Maximum Likelihood Distance Research Articles

A comparative methodological approach for argan forest classification using Landsat imagery

In the last decades, natural and anthropogenic pressures have caused observable changes in the argan landscape despite its significance in Morocco. Remote sensing data can be used to monitor these changes over time and provide information on vegetation health and land cover changes. This study assesses the performance of supervised methods (support vector machine, maximum likelihood, and minimum distance) and unsupervised classification method (Isodata) for mapping the argan forest in the Smimou area of Essaouira province using remote sensing data from Landsat-5 and Landsat-8 (1985 and 2019). Additionally, the impact of the resampling method and the digital elevation model (DEM) integration on the classification results have been examined. The ground truth data were collected and randomly divided into two categories: 234 samples to calibrate the classification algorithms and 340 samples for validation. Maximum likelihood supervised classification achieved an overall accuracy (OA) of 89.62% (kappa = 0.84) and 87.58% (kappa = 0.81) in 1985 and 2019, respectively. Using resampling techniques on normalized difference vegetation index (NDVI) products, aiming for a 10 m resolution, the NDVI results yielded an OA of 91.60% in 1985 and 88.85% in 2019. Further integration of DEM (30-m resolution) with NDVI, which was resampled to a 10 m resolution, achieved an OA of 92.27% and 92.37% for 1985 and 2019, respectively.

Taxonomy of Tobrilidae species from the Alkaline Lakes of the western Nebraska Sandhills.

Six distinct COI mitochondrial Haplotype Groups (HG) are morphologically, ecologically, and genetically characterized from the aquatic nematode family Tobrilidae. Collection locations included the extreme habitats of the Alkaline Lakes in the western Nebraska Sandhills and the contaminated stream, Johnson Creek, bordering the AltEn 2021 catastrophic pesticide release near the village of Mead in eastern Nebraska. Maximum likelihood and genetic distance metrics supported the genetic integrity of the haplotype groups. Discriminant function analysis of COI haplotype group datasets of combined morphological characters and soil chemistry attributes for both male and female Tobrilidae were classified correctly in all but one case. Scanning electron microscopy revealed new details about amphid apertures, male supplements, and spicules. Partial 18S gene phylogeny suggests that the genus Semitobrilus may not be a member of the subfamily Neotobrilinae, and three specimens in the 226 tobrilid dataset provide evidence of incongruence between COI and 18S derived phylogenies. Given the strong signal provided by the environmental chemistry data, tobrilid mitochondrial haplotypes may well have value as environmental indicators.

Perbandingan Beberapa Metode Klasifikasi Menggunakan Citra Landsat dan Sentinel Untuk Pemetaan Sebaran Mangrove Di Kawasan Ekowisata Mangrove PIK Jakarta Utara

Mangroves are one of the coastal ecosystems that have many functions and have a very large role for living things associated with mangroves. One of the mangrove ecosystems in DKI Jakarta is in the Mangrove Ecotourism Area of the Sedyatmo Toll Road, North Jakarta. Remote sensing technology is the right method for mangrove monitoring activities that are efficient, effective and wide area coverage. Mangrove mapping is one of the remote sensing techniques to determine the distribution of mangroves in a region. This study aims to assess mangroves and compare the accuracy of the supervised classification method (Maximum Likelihood, Minimum Distance) and object based classification in the distribution of mangroves in the Mangrove Ecotourism Area of the Sedyatmo Toll Road using Landsat 8 and Sentinel-2A images. Comparison of the results of the supervised classification and object based classification of the two images to be tested for accuracy using a confusion matrix. The results showed that the mangroves were evenly distributed along the Sedyatmo toll road and its surroundings with an average mangrove area of >60 ha. In Landsat 8 imagery, the Overall Accuracy for the classification of Maximum Likelihood is 87,50%, Minimum Distance is 75,00% and object based classification is 89,83%. Sentinel-2A image classification results Overall Accuracy classification method Maximum Likelihood 89,83%, Minimum Distance 77,50%, and object-based classification 90,00%. The results of the Overall Accuracy show that object-based classification is better than the Maximum Likelihood and Minimum Distance classifications in mapping the distribution of mangroves on Landsat 8 and Sentinel-2A images.

A Comparison between Supervised Classification Methods: Study Case on Land Cover Change Detection Caused by a Hydroelectric Complex Installation in the Brazilian Amazon

The Volta Grande do Xingu (VGX) in the Amazon Forest of Brazil was chosen to analyze the land use and land cover changes (LULCC) from 2000 to 2017, with the aim of assessing the most suitable classification method for the area. Three parametric (Mahalanobis distance, maximum likelihood and minimum distance) and three non-parametric (neural net, random forest and support vector machine) classification algorithms were tested in two Landsat scenes. The accuracy assessment was evaluated through a confusion matrix. Change detection of the landscape was analyzed through the post-classification comparison method. While maximum likelihood was more capable of highlighting errors in individual classes, support vector machine was slightly superior when compared with the other non-parametric options, these being the most suitable classifiers within the scope of this study. The main changes detected in the landscape were from forest to agro-pasture, from forest/agro-pasture to river, and from river to non-river, resulting in rock exposure. The methodology outlined in this research highlights the usefulness of remote sensing tools in follow-up observations of LULCC in the study area (with the possibility of application to the entire Amazon rainforest). Thus, it is possible to carry out adaptive management that aims to minimize unforeseen or underestimated impacts in previous stages of environmental licensing.

Genetic characterization of Staphylococcus aureus isolated from Norway rats in Boston, Massachusetts.

Despite the importance of domesticated animals in the generation and transmission of antibiotic-resistant Staphylococcus aureus, the role of wild animals, specifically rodents, in the ecology of S. aureus remains unclear. We recovered and genotyped S. aureus isolates from wild Norway rats (Rattus norvegicus) in Boston, Massachusetts to examine genetic relationships between common human and animal S. aureus isolates in a large US metropolitan area. We collected and necropsied 63 rats from June 2016 to June 2017. Nasal, foot pad, fur, and fecal swabs were collected. Staphylococcus aureus was isolated using culture-based methods and polymerase chain reaction confirmation. S. aureus isolates were spa typed, tested for antibiotic susceptibility, and whole genome sequenced. Assembled sequences were uploaded to the Comprehensive Antibiotic Resistance Database to identify antibiotic resistance elements. A phylogenetic tree was constructed using the neighbor-joining method with the maximum composite likelihood distance in MEGA7. We recovered 164 Gram-positive bacterial isolates from Norway rats. Nineteen isolates from eight individual rats were confirmed as S. aureus (prevalence: 12.9% (8/63)). All S. aureus isolates were methicillin-susceptible S. aureus (MSSA), pvl-negative, and resistant to penicillin. Two isolates displayed resistance to erythromycin. Four different S. aureus spa types were detected (t933, t10751, t18202, and t189). Thirteen unique antibiotic resistance elements were identified, and all isolates shared genes mepR, mgrA, arlR, and S. aureus norA. Phylogenetic analysis if the 19 S. aureus isolates revealed they were genetically similar to four clades of S. aureus with similar resistance gene profiles isolated from both human- and animal-derived S. aureus, as well as formed a distinct phylogenetic cluster composed only of rat isolates. Wild rodents may serve as a reservoir or vector of antibiotic resistance genes in the urban environment with relevance for human and animal health.

Distance Measures of Polarimetric SAR Image Data: A Survey

Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields.

Ecological potential assessment and land use area estimation of agricultural lands based on multi-time images of Sentinel-2 using ANP-WLC and GIS in Bastam, Iran.

The use of land-based ecological potential is a key management factor in achieving sustainable development and conserving soil and water resources. The purpose of this study is to use multi-time images of Sentinel-2 to determine the area of agricultural lands and evaluate their ecological potential in Bastam, Semnan Province, Iran. Therefore, in the first step, the most common agricultural lands (including apricot and grape orchards), field crops (including wheat and forage maize), and their phenological period were determined. Then, the information was classified using, namely support vector machine, maximum likelihood, and minimum distance. In the next step, soil, topography, and climate data were extracted according to expert opinions and the analysis of the basin's natural potential. The ecological evaluation models of the region were applied to homogeneous units, and after calculating the ecological potential index by the integrated ANP-WLC model, the ecological potential map was generated. The results of the land use classification showed that the support vector machine model and the minimum distance, respectively, had the best and worst performance with kappa coefficient of 0.82 and 0.61. The highest area of cultivated lands (3423 hectares) was estimated for wheat and the lowest (738 hectares) for forage maize. Moreover, the results of the ecological potential evaluation showed that 60% of apricot orchards, and 40.19% of the wheat fields, were in the class of good ecological potential. Overall, 2290 hectares were in the poor class, and 4030 hectares in the excellent class in terms of ecological potential.

On Comparing Different Methods of Estimation for the Parameters of a Pathological Distribution with Application to Climate Data

In statistical literature, various probability distributions exist with advantageous properties, while others are considered pathological since their properties are counterintuitive. A well-known pathological probability distribution is the Cauchy distribution, and it has applications in areas related to environmental and financial research. Both the log-Cauchy and half-Cauchy distributions, which have close connections to the Cauchy distribution, are pathological distributions. This paper considers another pathological model called the Cauchy Birnbaum-Saunders distribution. Some of the statistical properties of this distribution are discussed briefly, and its parameters are estimated using eight frequentist estimation methods, including the maximum likelihood, least-squares-based, and minimum distance estimation methods. Monte Carlo simulations are carried out to compare and examine the performance of each estimator numerically. Furthermore, a recent climate data set is analyzed to show the practical applicability of this model.

Monitoring the Wetland of the Yellow River Delta by Combining GF-3 Polarimetric Synthetic Aperture Radar and Sentinel-2A Multispectral Data

Wetlands in estuary deltas functionally protect biodiversity, store water, and regulate ecological balance. However, wetland monitoring accuracy is low when using only synthetic aperture radar (SAR) images or optical images. This study proposes a novel method for extracting ground objects in a wetland using principal component analysis (PCA) and random forest (RF) classification, which combines the features of fully polarimetric SAR images and optical images. Firstly, polarization decomposition features and texture features were extracted based on polarimetric SAR data, and spectral features were extracted based on optical data. Secondly, the optical image was registered to SAR image. Then PCA was performed on the nine polarimetric features of the SAR images and the four spectral features of the optical images to obtain the first two principal components of each. After combining these components, a RF classification algorithm was used to extract the objects. The objects in the Yellow River Delta wetland were successfully extracted using our proposed method with Gaofen-3 fully polarimetric SAR data and Sentinel-2A optical data acquired in November 2018. The overall accuracy of the proposed method was 86.18%, and the Kappa coefficient was 0.84. This was an improvement of 18.96% and 0.22, respectively, over the GF-3 polarimetric features classification, and 11.02% and 0.13, respectively, over the Sentinel-2A spectral features classification. Compared with the results of the support vector machine, maximum likelihood, and minimum distance classification algorithms, the overall accuracy of the RF classification based on joint features was 2.03, 5.69, and 23.36% higher, respectively, and the Kappa coefficient was 0.03, 0.07, and 0.27 higher, respectively. Therefore, this novel method can increase the accuracy of the extraction of objects in a wetland, providing a reliable technical means for wetland monitoring.

A nonlinear dynamic factor model of health and medical treatment.

Quantitative assessments of the relationship between health and medical treatment are of great importance to policy makers. To overcome endogeneity problems we formulate and estimate a tractable dynamic factor model where observed health outcomes are driven by the individual's latent health. The dynamics of latent health reflects both exogenous health deterioration and endogenous health investments. Our model allows us to investigate the effect of medical treatment on current health, as well as on future medical treatment and health outcomes. We estimate the model by maximum simulated likelihood and minimum distance methods using a rich longitudinal data set from Italy obtained by merging a number of administrative archives. These data contain detailed information on medical drug purchase, hospitalization, and mortality for a representative sample of elderly hypertensive patients. Our findings show that the observed autocorrelation in medical treatment reflects both permanent and time-varying observed and unobserved heterogeneity. They also show that medical drug purchase significantly maintains future health levels and prevents transitions to worse health. This suggests that policies aimed at increasing the awareness and the compliance of hypertensive patients help reduce cardiovascular risks and consequent hospitalization and mortality.

Acroneuria kirchneri Stark amp; Kondratieff, 2004 is a synonym of Acroneuria kosztarabi Kondratieff amp; Kirchner, 1993 (Plecoptera: Perlidae).

Acroneuria Pictet, 1841 (Plecoptera: Perlidae) is a genus of stoneflies which presently includes 18 summer emerging Nearctic species. Diagnosis of species relies primarily on male aedeagal setal patterns, details of the ovum chorion, and larval dorsal maculations. Several species are morphologically cryptic in one or more life stages. In 2010, an apparently rare species, A. kosztarabi Kondratieff Kirchner, 1993, was petitioned for listing under the Endangered Species Act. A status survey conducted in 20162017 produced new specimen collections from near the type locality and surrounding areas. Examination of new material and the holotype of A. kirchneri Stark Kondratieff, 2004 demonstrates that it is a junior subjective synonym of A. kosztarabi. All life stages of A. kosztarabi are redescribed using color photographs and scanning electron microscope photomicrographs. Supporting data include maximum likelihood and pairwise genetic distance analyses based on mitochondrial cytochrome c oxidase subunit I fragment data. Acroneuria kosztarabi, previously known only from the type locality, is now reported from six states in the southeastern United States ranging from central North Carolina to western Tennessee and north to northern Virginia and northern Ohio.

Radiometric Identification of Signals by Matched Whitening Transform

Radiometric identification is the problem of attributing a signal to a specific source. In this work, a radiometric identification algorithm is developed using the whitening transformation. The approach stands out from the more established methods in that it works directly on the raw IQ data and hence is featureless. As such, the commonly used dimensionality reduction algorithms do not apply. The premise of the idea is that a data set is “most white” when projected on its own whitening matrix than on any other. In practice, transformed data are never strictly white since the training and the test data differ. The Förstner-Moonen measure that quantifies the similarity of covariance matrices is used to establish the degree of whiteness. The whitening transform that produces a data set with the minimum Förstner-Moonen distance to a white noise process is the source signal. The source is determined by the output of the mode function operated on the Majority Vote Classifier decisions. Using the Förstner-Moonen measure presents a different perspective compared to maximum likelihood and Euclidean distance metrics. The whitening transform is also contrasted with the more recent deep learning approaches that are still dependent on feature vectors with large dimensions and lengthy training phases. It is shown that the proposed method is simpler to implement, requires no features vectors, needs minimal training and because of its non-iterative structure is faster than existing approaches.

A New Method for Winter Wheat Mapping Based on Spectral Reconstruction Technology

Timely and accurate estimation of the winter wheat planting area and its spatial distribution is essential for the implementation of crop growth monitoring and yield estimation, and hence for the development of national agricultural production and food security. In remotely sensed winter wheat mapping based on spectral similarity, the reference curve is obtained by averaging multiple standard curves, which limits mapping accuracy. We propose a spectral reconstruction method based on singular value decomposition (SR-SVD) for winter wheat mapping based on the unique growth characteristics of crops. Using Sentinel-2 A/B satellite data, we tested the SR-SVD method in Puyang County, and Shenzhou City, China. Performance was increased, with the optimal overall accuracy and the Kappa of Puyang County and Shenzhou City were 99.52% and 0.99, and 98.26% and 0.97, respectively. We selected the spectral angle mapper (SAM) and Euclidean Distance (ED) as the similarity measures. Compared to spectral similarity methods, the SR-SVD method significantly improves mapping accuracy, as it avoids excessive extraction, can identify more detailed information, and is advantageous in distinguishing non-winter wheat pixels. Three commonly used supervised classification methods, support vector machine (SVM), maximum likelihood (ML), and minimum distance (MD) were used for comparison. Results indicate that SR-SVD has the highest mapping accuracy and greatly reduces the number of misidentified pixels. Therefore, the SR-SVD method can achieve high-precision crop mapping and provide technical support for monitoring regional crop planting structure information.

A Comparative Study of the different remote sensing techniques for evaluating land use/ cover in Basra city, Iraq.(Dept. C)

Basra governorate represents one of the most important governorates in Iraq, where it is the only seaport for exporting oil and agriculture products. However, this governorate is facing serious land degradation problems results in major changes in land use/ cover (LULC) within the area. Accordingly, the main objective of this study was to evaluate LULC in Basra city using different techniques on remotely sensed data. For that purpose, Landsat 8, Sentinel 2A images were used in 2018 to study extent of urban areas, agricultural lands, water bodies and bare lands areas in Basra city center. The studied techniques include: the supervised classification in three methods (minimum distance, maximum likelihood, and Mahalanobis distance), spectral indices, manual digitizing of features and land surface temperature (LST). These methods were applied on both Sentinel 2A and Landsat data at three spatial resolutions 10, 15 and 30 m, respectively. The obtained results indicated that the minimum distance technique has the highest accuracy in identifying LULC when compared with the other classification methods. It was found that the higher the spatial resolution the higher the accuracy of the results. The spectral indices were more accurate than the classification methods in identifying agricultural areas and water bodies. There was a higher inclusion between urban areas and bare lands due to the similarity in their spectral reflectance. Accordingly, it is recommended to manually digitize urban areas than classifying it. The LST can be used as an indirect and fairly accurate method for evaluating LULC in Al-Basra city. In conclusion, remote sensing data and techniques could help in providing more accurate information about LULC in Basra City to be used in its future planning and sustainable development

Vegetation mapping of No Name Key, Florida using lidar and multispectral remote sensing

ABSTRACT LIght Detection And Ranging (lidar) data have been widely used in the areas of ecological studies due to lidar’s ability to provide information on the vertical structure of vegetation in wildlife habitats. The overall objective of this project was to map the vegetation on No Name Key, Florida where endangered wildlife species reside using publicly available remote sensing data such as lidar data and high resolution aerial images (including National Agricultural Imagery Program (NAIP) images). The methods involved the use of 4 different classification algorithms (Support Vector Machine, Random Forest, Maximum Likelihood, and Mahalanobis Distance), different classification settings (default and custom settings), and normalization (original and normalized input bands) on 2 different input stacked images, NAIP image alone and NAIP combined with lidar data. A majority filter was applied to each classification output before performing the accuracy assessment. The result of performing the image classifications showed the following: across all inputs and classification algorithms, the highest overall accuracy (OA) and kappa coefficient (к) were achieved by the Random Forest classification on the NAIP-lidar stacked image with an applied majority filter and original input bands. The result also showed the airborne data combined with the lidar data resulted in a higher classification accuracy than the airborne data alone; and when normalization was applied to all the input bands or layers, the classification accuracies were not increased in most cases compared to when original bands were used in the classification. In most cases, the application of the majority filter increased the accuracy of the classification results as opposed to when no majority filter was applied. Instead of using default values, when the new parameter values (i.e., custom settings) for the penalty parameter (C) and the gamma (ɤ) were used, the accuracies of the Support Vector Machine (SVM) classified images were slightly increased compared to using the default values for the 2 parameters. With increased availability of public lidar data, combining the aerial image with lidar data would enhance the accuracy of vegetation mapping, which would lead to more effective and accurate wildlife management.

Spatio-temporal investigation of surface soil hardness on professional football field.

The present study was conducted to identify the minimum number of sampling points to monitor surface hardness of the pitches through geostatistical methods and to determine spatial and temporal distribution of surface hardness in autumn, winter, spring, and summer periods. Initial samplings were performed from 126 points and with data reduction, the optimum number of sampling points was identified as 77. In upcoming sampling periods, surface hardness and soil temperature were directly measured in situ and disturbed soil samples taken from 77 points were subjected to moisture content, bulk density, and texture analyses (clay-C, silt-Si, and sand-S). In autumn period, surface hardness highly correlated with soil temperature and moisture content (r2 = - 0.438 and - 0.344, p < 0.01). Surface hardness significantly correlated only with soil temperature in winter period and only with bulk density in summer period (respectively r2 = - 0.366 and 0.234, p < 0.01). Average surface hardness values in autumn, winter, spring, and summer periods were respectively measured as 5.99, 6.55, 5.84, and 5.92%. Semivariograms generated for hardness were modeled with spherical model in all periods and a certain nugget effect was detected in all periods. Maximum likelihood distance for autumn, winter, spring, and summer periods was respectively measured as 65, 40, 45, and 46m. It was concluded based on present findings that geostatistical methods could reliably be used to monitor surface hardness of football pitches and then proper and timely interventions could be made to sections not complying with FIFA standards.

Spatial and quantitative assessment of wind erosion from adjacent dunes with different surface cover ratios.

Direct measurements, models, and risk maps play significant roles in assessment and monitoring of wind erosion cases. Although active and passive traps allow researchers to measure point sediment transports directly, it is also possible to make geostatistical analysis of wind erosion with grid and random sampling at multiple points. Geostatistical models can be used in multi-sample eolian researches to improve model success and update model parameters. The present study was conducted for case-based geostatistical analysis of sediment transport rates (STRs) over two adjacent dunes (plot A and B) with different vegetation cover rates between 22 May and 15 June 2011. The plot A has a vegetative cover ratio of 30%, while the plot B has a vegetation cover ratio of 2% and sand content of the plots is 88%. Actual mass transports were measured with BEST sediment traps. A total of 19 BEST sediment trap assemblies were placed randomly over the plot A and 21 were placed over the plot B. A climate station was installed over the research site to record climate data throughout the experimental period. There were two wind erosion cases during the research period. U test indicated that differences in sediment transport rates of the plots for each case were significant (p < 0.00). Spatial analyses of STRs (kgm-1h-1) also exhibited case-based differences. While nugget effect was observed in case 1 of the plot B, the other case in both plots were modeled with spherical model. Maximum likelihood distances in plot A and B were respectively identified as 61m and 1m in the first case and as 13m and 30m in the second case. Total mass transport was measured as 112kgm-1 in plot A and as 2162kgm-1 in plot B. Consequently, it was found that 30% vegetation cover reduced the total mass transport dramatically.

Maximum likelihood distance estimation algorithm for multi‐carrier radar system

In this study, a maximum likelihood distance estimation algorithm is proposed for a multi-carrier radar system to estimate the time delay of non-integer sampling intervals. Particularly, the equivalent relationship between time domain delay and frequency domain phase shift and the relationship between target delay and channel impulse response is used. The idea of maximum likelihood estimation is to find the estimation value that maximises the likelihood function. Therefore, the core idea of this algorithm is the peak search. First, channel estimation is performed on the sampled signal in frequency domain to obtain the channel impulse response. Second, phase compensation and peak search is done to estimate the target delay. Finally, for multiple estimation results, the authors statistic the probability density distribution of the target. In addition, they deduce the Cramer–Rao bound (CRB) of the variance of time delay estimation. The measurement accuracy of the algorithm that they proposed approaches the CRB in the high signal-to-noise regime.

Characterization of the complete mitochondrial genome of Chinese Triops granarius and implications for species delimitation

The mt genome of T. granarius (15.121 bp), containing 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), 2 ribosomal RNA genes (rRNAs) and one control region, present the typical pattern of phyllopod mt genomes from the perspective of gene content and order. All PCGs initiate with standard start codon of ATN and share the complete stop codon of TAA or TAG, whereas nd1 initiates with GTG, nd2 terminates with AGA and nd4 terminates with a single T. The secondary structures of rrnL and rrnS comprise of 48 helices and 32 helices, respectively. The patterns of evolutionary rate heterogeneity were observed at different levels in Triops. Phylogenetic analyses were performed based on the nucleotide sequences and amino acid sequences showed that Asian T. granarius was a sister group to (T. longicaudatus + T. australiensis), and T. cancriformis was the basal clade of Triops. Furthermore, we investigated the genetic diversity and phylogenetic relationships of different T. granarius s.l. populations. Ten phylogenetic lineages are separated on species level by mean maximum likelihood distance ≥ 12.2% in the cox1 gene. While the six populations occurring in Inner Mongolia of China, Mongolia and Russia, investigated for haplotypes belonged to the same haplotype networks.

Deciphering the phylogeny of violets based on multiplexed genetic and metabolomic approaches

Molecular phylogenetics based on nucleotide sequence comparisons has profoundly influenced plant taxonomy. A comprehensive chemotaxonomical approach based on GC-MS and UHPLC-HRMS profiling was evaluated for its ability to characterize a large collection of plants all in the violet family Violaceae (n = 111) and thus decipher the taxonomy. A thorough identification of violets is challenging due to their natural hybridization and phenotypic variability. Phylogenetic inference performed on ribosomal internal transcribed spacer sequences using maximum likelihood and neighbor-joining distance methods allowed the clear identification of 58% of the collection. Metabolomic approaches with multivariate data analysis were performed on SPME/GC-MS chromatograms of volatile compounds emitted by fresh mature flowers and on UHPLC-HRMS/MS leaf extracts for non-volatile compounds. Interestingly, molecular and biochemical approaches provided separate classifications while highlighting several common clusters. The profiling of secondary metabolites was proved most suitable for the classification of hundreds of extracts. The combination of phylogenetic and chemotaxonomic approaches, allowed the classification of 96% of the entire collection. A correlation network revealed specific chemotaxonomic biomarkers, in particular flavonoids, coumarins and cyclotides. Overall, our pioneering approach could be useful to solve misclassification issues within collections of close plant species.