Spectral Variables Research Articles

Estimation of aboveground biomass and carbon in palustrine wetland using bands and multispectral indices derived from optical satellite imageries PlanetScope and Sentinel-2A

Facing the climate change and anthropogenic activities that have been discharging a large proportion of carbon dioxide CO2) into the atmosphere, wetlands stand out as an important sink for CO2 that is fixed in plant biomass and peatlands. Therefore, quantifying and monitoring wetland biomass is of great importance to preserve carbon stocks. This study aims to explore the potential of multispectral bands and vegetation indices (VIs) derived from PlanetScope and Sentinel-2A sensors to estimate of aboveground biomass (AGB) and organic carbon in AGB (Corg) in the emergent vegetation of a palustrine wetland. We use correlation analysis and linear regression models to examine the relationships between spectral and biophysical variables and verify the best predictor spectral variables for AGB and Corg. Scirpus giganteus vegetation was sampled in the Banhado Grande wetland, in southern Brazil. The VIs were best correlated and preferred as predictor variables. The most accurate model used data from the PlanetScope sensor and VI of photochemical reflectance. Both sensors showed potential for pixel-based estimates of AGB and Corg due to their low RMSE values and their contribution as predictors of biophysical variables, which can contribute to opening new avenues in scientific research focusing on the management, monitoring, and conservation of marshes and your ecosystem service of carbon sink.

Variable Optimization of Seaweed Spectral Response Characteristics and Species Identification in Gouqi Island.

Probing the coverage and biomass of seaweed is necessary for achieving the sustainable utilization of nearshore seaweed resources. Remote sensing can realize dynamic monitoring on a large scale and the spectral characteristics of objects are the basis of remote sensing applications. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions from the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Sargassum fusifarme. The different seaweed spectra were identified and analyzed using a combination of one-way analysis of variance (ANOVA), support vector machines (SVM), and a fusion model comprising extreme gradient boosting (XGBoost) and SVM. In total, 14 common spectral variables were used as input variables, and the input variables were filtered by one-way ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a ratio of 3:1 for input into the SVM and fusion model. The results showed that when the input variables were the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), Vre, Abe, Rg, Lre, Lg, and Lr and the model parameters were g = 1.30 and c = 2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.99%, and the highest accuracy was 93.94% when distinguishing between the different seaweed phyla (g = 6.85 and c = 2.55). The classification of the fusion model also shows similar results: The overall accuracy is 73.98%, and the mean score of the different seaweed phyla is 97.211%. In this study, the spectral data of intertidal seaweed with different dry and wet states were classified to provide technical support for the monitoring of coastal zones via remote sensing and seaweed resource statistics.

Comparison of the use of Landsat 8, Sentinel-2, and Gaofen-2 images for mapping soil pH in Dehui, northeastern China

Mapping the distribution and quantity of soil properties is important for black soil protection, management, and restoration in northeastern China. The objective of this study was to evaluate the effect of the spatial resolution on soil pH mapping using satellite images of the black soil region in northeastern China. A high spatial resolution Gaofen (GF)-2 high-definition image and multispectral images acquired by the Landsat 8 operational land imager and Sentinel-2 multi-spectral instrument were used to compare their performance in soil pH prediction. The spectral variables, including the original bands of the three satellite images and a variety of spectral indices derived from the original bands, were employed. Then, a machine learning model (quantile regression forest) was used to determine the relationships between the spectral variables and the measured soil pH, and prediction models were established to estimate the soil pH and to characterize the spatial pattern of the soil pH. The results revealed that the soil pH prediction model based on the GF-2 image had a slightly higher prediction accuracy than the models constructed using the Landsat 8 and Sentinel-2 images. The prediction models for Landsat 8, Sentinel-2, and GF-2 had root mean square errors of 0.34, 0.39, and 0.31, respectively. The use of remote sensing images with a high spatial resolution may not substantially increase the prediction accuracy of soil pH mapping compared with the results derived from medium-resolution images.

Inversion of Coniferous Forest Stock Volume Based on Backscatter and InSAR Coherence Factors of Sentinel-1 Hyper-Temporal Images and Spectral Variables of Landsat 8 OLI

Forest stock volume (FSV) is a basic data source for estimating forest carbon sink. It is also a crucial parameter that reflects the quality of forest resources and forest management level. The use of remote sensing data combined with a support vector regression (SVR) algorithm has been widely used in FSV estimation. However, due to the complexity and spatial heterogeneity of the forest biological community, in the FSV high-value area with dense vegetation, the optical re-mote sensing variables tend to be saturated, and the sensitivity of synthetic aperture radar (SAR) backscattering features to the FSV is significantly reduced. These factors seriously affect the ac-curacy of the FSV estimation. In this study, Landsat 8 (L8) Operational Land Imager multispectral images and C-band Sentinel-1 (S1) hyper-temporal SAR images were used to extract three re-mote sensing feature datasets: spectral variables (L8), backscattering coefficients (S1), and inter-ferometric SAR factors (S1-InSAR). We proposed a feature selection method based on SVR (FS-SVR) and compared the FSV estimation performance of FS-SVR and stepwise regression analysis (SRA) on the aforementioned three remote sensing feature datasets. Finally, an estima-tion model of coniferous FSV was constructed using the SVR algorithm in Wangyedian Forest Farm, Inner Mongolia, China, and the spatial distribution map of coniferous FSV was predicted. The experimental results show the following: (1) The coherence amplitude and DSM data ob-tained based on S1 images contain information relat-ed to forest canopy height, and the hy-per-temporal S1 image data significantly enrich the diversity of S1-InSAR feature factors. There-fore, the S1-InSAR dataset has a better FSV response than remote sensing factors such as the S1 backscattering coefficient and L8 vegetation index, and the corresponding root mean square er-ror (RMSE) and relative RMSE (rRMSE) values reached 47.6 m3/ha and 20.9%, respectively. (2) The integrated dataset can provide full play to the synergy of the L8, S1, and S1-InSAR remote sensing data. Its RMSE and rRMSE values are 44.3 m3/ha and 19.4% respectively. (3) The proposed FS-SVR method can better select remote sensing variables suitable for FSV estimation than SRA. The average value of the rRMSE (23.17%) based on the three datasets was 13.8% lower than that of the SRA method (26.87%). This study provides new insights into forest FSV retrieval based on active and passive multisource remote sensing joint data.

Uni- and multivariate calibration of tempe­rature from the neodymium fluorescence spectra in nanocrystals of yttrium-gadolinium oxide and yttrium-gadolinium garnet

The use of neodymium-doped nanocrystalline powders of yttrium-gadolinium oxide and yttrium gadolinium garnet to increase the sensitivity of local fluorescent optical temperature sensors is considered. Based on the temperature dependences of the neodymium fluorescence spectra in this powders, univariate (using fluorescence intensity ratio from thermally coupled energy levels of the activator) and multivariate (using the partial least squares method) calibration models are developed. When using the spectral range 860 – 950 nm falling into the first biological transparency window (700 – 980 nm), both calibration models have a standard deviation of about 10 % and are comparable in accuracy. The spectral variables selection by searching combination moving window in the multivariate model made it possible to reduce the root mean square error for yttrium-gadolinium oxide by more than 12 times (from 9.8 to 0.8 °C), and for yttrium-gadolinium garnet by more than 2 times(from 8.7 to 4.0 °С). The result obtained indicatesthe proposed neodymium-doped nanocrystalline powders and multivariate methods of calibration can be used to localise areas with febrile temperatures for biological and medical purposes.

Prediction of the Nitrogen, Phosphorus and Potassium Contents in Grape Leaves at Different Growth Stages Based on UAV Multispectral Remote Sensing

The rapid and accurate acquisition of nitrogen, phosphorus and potassium nutrient contents in grape leaves is critical for improving grape yields and quality and for industrial development. In this study, crop growth was non-destructively monitored based on unmanned aerial vehicle (UAV) remote sensing technology. Three irrigation levels (W1, W2 and W3) and four fertilization levels (F3, F2, F1 and F0) were set in this study, and drip irrigation fertilization treatments adopted a complete block design. A correlation analysis was conducted using UAV multispectral image data obtained from 2019 to 2021 and the field-measured leaf nitrogen content (LNC), leaf potassium content (LKC) and leaf phosphorus content (LPC) values; from the results, the vegetation indices (VIs) that were sensitive to LNC, LKC and LPC were determined. By combining spectral indices with partial least squares (PLS), random forest (RF), support vector machine (SVM) and extreme learning machine (ELM) machine-learning algorithms, prediction models were established. Finally, the optimal combinations of spectral variables and machine learning models for predicting LNC, LPC and LKC in each grape growth period were determined. The results showed that: (1) there were high demands for nitrogen during the new shoot growth and flowering periods, potassium was the main nutrient absorbed in the fruit expansion period, and phosphorus was the main nutrient absorbed in the veraison and maturity periods; (2) combining multiple spectral variables with the RF, SVM and ELM models could result in improved LNC, LPC and LKC predictions. The optimal prediction model determination coefficient (R2) derived during the new shoot growth period was above 0.65, and that obtained during the other growth periods was above 0.75. The relative root mean square error (RRMSE) of the above models was below 0.20, and the Willmott consistency index (WIA) was above 0.88. In conclusion, UAV multispectral images have good application effects when predicting nutrient contents in grape leaves. This study can provide technical support for accurate vineyard nutrient management using UAV platforms.

Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method

Direct quantification analysis of near-infrared (NIR) spectra is challenging because the number of spectral variables is usually considerably higher than the number of samples. To mitigate the so-called curse of dimensionality, variable selection is often performed before multivariate calibration. There has been much work in this regard, where the developed variable selection method can be categorized as individual variable selection, such as uninformative variable elimination or variable importance in projection, and continuous interval variable selection method such as interval partial least squares or moving window partial least squares. In this study, a new individual variable selection method, modified simulated annealing (MSA), was proposed and used in conjunction with the partial least squares regression (PLSR) model. The interpretability of the selected variables in the determination of aflatoxin B1 levels in white rice was assessed. The results revealed that the PLSR model combined with MSA not only yielded higher accuracy than the full-spectrum PLSR but also successfully shrank the variable space. The developed simplified PLSR model using MSA produced satisfactory performances, with root mean square error of calibration (RMSEC) of 0.11 μg/kg and 0.56 μg/kg, and root mean square error of prediction (RMSEP) of 7.16 μg/kg and 14.42 μg/kg, were obtained for the low-aflatoxin B1-level- and high-aflatoxin-B1-level samples, respectively. Specifically, the MSA-based models yielded improvements of 97.80% (calibration set) and 44.62% (prediction set) as well as 95.85% (calibration set) and 62.57% (prediction set) for both datasets when compared with the full-spectrum PLSR (low aflatoxin: RMSEC = 5.02 μg/kg, RMSEP = 12.93 μg/kg; high aflatoxin: RMSEC = 13.50 μg/kg, RMSEP = 38.53 μg/kg). Compared with the baseline method of simulated annealing (SA) (low aflatoxin: RMSEC = 0.21 μg/kg, RMSEP = 9.78 μg/kg; high aflatoxin: RMSEC = 12.27 μg/kg, RMSEP = 38.53 μg/kg), the MSA significantly improved the predictive performance of the regression models, with the number of selected variables being almost half of that in the SA. A comparison with other commonly used variable selection methods of selectivity ratio (low aflatoxin: RMSEC = 6.09 μg/kg, RMSEP = 13.75 μg/kg; high aflatoxin: RMSEC = 13.74 μg/kg, RMSEP = 41.13 μg/kg), uninformative variable elimination (low aflatoxin: RMSEC = 0.32 μg/kg, RMSEP = 5.11 μg/kg; high aflatoxin: RMSEC = 3.80 μg/kg, RMSEP = 17.76 μg/kg), and variable importance in projection (low aflatoxin: RMSEC = 2.67 μg/kg, RMSEP = 10.71 μg/kg; high aflatoxin: RMSEC = 13.51 μg/kg, RMSEP = 32.53 μg/kg) also indicated the promising efficacy of the proposed MSA.

Study on the Effect of Apple Size Difference on Soluble Solids Content Model Based on Near-Infrared (NIR) Spectroscopy

Soluble solids content (SSC) is a vital evaluation index for the internal quality of apples, and NIR spectroscopy is the preferred technique for predicting the SSC of apples. Due to the differences in fruits’ sizes, their SSC prediction models have poor robustness and low prediction accuracy, so it is important to eliminate the effects brought by the differences in fruit sizes to improve the accuracy of fruit sorting models. The NIR spectra of apples with different fruit sizes were collected by applying NIR spectroscopy online detection device, and after various preprocessing of the spectra, the partial least squares (PLS) models of apple SSC were established, respectively, and then the modeling set in the apple fruit size group of 75 mm–85 mm was used to predict the prediction set samples in the apple fruit size group of 65 mm–75 mm and 85 mm–95 mm, respectively. To better address the effects of apple size differences, data fusion techniques were used to perform an intermediate fusion of apple fruit diameter and spectra, firstly, the competitive adaptive reweighting algorithm (CARS) and the continuous projection algorithm (SPA) were used to select spectral variables and build their prediction models for apple SSC, respectively, and the results showed that the models built with 61 spectral variables selected by CARS had better performance, greatly reduced the amount of data involved in modeling, effectively simplified the model, and improved the stability of the model. The apple size variables were added to the wavelength variables selected by CARS, and the data were normalized to establish a PLS model of apple SSC with the normalized spectral and apple fruit diameter data, and the results showed that the size compensation model based on intermediate fusion had the best prediction performance, with the prediction set Rp of 0.886 for fruit diameter of 65 mm–75 mm, RMSEP of 0.536%, and its prediction set Rp was 0.913 and RMSEP was 0.497% for the fruit diameter of 85 mm–95 mm. Therefore, adding the fruit diameter variable to establish the size-compensated model of apple SSC can improve the prediction performance of the model.

Fusion of THz-TDS and NIRS Based Detection of Moisture Content for Cattle Feed

As an essential index to evaluate feed quality, feed moisture content which is too high or too low will impose an adverse impact on feed nutritional value. Therefore, the quantitative analysis of feed moisture content is significant. In this paper, the detection of feed moisture content based on terahertz (THz) and near-infrared (NIR) spectroscopy and data fusion technology of THz and NIR (THz-NIR) was investigated. First, feed samples with different water content (29.46%–49.46%) were prepared, and THz (50–3000 μm) and NIR (900–1700 nm) spectral data of samples was collected and preprocessed, and the feed samples were divided into correction set and verification set by 2:1. Second, the spectral data was fused through the head-to-tail splicing, and the feed moisture content prediction model was established combined with partial least squares regression (PLSR). Third, competitive adaptive reweighting sampling (CARS) was applied to extract spectral characteristic variables for feature layer fusion, and the feed moisture content prediction model in feature level was constructed combined with PLSR. Finally, the evaluation parameters validation set correlation coefficient (Rp), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD) were employed to evaluate the prediction effect of the model. The results indicated that THz, NIR spectra, and data fusion technology could quickly and effectively predict feed moisture content. Among them, the characteristic layer spectral data fusion model achieved the optimal prediction effect while Rp, RMSEP, and RPD reached 0.9933, 0.0069, and 8.7386 respectively. In conclusion, compared with the prediction model established by single THz and NIR spectrum, THz-NIR spectrum data fusion could more accurately predict feed moisture content and provide certain theoretical and technical support for inspirations and methods for quantitative analysis of feed moisture content of livestock and poultry.

Design and performance analysis of smart photonic sensors for industrial applications

Toxic gas has a median fatal concentration in the oxygen of much more than 200 parts per million (ppm) but far less than 2000 ppm by volume of gas. Many industries, mines and thermal plants emit perilous gases that are more harmful to our human life. The Proposed nanosensor senses the various perilous gases and averts many accidents. In this paper, a two-dimensional Photonic Crystal (2D-PhC) resonator and PhC-based poisonous gas sensor based on the hexagonal and square crystal lattice are built-in smart way. The PhCs are artificial constructs of any material with an occasional enunciation of refractive index (RI). It has effective light manipulation and it would be helpful to obtain light migration in the handling of sensing applications. The TE/TM wave transmission can shift as per the RI value of different gases in the PhCs. The wavelength variations obtained agree well with the Finite Difference Time Domain (FDTD) study, and the simulation is performed by the tool RSoft. The spectral variables such as quality factor (QF), sensitivity (Se), transmitted output power and detection limit (DL) are evaluated using the RI value over the spectrum of different toxic gases. The proposed square crystal structure have acquired a QF range of 500.6, high efficiency of 99%, and a better Se of 716.6 nm/RIU at 1502 nm. The designed hexagonal crystal structure have acquired a QF range of 165.8, high efficiency of 99%, and a better Se of 798.24 nm/RIU at 1630 nm respectively. The DL for both the proposed sensors is very low. So, the designed smart sensor helps promptly recognize the contaminated gases in several places. The proposed nanosensor is helpful in industrial safety, health care applications, aerospace, agricultural, transportation, environmental monitoring, thermal plants and mines.

Fourier transform near-infrared spectroscopy coupled with variable selection methods for fast determination of salmon fillets storage time

To address the fast and nondestructive determination of salmon fillets storage time associated with its freshness, Fourier transform near-infrared (FT-NIR) spectroscopy coupled with advanced variable selection methods was attempted in this work. Fresh salmon fillets were divided into three groups and stored at -18℃, 4℃ and 20℃, respectively. The spectra under each temperature were collected by a FT-NIR spectrometer with the range of 4000–11000 cm−1. Advanced variable selection methods, including random frog (RF), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), CARS combined with SPA (CARS-SPA) and variable combination population analysis combined with iteratively retaining informative variables (VCPA-IRIV), were utilized to extract key feature variables and shrink variable space. The prediction models were constructed by using partial least squares regression (PLSR) based on full spectral variables and selected feature variables. Variable selection method VCPA-IRIV coupled with PLSR (VCPA-IRIV-PLSR) showed the best prediction performance in each temperature, with determination coefficients of prediction set (R2P) of 0.9988, 0.9976 and 0.9998, and root mean square error of prediction set (RMSEP) of 0.145, 0.209 and 0.024 for -18℃, 4℃ and 20℃, respectively. The overall results showed the great feasibility of FT-NIR combined with variable selection methods in the rapidly, nondestructively and accurately predicting freshness of salmon fillets stored at different temperatures.

UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages

• LCC was estimated based on the UAV-based image analysis. • The response stability of vegetation index and LCC was analyzed under different coverage. • Different variable screening methods were used to optimize the spectral parameters. • The spatial distribution map of maize LCC was constructed to provide potentials for fertilization application. Efficiently estimating chlorophyll content is important in monitoring the photosynthesis capacity and growth status of maize canopy in precision agriculture management. Vegetation index (VI) easily obtained by proximal remote sensing has been used as a non-destructive and high-throughput way in crop monitoring, especially in chlorophyll estimation. However, the estimated results of the field chlorophyll content by VIs always face challenges from soil background inhibition and estimation stability under the dynamic changes of vegetation biomass. Thus, an unmanned aerial vehicle (UAV)-based chlorophyll content estimation was conducted by evaluating VI responses under different crop coverages. An analysis was conducted on 36 VIs under different crop coverage conditions to explore their response differences and robustness for chlorophyll estimation. This work focused on the three kinds of VIs named simple vegetation index, modified vegetation index, and functional vegetation index. In 2020, at the experimental station of Dryland Farming Institute of Hebei Academy of Agriculture and Forestry Sciences, UAV carrying multispectral sensor was used to collect visible and near-infrared images of the canopy at the jointing stage of maize under six fertilization levels to obtain VIs. After the UAV fled, ground calibration and sample collection were performed simultaneously, and chlorophyll content was measured. For data processing, correlation coefficient method (CCM) and maximal information coefficient (MIC) were first used to analyze the correlation response characteristics of VIs and chlorophyll content under three different coverage levels. The results showed that when the level of canopy coverage was increased, the linear correlation between VIs and chlorophyll content was substantially reduced. The MIC response indicating linear and non-linear combination relationship was more robust. In addition, the VIs obtained by UAV had a significant linear correlation with maize canopy chlorophyll under low (0.05–0.35) and medium (0.35–0.48) coverage, but an obvious non-linear correlation under high (0.48–0.75) coverage. Chlorophyll-sensitive parameters were then screened based on methods of CCM, MIC, and random frog method (RFM), respectively. Partial least squares regression (PLS) and random forest (RF) algorithms were used to establish the maize canopy chlorophyll content detection models. The findings showed that when Green minus red vegetation index (GMR), Red light normalized value (NRI), Normalized difference red edge (NDRE), Modified simple ratio with red edge (MSRREG), Enhanced Vegetation Index (EVI), Normalized red green difference vegetation index (NDIg), Normalized red blue difference vegetation index (NDIb), Soil-adjusted vegetation index (SAVI), Optimized soil-adjusted vegetation index with red edge (OSAVIREG), Soil-atmospherically resistant vegetation index (SARVI) were selected based on RFM as the optimal spectral variables, the chlorophyll content detection model constructed based on PLS had the least numbers of characteristic variables and the best model accuracy. The training set R 2 and RMSE were 0.753 and 2.089 mg/L, respectively, and the verification set R 2 and RMSE were 0.682 and 2.361 mg/L, respectively. Field chlorophyll content and detection error distribution maps were also drawn and combined with the distribution of fertilization management to provide support for the UAV monitoring of crop growth in the field and variable fertilization management decisions.

Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass

Above-ground biomass (AGB) is a key indicator for studying grassland productivity and evaluating carbon sequestration capacity; it is also a key area of interest in hyperspectral ecological remote sensing. In this study, we use data from a typical alpine meadow in the Qinghai–Tibet Plateau during the main growing season (July–September), compare the results of various feature selection algorithms to extract an optimal subset of spectral variables, and use machine learning methods and data mining techniques to build an AGB prediction model and realize the optimal inversion of above-ground grassland biomass. The results show that the Lasso and RFE_SVM band filtering machine learning models can effectively select the global optimal feature and improve the prediction effect of the model. The analysis also compares the support vector machine (SVM), least squares regression boosting (LSB), and Gaussian process regression (GPR) AGB inversion models; our findings show that the results of the three models are similar, with the GPR machine learning model achieving the best outcomes. In addition, through the analysis of different data combinations, it is found that the accuracy of AGB inversion can be significantly improved by combining the spectral characteristics with the growing season. Finally, by constructing a machine learning interpretable model to analyze the specific role of features, it was found that the same band plays different roles in different records, and the related results can provide a scientific basis for the research of grassland resource monitoring and estimation.

Quantifying the Aboveground Biomass (AGB) of Gobi Desert Shrub Communities in Northwestern China Based on Unmanned Aerial Vehicle (UAV) RGB Images

Shrubs are an important part of the Gobi Desert ecosystem, and their aboveground biomass (AGB) is an important manifestation of the productivity of the Gobi Desert ecosystem. Characterizing the biophysical properties of low-stature vegetation such as shrubs in the Gobi Desert via conventional field surveys and satellite remote sensing images is challenging. The AGB of shrubs had been estimated from spectral variables taken from high-resolution images obtained by unmanned aerial vehicle (UAV) in the Gobi Desert, Xinjiang, China, using vegetation feature metrics. The main results were as follows: (1) Based on the UAV images, several RGB vegetation indices (RGB VIs) were selected to extract the vegetation coverage, and it was found that the excess green index (EXG) had the highest accuracy and the overall extraction accuracy of vegetation coverage reached 97.00%. (2) According to field sample plot surveys, the AGB and shrub crown area of single shrubs in the Gobi Desert were in line with a power model. From the bottom of the alluvial fan to the top of the alluvial fan, as the altitude increased, the AGB of the vegetation communities showed an increasing trend: the AGB of the vegetation communities at the bottom of the alluvial fan was 2–90 g/m2, while that at the top of the alluvial fan was 60–201 g/m2. (3) Vegetation coverage (based on the UAV image EXG index) and AGB showed a good correlation. The two conform to the relationship model (R2 = 0.897) and the expression is Y = 1167.341 x0.946, where Y is the AGB of the sample plots in units g/m2 and x is the vegetation coverage extracted by the VI. (4) The predicted AGB values of Gobi Desert shrubs using UAV RGB images based on a power model were closer to the actual observed AGB values. The study findings provide a more efficient, accurate, and low-cost method for estimating vegetation coverage and AGB of Gobi Desert shrubs.

Floristic types of high‐Andean wetlands from northwest Argentina and their remote‐sensed characterization at a regional scale

AbstractAimsHigh‐Andean vegas are key functional wetlands in the Puna ecoregion. Plant communities in combination with ecogeographic characteristics determine their functional processes. In this study, we identified groups of vegas based on their plant composition and characterized these groups with spatial and spectral variables representing their ecogeographic context.LocationArgentine Puna and High‐Andean ecoregions.MethodsWe recorded the species composition and cover of plants in 50 vegas distributed along a ecogeographic gradient. We calculated six spatial and 14 spectral variables for each vega. We performed a correspondence analysis (CA) to explore species data and used the site's scores in a k‐means analysis to identify groups of vegas. Then, we characterized each group of vegas with spatial and spectral variables with the v.test using the ‘catdes’ function in the FactoMineR package.ResultsThe CA showed five groups of vegas segregated by the plant species composition. Each group was related to different spatial and spectral variables showing an ecogeographic gradient. Vegas with Poaceas were located at higher altitude and lower latitude and longitude (Group 1, Festuca nardifolia and Deschampsia hackelii). Vegas dominated by cushion species had higher humidity (Group 2, Oxychloe andina), and higher and more stable productivity (Group 3, Eleocharis pseudoalbibracteata), while vegas with halophytic species were associated with a larger area, higher salinity, and lower humidity (Group 4, Amphiscirpus nevadensis), and lower productivity (Group 5, Lycium humile and Salicornia pulvinata).ConclusionsOur results are the first floristic classification and remote‐sensing characterization of high‐Andean vegas at a regional scale. This information shows the variation of these ecosystems and suggests that remote sensing, complemented with field information, could help to identify types of vegas at regional scales. This information is relevant for land planning and sustainable management of these key ecosystems in the context of threats of global change.

Rapid identification of A1 and A2 milk based on the combination of mid-infrared spectroscopy and chemometrics

The milk containing only A2 β-casein (called A2 milk) is globally popular because of its unique health benefits. Traditionally, genetic testing (such as gene sequencing) is used to identify the cows with A2 β-casein gene that can only produce A2 milk, which is a time-consuming and costly method. The objective of this study was to directly identify A1 and A2 milk from a large quantity of milk using mid-infrared (MIR) spectroscopy and chemometrics without genotyping cows. Before establishing the predictive model, we firstly genotyped the A1 β-casein and A2 β-casein of cows from blood as reference values. Further, the MIR spectra of the milk collected from these cows were obtained using a dairy product analyzer. The MIR spectroscopy data and the reference values were used as the independent and dependent variables, respectively, to establish a category classification model for A1 and A2 milk. Seven preprocessing methods were combined with two feature extraction algorithms to establish the model. Subsequently, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models were developed. The average accuracy of the test set of the two models were 94.9% and 94.4%, respectively, while the PLS-DA model exhibited better effect, and the accuracy of training set and test set reached 96.6% and 96.0%, respectively. We used a set of independent samples for the external validation of the PLS-DA model, and the prediction accuracy was 95.2%. Overall, the proposed prediction models based on MIR spectroscopy can be used for low-cost, rapid, and large-scale classification of A1 and A2 milk, which may be extremely beneficial in milk production industries. • A1 and A2 milk identification was feasible using MIR spectroscopy. • UVE and CARS algorithm can used to extract spectral characteristic variables. • PLS-DA model exhibited better effect than SVM model.

Balance Adaptation While Standing on a Compliant Base Depends on the Current Sensory Condition in Healthy Young Adults.

BackgroundSeveral investigations have addressed the process of balance adaptation to external perturbations. The adaptation during unperturbed stance has received little attention. Further, whether the current sensory conditions affect the adaptation rate has not been established. We have addressed the role of vision and haptic feedback on adaptation while standing on foam.MethodsIn 22 young subjects, the analysis of geometric (path length and sway area) and spectral variables (median frequency and mean level of both total spectrum and selected frequency windows) of the oscillation of the centre of feet pressure (CoP) identified the effects of vision, light-touch (LT) or both in the anteroposterior (AP) and mediolateral (ML) direction over 8 consecutive 90 s standing trials.ResultsAdaptation was obvious without vision (eyes closed; EC) and tenuous with vision (eyes open; EO). With trial repetition, path length and median frequency diminished with EC (p < 0.001) while sway area and mean level of the spectrum increased (p < 0.001). The low- and high-frequency range of the spectrum increased and decreased in AP and ML directions, respectively. Touch compared to no-touch enhanced the rate of increase of the low-frequency power (p < 0.05). Spectral differences in distinct sensory conditions persisted after adaptation.ConclusionBalance adaptation occurs during standing on foam. Adaptation leads to a progressive increase in the amplitude of the lowest frequencies of the spectrum and a concurrent decrease in the high-frequency range. Within this common behaviour, touch adds to its stabilising action a modest effect on the adaptation rate. Stabilisation is improved by favouring slow oscillations at the expense of sway minimisation. These findings are preliminary to investigations of balance problems in persons with sensory deficits, ageing, and peripheral or central nervous lesion.

Soil chemical variables improve models of understorey plant species distributions

AbstractAimSpecies distribution models (SDMs) assume that all ecologically relevant predictor variables are included. This assumption is frequently violated in SDMs of plant species, as soil variables are rarely included. Here, we used in‐situ, soil geochemical variables along with more commonly used topo‐climatic and remotely sensed variables to create SDMs of understorey plant species. We evaluated whether the potential importance of soil variables is greater than generally described in SDM literature, which predictors are most important, and whether a standard subset of predictors can be used to effectively model all species.LocationNorthern Appalachian Mountains.TaxonVascular, forest understorey plants.MethodsWe fit models for the presence of 41 forest understorey plant species across 158 plots using soil, topographic and spectral predictors.ResultsModels containing all three predictor types performed best. Soil and topographic variables had comparable importance; spectral variables were of lesser importance. The best predictor variable was B horizon carbon to nitrogen ratio (B C:N), followed by topographic position index, elevation and B horizon exchangeable calcium (B Ca). A standard subset of variables did not effectively model all species.Main conclusionsOur results and those of other SDMs that include in‐situ soil geochemical data suggest that soil variables are increasingly important with more detailed descriptions of soils. Soil fertility data, such as B C:N and B Ca, are particularly important in acidic, forest soils where pH is a poor indicator of fertility. Commonly used topo‐climatic variables provide meaningful predictions but are limited by their use of indirect predictor variables, inhibiting transferability and interpretability. The poor performance of a standard subset of variables highlights the uniqueness of each species' niche and the need to have a variety of predictor variables.

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage.

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

ATR-FTIR-based rapid solution for the discrimination of lentils from different origins, with a special focus on PGI and Slow Food typical varieties

Three hundred forty-six (346) samples of lentils have been collected and analyzed by ATR-MIR spectroscopy. Of the investigated individuals, 283 were harvested in two Central-Italy regions (Umbria and Abruzzo), whereas the others were grown in Canada. At first, Partial Least Squares Discriminant Analysis (PLS-DA) was used to discriminate samples among the three origins. The outcome of PLS-DA analysis was noteworthy: only one individual (over 86 of the external test set) was erroneously assigned by the model, indicating the suitability of the proposed approach. Furthermore, Variable Importance in Projection (VIP) was exploited to inquire which spectral variables significantly contribute to the discrimination. Eventually, the focus has been circumscribed to two categories of high-valued lentils, e.g., lentils from Castelluccio di Norcia (CDN), a sub-set of the Umbria class, and from Santo Stefano di Sessanio (SSS), a sub-set of the Abruzzo class. These are of particular interest because CDN presents the Protected Geographical Indication (PGI) while SSS belongs to the Slow Food Presidium. The models for these classes provided interesting results.