Use Of Spectral Data 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.

Towards a nonperturbative construction of the S-matrix

We present a nonperturbative recipe for directly computing the S-matrix in strongly-coupled QFTs. The method makes use of spectral data obtained in a Hamiltonian framework and can be applied to a wide range of theories, including potentially QCD. We demonstrate the utility of this prescription in the specific example of the 2+1d O(N) model at large N, using energy eigenstates computed with Hamiltonian truncation to reproduce the full 2 → 2 scattering amplitude for arbitrary (complex) center-of-mass energy.

ОЦЕНОЧНЫЕ ПРИЗНАКИ ВИНОГРАДОПРИГОДНЫХ ЗЕМЕЛЬ ПО СПЕКТРАЛЬНЫМ ПАТТЕРНАМ

The purpose of the study is to determine the necessary set of estimated signs of vineyards with terroir properties based on the generalization of literary sources and the setting up of field experience in the application of spectral indices from satellite images. The use of spectral data on the state of soils, plants and the environment makes it possible to evaluate the growth force of bushes and the yield of vineyards by difference normalized indices. Spectral analysis of vineyards using satellite images allows obtaining data on the vegetative and moisture variability of vine plantations with a frequency of 2-7 days. The correct interpretation of spectral satellite images is possible when they are verified by ground studies of soil indicators, vegetation of bushes by phenophases, agricultural work performed in the aisle, soil, air and leaf moisture. In the future, verification of satellite and ground-based data will reduce the number of route-field surveys and laboratory tests. To solve this problem, spectral data from the public satellite platforms Sentinel-2 and Landsat-8 must be calibrated using ground samples from sample plots of vineyard lands with terroir properties, which are defined as spectral patterns (samples) of vineyards. The presence of a relationship between soil moisture, leaf area and yield makes it possible, on the basis of the normalized NDVI vegetation index and NDMI soil moisture, to determine the micro-sites of vine plantations of different productivity and quality of grape harvest. Microzoning of the area according to such parameters as physical and chemical composition, soil moisture, morphometry, exposure and slope slopes makes it possible to identify viticultural areas with terroir properties. Identification of the boundaries and areas of vineyards with terroir properties using spectral raster satellite images with vectorization of difference soil layers in a geographic information system allows you to quickly assess viable lands, the productivity of various areas of vineyards and optimize agricultural work in accordance with a precision approach in viticulture.

ОЦЕНОЧНЫЕ ПРИЗНАКИ ВИНОГРАДОПРИГОДНЫХ ЗЕМЕЛЬ ПО СПЕКТРАЛЬНЫМ ПАТТЕРНАМ

The purpose of the study is to determine the necessary set of estimated signs of vineyards with terroir properties based on the generalization of literary sources and the setting up of field experience in the application of spectral indices from satellite images. The use of spectral data on the state of soils, plants and the environment makes it possible to evaluate the growth force of bushes and the yield of vineyards by difference normalized indices. Spectral analysis of vineyards using satellite images allows obtaining data on the vegetative and moisture variability of vine plantations with a frequency of 2-7 days. The correct interpretation of spectral satellite images is possible when they are verified by ground studies of soil indicators, vegetation of bushes by phenophases, agricultural work performed in the aisle, soil, air and leaf moisture. In the future, verification of satellite and ground-based data will reduce the number of route-field surveys and laboratory tests. To solve this problem, spectral data from the public satellite platforms Sentinel-2 and Landsat-8 must be calibrated using ground samples from sample plots of vineyard lands with terroir properties, which are defined as spectral patterns (samples) of vineyards. The presence of a relationship between soil moisture, leaf area and yield makes it possible, on the basis of the normalized NDVI vegetation index and NDMI soil moisture, to determine the micro-sites of vine plantations of different productivity and quality of grape harvest. Microzoning of the area according to such parameters as physical and chemical composition, soil moisture, morphometry, exposure and slope slopes makes it possible to identify viticultural areas with terroir properties. Identification of the boundaries and areas of vineyards with terroir properties using spectral raster satellite images with vectorization of difference soil layers in a geographic information system allows you to quickly assess viable lands, the productivity of various areas of vineyards and optimize agricultural work in accordance with a precision approach in viticulture.

Automatic Spectral Classification of Stars using Machine Learning: An Approach based on the use of Unbalanced Data

With the increase in astronomical surveys, astronomers are faced with the challenging task of analyzing a large amount of data in order to classify observed objects into hard-to-distinguish classes. This article presents a machine learning-based method for the automatic spectral classification of stars from the latest release of the SDSS database. We propose the combinatorial use of spectral data, derived stellar data, and calculated data to create patterns. Using these patterns as inputs, we develop a Random Forest model that outputs the spectral class of the observed star. Our model is able to classify data into six complex classes: A, F, G, K, M, and Carbon stars. Due to the unbalanced nature of the data, we train our model considering three data use cases: using the original data, using under-sampling, and over-sampling data techniques. We further test our model by using a fixed dataset and a stratified dataset. From this, we analyze the performance of our model through statistical metrics. The experimental results showed that the combinatorial use of data as an input pattern contributes to improve the prediction scores in all data use cases, meanwhile, the model trained with augmented data outperforms the other cases. Our results suggest that machine learning-based spectral classification of stars may be useful for astronomers.

Printer spectral color characterization adjustment for change in substrates

Characterization data for printers is obtained by printing a test chart on the intended production substrate. In practice it is common for a different substrate to that used to obtain the characterization data to be used in proofing and in production, and this requires either reprinting and re-measuring the test chart or estimating new characterization data. Methods to do this exist for colorimetric characterization data, but with the increasing use of spectral data in the workflow, there is a need for a method that can be applied to spectral. reflectances. This paper proposes two different methods of adjusting printer spectral color characterization data for a change in substrates. In the first part, a Spectral Correction Technique was applied to spectral reflectance data obtained from different printers to predict a spectral color characterization data for an additional substrate. In the second part, the reference printing condition was used to adjust spectral color characterization data. The results were evaluated, and it was found that a good prediction is achieved with the use of machine learning.

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO2-saturated photosynthesis rate, Vmax,27), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, Vmax,27, Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R2 = 0.73) and LAI (R2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R2 = 0.79), nitrogen concentration (R2 = 0.83), nitrogen content (R2 = 0.77), and Vmax,27 (R2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R2 = 0.80), nitrogen (R2 = 0.85) and Vmax,27 (R2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy Vmax,27, have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

Hyperspectral inversion of nitrogen content in maize leaves based on different dimensionality reduction algorithms

Fast, accurate, and non-destructive detection of the nitrogen (N) content in corn leaves is of great significance for the precise dynamic management of nitrogen fertilizer application for corn. Hyperspectral data can provide an important means for detecting the nitrogen content in plants. Existing research has mainly focused on using various vegetation indices or 3–5 band combinations to estimate leaf nitrogen content, ignoring the different in spectral characteristics of hyperspectral data and failing to characterize most of the spectral information. Some scholars have used principal component analysis and wavelet analysis dimensionality reduction algorithms, but used different bands for these models. Therefore, more and different inversion models need to be introduced to improve the use of spectral data and increase the universality of the model. The present study selected three different methods to reduce data dimensionality, including the Successful Projections Algorithm (SPA) and the Least Absolute Shrinkage and Selection Operator (LASSO) and the Elastic Net (EN) algorithms. Then the processed spectral reflectance information and observational data for synchronous leaf nitrogen content were used to construct an inversion model used to predict leaf nitrogen content. Nine inversion models were constructed based on different dimensionality reduction and regression methods. Based on the coefficient of determination (R2) and root mean square error (RMSE), the accuracy of each model was tested. The main results follow: (1) Dimensionality reduction processing of hyperspectral data can effectively prevent data from overfitting, limit the correlation between adjacent frequency bands, and reduce data redundancy. An EN dimensionality reduction algorithm (EN-Partial Least Squares Regression (PLSR)) model R2 = 0.96, RMSE = 0.19) was better than a SPA (SPA-PLSR model R2 = 0.90, RMSE = 0.26) and LASSO (LASSO-PLSR model R2 = 0.89, RMSE = 0.37) dimensionality reduction algorithm. (2) For the same dimensionality reduction method, the accuracy of the regression model based on PLSR was higher than that of other models. Among the nine inversion models in this paper, the EN-PLSR inversion model has the best fitting effect (R2 = 0.96, RMSE = 0.19). (3) Obvious changes in nitrogen content have little effect on the overall hyperspectral reflectance curve. This study provides a reference for high-efficiency and non-destructive testing of corn nitrogen content using hyperspectral technology.

Gene Validation and Remodelling Using Proteogenomics of Phytophthora cinnamomi, the Causal Agent of Dieback.

Phytophthora cinnamomi is a pathogenic oomycete that causes plant dieback disease across a range of natural ecosystems and in many agriculturally important crops on a global scale. An annotated draft genome sequence is publicly available (JGI Mycocosm) and suggests 26,131 gene models. In this study, soluble mycelial, extracellular (secretome), and zoospore proteins of P. cinnamomi were exploited to refine the genome by correcting gene annotations and discovering novel genes. By implementing the diverse set of sub-proteomes into a generated proteogenomics pipeline, we were able to improve the P. cinnamomi genome annotation. Liquid chromatography mass spectrometry was used to obtain high confidence peptides with spectral matching to both the annotated genome and a generated 6-frame translation. Two thousand seven hundred sixty-four annotations from the draft genome were confirmed by spectral matching. Using a proteogenomic pipeline, mass spectra were used to edit the P. cinnamomi genome and allowed identification of 23 new gene models and 60 edited gene features using high confidence peptides obtained by mass spectrometry, suggesting a rate of incorrect annotations of 3% of the detectable proteome. The novel features were further validated by total peptide support, alongside functional analysis including the use of Gene Ontology and functional domain identification. We demonstrated the use of spectral data in combination with our proteogenomics pipeline can be used to improve the genome annotation of important plant diseases and identify missed genes. This study presents the first use of spectral data to edit and manually annotate an oomycete pathogen.

Spectral Curves for the Derivative Nonlinear Schrödinger Equations

Currently, in nonlinear optics, models associated with various types of the nonlinear Schrödinger equation (scalar (NLS), vector (VNLS), derivative (DNLS)), as well as with higher and mixed equations from the corresponding hierarchies are usually studied. Typical tools for solving the problem of propagation of optical nonlinear waves are the forward and inverse nonlinear Fourier transforms. One of the methods for reconstructing a periodic nonlinear signal is based on the use of spectral data in the form of spectral curves. In this paper, we study the properties of the spectral curves for all the derivatives NLS equations simultaneously. For all the main DNLS equations (DNLSI, DNLSII, DNLSIII), we have obtained unified Lax pairs, unified hierarchies of evolutionary and stationary equations, as well as unified equations of spectral curves of multiphase solutions. It is shown that stationary and evolutionary equations have symmetries, the presence of which leads to the existence of holomorphic involutions on spectral curves. Because of this symmetry, spectral curves of genus g are covers over other curves of genus M and N=g−M, where M is a number of phase of solutions. We also showed that the number of the genus g of the spectral curve is related to the number of phases M of the solution of one of the two formulas: g=2M or g=2M+1. The third section provides examples of the simplest solutions.

Synthesis and Characterization of New Pyrazolopyrimidine Derivatives Incorporated to Benzothiaazole

Reaction of 2-Amino-6-methyl benzothiazole with 1-cyano-acetyl-3,5-dimethylpyrazole afforded cyanoacetamide derivative (2). Compound 2 was used as the precursor for the synthesis of novel aminopyrazole derivative (5) by reaction with phenyl isothiocyanate and methyl iodide followed by addition of hydrazine hydrate. Treatment of aminopyrazole (5) with different reagents gave pyrazolopyrimidine derivatives (7-12). The newly synthesized compounds were characterized by elemental analysis and the use of spectral data (IR, 1H NMR, and MS).

Airborne hyperspectral remote sensing applied to determine the texture of a Cambisol in the Chapada do Apodi, Ceará

ABSTRACT Texture is of great importance in soil management, as it strongly influences the physical, hydraulic and chemical behaviour of soils. It is therefore necessary to determine texture, with the spectral band ratio being a fast and precise alternative method for this purpose. The aim of this study was to evaluate the use of spectral data, acquired by the ProSpecTIR-VS airborne sensor with a spatial resolution of 1 m, in selecting two bands for building a Normalised Difference Index that allows the textural attributes of the soil to be estimated, besides preparing a texture map of the soil in the image. Sixty-four samples were collected from several areas inserted in the Jaguaribe-Apodi irrigated perimeter, which is located in the Chapada do Apodi, in the Lower Jaguaribe Basin, where the predominant soil classes are Cambisols. The samples were collected from exposed soil, based on the hyperspectral images of the ProSpecTIR-VS airborne sensor. The Normalised Difference Index (NDI) was constructed, carrying out all possible normalised band ratios, with the best indices selected based on the coefficient of determination (R²). The most promising results for R² were obtained when estimating sand in the 1045 and 1323 nm bands, with an R² of 0.5. The low values ​​for R² can be explained by interference in the spectral response from materials on the soil surface, such as crop residue, gravel and vegetation. Preparing the sand map using the best model resulted in 82.1% of the pixels having values ​​between 20 and 60% sand, falling between the minimum and maximum sand content of the soil samples.

Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation.

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.

Laboratory-based spectral data acquisition of roof materials

ABSTRACT Roof characteristics such as material type and their properties information are essential to integrating urban agriculture (UA), rainwater harvesting systems (RWHS), and energy systems on roofs. Roof materials can be identified from their spectral signatures. However, this identification requires a priori knowledge of the materials’ spectral characteristics. The main perspective of this work is the future use of spectral data for roof classification. A common practice in mapping materials is the use of spectral libraries. In this regard, this work describes a novel framework for laboratory-based spectral data acquisition. The reflectance data of common, recently introduced (plastics and metals), and representative roof materials from the Mediterranean region were obtained. Data acquisition was conducted in a laboratory under controlled conditions using a high-spatial-resolution (HSR) sensor, which is usually used for airborne surveys. Large variations in the spectral reflectance data were observed due to the composition of the roof material. Flat spectral signatures were found for fibre cement, concrete, gravels and some metals, especially from the near-infrared (NIR) spectral region. Colour and surface finish greatly influence the visible (VIS) spectral range. It was confirmed that the view angle did not modify the spectral shapes. A collection of 39 spectral data of roof materials (ceramics, concrete, fibre cement, metals, plastics, paints, stone, and wood) were compiled into a spectral library that is available online.

Investigation of the new possibility of mathematical processing of Raman spectra for dating documents

The use of spectral analysis methods to determine the age of writing inks is an important forensic task. However, the use of spectral data for this purpose has a number of limitations and difficulties. This paper considers the application of the Raman spectroscopy method to an urgent forensic task. The known mechanisms of dye degradation are analyzed; Raman bands are identified that are related to the age of the sample. In a sample of 5 randomly selected writing inks, temporary markers were identified. Narrow sections of Raman spectra containing characteristic lines were used for analysis. It was shown that processing narrow sections of the Raman spectra using the PCA chemometric method allowed the separation of writing inks into groups (clusters) corresponding to different creation intervals.

Influence of Time and Frequency Domain Wave Forcing on the Power Estimation of a Wave Energy Converter Array

Industry-specific tools for analyzing and optimizing the design of wave energy converters (WECs) and associated power systems are essential to advancing marine renewable energy. This study aims to quantify the influence of phase information on the device power output of a virtual WEC array. We run the phase-resolving wave model FUNWAVE-TVD (Total Variation Diminishing) to generate directional waves at the PacWave South site offshore from Newport, Oregon, where future WECs are expected to be installed for testing. The two broad cases presented correspond to mean wave climates during warm months (March–August) and cold months (September–February). FUNWAVE-TVD time series of sea-surface elevation are then used in WEC-Sim, a time domain numerical model, to simulate the hydrodynamic response of each device in the array and estimate their power output. For comparison, WEC-Sim is also run with wave energy spectra calculated from the FUNWAVE-TVD simulations, which do not retain phase information, and with wave spectra computed using the phase-averaged model Simulating WAves Nearshore (SWAN). The use of spectral data in WEC-Sim requires a conversion from frequency to time domain by means of random superposition of wave components, which are not necessarily consistent because of the linear assumption implicit in this method. Thus, power response is characterized by multiple realizations of the wave climates.

Methods of extraction of optical properties from diffuse reflectance measurements of ex-vivo human colon tissue using thin film silicon photodetector arrays.

Spatially resolved diffuse reflectance spectroscopy (SRDRS) is a promising technique for characterization of colon tissue. Herein, two methods for extracting the reduced scattering and absorption coefficients ( and ) from SRDRS data using lookup tables of simulated diffuse reflectance are reported. Experimental measurements of liquid tissue phantoms performed with a custom multi-pixel silicon SRDRS sensor spanning the 450 - 750 nm wavelength range were used to evaluate the extraction methods, demonstrating that the combined use of spatial and spectral data reduces extraction error compared to use of spectral data alone. Additionally, SRDRS measurements of normal and tumor ex-vivo human colon tissue are presented along with and extracted from these measurements.

Integration of ZiYuan-3 Multispectral and Stereo Data for Modeling Aboveground Biomass of Larch Plantations in North China

Data saturation in optical sensor data has long been recognized as a major factor that causes underestimation of aboveground biomass (AGB) for forest sites having high AGB, but there is a lack of suitable approaches to solve this problem. The objective of this research was to understand how incorporation of forest canopy features into high spatial resolution optical sensor data improves forest AGB estimation. Therefore, we explored the use of ZiYuan-3 (ZY-3) satellite imagery, including multispectral and stereo data, for AGB estimation of larch plantations in North China. The relative canopy height (RCH) image was calculated from the difference of digital surface model (DSM) data at leaf-on and leaf-off seasons, which were extracted from the ZY-3 stereo images. Image segmentation was conducted using eCognition on the basis of the fused ZY-3 multispectral and panchromatic data. Spectral bands, vegetation indices, textural images, and RCH-based variables based on this segment image were extracted. Linear regression was used to develop forest AGB estimation models, where the dependent variable was AGB from sample plots, and explanatory variables were from the aforementioned remote-sensing variables. The results indicated that incorporation of RCH-based variables and spectral data considerably improved AGB estimation performance when compared with the use of spectral data alone. The RCH-variable successfully reduced the data saturation problem. This research indicated that the combined use of RCH-variables and spectral data provided more accurate AGB estimation for larch plantations than the use of spectral data alone. Specifically, the root mean squared error (RMSE), relative RMSE, and mean absolute error values were 33.89 Mg/ha, 29.57%, and 30.68 Mg/ha, respectively, when using the spectral-only model, but they become 24.49 Mg/ha, 21.37%, and 20.37 Mg/ha, respectively, when using the combined model with RCH variables and spectral band. This proposed approach provides a new insight in reducing the data saturation problem.

Urban road classification in geometrically integrated high-resolution RGB aerial and laser-derived images using the artificial neural network classification method

ABSTRACTThe problem of automated urban road network extraction is extremely complex because roads in urban scenes strongly interact with other objects. This problem can be simplified if road regions are first isolated using a classification procedure. The isolated road regions can be posteriorly used in tasks of refinement and reconstruction of the road network. This article addresses only the problem of road regions’ detection using Artificial Neural Network as classification method. However, in urban areas, the use of spectral data alone commonly leads to the confusion of the road class with other classes in RGB images, such as building roofs and concrete, because these objects may present similar spectral characteristics. To overcome this problem, it is proposed the integration of a high-resolution RGB aerial image with laser-derived images. The classification results showed that the integration of the geometric (height) and radiometric (laser pulse intensity) laser data significantly improved the classification accuracy, also contributing for the better detection of road pixel. The laser intensity data help to overcome the effects of road obstructions caused by shadows and trees. On the other hand, the laser height data help to separate the aboveground objects from those on the ground level.

Spectral-temporal characterization of wheat cultivars through NDVI obtained by terrestrial sensors

ABSTRACT Remote sensing applications in agriculture are presented as a very promising reality, but research is still needed for the correct use of spectral data. The objective of this study was to evaluate the spectral-temporal patterns of eleven wheat cultivars (Triticum aestivum L.). The experiment was conducted in Cascavel, PR, in the year 2014. With the help of the GreenSeeker and FieldSpec 4 terrestrial sensors, spectral signatures were determined and the temporal profiles of the Normalized Difference Vegetation Index (NDVI) were created. Statistical differences between wheat cultivars were analysed using analysis of variance (ANOVA) and Scott-Knott test. Grain yields obtained with INSEY (In-Season Estimate of Yield) factors were correlated. NDVI normalized by degree-days accumulated from the Feekes growth stages 2 and 8 showed to be more consistent in the estimation of grain yield, explaining approximately 70% of the variation. At the Feekes stage 10.1, wheat cultivars presented different spectral patterns in the near and medium infrared bands. This suggests that these spectral bands can be used to differentiate wheat cultivars.