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Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.

The necessity of conserving and preserving the heritage is nowadays a developing study line for several researchers. From a civil engineering perspective, one of the most important issues for the conservation of historical buildings is to ensure the structural stability. For this purpose, it is necessary to determine the mechanical properties of the ancient construction materials. However, these structures cannot be evaluated by destructive tests due to politics and restrictions for preserving the heritage built. As a consequence, new methods that do not put in risk the integrity of the materials and the structure are becoming useful tools for researchers who work on structural engineering of heritage buildings. This paper presents a nondestructive method (NDM) that has been used to characterize the mechanical properties of different constructive systems of the San Antonio de Padua Temple, which is a religious icon in Aguascalientes, Mexico. This method includes the measuring of seismic waves’ travel time in order to obtain shear and compressional velocity waves. Results show that with this method mechanical properties of masonry can be obtained such as Young’s modulus, Poisson ratio and bulk density, which are needed for the structural analysis using numerical simulation models.

Monitoring communities of fish is important for the management and sustainability of fisheries and marine ecosystems. Baited remote underwater video systems (BRUVs) are among the most effective nondestructive techniques for sampling bony fishes and elasmobranchs (sharks, rays, and skates). However, BRUVs sample visually conspicuous biota; hence, some taxa are undersampled or not recorded at all. We compared the diversity of fishes characterized using BRUVs with diversity detected via environmental DNA (eDNA) metabarcoding. We sampled seawater and captured BRUVs imagery at 48 locales that included reef and seagrass beds inside and outside a marine reserve (Jurien Bay in Western Australia). Eighty-two fish genera from 13 orders were detected, and the community of fishes described using eDNA and BRUVs combined yielded >30% more generic richness than when either method was used alone. Rather than detecting a homogenous genetic signature, the eDNA assemblages mirrored the BRUVs’ spatial explicitness; differentiation of taxa between seagrass and reef was clear despite the relatively small geographical scale of the study site (~35 km²). Taxa that were not sampled by one approach, due to limitations and biases intrinsic to the method, were often detected with the other. Therefore, using BRUVs and eDNA in concert provides a more holistic view of vertebrate marine communities across habitats. Both methods are noninvasive, which enhances their potential for widespread implementation in the surveillance of marine ecosystems. El monitoreo de comunidades de peces es importante para el manejo y sustentabilidad de las pesquerías y los ecosistemas marinos. Los sistemas remotos de video submarino con carnada (SRVSC) están entre las técnicas no destructivas más efectivas para el muestreo de peces óseos y elasmobranquios (tiburones, mantarrayas y rayas). Sin embargo, los SRVSC muestrean biota que es conspicua visiblemente; entonces, algunos taxones están mal muestreados o simplemente no se registran en los muestreos. Comparamos la diversidad de peces caracterizada usando SRVSC con la diversidad detectada por medio del metacódigo de barras de ADN ambiental (eDNA, en inglés). Muestreamos el agua de mar y capturamos imágenes con SRVSC en 48 localidades que incluyeron el arrecife y los pastos marinos dentro y fuera de una reserva marina (Bahía Jurien en el oeste de Australia). Se detectaron 83 géneros de peces de 13 órdenes, y la comunidad de peces descrita con el uso combinado del eDNA y el SRVSC produjo >30% riqueza más genérica que cuando cualquiera de los dos métodos se usó individualmente. En lugar de detectar una firma genética homogénea, los ensamblados de eDNA reflejaron la claridad espacial del SRVSC; la diferenciación de los taxones entre los pastos marinos y el arrecife fue clara a pesar la escala geográfica relativamente pequeña del sitio de estudio (~35 km²). Los taxones que no fueron muestreados por uno de los métodos, por causa de limitaciones y sesgos intrínsecos al método, casi siempre fueron detectados usando el otro método. Por lo tanto, el uso de SRVSC y el eDNA en concreto proporciona una visión más holística de las comunidades marinas de vertebrados en todos los hábitats. Ambos métodos son no invasivos, lo que incrementa su potencial para ser una implementación de uso amplio en la vigilancia de los ecosistemas marinos.

Polymer composite materials are being increasingly used in primary load-bearing structures in several advanced industrial fields such as aerospace vessels, railway wagons and mega-scaled wind turbines where detection of subcritical damage initiation can significantly reduce safety issues and maintenance costs. It is therefore crucial to inspect these composite structures in order to assess their structural health and to ensure their integrity. Non-destructive testing techniques (NDT) are used for this purpose, making it possible to monitor mechanical damage of composite materials under in situ or ex situ service conditions. This paper reviews the capabilities of the most common NDT techniques used to inspect the integrity of composite materials. Each technique has a detection potential and cannot allow a full diagnosis of the mechanical damage state of the material. Thus, depending on the occurring damage mechanism and the conditions of use, one technique will be preferred over another, or several techniques should be combined to improve the diagnosis of the damage state of the structures.

The non-destructive testing and diagnosis of transport infrastructures is essential because of the need to protect these facilities for mobility, and for economic and social development. The effective and timely assessment of structural health conditions becomes crucial in order to assure the safety of the transportation system and time saver protocols, as well as to reduce excessive repair and maintenance costs. Ground penetrating radar (GPR) is one of the most recommended non-destructive methods for routine subsurface inspections. This paper focuses on the on-site use of GPR applied to transport infrastructures, namely pavements, railways, retaining walls, bridges and tunnels. The methodologies, advantages and disadvantages, along with up-to-date research results on GPR in infrastructure inspection are presented herein. Hence, through the review of the published literature, the potential of using GPR is demonstrated, while the main limitations of the method are discussed and some practical recommendations are made.

Background Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results The models with a single color feature from RGB images predicted chlorophyll content with R 2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R 2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R 2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R 2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.

In this work, freshwater microplastic samples collected from four different stations along the Italian Po river were characterized in terms of abundance, distribution, category, morphological and morphometrical features, and polymer type. The correlation between microplastic category and polymer type was also evaluated. Polymer identification was carried out developing and implementing a new and effective hierarchical classification logic applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000–2500 nm). Results showed that concentration of microplastics ranged from 1.89 to 8.22 particles/m 3 , the most abundant category was fragment, followed by foam, granule, pellet, and filament and the most diffused polymers were expanded polystyrene followed by polyethylene, polypropylene, polystyrene, polyamide, polyethylene terephthalate and polyvinyl chloride, with some differences in polymer distribution among stations. The application of hyperspectral imaging (HSI) as a rapid and non-destructive method to classify freshwater microplastics for environmental monitoring represents a completely innovative approach in this field.

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony’s unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. Key points • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes

Macroalgae are a potentially novel source of nutrition and biologically active molecules. Proliferative species such as Eucheuma denticulatum, Solieria chordalis (red algae) and Sargassum muticum (brown alga) constitute a huge biomass that can be exploited. In this study, we focus on the extraction of polysaccharides from these three macroalgae species and the characterization of cell wall polysaccharides such as carrageenans, fucoidans and alginates by Fourier Transform Infrared spectroscopy with Attenuated Reflectance Module (FTIR-ATR). The comparison of purified extracts with commercial solutions of fucoidans, alginates or carrageenans shows a strong similarity between the spectra. It demonstrates that the methods of extraction that have been used are also suitable purifying technics. Moreover, it validates infrared spectroscopy as a quick, simple and non-destructive method for the accurate analysis of polysaccharides. The FTIR technique applied to samples collected at different periods of the year allowed us to highlight differences in the composition of fucoidans, alginates and carrageenans. Different classes corresponding to the season can be distinguished by statistical multidimensionnal analysis (Principal Component Analysis) showing that the structure of algal polysaccharides, related to bioactivity, depends on the period of harvest. FTIR results showed that S. chordalis and E. denticulatum possess a dominant type of carrageenan called iota-carrageenan. This type of carrageenan is in the majority when the alga is at maturity in its development cycle. During its growth phase, iota-carrageenan precursors can be detected by FTIR spectra, enabling a better control of the extraction and an application of these compounds in various economic sectors. When the alga E. denticulatum is in its juvenile stage, we found traces of kappa-carrageenan and nu-carrageenan polysaccharides in some extracts.