Search Results (47)

Plantations left for natural succession play a significant role in Tropical Rainforest National Parks. Studying the succession and restoration of plantations is crucial for achieving a park’s authenticity and integrity, as well as for maximizing its ecological functions. However, the changes in vegetation and soil properties during the natural succession of these decommissioned plantations remain unclear. In this study, we examined rubber [(Hevea brasiliensis (Willd. Ex A. Juss.) Muell. Arg] plantations in the Yinggeling area of the National Park of Hainan Tropical Rainforest. We used community surveys, field sampling, and soil property analyses to investigate the species richness, diversity, and species composition of the aboveground plant communities during three succession periods of rubber plantations left for natural succession, including 0 years (ZY), 3 years (TY), and 7 years (SY). The soil pH, total organic carbon, total nitrogen, total phosphorus, available phosphorus, nitrate nitrogen, ammonium nitrogen, and total potassium contents in the three succession periods were analyzed. These results showed that there were 92 species of understory plants in the decommissioned rubber plantations, belonging to 72 genera in 39 families. The highest number of understory plant species was found in the plantations with 3 years of natural succession, totaling 66 species from 49 genera in 29 families. The number of families, genera, and species followed the pattern TY > SY > ZY. The Margalef richness index (F), Simpson index (D), and Shannon–Wiener index (H) of understory plants in the 0-year succession plantations were significantly lower than those in the 3-year and 7-year succession plantations. However, there was no significant difference in the Pielou (EH) index among the succession gradients. The soil pH, nitrate nitrogen (NO3--N), and available phosphorus (AP) in the 0-year succession plantations were significantly higher than those in the 3-year and 7-year succession plantations. There were no significant differences in soil total nitrogen (TN), total phosphorus (TP), total organic carbon (TOC), and ammonium nitrogen (NH4+-N) across the three succession gradients. The soil total potassium (TK) in the 3-year succession plantations was significantly higher than that in the 0-year and 7-year succession plantations. Soil available phosphorus and total phosphorus (TP) were positively correlated with the Margalef index, Simpson index, Shannon–Wiener index, and Pielou index. The recovery rate of understory vegetation in decommissioned rubber plantations was faster than that of the soil. This indicates that the construction of the National Park of Hainan Tropical Rainforest has significantly promoted the recovery of the number of plant species and plant species diversity that have been left from rubber plantation operations. These findings not only deepen our understanding of soil property changes during the vegetation succession of artificial forests, particularly rubber plantations, but they also hold significant implications for guiding tropical forest management and sustainable development. Full article

The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R² = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents. Full article

This study applied Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data and ZY1-02D hyperspectral data to map the structural distribution and hydrothermal alteration in the polymetallic ore district in southern Shangri-La City, Yunnan Province, China. The study area hosts several polymetallic deposits, including the Mahuaping tungsten–beryllium deposit, which has significant mineral exploration potential. The deposit type is mainly magmatic–hydrothermal, with average grades of 0.41% WO3 and 0.22% BeO, and substantial reserves, prominently controlled by faults. Based on this, this study employed ASTER data for the visual interpretation of structures through false-color composites combined with DEM data. Additionally, ASTER and ZY1-02D data were processed using the principal component analysis and spectral angle mapper methods to extract anomalies related to tungsten mineralization such as carbonate alteration, sericitization, chloritization, and hematization of the hydrothermal origin. The results indicated that the structural trends in the study area predominantly align in north–south and northeast directions, with alteration anomalies concentrated in the central and fold areas. Our analysis of typical deposits revealed their close association with north–south faults and east–west joints, as well as the enrichment level of alteration anomalies, identifying five high-potential target areas for mineral exploration. Further evaluation involved field validation through the spectral scanning of samples, field verification, and a comparison with known lithology. These assessments confirmed that the spectral curves matched those in the USGS database, the structural interpretations aligned with the field observations (84% accuracy from 25 sampling points, with 21 matching extracted alteration types), and the alteration results corresponded well with the lithological units, indicating high accuracy in alteration extraction. Finally, a comparative discussion highlighted that the results derived from ZY1-02D data were more applicable to the local area. The outcomes of this study can support subsequent mineral exploration efforts, enhancing the sustainability of important mineral resources. Full article

Remote sensing images (RSIs) are widely used in various fields due to their versatility, accuracy, and capacity for earth observation. Direct application of RSIs to harvest optimal results is generally difficult, especially for weak information features in the images. Thus, extracting the weak information in RSIs is reasonable to promote further applications. However, the current techniques for weak information extraction mainly focus on spectral features in hyperspectral images (HSIs), and a universal weak information extraction technology for RSI is lacking. Therefore, this study focused on mining the weak information from RSIs and proposed the deep multi-order spatial–spectral residual feature extractor (DMSRE). The DMSRE considers the global information and three-dimensional cube structures by combining low-rank representation, high-order residual quantization, and multi-granularity spectral segmentation theories. This extractor obtains spatial–spectral features from two derived sequences (deep spatial–spectral residual feature (DMSR) and deep spatial–spectral coding feature (DMSC)), and three RSI datasets (i.e., Chikusei, ZY1-02D, and Pasture datasets) were employed to validate the DMSRE method. Comparative results of the weak information extraction-based classifications (including DMSR and DMSC) and the raw image-based classifications showed the following: (i) the DMSRs can improve the classification accuracy of individual classes in fine classification applications (e.g., Asphalt class in the Chikusei dataset, from 89.12% to 95.99%); (ii) the DMSC improved the overall accuracy in rough classification applications (from 92.07% to 92.78%); and (iii) the DMSC improved the overall accuracy in RGB classification applications (from 63.25% to 63.6%), whereas DMSR improved the classification accuracy of individual classes on the RGB image (e.g., Plantain classes in the Pasture dataset, from 32.49% to 39.86%). This study demonstrates the practicality and capability of the DMSRE method to promote target recognition on RSIs and presents an alternative technique for weak information mining on RSIs, indicating the potential to extend weak information-based applications of RSIs. Full article

Vegetation classification has always been the focus of remote sensing applications, especially for tropical regions with fragmented terrain, cloudy and rainy climates, and dense vegetation. How to effectively classify vegetation in tropical regions by using multi-spectral remote sensing with high resolution and red-edge spectrum needs to be further verified. Based on the experiment in Wenchang, Hainan, China, which is located in the tropical monsoon region, and combined with the ZY-1 02D 2.5 m fused images in January, March, July, and August, this paper discusses whether NDVI and four red-edge vegetation indices (VIs), CIre, NDVIre, MCARI, and TCARI, can promote vegetation classification and reduce the saturation. The results show that the schemes with the highest classification accuracies in all phases are those in which the red-edge VIs are involved, which suggests that the red-edge VIs can effectively contribute to the classification of vegetation. The maximum accuracy of the single phase is 86%, and the combined accuracy of the four phases can be improved to 92%. It has also been found that CIre and NDVIre do not reach saturation as easily as NDVI and MCARI in July and August, and their ability to enhance the separability between different vegetation types is superior to that of TCARI. In general, red-edge VIs can effectively promote vegetation classification in tropical monsoon regions, and red-edge VIs, such as CIre and NDVIre, have an anti-saturation performance, which can slow down the confusion between different vegetation types due to saturation. Full article

The limited culinary utilizations of durum wheat (Triticum turgidum ssp. durum) are partly related to its very hard kernel texture, which is due to the softness genes Puroindoline a (Pina) and Puroindoline b (Pinb) on the Hardness (Ha) locus eliminated during allopolyploid formation. A previous study has reported that the softness genes Dina/Dinb, homologous to Pina/Pinb, were located on the chromosome arm 5VS of wild species Dasypyrum villosum. In the present study, we describe the process of transferring the soft grain texture from D. villosum into durum wheat through homoeologous recombination to develop a Robertsonian translocation. A durum wheat–D. villosum T5AL·5V#5S translocation line, S1286, was developed and characterized by molecular cytogenetic analysis from BC₄F₂ progeny of durum cv. ZY1286/D. villosum 01I140. The translocation line S1286 exhibited a soft grain texture as evidenced by observation through an electron microscope and a Single Kernel Characterization System (SKCS) hardness value of 5.5. Additionally, a newly developed 5VS/5AS co-dominant InDel marker, LW5VS-1, facilitated the transfer of the T5AL·5V#5S translocated chromosome into diverse durum wheat backgrounds. Subsequently, the T5AL·5V#5S translocated chromosome was transferred into five high-yielding durum wheat backgrounds by backcrossing and traced using marker LW5VS-1. Compared with each recurrent parent, T5AL·5V#5S lines showed good viability, similar development, and no yield penalty. Meanwhile, a significant decrease in plant height of about 6.0% was observed when comparing T5AL·5V#5S translocation lines with their recurrent parents. Accordingly, our results provide an efficient strategy for developing soft kernel durum wheat through the combination of T5AL·5V#5S translocation and the co-dominant marker LW5VS-1, which will be crucial for meeting the future challenges of sustainable agriculture and food security. Full article

The concept of DNA vaccination was introduced in the early 1990s. Since then, advancements in the augmentation of the immunogenicity of DNA vaccines have brought this technology to the market, especially in veterinary medicine, to prevent many diseases. Along with the successful COVID mRNA vaccines, the first DNA vaccine for human use, the Indian ZyCovD vaccine against SARS-CoV-2, was approved in 2021. In the current review, we first give an overview of the DNA vaccine focusing on the science, including adjuvants and delivery methods. We then cover some of the emerging science in the field of DNA vaccines, notably efforts to optimize delivery systems, better engineer delivery apparatuses, identify optimal delivery sites, personalize cancer immunotherapy through DNA vaccination, enhance adjuvant science through gene adjuvants, enhance off-target and heritable immunity through epigenetic modification, and predict epitopes with bioinformatic approaches. We also discuss the major limitations of DNA vaccines and we aim to address many theoretical concerns. Full article

A lanthanide contraction(LC) of 14 lanthanides (Ln) from ₅₈Ce to ₇₁Lu consists of the interaction of Ln nucleus with 4f-electrons. Rare earth elements (REEs—R) include Sc, Y, La, and 14 Ln. They are located in 4–6th periods of the subgroup of group III. The electronic structure divides R into short (d- Sc, Y, La) and long (14 f-elements Ce-Lu) homologous series. The most important chemical consequence of LC is the creation of a new conglomerate of 16 RF₃ by mixing fluorides of d- (Y, La) and f-elements. This determines the location of YF₃ among LnF₃. The location of YF₃ depends on the structural (formula volumes—V_form) and thermochemical (temperatures and heats of phase transformations, phase diagrams) properties. The location of YF₃ between HoF₃ and ErF₃ was determined by V_form at a standard pressure (P_st) and temperature (T_st). The location of YF₃ according to heats of phase transformations ΔH_fus and ΔH_trans is in a dimorphic structural subgroup (SSGr) D (Ln = Er-Lu), but without the exact “pseudo Z_Y”. According to the temperatures of phase transformations (T_trans) in LnF₃ (Ln = Dy-Lu), YF₃ is located in the SSGr D between ErF₃ and TmF₃. The ErF₃-YF₃ and YF₃-TmF₃ phase diagrams show it to be between ErF₃ and TmF₃. The crystals of five β-LnF₃ (Ln = Ho-Lu) and β-YF₃ were obtained in identical conditions and their crystal structures were studied. V_form (at P_st and T_st) with “pseudo” atomic number Z_Y = 67.42 was calculated from the unit cell parameters, which were defined with ±5 × 10⁻⁴ Å accuracy. It determines the location of YF₃ between HoF₃ and ErF₃. Full article

Water vapor is a crucial component of the atmosphere. Its absorption significantly influences remote sensing by impacting radiation signals transmitted through the atmosphere. Determining columnar water vapor (CWV) from hyperspectral remote sensing data is essential during the imagery atmospheric correction process. Over the past 40 years, numerous CWV inversion algorithms have been developed, with refinements to enhance retrieval accuracy and reliability. In this study, we proposed an enhanced atmospheric pre-corrected differential absorption (APDA) algorithm. This enhancement was achieved by thoroughly analyzing water vapor absorption in relation to elevation and aerosol optical depth and extending look up tables (LUTs). The enhanced method utilizes a pre-built MODTRAN lookup table and is applied to ZY1-02D hyperspectral data from a satellite launched in 2020. We compared the inversion results of 10 ZY1-02D scenes obtained using the improved method with AERONET measurements and inversion results from commonly used atmospheric correction software, namely, FLAASH and ATCOR. The updated algorithm demonstrated a lower average error (0.0568 g·cm⁻²) and relative average error (10.49%) compared to the ATCOR software (0.17 g·cm⁻² and 40.78%, respectively) and the FLAASH module (0.13 g·cm⁻² and 30.82%, respectively). Consequently, the enhanced method outperforms traditional CWV inversion algorithms, especially at high altitudes. Full article

Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images. Full article

This study aimed to develop a two-color pressure-sensitive paint (PSP) that has both high pressure sensitivity and high temperature sensitivity. Different nitrobenzoxadiazole (NBD) derivatives were used as the temperature probe. Among them, NBD-ZY37 demonstrated favorable stability against photodegradation, and its temperature sensitivity in an RTV118-based two-color PSP was −1.4%/°C. Moreover, temperature sensitivity was independent of pressure in the tested temperature range. PtTFPP was used, and its pressure sensitivity was measured to be 0.5% per kPa. The two-color PSP paint underwent further examination in jet impingement experiments. The experimental results indicated that the pressure fluctuation introduced by the shock waves occurred earlier at higher impingement angles. Specifically, when the pressure ratio was 2.38, increasing the impinging angle from 15° to 30° caused the location of the pressure wave to move from s/D at 0.8 to the exit of the nozzle. Simultaneously, the shape of the maximum pressure zone changed from a fan shape to a round shape. Additionally, the jet region expanded when the pressure ratio was increased. Full article

Lithium (Li) has grown to be a strategic key metal due to the enormous demand for the development of new energy industries over the world. As one of the most significant sources of Li resources, pegmatite-type Li deposits hold a large share of the mining market. In recent years, several large and super-large spodumene (Spd)-rich pegmatite deposits have been discovered successively in the Hoh-Xil–Songpan-Garzê (HXSG) orogenic belt of the northern Tibetan Plateau, indicative of the great Li prospecting potential of this belt. Hyperspectral remote sensing (HRS), as a rapidly developing exploration technology, is especially sensitive to the identification of alteration minerals, and has made important breakthroughs in porphyry copper deposit exploration. However, due to the small width of the pegmatite dykes and the lack of typical alteration zones, the ability of HRS in the exploration of Li-rich pegmatite deposits remains to be explored. In this study, Li-rich pegmatite anomalies were directly extracted from ZY1-02D hyperspectral imagery in the Zhawulong (ZWL) area of western Sichuan, China, using target detection techniques including Adaptive Cosine Estimator (ACE), Constrained Energy Minimization (CEM), Spectral Angle Mapper (SAM), and SAM with BandMax (SAMBM). Further, the Li-rich anomalies were superimposed with the distribution of pegmatite dykes delineated based on GF-2 high-resolution imagery. Our final results accurately identified the known range of Spd pegmatite dykes and further predicted two new exploration target areas. The approaches used in this study could be easily extended to other potential mineralization areas to discover new rare metal pegmatite deposits on the Tibetan Plateau. Full article

Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field. Full article

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. Full article

Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content. Full article