Variations In Spectral Reflectance Research Articles

Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles

Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed.

A hyperspectral evaluation approach for quantifying salt-induced weathering of sandstone

Salt-induced weathering is a common phenomenon in stone relics, and its traditional artificial evaluation of severity is greatly affected by subjective consciousness and lacks systematic standards. Here, we propose a hyperspectral evaluation method for quantifying salt-induced weathering on sandstone surfaces in laboratory tests. Our novel approach consists of two parts: data acquisition of microscopic observations of sandstone in salt-induced weathering environments, and machine learning technology for a predictive model. We first obtain the microscopic morphology of sandstone surfaces by near-infrared hyperspectral imaging technique. Then, a salt-induced weathering reflectivity index is proposed according to analyses of spectral reflectance variation. Next, a principal components analysis-Kmeans (PCA-Kmeans) algorithm is applied to bridge the gaps between the salt-induced weathering degree and the associated hyperspectral images. Furthermore, machine learning technologies, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN), are trained for better evaluating the salt-induced weathering degree of sandstone. Tests demonstrate that the RF algorithm is feasible and active in weathering classification based on spectral data. The proposed evaluation approach is finally applied to the analysis of salt-induced weathering degree on Dazu Rock Carvings.

Disentangling the effects of phosphorus, nitrogen and species identity on the vegetation reflectance spectrum

AbstractQuestionsEffective and successful restoration of species‐rich semi‐natural grasslands requires knowledge of the soil nutrient status, including soil phosphorus availability. Plants are solid indicators of soil nutrient status, because their growth reflects nutrient availability integrated over a certain period. The use of reflectance spectroscopy to estimate vegetation nutrient content has the potential to offer a fast and efficient approach to provide information about soil nutrient availability for plants. Here, we investigated the effect and relative importance of vegetation phosphorus content compared with vegetation nitrogen content and species identity in explaining variation in vegetation reflectance.LocationPot experiment mimicking Western European grassland communities on a restoration trajectory.MethodsWe combined a pot experiment with a broad range of mesotrophic grassland species growing along a soil phosphorus gradient with a multivariate modelling approach. We measured vegetation spectra and vegetation phosphorus and nitrogen content in monocultures and mixtures.ResultsAlthough vegetation biochemistry explained a considerable part of the variation in vegetation reflectance, we found no pronounced absorption features for vegetation nitrogen and phosphorus content across the reflectance spectrum. The relatively large effect of species and community identity suggest that other drivers, for example vegetation architecture, overruled the effect of vegetation biochemistry on the reflectance spectrum. Our findings indicate that species detection and indirect case‐specific estimation of nutrients is possible, especially in structurally less‐complex canopies such as monospecific grass swards.ConclusionsDisentangling the specific drivers of variation in spectral reflectance is challenging. Many studies confound the effect of species identity on the vegetation reflectance spectrum with the effect of vegetation biochemistry. Here, we showed the importance of explicitly taking species identity into account. Gaining insight into light–vegetation interactions and the in‐depth integration of ecological theory in remote sensing are the way forward.

Spectral reflectance variations of aubrites, metal‐rich meteorites, and sulfides: Implications for exploration of (16) Psyche and other “spectrally featureless” asteroids

AbstractThe 350–2500 nm reflectance spectra of five enstatite achondrites (aubrites), five metal‐rich chondrites (CBa, CBb, CH/CBb, and ungrouped), and seven sulfide mineral samples (three troilites, pyrrhotite, pentlandite, a mixture of pentlandite and chalcopyrite, and oldhamite) have been measured to search for spectral parameters that may offer insight into the surface composition of so‐called “spectrally featureless” asteroids. Spectral data were acquired from powders, slabs, and hand samples. Aubrites exhibit high reflectance, generally positive slopes at visible wavelengths, and low‐to‐negative infrared slopes, consistent with E‐/Xe‐type asteroids. The metal‐rich chondrites exhibit low reflectance, moderate visible slopes, and low near‐infrared slopes, somewhat consistent with M−/X‐complex asteroids. The metal‐rich chondrites exhibit absorption features at ~900 nm arising from Fe2+‐bearing silicates. Sulfides exhibit low to moderate reflectance and high visible and near‐infrared slope, intermediate to the T‐ and L‐type asteroids. The D‐type asteroids, which have high visible and near‐infrared slopes, are not well‐matched by sulfides. Spectral data of the largest M−/X‐type asteroid, (16) Psyche, are consistent with both powder from the Isheyevo CH/CBb chondrite and powder of meteoritic troilite. The data presented here will support interpretation of data returned from future spacecraft missions to “spectrally featureless” asteroids, like the Psyche, Lucy, and DART/Hera missions.

Longitudinal effects of habitat quality, body condition, and parasites on colour patches of a multiornamented lizard

Ontogeny is expected to be a determinant factor affecting production of colour patches in lizards, while immune challenges or sudden weight loss may impair the maintenance of pigment-based coloration within a breeding season. We translocated males of the lizard Psammodromus algirus between two sampling plots that differed in distance to a road, vegetation structure, and predator abundance. We analysed variation in spectral reflectance of their colour patches the same and the following year. The change in the reflectance of the lizard colour patches within the first breeding season was explained by the interaction between plot and treatment, but not body condition. The maintenance of the breeding coloration was impaired only in those males translocated close to the road, probably reflecting that it is a poor-quality habitat for P. algirus. The following year, lizards that produced a more elaborate coloration were those that increased their body condition and controlled some parasitic infections, although suffered an increase of others. This study shows that colour patch production is plastic in P. algirus. Lizards increasing parasites or losing weight reduced pigmentation, although habitat quality can cushion these negative effects on pigmentation. However, not all parasites constrain the investment in coloration. In fact, some increased in those lizards that allocated more pigments to colour patches. In conclusion, longitudinal studies following experimental manipulation can contribute to understand pigment allocation rules in lizards.Significance statementPigments involved in colour patches of animals are limiting resources that can be reallocated off the skin to other functions. However, longitudinal evidence of this phenomenon is scarce in reptiles. We designed a manipulative mark-recapture experiment to investigate effects of habitat and parasitic infections on colour patch maintenance (within-year variation) and production (between-year variation) in male free-ranging lizards that were reciprocally translocated between two patches of habitat that differed in quality. During the first year, lizards translocated to the habitat with more predators and worse vegetation impoverished their coloration, while lizards translocated to the more favourable habitat maintained it despite all translocated lizards loose body condition. The next year we detected different effects on the coloration of three different parasites investigated, suggesting that coloration can reflect the virulence of the infections.

Analysis of Changes in Vegetation Index Through Long-term Monitoring of Petroglyphs of Cheonjeon-ri, Ulju

In this study, vegetation index, the vegetation index calculated based on hyperspectral images was used to monitor Petroglyphs of Cheonjeon-ri, Ulju from 2014 to 2020. To select suitable the vegetation index for monitoring, indoor analysis was performed, and considering the sensitivity to biocontamination, Normalized Difference Vegetation Index (NDVI) and Triangular Vegetation Index (TVI) were selected. As a result of monitoring using the selected vegetation index, NDVI increased from 2014 to 2018 and then decreased in 2020, after preservation treatment. On the other hand, TVI was difficult to confirm the tendency during the monitoring. This difference was due to the variation in spectral reflectance according to the photographing conditions by year. Therefore NDVI is less sensitive to spectral reflectance deviation than TVI, so it can be used for monitoring. In order for TVI to be used, however, in-depth study is needed.

Convolutional Two-Stream Generative Adversarial Network-Based Hyperspectral Feature Extraction

Hyperspectral image processing is faced with difficulties considering its redundant features and complex information. Studies on hyperspectral feature extraction in the deep learning domain have become increasingly popular. The mainstream techniques fully consider the spatial information in local neighborhoods when extracting spectral features by constructing deep neural networks. Deep generative models simulate the intrinsic structure of samples by adequately training, showing their potential values for signal processing. In this article, a convolutional two-stream network (cs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN-FE) based on the improved Wasserstein generative adversarial network (WGAN) is proposed for unsupervised hyperspectral spatial–spectral feature extraction. The improved WGAN is composed of one generator and one discriminator; the former perceives real data distributions, and the latter determines the attribution of generated data. The designed two-stream strategy is not a simple extension of a one-stream strategy and considers both the static spectral–spatial information and the dynamic spectral reflectance variation in multiple bands. Intrinsic spatial–spectral features are extracted by the trained discriminator considering sample distributions and feature relationships. The loss function is also improved for the unique structure of cs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN-FE. Various state-of-the-art techniques are chosen for comparison. Experimental results show the feasibility and potential of this network. Besides, experiments with the random split and the disjointed split both show that the proposed method can outperform other comparison techniques.

Remote sensing of regional-scale maize lodging using multitemporal GF-1 images

Lodging stress affects the yield, quality, and mechanical harvesting capacity of maize, thereby resulting in a reduction in maize production. The timely monitoring of maize lodging assists in determining the extent and degree of the effects. This study proposed a method for this purpose using Gaofen-1 (GF-1) optical satellite images, which can be significant for lodging field management, production assessments, and implementation of the related remedial measures. The evaluation index of the lodging grade was constructed by measuring the proportion and angle of maize lodging after the lodging incident. Variations in spectral reflectance and vegetation index were calculated before and after lodging. The competitive adaptive reweighted sampling algorithm was used to screen the optimal combination of variables sensitive to the evaluation index of the maize lodging grade. A remote sensing monitoring model was established by using random forest (RF) and partial least squares (PLS). Results show that the maize lodging monitoring model established by RF is better than that of PLS. The accuracy of monitoring the lodging damage using the GF-1 images reaches 79%. Meanwhile, the classification map of the lodging grade we constructed using an RF model is consistent with the actual lodging area. Hence, this method can effectively characterize the intensity of lodging stress and can also be used to assess the range of damages caused by regional-scale lodging.

Canopy Nitrogen Concentration Monitoring Techniques of Summer Corn Based on Canopy Spectral Information.

Crop nitrogen monitoring techniques, particularly choosing sensitive monitoring bands and suitable monitoring models, have great significance both in theory and in practice for achieving non-destructive monitoring of nitrogen concentration and accurate management of water and fertilizer in large-scale areas. In this study, a lysimeter experiment was carried out to examine the characteristics of canopy spectral reflectance variation of summer corn under different fertilization levels. The relationship between canopy spectral reflectance and nitrogen concentration was investigated, based on which sensitive bands for the corn canopy nitrogen monitoring were selected and a suitable spectral index model was determined. The results suggest that under different fertilization levels, the canopy spectral reflectance of summer corn decreases with the increase of the canopy nitrogen concentration in the visible light band, but varies in the opposite direction in the near-infrared band, with a premium put on a higher correlation between the spectral reflectance of the characteristic bands and their first derivatives and the canopy nitrogen concentration. The most sensitive bands for monitoring the canopy nitrogen concentration using spectral reflectance and its first derivative are found to be 762 nm and 726 nm and the correlation coefficients are 0.550 and 0.795, respectively. The optimal band combination, generated by multivariate stepwise regression analysis, is composed of 762 nm, 944 nm and 957 nm bands. From the 55 reported spectral index models of crop nitrogen concentration monitoring, the most suitable index model, NDRE, is chosen such that this index model has the highest correlation with the canopy nitrogen concentration in summer corn. This model has a significant positive correlation with the canopy nitrogen concentration at each growth period, and the correlation coefficient is up to 0.738 during the whole growth period. Spectral monitoring models of canopy nitrogen concentration are constructed using sensitive bands, and a combination of bands and the spectral index, suggesting that these models perform well in monitoring. The models arranged in descending order of simulation accuracy are as follows: the suitable spectral index model, the optimal band combination model, the sensitive band reflectance first derivative model, the sensitive band reflectance model. The determination coefficients are 0.754, 0.711, 0.639 and 0.306, respectively.

Refining fine fraction soil extraction methods and analysis for mineral exploration

Soils are widely used as geochemical sample media. In transported cover, the soils that host the mobile element signature are the smallest size fractions, so we should consider concentrating the clay size fraction (&lt;2 µm) as an improved sample medium. Twenty-seven bulk reference soils were collected in the vicinity of known mineral deposits (including background areas) that reflect common soil types of Western Australia. A selection of these soils was used in replicate testing to assess differences in particle size, sample weight, dispersants, and how this relates to the Au and Cu geochemistry. Using multiple analysis of variance tests, the submicron fractions showed that, although the &lt;0.2 µm fraction was slightly different to the &lt;2 µm and &lt;0.75 µm fractions, there was no additional value in the extra effort required to extract these submicron fractions, and the &lt;2 µm fraction was favoured. Ultrasonics and rinsing were not required for Au analysis, and during wet separation no Au is lost in solution and only a small fraction of the overall Cu (0.5%) is lost. Using a dispersant was critical for solid recovery and Na-hexametaphosphate (technical or laboratory grade) was found to be the most effective reagent. The &lt;2 µm size fraction showed no significant differences for assays of Au using 0.1, 0.2, 0.5, 1 and 4 g ( P &lt; 0.05) with a microwave-assisted aqua regia digestion being the best analytical method for Au recovery. In addition to the geochemistry, the spectral reflectance variation was investigated with respect to particle size and weight, and showed more variation in the smaller weight fraction of material and a loss of quartz in the fine materials as expected. The integration of results from this study generated a new workflow called UltraFine+™. This workflow provides 40 elements, spectral mineralogy, particle-size distribution, pH, electrical conductivity (EC) and specific surface area, offering more quality data to improve mineral exploration using soil and regolith samples. Supplementary material: Spreadsheets of soils and regolith data from orientation sites and regional map sites analysed with the UltraFine+ method is available at https://doi.org/10.25919/5d1058f8b05fc

Deviation of white diffuse reflectance standards from perfect reflecting diffuser at visible and near-infrared spectral ranges

The assumption that the reflectance of white diffuse reflectance standards is identical to that of the perfect reflecting diffuser (PRD) allows these standards to be used to characterize reflectance or radiance factors of any surface at any irradiation/collection geometry simply by comparison. However, this assumption is only true within certain limits, and, for some applications, requirements may be out of those limits. PTB and IO-CSIC have studied the variation of the reflectance with respect to the bidirectional geometry for the four most typical white diffuse materials (barium sulfate, opal glass, ceramic and Spectralon), at in- and out-of plane geometries and at spectral range from 380 nm to 1700 nm. We have defined descriptors in order to more clearly quantify the spectral reflectance variation with the bidirectional geometries. The values obtained for these descriptors have been separately presented for the visible and near-infrared spectral ranges. In both spectral ranges, deviations of white diffuse reflectance standards with respect to the PRD were found, regarding both Lambertian behaviour and spectral constancy. The observed deviation from the BRDF is in general very large for high incidence and collection angles (reaching in many cases 20%). Therefore, it is not possible to assume Lambertianity in standards at those geometries when calibrating measuring systems.

Leaf Spectral Reflectance of Maize Seedlings and Its Relationship to Cold Tolerance

Increasing early‐season cold tolerance of maize (Zea mays L.) has the potential to lengthen its growing season, reduce its environmental impact, and enhance its yields. Cold‐ and warm‐grown plants differ for biomass accumulation and spectral reflectance, the latter caused by differences in leaf chlorophyll content, carotenoid content, or other chemical and morphological attributes. Here, we evaluate genetic leaf spectral reflectance diversity across 38 inbred and 14 hybrid maize genotypes grown in cold and control temperatures. Genotypes varied for spectral reflectance indices correlated with chlorophyll content and for an index correlated with the ratio of leaf carotenoids to chlorophylls. Genotypic differences greatly contributed to spectral reflectance variation across all visible wavelengths, with the greatest genetic variation between 500 and 600 nm and around 700 nm. Cold treatment effects were most significant across the same wavelengths. Spectral indices indicated lower chlorophyll and a higher carotenoid/chlorophyll level in cold‐exposed plants relative to control plants. Genotype × temperature interactions were small relative to genotype effects. Cold tolerance, measured as the ratio of dry matter accumulation in cold‐treated plants relative to control plants, varied among hybrids and inbreds. Correlations of cold tolerance with known spectral reflectance indices, reflectance first derivatives, and novel, normalized spectral indices were mostly low and occurred only in certain germplasm. One reflectance parameter, the reflectance curve slope at 828 nm, was consistently related to genotypic cold tolerance, even when measured on plants that had not been exposed to cold.

Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

Grasslands play important roles in ecosystem production and support a large farming and grazing industry. An accurate and efficient way is needed to estimate grassland health and production for monitoring and adjusting management to get sustainable products and other ecosystem services. Previous studies of grasslands have shown varying relationships between productivity and biodiversity, with most showing either a positive or a hump-shaped relationship where productivity peaks at intermediate diversity. In this study, we used airborne imaging spectrometry combined with ground sampling and eddy covariance measurements to estimate the spatial pattern of production and biodiversity for two sites of contrasting productivity in a southern Alberta prairie ecosystem. Resulting patterns revealed that more diverse sites generally had greater productivity, supporting the hypothesis of a positive relationship between production and biodiversity for this site. We showed that the addition of evenness to richness (using the Shannon Index of dominant species instead of the number of dominant species alone) improved the correlation with optical diversity, an optically derived metric of biodiversity based on the coefficient of variation in spectral reflectance across space. Similarly, the Shannon Index was better correlated with productivity (estimated via NDVI (Normalized Difference Vegetation Index)) than the number of dominant species alone. Optical diversity provided a potent proxy for other more traditional biodiversity metrics (richness and Shannon index). Coupling field measurements and imaging spectrometry provides a method for assessing grassland productivity and biodiversity at a larger scale than can be sampled from the ground, and allows the integrated analysis of the productivity–biodiversity relationship over large areas.

Spectral reflectance variations and mineral transformations during the formation of acid sulphate soil in an incubation experiment

Understanding the reflectance spectral variations associated with oxidation of iron sulphide materials is highly important to the detection of environmental changes affected by the detrimental acid mining drainage and acid sulphate soil (ASS) using hyperspectral sensing. An incubation experiment with iron-sulphide-rich sediments was set up to simulate the formation and evolution of ASS. The sediments were shaped into different micro-landforms. Spectral changes and mineral transformation were observed by comparing weekly measurements of the materials in the different landforms using an ASD (Analytical Spectral Devices) reflectance spectrometer, as well as comparing the measurements of the initial materials and the residual materials after oxidation. We found that the spectral changes usually occur at the spectral absorptions near 0.9 μm, which relate to ferric iron contents, near 1.17 μm, which relate to ferrous iron content, and near 2.265 μm, which relate to jarosite. Spectral variations are detected both in specific absorption position and depth. We also found that the causes of the spectral variations are mainly due to the variations in mineral transformation and consequent mineral composition, owing to lying in different oxidation stages and drainage conditions in the different landforms. This article shows not only oxidation of sulphides to different mineral phases spectrally but also the link of the phases with landforms and drainage conditions and the link between oxidation degree and oxidation progress, all of which have not been sufficiently studied before.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared (NIR) shoulder (750-900 nm) region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region (NSRI), defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices (water index and normalized difference water index) and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index (NDVI), water index (WI) and normalized difference water index (NDWI), with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

Laboratory Salinization of Brazilian Alluvial Soils and the Spectral Effects of Gypsum

Irrigation-induced salinization is an important land degradation process that affects crop yield in the Brazilian semi-arid region, and gypsum has been used as a corrective measure for saline soils. Fluvent soil samples (180) were treated with increasing levels of salinization of NaCl, MgCl2 and CaCl2. The salinity was gauged using electrical conductivity (EC). Gypsum was added to one split of these samples before they were treated by the saline solutions. Laboratory reflectance spectra were measured at nadir under a controlled environment using a FieldSpec spectrometer, a 250-W halogen lamp and a Spectralon panel. Variations in spectral reflectance and brightness were evaluated using principal component analysis, as well as the continuum-removed absorption depths of major features at 1450, 1950, 1750 and 2200 nm for both the gypsum-treated (TG) and non-treated (NTG) air-dried soil samples as a function of EC. Pearson’s correlation coefficients of reflectance and the band depth with EC were also obtained to establish the relationships with salinity. Results showed that NTG samples presented a decrease in reflectance and brightness with increasing CaCl2 and MgCl2 salinization. The reverse was observed for NaCl. Gypsum increased the spectral reflectance of the soil. The best negative correlations between reflectance and EC were observed in the 1500–2400 nm range for CaCl2 and MgCl2, probably because these wavelengths are most affected by water absorption, as Ca and Mg are much more hygroscopic than Na. These decreased after chemical treatment with gypsum. The most prominent features were observed at 1450, 1950 and 1750 nm in salinized-soil spectra. The 2200-nm clay mineral absorption band depth was inversely correlated with salt concentration. From these features, only the 1750 and 2200 nm ones are within atmospheric absorption windows and can be more easily measured using hyperspectral sensors.

Investigation of Tree Spectral Reflectance Characteristics Using a Mobile Terrestrial Line Spectrometer and Laser Scanner

In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt.

Testing for selection on color and pattern in a mimetic radiation

Abstract In this paper, we analyze variation in spectral reflectance and color pattern among populations to demonstrate dramatic divergence between four distinct morphs of the mimic poison frog Ranitomeya imitator. We also analyze genetic divergence in d-loop mtDNA sequences between populations. We then use coalescent-based simulations to demonstrate that the high levels of observed phenotypic divergence are not consistent with levels of genetic divergence expected under neutral drift among populations, implying an important role for selection in driving divergence between these populations.

Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons

There is an increasing need to monitor the dynamics of green LAI of field crops through the growing season. A simple approach is to use a regression model to estimate crop LAI from a vegetation index derived from optical remote sensing data. However, variations of interference factors in the signal path could induce variations in spectral reflectance, leading to uncertainty in LAI estimation. A semi-empirical equation was implemented to estimate green LAI of field crops from Landsat-5/7 data using a few vegetation indices, including the normalized difference vegetation index (NDVI), the optimized soil adjusted vegetation index (OSAVI), the two band enhanced vegetation index (EVI2) and the modified triangular vegetation index (MTVI2). Data were collected during several growing seasons, from 1999 to 2006, over corn, soybean, and spring wheat fields in an experimental farm in Ottawa (ON, Canada). LAI estimated for corn, soybean and wheat from Landsat data using the vegetation indices was compared to ground LAI. Except for NDVI, comparable results were obtained from the other three vegetation indices, with a coefficient of determination above 0.83 and a root mean square error (RMSE) not more than 0.60. The performance of NDVI was less satisfactory (RMSE>0.66). The uncertainties in LAI estimation induced by variations in soil reflectance, leaf optical properties, canopy structure, and atmospheric conditions were assessed through a global sensitivity analyses using the PROSPECT leaf model coupled to the SAIL canopy model along with the 6S atmospheric transmission model. The sensitivity analyses show that different indices are affected differently by the various interference factors. Comparatively, NDVI is the most influenced by leaf chlorophyll but the least affected by leaf inclination, OSAVI and the narrow band MTVI2 are more efficient in reducing soil effects, and EVI2 has a better performance in reducing aerosol perturbation. At high LAI, the uncertainty of NDVI is the smallest, but the uncertainty propagated to LAI estimation is the largest due to saturation. In this case, vegetation indices that are less prone to saturation should be considered, such as EVI2 and MTVI2. When MTVI2 is used on multispectral data, its ability to reduce soil and leaf chlorophyll perturbation is similar to EVI2 but weaker than when it is used on hyperspectral data. These results show that vegetation indices can be used in a simple regression model to generate baseline green LAI product for seasonal crop growth monitoring, however it is important to be aware of the sources of uncertainty and their relative amplitudes when using the product.

Functional Data Analysis of Multi-Angular Hyperspectral Data on Vegetation

Abstract - The surface reflectance anisotropy can be estimated by directional reflectance analysis through the collection of multi-angular spectral data. Proper characterization of the surface anisotropy is an important element in the successful interpretation of remotely sensed signals. A signal received by a sensor from a vegetation canopy is affected by several factors. One of them is the sensor zenith angle. Functional data analysis can be used to assess the distribution and variation of spectral reflectance due to sensor zenith angle. This paper examines the effect of sensor zenith angles on the spectral reflectance of vegetation, example on cotton leaves. The spectra were acquired in a green house trial in order to address the question ‘how much information can be obtained from multi-angular hyperspectral remote sensing on vegetation?’ The goals of the functional data analysis applied in this paper is to examine the Functional Data Analysis approach was applied to analysis multi-angular hyperspectral data on cotton, highlighting various characteristics of cotton spectra due to sensor view angles, and to infer directional variation in an outcome or dependent variable with different zenith angles.