEE) was evaluated for lithological mapping using a hyperspectral image encompassing a range of spectrally similar mafic and ultramafic rock units. The band selected by NSSA-SSEE showed a good agreement with known features of scene components identified by experts. Results showed an improvement in the selection of detailed endmembers, endmembers that are similar and that can be significant for mapping. The incorporation of NSSA into SSEE was feasible because both methods are well suited for this process. NSSA is one of the few methods of band selection that is suitable for the analysis of a small number of endmembers and SSEE provides such endmember sets via spatial subsetting. The automated NSSA-SSEE approach can reduce the need for field-based information to guide the feature selection process. [ABSTRACT FROM AUTHOR]

• Estimation of mineral chemistry from SWIR data. • Correlation between Au content and mineral chemistry. • Vectoring to mineralization using SWIR data on regional scales. • High-resolution downhole hyperspectral imaging. Canadian Malartic is a large-tonnage, low-grade Archean gold deposit (16.3 Moz, 1.08 g/t Au) located in the Abitibi region of Québec, Canada. A large part of the mineralization is hosted in the Pontiac Group metasedimentary rocks, which consist of mudstones to greywackes at upper greenschist to amphibolite facies. In exploration and production environments, these lithologies are challenging to characterize by conventional core logging methods, while in a research setting thin section-sized samples (2 cm × 4 cm) do not capture the full extent of mineralogical variability, which can extend from centimeters to meters away from mineralized zones. Here, high-resolution hyperspectral imagery (0.2–1.0 mm/pixel) in both shortwave infrared (SWIR, 1000–2500 nm) and longwave infrared (LWIR, 8000–12000 nm) is acquired for over two thousand meters of drill core, and is used to visualize changes in mineralogy and mineral chemistry related to metamorphism and hydrothermal alteration. Unaltered metasedimentary rocks contain metamorphic white mica with AlVI contents varying between 1.90 and 1.75 apfu (2195 to 2203 nm), depending on metamorphic grade. Hydrothermal alteration is characterized by white mica which becomes progressively more phengitic with increasing alteration intensity, with AlVI contents ranging from 1.70 to 1.50 apfu (2204 to 2212 nm). Phengitic white mica extends from meters to tens of meters away from major mineralized zones, and can be used as a vector towards mineralization in an exploration setting. White mica composition is correlated to Au content, and can be used to discriminate between unmineralized (<0.3 g/t Au, 2195–2204 nm), weakly mineralized (>0.3 g/t Au, 2205–2208 nm), and highly mineralized (>1.0 g/t Au, >2208 nm) samples, which is a simple metric that can directly be applied to sort ore in a production environment. The Mg# (molar Mg/[Mg + Fe]) of biotite, on the other hand, is unaffected by metamorphic grade, and consistently is Mg# 55–60 (2251–2250 nm) in unaltered samples. In mineralized samples, biotite is Mg-rich (Mg# >65, <2249 nm), but rapidly grades into background compositions outside of the ore zones. LWIR hyperspectral data is used in drill core to estimate silicification abundance from the strength of the 9200 nm quartz reflectance peak. Peak strength was generally in good agreement with the silicification abundance estimated by core logging, and is therefore a metric that can directly be used for characterization of this parameter in exploration and production environments. Over 1500 SWIR point measurements were also acquired in a 50 km by 15 km area around the deposit, and were used to delineate metamorphic isograds on regional scales, and a large 12 km by 3 km hydrothermal alteration halo around the Canadian Malartic deposit. Similar measurements, if performed along the over 200 km long Cadillac-Larder Lake Deformation zone, could be used to rapidly delineate other potentially mineralized areas. [ABSTRACT FROM AUTHOR]

Highlights • Longwave IR spectra were collected from twelve REE-bearing samples. • Stretching and bending modes of the X-O (X = C, Si, P) groups are observed. • No specific features were found for the direct detection of the REEs. Abstract Rare earth elements (REEs) are a group of metals essential to high technology industries. This high demand, combined with a high supply risk, has led to an understanding that REEs are critical to society. Despite the potential that hyperspectral imaging (HSI) data offers for a fast and non-invasive characterization of the REEs, it is still poorly understood whether REEs have some information in the long-wave infrared (LWIR; 8–12 μm) wavelength range that can be used for their identification. To partially fill this gap, we have investigated the spectroscopy of twelve REE-bearing mineral samples using relatively high spatial and spectral resolution LWIR hyperspectral imaging data. These samples were formerly characterized using electron probe microanalysis (EPMA), scanning electron microscopy (SEM), and hyperspectral imaging data acquired in the 0.4–2.5 μm wavelength range. Results from these analyses were compared to and used to guide the analysis of the HSI data recorded in the LWIR range. This information was further compared to a reference spectral library of rare earth oxides. Our findings suggest that the spectral features of the samples can generally be traced to the asymmetric degenerate stretching and bending modes of the X-O (X = C, Si, P) groups. Moreover and contrary to what has been observed in the shorter wavelengths, there are no definitive spectral features in the LWIR wavelength region that could be assigned to any specific REE. [ABSTRACT FROM AUTHOR]

This study investigates using the Airborne Hyperspectral Imaging Systems (AISA) visible and short-wave infrared (SWIR) and Spatially Enhanced Broadband Array Spectrograph System (SEBASS) longwave infrared (LWIR) (2 and 4 m spatial resolution, respectively) imagery independently and in combination to produce detailed lithologic maps in a subarctic region (Cape Smith Belt, Nunavik, Canada) where regionally metamorphosed lower greenschist mafic, ultramafic and sedimentary rocks are exposed in the presence of lichen coatings. We make use of continuous wavelet analysis (CWA) to improve the radiometric quality of the imagery through the minimization of random noise and the enhancement of spectral features, the minimization of residual errors in the ISAC radiometric correction and target temperature estimation in the case of the LWIR data, the minimization of line to line residual calibration effects that lead to inconsistencies in data mosaics, and the reduction in variability of the spectral continuum introduced by variable illumination and topography. The use of CWA also provides a platform to directly combine the wavelet scale spectral profiles of the SWIR and LWIR after applying a scalar correction factor to the LWIR such that the dynamic range of two data sets have equal weight. This is possible using CWA as the datasets are normalized to a zero mean allowing spectra from different spectral regions to be adjoined. Lithologic maps are generated using an iterative spectral unmixing approach with image spectral endmembers extracted from the SWIR and LWIR imagery based on locations defined from previous work of the study area and field mapping information. Unmixing results of the independent SWIR and LWIR data, and the combined data show clear benefits to using the CWA combined imagery. The analysis showed SWIR and LWIR imagery highlight similar regions and spatial distributions for the three ultramafic units (dunite, peridotite, pyroxenite). However, significant differences are observed for quartz-rich sediments, with the SWIR overestimating the distribution of these rocks whereas the LWIR provided more consistent results compared with existing maps. Both SWIR and LWIR imagery were impacted by the pervasive lichen coatings on the mafic rocks (basalts and gabbros), although the SWIR provided better results than the LWIR. Limitations observed for the independent data sets were removed using the combined spectral data resulting in all geologically meaningful units mapped correctly in comparison with existing geological maps. [ABSTRACT FROM AUTHOR]

The Tiangong-1 Hyperspectral Imager (HSI) is a relatively new spaceborne hyperspectral remote sensing system that was launched by the Chinese government on September 29th 2011. The system has 64 shortwave infrared (SWIR) spectral bands (1000–2500 nm) and imagery is at a spatial resolution of 20 m. This study represents an evaluation of Tiangong-1 data for the production of alteration mineral maps. Alteration mineral maps resulting from the analysis of Tiangong-1 HSI data and airborne SASI (Shortwave infrared Airborne Spectrographic Imager) data are compared for the Jintanzi area, Beishan, Gansu province, northwest China where gold bearing veins are documented. The results illustrate the detection of muscovite, kaolinite, chlorite, epidote, calcite and dolomite from Tiangong-1 HSI data and most anomalies seen in the airborne SASI data are captured. The Tiangong-1 data appears to be well suited for the detection of surface mineralogy in support of regional mapping and exploration. The data complements that which will be offered by the Chinese GF-5 Hyperspectral Imager and the German EnMAP system, both scheduled for launch in 2018. [ABSTRACT FROM AUTHOR]

Arctic environments provide a challenging ground for geological mapping and mineral exploration. Inaccessibility complicates ground surveys and the presence of ice, vegetation, and lichens hinders supportive remote sensing surveys. Spectral mixing of lichens and bare rock can shift the wavelength position of characteristic absorption features, thereby complicating the spectral mapping of minerals and lithologies. The extent to which diagnostic rock features are preserved despite the presence of lichens is of major concern in remotely sensed geological studies and estimates of the critical level of lichen coverage, below which spectral features of the mineral substrate can still be identified, are needed. We investigated how surficial lichen cover affects the characteristics of shortwave infrared (SWIR) mineral absorption features and the efficacy of automated absorption feature extraction. For this purpose, mixed spectra were synthetically generated from laboratory spectra of common rock forming SWIR absorbing minerals and lichens. Wavelength displacements of characteristic absorption features for each mixed spectrum were then analyzed as a function of percentage lichen cover. Distinctive trends were identified that can be used in future analysis: The strong spectral features of mica group minerals around 2200 and 2340–2350 nm maintain their integrity for up to 30% lichen cover, despite the related shift toward shorter wavelengths for higher percentage lichen cover. In contrast, very weak absorption bands around 2440 nm in (white) micas spectra are completely obscured for a lichen cover of ≥ 50%. Our observations suggest that the chlorite feature around 2250 nm is shifted toward longer wavelengths and the depth of this feature as well as the contrast between lichen and substrate spectra define the amount of lichen needed to mask it. Furthermore, lichens induce a spectral shift towards shorter wavelengths for the features around 2320 nm for the rocks containing amphibole, chlorite, carbonate and serpentine group minerals. In addition, no wavelength displacement is observed for chlorite, biotite and phlogopite features around 2380 nm in mixtures with lichens. By quantifying lichen cover effects on mineral absorption features, our study highlights the importance of being precautious in any interpretation in areas characterized by abundant lichen-covered outcrops. This can be of significant importance for mineral and deposit vectoring as the presence of abundant lichen coverage causing slightly shifted features for a given spectra can be erroneously identified as a path to a deposit. [ABSTRACT FROM AUTHOR]

The early identification of natural oil seepages or accidental leaks onshore is not a well explored theme in the literature. Location and mapping of contaminated areas can indicate the presence of a subsurface reservoir and guide direct exploration operations in the oil industry. Superficial occurrences of hydrocarbons (HCs) can also indicate damage to pipelines. Locating small leaks can guide containment, cleaning and repair operations. Remote sensing tools for such purposes usually focus on the effects that oil in the soil causes on the spectral signatures of vegetation. Studies investigating the spectral characteristics of soil and HC mixtures are not prevalent. This study comprises the conception and analysis of a spectral library of mixtures of several mineral substrates impregnated with various concentrations of light to heavy oils (API varying from 19.4 to 41.9), using data acquired with an automated hyperspectral imaging station and processed with wavelets. The wavelet transform allowed the extraction of key spectral features and the discard of secondary information (e.g. noise and continuum), resulting in hyperspectral imagery products that proved suitable to separate different phases in the soil-HC mixture, as well as to identify the type and concentration of HC in the soil. This comprehensive spectral library and the acquired information regarding spectral characteristics of soil-hydrocarbon mixtures, opens opportunities for the development of new processing methods for the direct mapping of onshore seeps and leaks using current and future orbital hyperspectral (Hyperion; EnMap; HyspIRI) and multispectral (WordView-3) sensors. [ABSTRACT FROM AUTHOR]

Organic matter (OM) accumulation in organic matter-rich mudstones, or black shales, is generally recognized to be controlled by combinations of bioproductivity, preservation, and dilution. However, specific triggers of OM deposition in these formations are commonly difficult to identify with geochemical proxies, in part because of feedbacks that cause geochemical proxies for these controls to vary synchronously. This apparent synchronicity is partly a function of sample spacing, commonly at decimeter to meter intervals, which may represent longer periods of time than is required for the development of feedbacks. Higher resolution data sets may be required to fully interpret OM accumulation.This study applies a novel combination of technologies to develop a high-resolution geochemical data set, integrating energy-dispersive X-ray fluorescence (EDXRF) and infrared imagery analyses, to record proxies for redox conditions, bioproductivity, and clastic and carbonate dilution in millimeter-resolution profiles of 133 core slabs from the Middle and Upper Devonian Horn River shale in the Western Canada Sedimentary Basin, which provides decadal-scale temporal resolution. A comparison to a more coarsely sampled data set from the same core results in substantially different interpretations of variations in bioproductivity, redox, and dilution proxies. Stratigraphic distributions of organic matter accumulation patterns (bioproductivity-control, siliciclastic/carbonate-dilution, and redox conditions-control) show that organic enrichment events were highly varied during deposition of the shale and were closely related to second- and third-order sea-level changes. High-resolution profiles indicate that bioproductivity was the predominant trigger for organic matter accumulation in a second-order highstand, particularly during deposition of third-order transgressive systems tracts. Organic matter accumulation was largely controlled by dilution from either carbonate or clastic sediments in a second-order lowstand. Bioproductivity-redox feedbacks developed on timescales of decades to centuries. [ABSTRACT FROM AUTHOR]

Spectral decorrelation (transformations) methods have long been used in remote sensing. Transformation of the image data onto eigenvectors that comprise physically meaningful spectral properties (signal) can be used to reduce the dimensionality of hyperspectral images as the number of spectrally distinct signal sources composing a given hyperspectral scene is generally much less than the number of spectral bands. Determining eigenvectors dominated by signal variance as opposed to noise is a difficult task. Problems also arise in using these transformations on large images, multiple flight-line surveys, or temporal data sets as computational burden becomes significant. In this paper we present a spatial–spectral approach to deriving high signal quality eigenvectors for image transformations which possess an inherently ability to reduce the effects of noise. The approach applies a spatial and spectral subsampling to the data, which is accomplished by deriving a limited set of eigenvectors for spatially contiguous subsets. These subset eigenvectors are compiled together to form a new noise reduced data set, which is subsequently used to derive a set of global orthogonal eigenvectors. Data from two hyperspectral surveys are used to demonstrate that the approach can significantly speed up eigenvector derivation, successfully be applied to multiple flight-line surveys or multi-temporal data sets, derive a representative eigenvector set for the full image data set, and lastly, improve the separation of those eigenvectors representing signal as opposed to noise. [ABSTRACT FROM AUTHOR]

Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol. [ABSTRACT FROM AUTHOR]

Leaf traits and physiological performance govern the amount of light reflected from leaves at visible and infrared wavebands. Information on leaf optical properties of tropical trees is scarce. Here, we examine leaf reflectance of Mesoamerican trees for three applications: (1) to compare the magnitude of within- and between-species variability in leaf reflectance, (2) to determine the potential for species identification based on leaf reflectance, and (3) to test the strength of relationships between leaf traits (chlorophyll content, mesophyll attributes, thickness) and leaf spectral reflectance. Within species, shape and amplitude differences between spectra were compared within single leaves, between leaves of a single tree, and between trees. We also investigated the variation in a species' leaf reflectance across sites and seasons. Using forward feature selection and pattern recognition tools, species classification within a single site and season was successful, while classification between sites or seasons was not. The implications of variability in leaf spectral reflectance were considered in light of potential tree crown classifications from remote airborne or satellite-borne sensors. Species classification is an emerging field with broad applications to tropical biologists and ecologists, including tree demographic studies and habitat diversity assessments. [ABSTRACT FROM AUTHOR]

Abstract: Studies of forest fragmentation in the tropics have systematically paid more attention to understanding processes contributing to the formation of landscapes that are the product of deforestation, while little or no attention has been paid to forested landscapes that are the product of secondary growth processes. In this paper, using information of forest cover from 1960 to 2000 derived from aerial photography and remote sensing data, we examine the dynamics associated with deforestation and secondary growth processes at the Chorotega region, Costa Rica. Our analysis is conducted using a comprehensive land capability map developed for the country. Our results indicate the occurrence of two well-defined land cover change processes over time. Extensive/intensive cattle ranching practices that contribute to significant forest degradation and forest cover loss characterize the first of these processes, occurring between 1960 and 1980. The second period (1980–2000) is characterized by a dynamic process of secondary forest growth resulting from a combination of internal and international market forces and conservation initiatives. Conclusions from this paper indicate that more attention needs to be paid to tropical secondary landscapes that are recuperating from years of constant degradation. This natural recovery process could be beneficial for biodiversity recovery, biological corridors and environmental services. [Copyright &y& Elsevier]

Tropical dry forests (TDFs) in the Americas are considered the first frontier of economic development with less than 1% of their total original coverage under protection. Accordingly, accurate estimates of their spatial extent, fragmentation, and degree of regeneration are critical in evaluating the success of current conservation policies. This study focused on a well-protected secondary TDF in Santa Rosa National Park (SRNP) Environmental Monitoring Super Site, Guanacaste, Costa Rica. We used spectral signature analysis of TDF ecosystem succession (early, intermediate, and late successional stages), and its intrinsic variability, to propose a new multiple criteria spectral mixture analysis (MCSMA) method on the shortwave infrared (SWIR) of HyMap image. Unlike most existing iterative mixture analysis (IMA) techniques, MCSMA tries to extract and make use of representative endmembers with spectral and spatial information. MCSMA then considers three criteria that influence the comparative importance of different endmember combinations (endmember models): root mean square error (RMSE); spatial distance (SD); and fraction consistency (FC), to create an evaluation framework to select a best-fit model. The spectral analysis demonstrated that TDFs have a high spectral variability as a result of biomass variability. By adopting two search strategies, the unmixing results showed that our new MCSMA approach had a better performance in root mean square error (early: 0.160/0.159; intermediate: 0.322/0.321; and late: 0.239/0.235); mean absolute error (early: 0.132/0.128; intermediate: 0.254/0.251; and late: 0.191/0.188); and systematic error (early: 0.045/0.055; intermediate: −0.211/−0.214; and late: 0.161/0.160), compared to the multiple endmember spectral mixture analysis (MESMA). This study highlights the importance of SWIR in differentiating successional stages in TDFs. The proposed MCSMA provides a more flexible and generalized means for the best-fit model determination than common IMA methods. [ABSTRACT FROM AUTHOR]

Aim: The general goal of this study is to investigate and analyze patterns of ecophysiological leaf traits and spectral response among life forms (trees, shrubs and lianas) in the Cerrado ecosystem. In this study, we first tested whether life forms are discriminated through leaf level functional traits. We then explored the correlation between leaf-level plant functional traits and spectral reflectance. Location: Serra do Cipo National Park, Minas Gerais, Brazil. Methods: Six ecophysiological leaf traits were selected to best characterize differences between life forms in the woody plant community of the Cerrado. Results were compared to spectral vegetation indices to determine if plant groups provide means to separate leaf spectral responses. Results: Values obtained from leaf traits were similar to results reported from other tropical dry sites. Trees and shrubs significantly differed from lianas in terms of the percentage of leaf water content and Specific Leaf Area. Spectral indices were insufficient to capture the differences of these key traits between groups, though indices were still adequately correlated to overall trait variation. Conclusion: The importance of life forms as biochemical and structurally distinctive groups is a significant finding for future remote sensing studies of vegetation, especially in arid and semi-arid environments. The traits we found as indicative of these groups (SLA and water content) are good candidates for spectral characterization. Future studies need to use the full wavelength (400 nm–2500 nm) in order to capture the potential response of these traits. The ecological linkage to water balance and life strategies encourages these traits as starting points for modeling plant communities using hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

Abstract: Research suggests that secondary forest structure and composition subsequent to human disturbance could be related to wind or vertebrate dependent seed dispersal mechanisms. This paper examines the capability of a waveform LiDAR system (LVIS: LiDAR Vegetation Imaging System) to detect the location of wind-dispersed or vertebrate-dispersed forest patches in a mosaic of a secondary Tropical Dry Forest in the Santa Rosa National Park, Guanacaste, Costa Rica. Canopy-height estimations derived from LVIS data were used to identify and locate two types of dispersal-dependent arrangements of canopy heights: flat-topped (windborne seed dispersed) or dome-shaped (vertebrate-dispersed) fragments. Following identification, a Ripley''s K indicator was used to compare the locations for each confirmed canopy type to those reported in existing maps. Results indicated that dome-shaped patches tended to be arranged in clusters at the middle of former pastures (far from older forests), and that flat-topped patches tended to be located adjacent to older forests, forming extensions from the old growth patches and at the downwind side of the slope. Such characteristics agree with the theoretical observation of the spatial configuration of vertebrate- and wind-dispersed fragments reported in the study site. Our findings demonstrate that processes controlling forest regeneration in the TDF can be successfully identified through LiDAR data. [Copyright &y& Elsevier]

Abstract: Increasing dominance of lianas in many tropical forests is considered a fingerprint of global environmental change. Despite the key role they play in ecosystem functioning, lianas remain one of the least studied life forms in tropical environments. This paper contrasts leaf traits and spectral properties (400–1100 nm) of liana and tree communities from a tropical dry forest and a tropical rainforest in Panama, Central America. Differences between lianas and tree leaf traits were analyzed using spectroscopy, leaf histology and pigment extractions. Results from this study indicate that many of the biochemical, structural, and optical properties of lianas and trees are different in the dry forest site but not in rainforest sites. In the dry forest site, liana leaves exhibited significantly lower chlorophyll and carotenoid contents and were thinner than the leaves of their host trees. Specific leaf area, dry to fresh mass ratio, and mean water content of liana leaves were significantly higher when compared with tree leaves. The differences observed in the tropical dry forest site indicate that lianas may have a higher rate of resource acquisition and usage, whereas trees tend to conserve acquired resources. We suggest that our results may be indicative of the presence of a liana syndrome related to water availability and thus best exhibited in tropical dry forests. Our findings have important implications for using remote sensing to accurately map the distribution of liana communities at regional scales and for the continued expansion of lianas in tropical environments as a result of global change. [Copyright &y& Elsevier]

Abstract: A number of recent studies have focused on estimating gross primary production (GPP) using vegetation indices (VIs). In this paper, GPP is retrieved as a product of incident light use efficiency (LUE), defined as GPP/PAR, and the photosynthetically active radiation (PAR). As a good correlation is found between canopy chlorophyll content and incident LUE for six types of wheat canopy (R 2 =0.87, n =24), indices aimed for chlorophyll assessment can be used as an indicator of incident LUE and the product of chlorophyll indices and PAR will be a proxy of GPP. In a field experiment, we investigated four canopy chlorophyll content related indices (Red edge Normalized Difference Vegetation Index [Red Edge NDVI], modified Chlorophyll Absorption Ratio Index [MCARI710], Red Edge Chlorophyll Index [CIred edge] and the MERIS Terrestrial Chlorophyll Index [MTCI]) for GPP estimation during the growth cycle of wheat. These indices are validated for leaf and canopy chlorophyll estimation with ground truth data of canopy chlorophyll content. With ground truth data, a strong correlation is observed for canopy chlorophyll estimation with correlation coefficients R 2 of 0.79, 0.84, 0.85 and 0.87 for Red Edge NDVI, MCARI710, CIred edge and MTCI, respectively (n =24). As evidence of the existence of a relationship between canopy chlorophyll and GPP/PAR, these indices are shown to be a good proxy of GPP/PAR with R 2 ranging from 0.70 for Red Edge NDVI and 0.75 for MTCI (n =240). Remote estimation of GPP from canopy chlorophyll content×PAR is proved to be relatively successful (R 2 of 0.47, 0.53, 0.65 and 0.66 for Red edge NDVI, MCARI710, CIred edge and MTCI respectively, n =240). These results open up a new possibility to estimate GPP and should inspire new models for remote sensing of GPP. [Copyright &y& Elsevier]

Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration. [ABSTRACT FROM AUTHOR]

In this study, we investigated the use of Bayesian networks for inferring tropical dry forest leaf area index (LAI) from satellite imagery in dry and wet seasons. LAI was chosen as the variable of interest because leaf area is the exchange surface between the photosynthetically active component of the canopy and the atmosphere. Initial network estimates were obtained from ground truth plot data with known forest structure, LAI, and satellite reflectance in the red and near-infrared bands (as observed by the Landsat 7 Enhanced Thematic Mapper Plus sensor). We tested the performance of the Bayesian networks with scoring rules and also with confidence and surprise scores. We evaluated the networks on a per-pixel basis and created both LAI maps of the study area as well predicted the probability maps for the highest LAI states. Results not only demonstrate the predictive power of a Bayesian network but also its explanatory power which is far beyond what is typically available with current pixel classifier approaches such as spectral vegetation indices or other approaches such as neural networks. [ABSTRACT FROM AUTHOR]

Reflectance spectroscopy has made it possible to rapidly and nondestructively assess the chlorophyll content of plants and natural waters. However, to date this approach has not been applied to chlorophyll and chlorophyll derivatives preserved in lake sediments. Here, we explore the relationships between visible-near-infrared spectral properties of lake sediments and measured pigment concentrations for lakes that have been exposed recently to anthropogenic nitrogen deposition. Down-core decreases in pigment concentrations and changes in reflectance properties effectively chronicle increases in whole-lake primary production since 1950. Specifically, reflectance spectra of sediments from four alpine lakes in Rocky Mountain National Park (Colorado Front Range, USA) preserve salient troughs near 675 nm that covary in magnitude with concentrations of chlorophyll a and associated pheopigments. The area of the trough in reflectance between 600 and 760 nm best explains the sum of total chlorophyll a and its derivatives (r2 = 0.82, n = 23, P < 0.01). This result suggests that chlorophyll a preserved in lake sediments can be remotely sensed using a simple index derived from reflectance spectroscopy, thus providing a new paleolimnological strategy for rapid exploratory assessments of changing lake trophic status. [ABSTRACT FROM AUTHOR]

Abstract: The National Oceanic and Atmospheric Administration (NOAA) weekly snow cover dataset (1966–) is the longest available record of snow cover extent (SCE) over the Northern Hemisphere (NH). This dataset has been used extensively to derive trends in continental SCE and in climate-related studies, but it has received only limited validation, particularly in high latitude areas of the NH. This study evaluated spring snow cover depletion in the NOAA dataset over a study area in the Canadian Arctic mainland north of the tree line. The evaluation used four sources of information: (1) surface snow depth and snow survey observations, (2) snow cover extent produced from the Advanced Very High Resolution Radiometer (AVHRR), (3) snow cover extent derived from Special Sensor Microwave/Imager (SSM/I), and (4) Landsat 5 TM browse images. Six spring seasons from the period 1981–2000 with low (1984, 1988, and 1998) and high (1985, 1995, and 1997) spring snow cover extent were evaluated. The evaluation revealed that the NOAA weekly dataset consistently overestimated snow cover extent during the spring melt period, with delays of up to 4 weeks in melt onset. A number of possible reasons for this delay were investigated. The most likely causes for the delayed melt onset were frequent cloud cover in the spring melt period, and the low frequency of data coverage over higher latitudes. The results suggest that caution should be exercised when using this dataset in any studies related to the timing of snowmelt in the high latitudes of the Northern Hemisphere. [Copyright &y& Elsevier]

• Corticolous lichen classification using visible-near infrared spectra. • Linear spectral mixing between lichen and bark was evaluated. • Overall lichen signatures tend to present low spectral bark mixing. • Lichen classification is more accurate at higher, rather than lower taxonomic ranks. • Linear discriminant analysis presents the highest performance classifying species. The optical properties of lichens have been traditionally explored in the context of geological mapping where the encrustation of lichens on rocks may influence the detection of minerals of interest. As of today, few studies have looked into the potential of using the optical properties of lichens to classify them; however, none has investigated the classification of tropical lichens using spectroscopy. Here we explore the use of the visible-near infrared reflectance (VNIR; 450–1000 nm) to discriminate Neotropical corticolous lichens; the most abundant lichens in tropical forests. Reflectance measurements on lichens and their bark substrate were performed on 282 lichens samples of 32 species attached to their host's bark. Using these measurements, we first explored the degree of spectral mixing of bark and lichens by linear unmixing each lichen spectrum with the corresponding average species spectrum and bark spectrum. Overall, the results reveal that the lichen signatures tend to mask the spectral contributions from bark; however, there are some specific groups of species with high bark mixing probably due to their nature and the similarities between the lichen and bark spectra. Next, we classified the lichen spectra based on growth forms and taxonomic ranks (i.e., family, genus, species) using five machine learning classifiers. This analysis was conducted on raw reflectance spectra and wavelet-transformed spectra to enhance the absorption features prior to classification. As expected, the classification of lichen spectra is less accurate at species-specific levels, rather than higher taxonomic ranks. The wavelet transformation was found to enhance the general performance of classification; however, the accuracy of the classification depends on the classifier. Of the classifiers used in this study, linear discrimination applied to reflectance spectra presents the highest performance at the species level. Our results reveal the potential of using the VNIR reflectance as a method to discriminate Neotropical lichens. The introduced methodology may be conducted in the field, thus allowing the monitoring of lichen communities in forests; thereby furthering the current understanding of the role of lichens in ecosystem functioning. [ABSTRACT FROM AUTHOR]