Spectral Characteristic Parameters Research Articles

On-orbit spectral characteristics analysis of the greenhouse gases monitoring instrument: Spectral wavelength stability and instrumental line shape stability

The Greenhouse Gases Monitoring Instrument (GMI) is a carbon satellite payload developed based on the principle of spatial heterodyne spectroscopy technology, which is specifically designed for the global analysis of greenhouse gases (CO2 and CH4) “sources” and “sinks”. Due to the low concentration and minimal gradient variations of CO2 and CH4 in the atmosphere, higher precision is required for their retrieval. Vibrations during satellite launch and harsh space environments during on-orbit operation may cause changes in the performance of various payload components, resulting in decreased quality of spectrum products and retrieval precision. This paper proposes a set of on-orbit spectral characteristic evaluation methods tailored for the GMI to monitor the quality of its spectrum. It also develops an adaptive blind pixel detection and correction algorithm specifically for the GMI and optimizes the interferogram processing flow algorithm. On-orbit spectral calibration is performed, and methods to evaluate spectral characteristic parameters have been established. The analysis focuses on the variations of the spectral characteristics in the CO2-1 bands (1.575 μm) of the GMI during one-year of on-orbit calibration spectrum. The initial spectral wavenumber offset is 0.133 cm−1 after entering orbit, and the average spectral wavenumber offset is 0.0313 cm−1 during stable operation. During the one-year period, the maximum variation in the instrumental line shape function of the GMI is 0.006 cm−1. The observed spectrum obtained in nadir observation mode, underwent to accuracy verification. The results demonstrate consistency between the spectral peak wavenumbers and the relative trend changes of the observed and theoretical spectrum, with an average relative residual of 0.987%.

Rapid on-site nondestructive surface corrosion characterization of sintered nanocopper paste in power electronics packaging using hyperspectral imaging

Sintered nanocopper (nanoCu) paste, exhibiting excellent electrical, thermal, and mechanical performances, offers promise for interconnections in wide bandgap (WBG) semiconductors operating at higher temperatures. However, sintered nanoCu is prone to severe corrosion in environments containing H2S, with on-site characterization methods for the composition of corrosion products currently lacking. In this study, a novel method was proposed for the rapid characterization of corrosion products during the corrosion process based on hyperspectral imaging (HSI) technology. Sintered nanoCu samples were subjected to 336 h H2S gas corrosion tests with bulk Cu as the reference, followed by correlating the corrosion element content with hyperspectral characteristic parameters. Then, the morphology and composition of corrosion products were researched using focused ion beam scanning electron microscope (FIB-SEM) and transmission electron microscope (TEM) analysis. The results showed that (1) during the corrosion process, a linear relationship was established between the Cu, O elemental atomic contents on the sample surfaces and their hyperspectral characteristic parameters. (2) The elemental atomic content of S exhibited an exponential relationship with the characteristic parameter. (3) The change rate in the spectral characteristic parameters during the corrosion process reflected the severity of corrosion, which was confirmed by comparing the thickness of the corrosion products of the sintered nanoCu and bulk Cu. This study offers a foundation for the further investigation of rapid on-site characterization of sintered nanoCu corrosion involving H2S.

Statistical Analysis of Pulsar Flux Density Distribution

This study presents a comprehensive analysis of the spectral properties of 886 pulsars across a wide frequency range from 20 MHz–343.5 GHz, including a total of 86 millisecond pulsars (MSPs). The majority of the pulsars exhibit power-law behavior in their spectra, although some exceptions are observed. Five different spectral models, namely, simple power law, broken power law, low-frequency turnover, high-frequency cutoff, and double turnover, were employed to explore the spectral behaviors. The average spectral index for pulsars modeled with a simple power law is found to be −1.64 ± 0.80, consistent with previous studies. Additionally, significant correlations between the spectral index and characteristic parameters are observed, particularly in MSPs, while no strong correlation is observed in normal pulsars. Different models show variations in the most influential characteristic parameters associated with the spectral index, indicating diverse dominant radiation mechanisms in MSPs. Finally, this study identifies 22 pulsars of the gigahertz-peaked spectra type for the first time based on the Akaike information criterion.

Classification of major species in the sericite-Artemisia desert grassland using hyperspectral images and spectral feature identification.

The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring. Ground hyperspectral images of a sericite-Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of variance was used to extract the main species, namely, Seriphidium transiliense (Poljak), Ceratocarpus arenarius L., Petrosimonia sibirica (Pall), bare land and the spectral characteristic parameters and vegetation indices in different seasons. On this basis, Fisher discriminant analysis was used to divide the samples into a training set and a test set at a ratio of 7:3. The spectral characteristic parameters and vegetation indices were used to identify the three main plants and bare land. The selection of parameters with significant differences (P < 0.05) between the recognition objects effectively distinguished different land features, and the identification parameters also differed due to differences in growth period and species. The overall accuracy of the recognition model established by the vegetation index decreased in the following order: June (98.87%) > September (91.53%) > April (90.37%). The overall accuracy of the recognition model established by the feature parameters decreased in the following order: September (89.77%) > June (88.48%) > April (85.98%). The recognition models based on vegetation indices in different months are superior to those based on feature parameters, with overall accuracies ranging from 1.76% to 9.40% higher. Based on hyperspectral image data, the use of vegetation indices as identification parameters can enable the identification of the main plants in sericite-Artemisia desert grassland, providing a basis for further quantitative classification of the species in community images.

Fast and nondestructive discrimination of coal types based on spectral feature parameters

Coal type identification is the basic work of coal quality inspection, which is of great significance to the normal operation of power generation, metallurgy, and other industries. The traditional coal-type identification method is complicated and requires comprehensive determination of various chemical parameters to obtain more accurate analysis results. Hyperspectral detection and analysis technology has the advantages of being simple, fast, nondestructive, and safe, and is widely used in a variety of fields. In this study, typical spectral feature parameters of coal samples were extracted based on hyperspectral data, and the parameters' sensitivity to coal types was explored using one-way ANOVA. The results showed that the coal spectral feature parameters of DI1-2μm and AD2.2μm significantly differed with coal species, indicating that the two parameters were class-sensitive features. When DI1-2μm and AD2.2μm were used to construct the Fisher discriminant model, the coal types could be discriminated with high accuracy. At the same time, the correlation between the extracted spectral feature parameters and the physicochemical parameters of bituminous coal and anthracite was analyzed. The results showed that there was a certain basis for using the extracted spectral feature parameters as the sensitive spectral characteristics of the model, and the application potential of the spectral characteristics of coal in the nondestructive prediction analysis of coal parameters was further discussed.

Study of high-power breakdown accompanied by sliding discharge mode in a three-dimensional sliding arc plasma igniter

In order to improve the ignition reliability of a small-thrust rocket engine, a three-dimensional sliding arc igniter discharge characteristic research experimental system was constructed. The work gas flow rate and excitation parameters were changed, the plasma discharge characteristic and spectral characteristic parameters were measured, and the characteristics of the igniter discharge modes were investigated. The influence of the excitation parameters on the discharge modes was explored, and the effect of the discharge modes on the ignition performance of the igniter was analyzed as well as the mechanism of the discharge modes. The results show that the igniter has three different discharge modes during the discharge process, i.e., breakdown with sliding mode, high-power breakdown with sliding mode, and the transition state between the two modes, the shift of the discharge mode is mainly affected by the excitation power, and the igniter is in the breakdown with sliding mode when the excitation power is low, and in the high-power breakdown with sliding mode when the power is high. High-power breakdown with sliding mode has both breakdown with sliding and stable sliding characteristics in the early stage of the sliding arc movement for the breakdown with sliding and then transformed into sliding. Compared with the breakdown with sliding mode and high-power mode, the sliding arc discharge area is wider, the concentration of O * than the breakdown with sliding mode increased by 2.86 times, the electron temperature increased by 31.76%, and it can significantly improve plasma ignition effect, reduce the activation energy and enhance the performance of the igniter.

Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

Soil organic matter (SOM) is an essential component of soil and is crucial for increasing agricultural production and soil fertility. The combination of hyperspectral remote sensing and deep learning can be used to predict the SOM content efficiently, rapidly, and cost-effectively on various scales. However, determining the optimal groups, inputs, and models for reducing the spatial heterogeneity of soil nutrients in large regions and to improve the accuracy of SOM prediction remains a challenge. Hyperspectral reflectance data from 1477 surface soil samples in Northeast China were utilized to evaluate three grouping methods (no groups (NG), traditional grouping (TG), and spectral grouping (SG)) and four inputs (raw reflectance (RR), continuum removal (CR), fractional-order differentiation (FOD), and spectral characteristic parameters (SCPs)). The SOM prediction accuracies of random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM) models were assessed. The results were as follows: (1) The highest accuracy was achieved using SG, SCPs, and the LSTM model, with a coefficient of determination (R2) of 0.82 and a root mean squared error (RMSE) of 0.69%. (2) The LSTM model exhibited the highest accuracy in SOM prediction (R2 = 0.82, RMSE = 0.89%), followed by the CNN model (R2 = 0.72, RMSE = 0.85%) and the RF model (R2 = 0.69, RMSE = 0.91%). (3) The SG provided higher SOM prediction accuracy than TG and NG. (4) The SCP-based prediction results were significantly better than those of the other inputs. The R2 of the SCP-based model was 0.27 higher and the RMSE was 0.40% lower than that of the RR-based model with NG. In addition, the LSTM model had higher prediction errors at low (0–2%) and high (8–10%) SOM contents, whereas the error was minimal at intermediate SOM contents (2–8%). The study results provide guidance for selecting grouping methods and approaches to improve the prediction accuracy of the SOM content and reduce the spatial heterogeneity of the SOM content in large regions.

Estimation of the distribution patterns of heavy metal in soil from airborne hyperspectral imagery based on spectral absorption characteristics

Though soil is widely known as one of the most valuable resources for the world, its quality is going to be lower because of unsustainable economic development and social progress. Therefore, it is important for us to monitor and evaluate the quality of soil, especially its heavy metal contents which is too scarce to identify in soil spectra easily but poisonous enough to affect human health in a long run. Most of the existing estimation methods have based the characteristic bands on statistical analysis to a large extent, which is hard to accurately explain the retrieval mechanism. In this paper, the absorption characteristics of heavy metal are studied based on the soil spectra, and the distribution pattern is mapped in a large-scale continuous space, for environmental monitoring and further decision support. Taking Yitong County, China as the study area. After spectra continuum removal, the heavy metal contents were estimated by 11 features including the absorption depth, absorption area, and band ratio around 2200 nm, which showed the best performance. For arsenic (As), the best model yields Rp2 value of 0.8474, and the RMSEP value is 36.1542 (mg/kg). It is concluded that As is adsorbed by organic matter, clay minerals, and iron/manganese oxides in soil, and the adsorption of As by first two components is greater than that of the last. For airborne spectra after continuum removal, combining the spectral absorption characteristic parameters and the highly correlated bands is more accurate than using the spectral absorption characteristic parameters or bands alone. AdaBoost is presented for the heavy metal estimation, and the fitting ability of the method is found to be stronger than that of the traditional classical methods, with the Rp2 values of 0.6242 and the RMSEP value of 43.6481 (mg/kg). In summary, these results will provide a prospective basis for the rapid estimation of soil heavy metals, the risk assessment of soil heavy metals and soil environmental monitoring in a large scale.

Trace metal content prediction along an AMD (acid mine drainage)-contaminated stream draining a coal mine using VNIR–SWIR spectroscopy

The current study investigated the use of VNIR–SWIR (visible/near infrared to short-wavelength infrared: 400–2500 nm) spectroscopy for predicting trace metals in overbank sediments collected in the study site. Here, we (i) derived spectral absorption feature parameters (SAFPs) from measured ground spectra for correlation with trace metal (Pb, Cd, As, and Cu) contents in overbank sediments, (ii) built univariate regression models to predict trace metal concentrations using the SAFPs, and (iii) evaluated the predictive capacities of the regression models. The derived SAFPs associated with goethite in overbank sediments were Depth433b, Asym433b, and Width433b, and those associated with kaolinite in overbank sediments were Depth1366b, Asym1366b, Width1366b, Depth2208b, Asym2208b, and Width2208b. Cadmium in the overbank sediments showed the strongest correlations with the goethite-related SAFPs, whereas Pb, As, and Cu showed strong correlations with goethite- and kaolinite-related SAFPs. The best predictive models were obtained for Cu (R2 = 0.73, SEE = 0.15) and Pb (R2 = 0.73, SEE = 0.21), while weaker models were obtained for As (R2 = 0.46, SEE = 0.31) and Cd (R2 = 0.17, SEE = 0.81). The results suggest that trace metals can be predicted indirectly using the SAFPs associated with goethite and kaolinite. This is an important benefit of VNIR–SWIR spectroscopy considering the difficulty in analyzing “trace” metal concentrations, on large scales, using conventional geochemical methods.

Utilization of the Fusion of Ground-Space Remote Sensing Data for Canopy Nitrogen Content Inversion in Apple Orchards

Utilizing multi-source remote sensing data fusion to achieve efficient and accurate monitoring of crop nitrogen content is crucial for precise crop management. In this study, an effective integrated method for inverting nitrogen content in apple orchard canopies was proposed based on the fusion of ground-space remote sensing data. Firstly, ground hyper-spectral data, unmanned aerial vehicles (UAVs) multi-spectral data, and apple leaf samples were collected from the apple tree canopy. Secondly, the canopy spectral information was extracted, and the hyper-spectral and UAV multi-spectral data were fused using the Convolution Calculation of the Spectral Response Function (SRF-CC). Based on the raw and simulated data, the spectral feature parameters were constructed and screened, and the canopy abundance parameters were constructed using simulated multi-spectral data. Thirdly, a variety of machine-learning models were constructed and verified to identify the optimal inversion model for spatially inverting the canopy nitrogen content (CNC) in apple orchards. The results demonstrated that SRF-CC was an effective method for the fusion of ground-space remote sensing data, and the fitting degree (R2) of raw and simulated data in all bands was higher than 0.70; the absolute values of the correlation coefficients (|R|) between each spectral index and the CNC increased to 0.55–0.68 after data fusion. The XGBoost model established based on the simulated data and canopy abundance parameters was the optimal model for the CNC inversion (R2 = 0.759, RMSE = 0.098, RPD = 1.855), and the distribution of the CNC obtained from the inversion was more consistent with the actual distribution. The findings of this study can provide the theoretical basis and technical support for efficient and non-destructive monitoring of canopy nutrient status in apple orchards.

Characteristics of optical properties of DOM and nutrients in rainwater of different ecological areas of a large reservoir in China

Numerous studies have focused on the spectral characteristics and seasonal variations of dissolved organic matter (DOM) in rainwater. However, the relationship between the optical indices of DOM and nutrients in rainwater from different ecological areas of large reservoirs is poorly understood. A one-year monitoring study was conducted between March 2019 and February 2020 in the Danjiangkou Reservoir in Henan Province, China, to compare the composition, spectral characteristic parameters, and relationship between the optical indices of DOM and nutrients in rainwater under different ecological environments. The study showed that the average value of a300 in all samples was 5.29 ± 2.16 m-1 and showed a seasonal trend of higher in spring and winter and lower in summer and autumn as well as a regional difference of agricultural area > urban area > reservoir area. A three-dimensional fluorescence with parallel factor analysis (EEM-PARAFAC) revealed four components of the rainwater: C1 and C2 as UV humic-like substances, and C3 and C4 as protein-like substances. The protein-like components of rainwater from agricultural areas had a high fluorescence intensity, whereas the UV humic-like components of rainfall from urban and reservoir areas had a high fluorescence intensity. Analysis of the fluorescence indices showed that rainwater DOM humification was low and had a strong endogenous character in the Danjiangkou Reservoir. The redundancy analysis revealed that NO3−-N, DTN, and SO42− mainly influenced the DOM optical indices of rainwater in urban areas, EC, DTN, and DOC had the highest interpretation of the DOM optical indices of rainwater in agricultural areas, and SO42−, DOC, and DTN had the highest interpretation of the DOM optical indices of rainwater in the reservoir. Overall, understanding the characteristics of rainfall DOM fluorescence and the relationships with nutrients in different ecological regions provides important information for comprehending biogeochemical processes in reservoirs.

Response of soil oxides in complex terrain region to environment revealed by hyperspectral observations

Soil oxides are important diagnostic indicators for soil quality assessment, and their content has a positive effect on adsorption of heavy metals, remediation of pollution, and enhancement of organic matter enrichment. Moreover, soil oxides are affected by multiple environmental factors such as soil pH, geomorphological, and elevation, and their distribution is highly regional. In this study, 421 ground soil samples and 2 hyperspectral satellite data were collected. The content distribution of 8 main oxides in soil, including SiO2, Al2O3, Fe2O3, MgO, CaO, Na2O, K2O, and Corg. were obtained according to the process of data preprocessing, spectral transformation, feature extraction, band combination, and model establishment. The results indicated that the homogenization spectral transformation method is the most suitable; The calculation accuracy of complex spectral characteristic parameters is inferior to that of characteristic bands; The difference model is more suitable for the extraction of soil oxide content in this study area; The average content of SiO2 and CaO shows a decreasing trend with the elevation increasing; The average content of SiO2 and Fe2O3 shows an upward trend with the increase of slope; East is the main geomorphic direction of soil oxide enrichment. SiO2, Al2O3, and MgO are the three soil oxides with the highest correlation. The response relationship between soil oxides, topography and pH is discussed, and the distribution rule of soil oxides and environmental determinants are obtained, which provides a set of technical schemes for digital soil research. This study can extract the content of soil components in a large area, analyze the causes, and provide a new technical scheme for soil investigation and treatment.

Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas

As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data.

Visual characterization of pollution and moisture parameters of the polluted HTV silicone rubber surface with hyperspectral imaging technology

The application of high temperature vulcanized (HTV) silicone rubber materials in transmission lines insulators has been extensive. However, with the accumulation of pollution and moisture on the surface of HTV silicone rubber, the insulation reliability gradually decreases, which may even lead to flashover. In this paper, hyperspectral imaging technology is utilized as a rapid and effective tool for the visual characterization of pollution and moisture parameters of the polluted HTV silicone rubber. Based on the analysis of reflection spectra, it has been determined that the pollution degree has a positive effect and the moisture content has a negative effect on the reflection characteristics of HTV silicone rubber, respectively. Further quantitative analysis of reflectance values at various moisture conditions reveals that exponential relationships can be observed between reflectance values and moisture contents at a certain wavelength. At the same time, linear correlations between reflectance values and pollution degrees (quantified as equivalent salt deposit density (ESDD)) at a certain wavelength are also proposed. By integrating the exponential and linear relationships, a double-exponential model is formulated to concurrently characterize the ESDD and moisture content (MC). Furtherly, from the perspective of engineering application, to enhance the model’s robustness, a set of equations combining spectral indexes and spectral shape feature parameters is initially formulated for the calculating of ESDD and MC. Given the synchronous inversion and visualization capabilities of pollution and moisture parameters of the polluted HTV silicone rubber surface, the visual characterization model serves as a valuable tool for protecting against pollution flashover failures and designing power equipment insulation.

Study on explosion reaction mechanism and energy release characteristics of HMX-based explosives containing B-Al

To explore the application of the micro-sized B-Al (boron-aluminum) compound powder in enhanced blast explosive(EBX) and thermobaric explosive(TBX), three HMX-based explosives containing B-Al (PF-1, PF-2 and PF-3)were designed and prepared. The ignition and combustion characteristics of various samples (micro- and nano-sized B, B-Al compound powders without and with HMX) under different pressures and atmospheres were studied by a laser ignition and combustion experimental system, and the flame evolution images and emission spectral characteristic parameters of the ignition and combustion process were obtained. The structures of detonation reaction zone of ideal and non-ideal explosives were studied by PDV (photonic doppler velocimeter), and the characteristic parameters and explosion reaction mechanism of wave profile were obtained. The energy output characteristics of explosives containing B-Al were evaluated by air blast and underwater explosion test. The results show that nano-sized B has better ignition and combustion characteristics than micro-sized B; the ignition and combustion property of B-Al compound powder with HMX is obviously superior to that of B-Al compound powder. PBX-1 is an ideal explosive, and the reaction zone width is 44ns, whilePF-1 and PF-3 are typical non-ideal explosives, the reaction zone widths are 112ns and 102ns, respectively. In the air blast and underwater explosion test, under the detonation of HMX, the combustion of micro-Al can promote the afterburning effect of micro-B, which releases a great amount of combustion heat, then generates expansible products with higher temperature and pressure, and finally increases the duration of fireball and total energy in underwater explosion.

Spectral characteristics analysis of typical ground objects in Poyang Lake wetland based on “Zhuhai-1” hyperspectral data

Hyperspectral image data provides new ideas and methods for the fine classification of wetland. Research on wetland spectral characteristics lays the foundation for wetland classification and improves classification accuracy. Based on “Zhuhai-1” hyperspectral image data, combined with the Pure Pixel Index (PPI), this paper extracted the average spectral curves of 11 typical ground objects in Poyang Lake wetland. Two mathematical transformation methods, namely, the continuum removal and the first derivative, were used to analyze the spectral characteristics of the ground objects before and after transformation, in combination with spectral characteristic parameters. The method of combining the mean distance of ground objects with the range of data error was used to select characteristic wavelengths before and after spectral transformation. Finally, the Mahalanobis distance method was used to verify the recognition effect. The results demonstrate that: (1) Spectral transformation can effectively amplify the differences between ground objects. The continuum removal led to an increase in the characteristic bands of four types of vegetation and the emergence of characteristic bands of four types of other ground objects. The first derivative method extended the characteristic bands of four vegetation from red-edge to near-infrared. (2) The method of combining the mean distance and the data error range can effectively screened out the spectral characteristic bands of typical ground objects in Poyang Lake wetland, resulting in hyperspectral dimensionality reduction. (3) The Mahalanobis distance method validates the recognition effect of the characteristic bands. In the screened characteristic bands, the Mahalanobis distance between different ground objects is larger than that between the same ground object, except between shoaly land and road. This study offers valuable insights into spectral recognition and classification of freshwater lake wetlands, and also demonstrates the potential of “Zhuhai-1” hyperspectral image data in wetland research.

Study on Estimating Total Nitrogen Content in Sugar Beet Leaves Under Drip Irrigation Based on Vis-NIR Hyperspectral Data and Chlorophyll Content

The relationship between the leaf nitrogen content (LNC) and hyperspectral remote sensing imagery (HYP) was determined to construct an estimation model of the LNC of drip-irrigated sugar beets, aiming to provide supports for the in-time monitoring of sugar beet growth and nitrogen management in arid areas. In this study, a field hyperspectrometer was used to collect the leaf reflectance at the 350–2500 nm for each treatment on the 65th, 85th, 104th, 124th, and 140th day after emergence, and the LNC and leaf chlorophyll content (CHL) of sugar beets were also determined. The spectral characteristic parameters were selected to construct the vegetation indices. The LNC estimation model using HYP as the independent variable (HYP-LNC), and that using CHL and HYP as the independent variables (HYP-CHL-LNC), were compared. The results shows that the HYP-CHL-LNC models had a better linear relationship and a higher fitting accuracy than the HYP-LNC models.

Feasibility Study on the Classification of Persimmon Trees’ Components Based on Hyperspectral LiDAR

Intelligent management of trees is essential for precise production management in orchards. Extracting components' information from individual fruit trees is critical for analyzing and understanding their general growth. This study proposes a method to classify persimmon tree components based on hyperspectral LiDAR data. We extracted nine spectral feature parameters from the colorful point cloud data and performed preliminary classification using random forest, support vector machine, and backpropagation neural network methods. However, the misclassification of edge points with spectral information reduced the accuracy of the classification. To address this, we introduced a reprogramming strategy by fusing spatial constraints with spectral information, which increased the overall classification accuracy by 6.55%. We completed a 3D reconstruction of classification results in spatial coordinates. The proposed method is sensitive to edge points and shows excellent performance for classifying persimmon tree components.

Identification of Constructive Species and Degraded Plant Species in the Temperate Typical Grassland of Inner Mongolia Based on Hyperspectral Data

In recent years, grassland degradation has become a global ecological problem. The identification of degraded grassland species is of great significance for monitoring grassland ecological environments and accelerating grassland ecological restoration. In this study, a ground spectral measurement experiment of typical grass species in the typical temperate grassland of Inner Mongolia was performed. An SVC XHR-1024i spectrometer was used to obtain field measurements of the spectra of grass species in the typical grassland areas of the study region from 6–29 July 2021. The parametric characteristics of the grass species’ spectral data were extracted and analyzed. Then, the spectral characteristic parameters + vegetation index, first-order derivative (FD) and continuum removal (CR) datasets were constructed by using principal component analysis (PCA). Finally, the RF, SVM, BP, CNN and the improved CNN model were established to identify Stipa grandis (SG), Cleistogenes squarrosa (CS), Caragana microphylla Lam. (CL), Leymus chinensis (LC), Artemisia frigida (AF), Allium ramosum L. (AL) and Artemisia capillaris Thunb. (AT). This study aims to determine a high-precision identification method based on the measured spectrum and to lay a foundation for related research. The obtained research results show that in the identification results based on ground-measured spectral data, the overall accuracy of the RF model and SVM model identification for different input datasets is low, but the identification accuracies of the SVM model for AF and AL are more than 85%. The recognition result of the CNN model is generally worse than that of the BP neural network model, but its recognition accuracy for AL is higher, while the recognition effect of the BP neural network model for CL is better. The overall accuracy and average accuracy of the improved CNN model are all the highest, and the recognition accuracy of AF and CL is stable above 98%, but the recognition accuracy of CS needs to be improved. The improved CNN model in this study shows a relatively significant grass species recognition performance and has certain recognition advantages. The identification of degraded grassland species can provide important scientific references for the realization of normal functions of grassland ecosystems, the maintenance of grassland biodiversity richness, and the management and planning of grassland production and life.

Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China

Lithium (Li) resources are widely used in many strategic emerging fields; recently, several large-scale to super-large-scale pegmatite-type lithium deposits have been discovered in Dahongliutan, NW China. However, the natural environmental conditions in the Dahongliutan area are extremely harsh; hence, manpower in field exploration is difficult to achieve. Efficient and rapid methods for identifying Li-rich pegmatites, based on hyperspectral remote sensing technology, have great potential for promoting the discovery of lithium resources. Ground spectral research is the cornerstone of regional hyperspectral imaging (HSI) for geological mapping. Direct observation and analysis by the naked eye are part of a process that is mainly dependent upon abundant experience and knowledge from experts. Machine learning (ML) technology has the advantages of automatic feature extraction and relationship characterization. Therefore, identifying the spectral features of Li-rich pegmatite via ML can accurately and efficiently distinguish the spectral characteristics of Li-rich pegmatites and Li-poor pegmatites, enabling further excavation to identify the strongest predictors of Li-pegmatite and laying a foundation for the accurate extraction of Li-rich pegmatites in the West Kunlun region using HSI. The spectral characteristics of pegmatite in the visible near-infrared and shortwave infrared (VNIR–SWIR) spectra were observed and analyzed. Li-rich pegmatite was identified based on the diagnostic spectral waveform characteristic parameters of the local wavelength range. The results demonstrated that the pegmatite ML recognition model was based on spectral characteristic parameters of the local wavelength range, with good model explicability, and the area under the curve (AUC) calculated for the model is 0.843. A recognition model based on full-range spectrum data achieved a higher precision, and the AUC value was up to 0.977. The evaluation of the Gini coefficient presented the strongest predictors, which were used to map the spatial distribution lithology, based on GF-5, in Akesayi and the 509 mines, producing encouraging lithological mapping results (Kappa &gt; 0.9, OA &gt; 94%).