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In China, cemented paste backfill (CPB) is a common treatment method after the exploitation of basic energy. The homogeneity of slurry influences the performance of CPB. However, the online monitoring and characterization of homogeneity lack relevant technologies and unified standards. This article discusses an online image analysis technique applied to the online monitoring of cemented paste backfill mixing, which is based on the evolution of the texture of images taken at the surface of the mixing bed. First, the grayscale distribution of the image obtained by the high-speed camera in the CPB preparation process was analyzed by Matlab and its variance (s2) was solved, and the texture features of the image were analyzed by the variance of grayscale distribution. Then, a homogeneity discriminant model ( c s t ) was established. The results show that the variance value of the grayscale distribution of the slurry image increases rapidly at first, then gradually decreases, and becomes stable in the final stage since it turns a constant value. When the s2 value tends to be stable, the slurry gradually reaches homogenization, and the discriminant coefficient of paste homogenization based on the homogenization discriminant model reaches 0.05. The homogenization prediction of CPB proves to be consistent with the backfill performance comparison results. The evolution of the texture allows obtaining important information on the evolution of different formulations during mixing, which can be used for intelligent monitoring of CPB preparation process. [ABSTRACT FROM AUTHOR]

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

The quality of data in a pavement management system (PMS) has a direct impact on pavement maintenance and rehabilitation (M&R) decisions and management. However, pavement performance data and M&R action data often suffer from problems such as omissions and anomalies. To solve these problems, this study proposes a data cleaning framework based on artificial neural networks (ANNs) that can clean pavement performance data and M&R action data simultaneously. First, data are classified and labeled by the framework using the percentile method and considering the nature of the PMS data itself. Then two ANNs are established, one to clean data from anomalous or omitted pavement performance, and another to fill in data from omitted M&R actions. Applying the framework to the PMS in Shanxi Province, China, the following conclusions can be drawn. In terms of filling in the omitted M&R action, ANN calculated less average loss and improved the average prediction accuracy by 7.88% compared to the logistic regression model, proving the superiority of ANN in filling in the omitted M&R action data. Compared to the framework of filling in the omitted M&R action data by ANN without cleaning the pavement performance data, the proposed framework resulted in less average loss values and 5.71% improvement in average accuracy, demonstrating the need for cleaning both types of data simultaneously. The framework can provide a higher-quality data set for pavement M&R decisions and management. [ABSTRACT FROM AUTHOR]

Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User's accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions. [ABSTRACT FROM AUTHOR]

The Yellow River Delta (YRD), known for its vast and diverse wetland ecosystem, is the largest estuarine delta in China. However, human activities and climate change have significantly degraded the wetland ecosystem in recent decades in the YRD. Therefore, an understanding of the land use modifications is essential for the efficient management and preservation of ecosystems in this region. This study utilized time series of remote sensing data and the extreme gradient boosting method to generate land use maps of the YRD from 2000 to 2020. Several methods, including transition matrix, land use dynamic degree, and standard deviation ellipse, were employed to explore the characteristics of land use transitions. The results underscore significant spatial variations in land use over the past two decades. The most rapid increase was observed in built-up area, followed by terrestrial water and tidal flats, while unutilized land experienced the fastest decrease, followed by forest–grassland. The spatial distribution patterns of agricultural land, built-up area, terrestrial water, and forest–grassland demonstrated stronger directionality compared to other land use types. The wetlands have expanded in size and improved in structure. Unutilized land has been converted into artificial wetlands comprising ponds, reservoirs, salt ponds, shrimp and crab ponds, and natural wetlands featuring mudflats and forest–grassland. The wetland conservation efforts after 2008 have proven very effective, playing a positive role in ecological and environmental preservation, as well as in regional sustainable development. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of crop planting units plays a critical role in crop yield estimation, soil management, food supplies, and disaster warnings. However, precisely mapping crop types is challenging in smallholder farming systems due to heterogeneous and mixed pixels, where field sizes are small, and crop types are very diverse. In this paper, the crop type system of Huocheng County in northwest China as an example area, crop classification feature variables are constructed using Sentinel-2 remote sensing images, and combining the ReliefF algorithm, three feature selection classification models are established by 3092 crop type sampling points data. The crop planting units are extracted using pixel-based and object-based classification methods, respectively, and unmanned aerial vehicle (UAV) data assess the accuracy as ground truth. The Sentinel-2 image based smallholder crops classification results indicate that: The effective combination of optimal input feature variables selection and classification models significantly improves crop classification accuracy. This is particularly evident when integrating this approach into pixel-based classification, addressing challenges such as low pixel classification accuracy in regions of this type. The validation results from UAV data also confirm these findings. At validation points 2, 3, and 4, pixel-based classification demonstrates significantly higher accuracy compared to object-based classification, with pixel-based classification accuracy improving by 9.16%, 7.83%, and 31.83%, respectively. Although the UAV validation accuracy does not reach a high level, pixel-based classification remains the optimal choice for smallholder crop classification. This method offers new insights and references for research related to the classification of smallholder crops with complex planting structures. [ABSTRACT FROM AUTHOR]

Background: Automatic extraction of roads from remote sensing images can facilitate many practical applications. However, thus far, thousands of kilometers or more of roads worldwide have not been recorded, especially low-grade roads in rural areas. Moreover, rural roads have different shapes and are influenced by complex environments and other interference factors, which has led to a scarcity of dedicated low level category road datasets. Methods: To address these issues, based on convolutional neural networks (CNNs) and tranformers, this article proposes the Dual Path Information Fusion Network (DPIF-Net). In addition, given the severe lack of low-grade road datasets, we constructed the GaoFen-2 (GF-2) rural road dataset to address this challenge, which spans three regions in China and covers an area of over 2,300 km, almost entirely composed of low-grade roads. To comprehensively test the low-grade road extraction performance and generalization ability of the model, comparative experiments are carried out on the DeepGlobe, and Massachusetts regular road datasets. Results: The results show that DPIF-Net achieves the highest IoU and F1 score on three datasets compared with methods such as U-Net, SegNet, DeepLabv3+, and D-LinkNet, with notable performance on the GF-2 dataset, reaching 0.6104 and 0.7608, respectively. Furthermore, multiple validation experiments demonstrate that DPIF-Net effectively preserves improved connectivity in low-grade road extraction with a modest parameter count of 63.9 MB. The constructed low-grade road dataset and proposed methods will facilitate further research on rural roads, which holds promise for assisting governmental authorities in making informed decisions and strategies to enhance rural road infrastructure. [ABSTRACT FROM AUTHOR]

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands. [ABSTRACT FROM AUTHOR]

Simple Summary: In response to the quantitative demand for the practical deployment of applications and the need to enhance the detection accuracy of wildlife in complex field environments in southwest China and neighboring regions, we have refined the wildlife detection algorithm based on re-parameterized convolution. This refinement is specifically targeted at addressing the challenges posed by the low quality of wildlife images captured by camera traps and the limitations of traditional object detection algorithms in feature extraction capability. To address these issues, we have introduced a series of improvement schemes. As a result of these enhancements, there has been a noteworthy improvement in both the accuracy of wildlife detection and the speed of model inference. This advancement offers a convenient and efficient method for the preliminary detection in the context of automated wildlife monitoring. To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications. [ABSTRACT FROM AUTHOR]

Background: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. Methods: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017–2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018–2021) and another from center 2 (n = 43, 2020–2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. Results: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. Conclusion: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications. [ABSTRACT FROM AUTHOR]

Garlic (Allium sativum) is an important economic crop in China. In terms of using remote sensing technology to identify it, there is still room for improvement, and the high-precision classification of garlic has become an important issue. However, to the best of our knowledge, few studies have focused on garlic area mapping. Here, we propose a method for identifying garlic crops using samples and a multi-feature dataset under limited conditions. The results indicate the following: (1) In the land-use classification of the Erhai Lake Basin, the importance ranking of the characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. (2) The random forest method based on feature selection demonstrates high accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reached 95.79%, with a Kappa coefficient of 0.95. (3) From 1999 to 2023, the expansion of garlic cultivation in the Erhai Lake Basin showed a trend of initially strengthening from north to south, which was followed by weakening. The vertical development of garlic cultivation reached saturation, showing a slow trend toward horizontal expansion between 2005 and 2018. The planting distributions in various townships in the Erhai Lake Basin gradually shifted from relatively uniform distributions to upstream development. This study utilized the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Moreover, it accurately, rapidly, and efficiently extracted planting information, demonstrating significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas. [ABSTRACT FROM AUTHOR]

Background: This study aims to report the clinical characteristics of a child with autosomal recessive polycystic kidney disease (ARPKD) within a Chinese Zhuang ethnic family., Methods: We used whole exome sequencing (WES) in the family to examine the genetic cause of the disease. Candidate pathogenic variants were validated by Sanger sequencing., Results: We identified previously unreported mutations in the PKHD1 gene of the proband with ARPKD through WES: a splice site mutation c.6809-2A > T, a nonsense mutation c.4192C > T(p.Gln1398Ter), and a missense mutation c.2181T > G(p.Asn727Lys). Her mother is a heterozygous carrier of c.2181T > G(p.Asn727Lys) mutation. Her father is a carrier of c.6809-2A > T mutation and c.4192C > T(p.Gln1398Ter) mutation., Conclusions: The identification of novel mutations in the PKHD1 gene through WES not only expands the spectrum of known variants but also potentially enhances genetic counseling and prenatal diagnostic approaches for families affected by ARPKD., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Soil salinization is a crucial type in the degradation of coastal land, but its spatial distribution and drivers have not been sufficiently explored especially at the depth scale owing to its multidimensional nature. In this study, we proposed a multi-depth soil salinity prediction model (0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm) fully using the advantages of satellite image data and field sampling to rapidly estimate the multi-depth soil salinity in the Yellow River Delta, China. Firstly, a multi-depth soil salinity predictive factor system was developed through correlation analysis of soil sample electrical conductivity with a series of remote-sensing parameters containing heat, moisture, salinity, vegetation indices, spectral value, and spatial location. Then, three machine learning methods including back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF) were adopted to construct a coastal soil salinity inversion model. By using the best inversion model, we obtain the spatial distribution of soil salinity in the Yellow River Delta. The results show the following: (1) Environmental variables in this study are all effective variables for soil salinity prediction. The most sensitive indicators to multi-depth soil salinity are GDVI, ENDVI, SI-T, NDWI, and LST. (2) The RF model was chosen as the optimal approach for predicting and mapping soil salinity based on performance at four soil depths. (3) The soil salinity profiles exhibited intricate coexistence of two distinct types: surface aggregated and homogeneous. The former was dominant in the east, where salinity was higher. The central and southwestern parts were mostly homogeneous, with lower soil salinity. (4) The soil salinity throughout the four depths examined was found to be most elevated in saltern and bare land and lowest in wetland vegetation and farmland, according to land-cover type. This study proposed a remote sensing prediction method for salinization in multiple soil layers in the coastal plain, which could provide decision support for spatial monitoring of land salinization and achieving land degradation neutrality targets. [ABSTRACT FROM AUTHOR]

Due to the effects of global climate change and altered human land-use patterns, typical shrub encroachment in grasslands has become one of the most prominent ecological problems in grassland ecosystems. Shrub coverage can quantitatively indicate the degree of shrub encroachment in grasslands; therefore, real-time and accurate monitoring of shrub coverage in large areas has important scientific significance for the protection and restoration of grassland ecosystems. As shrub-encroached grasslands (SEGs) are a type of grassland with continuous and alternating growth of shrubs and grasses, estimating shrub coverage is different from estimating vegetation coverage. It is not only necessary to consider the differences in the characteristics of vegetation and non-vegetation variables but also the differences in characteristics of shrubs and herbs, which can be a challenging estimation. There is a scientific need to estimate shrub coverage in SEGs to improve our understanding of the process of shrub encroachment in grasslands. This article discusses the spectral differences between herbs and shrubs and further points out the possibility of distinguishing between herbs and shrubs. We use Sentinel-2 and Gao Fen-6 (GF-6) Wide Field of View (WFV) as data sources to build a linear spectral mixture model and a random forest (RF) model via space–air–ground collaboration and investigate the effectiveness of different data sources, features and methods in estimating shrub coverage in SEGs, which provide promising ways to monitor the dynamics of SEGs. The results showed that (1) the linear spectral mixture model can hardly distinguish between shrubs and herbs from medium-resolution images in the SEG. (2) The RF model showed high estimation accuracy for shrub coverage in the SEG; the estimation accuracy (R2) of the Sentinel-2 image was 0.81, and the root-mean-square error (RMSE) was 0.03. The R2 of the GF6-WFV image was 0.72, and the RMSE was 0.03. (3) Texture feature introduced in RF models are helpful to estimate shrub coverage in SEGs. (4) Regardless of the linear spectral mixture model or the RF model being employed, the Sentinel-2 image presented a better estimation than the GF6-WFV image; thus, this data has great potential to monitor shrub encroachment in grasslands. This research aims to provide a scientific basis and reference for remote sensing-based monitoring of SEGs. [ABSTRACT FROM AUTHOR]

Most current research on aquaculture ponds focuses on coastal areas, leaving a gap in understanding of inland regions, such as the strategically significant Yangtze River Economic Belt in China. This study introduces an intelligent extraction method for extensive monitoring of aquaculture ponds in Yangtze River Economic Belt, using Landsat and Sentinel data from 1985 to 2020 with five-year intervals based on the Google Earth Engine (GEE) platform. Land cover change data were also analyzed to understand the impact of aquaculture-related changes. Results indicate a significant increase in aquaculture ponds in the Yangtze River Economic Belt from 3235.51 km2 to 14,207.08 km2 between 1985 and 2020. Aquaculture activity primarily shifted eastward from 1985 to 2015, then westward from 2015 to 2020. Approximately 2018.36 km2 of aquatic areas underwent conversion, mainly to water bodies or croplands, with fewer transitions to impervious surfaces, grasslands, or forests. This study highlights that inland areas can also experience significant increases in aquaculture ponds, particularly alongside large rivers, and that the environmental impacts of these changes differ from those in coastal areas, warranting specific attention. [ABSTRACT FROM AUTHOR]

The novel coronavirus pneumonia (COVID-19) pandemic has caused enormous impacts around the world. Characterizing the risk dynamics for urgent epidemics such as COVID-19 is of great benefit to epidemic control and emergency management. This article presents a novel approach to characterizing the space-time risks of the COVID-19 epidemic. We analyzed the heavy-tailed distribution and spatial hierarchy of confirmed COVID-19 cases in 367 cities from 20 January to 12 April 2020, and population density data for 2019, and modelled two parameters, COVID-19 confirmed cases and population density, to measure the risk value of each city and assess the epidemic from the perspective of spatial and temporal changes. The evolution pattern of high-risk areas was assessed from a spatial and temporal perspective. The number of high-risk cities decreased from 57 in week 1 to 6 in week 12. The results show that the risk measurement model based on the head/tail breaks approach can describe the spatial and temporal evolution characteristics of the risk of COVID-19, and can better predict the risk trend of future epidemics in each city and identify the risk of future epidemics even during low incidence periods. Compared with the traditional risk assessment method model, it pays more attention to the differences in the spatial level of each city and provides a new perspective for the assessment of the risk level of epidemic transmission. It has generality and flexibility and provides a certain reference for the prevention of infectious diseases as well as a theoretical basis for government implementation strategies. [ABSTRACT FROM AUTHOR]

In recent years, grassland ecosystems have faced increasingly severe desertification, which has caused continuous changes in the vegetation composition in grassland ecosystems. Therefore, effective research on grassland plant taxa is crucial to exploring the process of grassland desertification. This study proposed a solution by constructing a UAV hyperspectral remote sensing system to collect the hyperspectral data of various species in desert grasslands. This approach overcomes the limitations of traditional grassland survey methods such as a low efficiency and insufficient spatial resolution. A streamlined 2D-CNN model with different feature enhancement modules was constructed, and an improved depth-separable convolution approach was used to classify the desert grassland plants. The model was compared with existing hyperspectral classification models, such as ResNet34 and DenseNet121, under the preprocessing condition of data downscaling by combining the variance and F-norm2. The results showed that the model outperformed the other models in terms of the overall classification accuracy, kappa coefficient, and memory occupied, achieving 99.216%, 98.735%, and 16.3 MB, respectively. This model could effectively classify desert grassland species. This method provides a new approach for monitoring grassland ecosystem degradation. [ABSTRACT FROM AUTHOR]

Due to the lack of clarity in the strength design of underground tailings reservoirs, it is imperative to investigate the interaction between the tailings deposit body (TDB) and surrounding rock. Taking the TDB as the subject of analysis, a differential equation for vertical stress on the TDB is proposed, considering the stresses from the hanging wall of the surrounding rock and physical and structural parameters of the TDB. Considering the similarity between the underground tailings reservoir and one-step subsequent filling, in situ data of the one-step subsequent filling body from a mine was utilized to compare calculated values of the theoretical model. The resulting theoretical prediction error was less than 10%, thus verifying the reliability of the proposed model. According to the theoretical model analysis, the height of the TDB exerts the most significant influence on vertical stress, while the width and length of the TDB have a negligible impact. Moreover, internal friction angle has a more pronounced effect on vertical stress than cohesion force. A case study for a lead–zinc mine in China is presented in this work. Through uniaxial compressive strength and triaxial shear experiments, the key mechanical parameters of TDB at different ratios of cement to tailings are obtained. According to the theoretical model proposed herein, the distribution law of vertical stress in the height direction of TDB is determined for various ratios of cement to tailings. The original technical scheme of the mine has been optimized by using uniaxial compressive strength greater than vertical stress as the evaluation index, achieving both storage safety and cost reduction goals. [ABSTRACT FROM AUTHOR]

As one of the world's major forestry countries, accurate forest-type maps in China are of great importance for the monitoring and management of forestry resources. Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km 2 , of which 1,127,294.42 km 2 of coniferous forests account for 52% of the total area, 981,690.98 km 2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km 2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development. [ABSTRACT FROM AUTHOR]

In China's second-largest wheat-producing region, the mid-lower Yangtze River area, cold stress impacts winter wheat production during the pre-heading growth stage. Previous research focused on specific growth stages, lacking a comprehensive approach. This study utilizes Unmanned Aerial Vehicle (UAV) multispectral imagery to monitor Soil-Plant Analysis Development (SPAD) values throughout the pre-heading stage, assessing crop stress resilience. Vegetation Indices (VIs) and Texture Indices (TIs) are extracted from UAV imagery. Recursive Feature Elimination (RFE) is applied to VIs, TIs, and fused variables (VIs + TIs), and six machine learning algorithms are employed for SPAD value estimation. The fused VIs and TIs model, based on Long Short-Term Memory (LSTM), achieves the highest accuracy (R2 = 0.8576, RMSE = 2.9352, RRMSE = 0.0644, RPD = 2.6677), demonstrating robust generalization across wheat varieties and nitrogen management practices. This research aids in mitigating winter wheat frost risks and increasing yields. [ABSTRACT FROM AUTHOR]

The paper aims to develop prediction model that integrates clinical, radiomics, and deep features using transfer learning to stratifying between high and low risk of thymoma. Our study enrolled 150 patients with thymoma (76 low-risk and 74 high-risk) who underwent surgical resection and pathologically confirmed in Shengjing Hospital of China Medical University from January 2018 to December 2020. The training cohort consisted of 120 patients (80%) and the test cohort consisted of 30 patients (20%). The 2590 radiomics and 192 deep features from non-enhanced, arterial, and venous phase CT images were extracted and ANOVA, Pearson correlation coefficient, PCA, and LASSO were used to select the most significant features. A fusion model that integrated clinical, radiomics, and deep features was developed with SVM classifiers to predict the risk level of thymoma, and accuracy, sensitivity, specificity, ROC curves, and AUC were applied to evaluate the classification model. In both the training and test cohorts, the fusion model demonstrated better performance in stratifying high and low risk of thymoma. It had AUCs of 0.99 and 0.95, and an accuracy of 0.93 and 0.83, respectively. This was compared to the clinical model (AUCs of 0.70 and 0.51, accuracy of 0.68 and 0.47), the radiomics model (AUCs of 0.97 and 0.82, accuracy of 0.93 and 0.80), and the deep model (AUCs of 0.94 and 0.85, accuracy of 0.88 and 0.80). The fusion model integrating clinical, radiomics and deep features based on transfer learning was efficient for noninvasively stratifying high risk and low risk of thymoma. The models could help to determine surgery strategy for thymoma cancer. [ABSTRACT FROM AUTHOR]

Tuna resources are an important part of China's pelagic fishery production. However, for China's tuna fishery, tuna species caught at sea are still manually classified, which is a time-consuming and inefficient process; so China's tuna fishery needs to develop toward automation. This study uses gray-level co-occurrence matrix (GLCM) and VGG16 to visualize phenotypic texture through local images of three Thunnus species. At the same time, texture feature index data (TFD), deep feature data (DFD), and their combined feature data (CFD) are obtained from texture images. Support vector machine (SVM) with different kernel functions is used to classify phenotypic texture of tuna automatically. The study shows that visualized texture images of different tuna using GLCM and VGG16 have biological characteristics. In the classification results without cross-validation, the average classification accuracy of TFD in polynomial was 83%, the average classification accuracy of DFD in RBF (Radial basis function) was 93%, and the average classification accuracy of CFD in RBF was 95%. It is concluded that tuna phenotype texture can be efficiently classified by using SVM with different kernel functions. [ABSTRACT FROM AUTHOR]

Landslide detection is crucial for natural disaster risk management. Deep-learning-based object-detection algorithms have been shown to be effective in landslide studies. However, advanced algorithms currently used for landslide detection require high computational complexity and memory requirements, limiting their practical applicability. In this study, we developed a high-resolution dataset for landslide-prone regions in China by extracting historical landslide remote sensing images from the Google Earth platform. We propose a lightweight LP-YOLO algorithm based on YOLOv5, with a more-lightweight backbone that incorporates our designed PartitionNet and neck equipped with CSPCrossStage. We constructed and added the vertical and horizontal (VH) block to the backbone, which explores and aggregates long-range information with two directions, while consuming a small amount of computational cost. A new feature fusion structure is proposed to boost information flow and enhance the location accuracy. To speed up the model learning process and improve the accuracy, the SCYLLA-IoU (SIoU) bounding box regression loss function was used to replace the complete IoU (CIoU) loss function. The experimental results demonstrated that our proposed model achieved the highest detection performance (53.7% of Precision, 49% of AP50 and 25.5% of AP50:95) with a speed of 74 fps. Compared to the YOLOv5 model, the proposed model achieved 4% improvement for Precision, 2.6% improvement for AP50, and 2.5% for AP50:95, while reducing the model parameters and FLOPs by 38.4% and 53.1%, respectively. The results indicated that the proposed lightweight method provides a technical guidance for achieving reliable and real-time automatic landslide detection and can be used for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant's continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah. [ABSTRACT FROM AUTHOR]

Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land–atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial-resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436 967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use and land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1–31-year) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method were validated by comparing the forest age map with Hansen's forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets, and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services as well as important guidance for government departments, such as the National Forestry and Grassland Administration and the National Development and Reform Commission in China. Data presented in this study is available at 10.6084/m9.figshare.21627023.v7 (Xiao, 2022). [ABSTRACT FROM AUTHOR]

Rapid and accurate estimation of forest aboveground biomass (AGB) with fine details is crucial for effective forest monitoring and management, where forest height plays a key role in AGB quantification. In this study, we propose a random forest (RF)-based down-scaling method to map forest height and biomass at a 15-m resolution by integrating Landsat 8 OLI and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data. ICESat-2 photon data are used to derive canopy parameters along 15-m segments, which are considered sample plots for the extrapolation of discrete forest height. Fourteen variables associated with spectral features, textual features and vegetation index are extracted from pan-sharpened Landsat 8 images. A regression function is established between these variables and ICESat-2-derived forest height to produce a 15-m continuous forest height distribution data based on the 30-m forest height product using the RF algorithm. Finally, a wall-to-wall forest AGB at 15-m spatial resolution is achieved by using an allometric model specific to the forest type and height. The Jilin Province in northeast China is taken as the study area, and the forest AGB estimation results reveal a density of 61.15 Mg/ha with a standard deviation of 89.46 Mg/ha. The R 2 between our predicted forest heights and the ICESat-2-derived heights reaches 0.93. Validation results at the county scale demonstrate reasonable correspondence between the estimated AGB and reference data, with consistently high R 2 value exceeding 0.65. This downscaling method provides a promising scheme to estimate spatial forest AGB with fine details and to enhance the accuracy of AGB estimation, which may facilitate carbon stock measurement and carbon cycle studies. [ABSTRACT FROM AUTHOR]

Background: The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Methods: Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. Results: On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Conclusion: Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro- environment of tumors. [ABSTRACT FROM AUTHOR]

Forest growing stock volume (GSV, m3/ha) is an indispensable variable for forest resources supervision. Precise measurement of GSV is conducive to monitoring forest dynamics. Nevertheless, little research has explored the pattern pixel values and the function of texture features derived from a vegetation index (VI) for GSV prediction. In this study, we investigated combining linear regression or Random Forest with Sentinel-2 spectral predictors and image textures derived from spectral bands and vegetation indices, which were based on standard deviation values or mean values at the plot level, for predicting GSV of Masson pine, theropencedrymion and all the survey plots in Anji County, China. Specifically, ten groups of experiments encompassing combinations of spectral parameters and texture measures were established for detecting the potential of image textures based on two different image pixel statistics. The results showed that texture measures derived from VI were superior to spectral parameters or texture measures derived from spectral bands for estimating the GSV of single tree species. Moreover, texture features based on shortwave infrared bands or their related VI and the standard deviation at pixel level for spectral bands/indices were emphasized. Finally, the mean value at the plot level exhibited slightly stronger potential than the standard deviation in general. [ABSTRACT FROM AUTHOR]

Farmland is the basis for human survival and development. The phenomenon of cropland abandonment has seriously affected national agricultural production and food security. In this study, remote sensing monitoring of abandoned cropland is carried out based on multisource time series remote sensing data using the Google Earth Engine (GEE) cloud platform. Landsat and Sentinel-2 time series data from 2010–2021 were used to obtain monthly synthetic cloud-free image sets in combination with cropland plot data. The time series farmland probability dataset was generated using the random forest classification method. The LandTrendr algorithm was used to extract and analyse the time series cropland probability dataset. Finally, this study also explored the drivers of change in abandoned cropland in Fujian Province. The results show that (1) the LandTrendr algorithm can effectively extract abandoned farmland and avoid the impact of pseudovariation resulting from non-farmland categories. A total of 87.02% of the abandoned farmland was extracted in 2018; 87.50% of the abandoned farmland was extracted in 2020. (2) The abandoned area in Fujian Province fluctuated after a significant increase in 2012, with the abandoned area exceeding 30 thousand hectares. Since 2017, the abandoned area has decreased to slightly below 30 thousand hectares. (3) The regression results of the factors affecting abandoned cropland in Fujian Province show that the increase in the number of agricultural workers and the improvement in soil organic matter content will significantly reduce the area of abandoned cropland in Fujian Province, while the increase in the rate of urbanization, poor road accessibility, and insufficient irrigation conditions will increase the area of abandoned cropland. The results of this study are useful for conducting surveys of cropland abandonment and obtaining timely and accurate data on cropland abandonment. The results of this study are of great significance for the development of effective measures to stop the abandonment of cropland, and ensure the implementation of food security strategies. [ABSTRACT FROM AUTHOR]

Floodplain wetlands are among the most dynamic ecosystems on Earth, featuring high biodiversity and productivity. They are also sensitive to anthropogenic disturbances and are globally threatened. Understanding how flow regime drives the spatiotemporal dynamics of wetland habitats is fundamental to effective conservation practices. In this study, using Landsat imagery and the random forest (RF) machine learning algorithm, we mapped the winter distribution of four wetland habitats (i.e., Carex meadow, reedbed, mudflat, and shallow water) in East Dongting Lake, a Ramsar wetland in the middle to lower Yangtze Basin of China, for 34 years (1988–2021). The dynamics of wetland habitats were explored through pixel-by-pixel comparisons. Further, the response of wetland habitats to flow regime variations was investigated using generalized additive mixed models (GAMM). Our results demonstrated the constant expansion of reedbeds and shrinkage of mudflats, and that there were three processes contributing to the reduction in mudflat: (1) permanent replacement by reedbed; (2) irreversible loss to water; and (3) transitional swapping with Carex meadow. These changes in the relative extent of wetland habitats may degrade the conservation function of the Ramsar wetland. Moreover, the duration of the dry season and the date of water level withdrawal were identified as the key flow regime parameters shaping the size of wetland habitats. However, different wetland vegetation showed distinct responses to variations in flow regime: while Carex meadow increased with earlier water withdrawal and a longer dry season, reedbed continuously expanded independent of the flow regime corresponding to the increase in winter rainfall. Our findings suggested that flow regime acts in concert with other factors, such as climate change and sand mining in river channels, driving wetland habitat transition in a floodplain landscape. Therefore, effective conservation can only be achieved through diverse restoration strategies addressing all drivers. [ABSTRACT FROM AUTHOR]

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data. [ABSTRACT FROM AUTHOR]

Crop residue cover is vital for reducing soil erosion and improving soil fertility, which is an important way of conserving tillage to protect the black soil in Northeast China. How much the crop residue covers on cropland is of significance for black soil protection. Landsat-8 and Sentinel-2 images were used to estimate corn residue coverage (CRC) in Northeast China in this study. The estimation model of CRC was established for improving CRC estimation accuracy by the optimal combination of spectral indices and textural features, based on soil texture zoning, using the random forest regression method. Our results revealed that (1) the optimization C5 of spectral indices and textural features improves the CRC estimation accuracy after harvesting and before sowing with determination coefficients (R2) of 0.78 and 0.73, respectively; (2) the random forest improves the CRC estimation accuracy after harvesting and before sowing with R2 of 0.81 and 0.77, respectively; (3) considering the spatial heterogeneity of the soil background and the usage of soil texture zoning models increase the accuracy of CRC estimation after harvesting and before sowing with R2 of 0.84 and 0.81, respectively. In general, the CRC estimation accuracy after harvesting was better than that before sowing. The results revealed that the corn residue coverage in most of the study area was 0.3 to 0.6 and was mainly distributed in the Songnen Plain. By the estimated corn residue coverage results, the implementation of conservation tillage practices is identified, which is vital for protecting the black soil in Northeast China. [ABSTRACT FROM AUTHOR]

High-resolution remote-sensing imagery has proven useful for building extraction. Unfortunately, due to the high acquisition costs and infrequent availability of high-resolution imagery, low-resolution images are more practical for large-scale mapping or change tracking of buildings. However, extracting buildings from low-resolution images is a challenging task. Compared with high-resolution images, low-resolution images pose two critical challenges in terms of building segmentation: the effects of fuzzy boundary details on buildings and the lack of local textures. In this study, we propose a sparse geometric feature attention network (SGFANet) based on multi-level feature fusion to address the aforementioned issues. From the perspective of the fuzzy effect, SGFANet enhances the representative boundary features by calculating the point-wise affinity of the selected feature points in a top-down manner. From the perspective of lacking local textures, we convert the top-down propagation from local to non-local by introducing the grounding transformer harvesting the global attention of the input image. SGFANet outperforms competing baselines on remote-sensing images collected worldwide and multiple sensors at 4 and 10 m resolution, thereby, improving the IoU by at least 0.66%. Notably, our method is robust and generalizable, which makes it useful for extending the accessibility and scalability of building dynamic tracking across developing areas (e.g., the Xiong'an New Area in China) by using low-resolution images. [ABSTRACT FROM AUTHOR]

Understory vegetation plays an important ecological role in maintaining the diversity of the ecosystem, the stability of ecosystem services, and the accumulation of nutrient elements, as an important part of a forest ecosystem. In this study, a new method of recognizing areas without understory vegetation is proposed. The method makes full use of the advantages of spectral characteristics, spatial structure information and temporal resolution of UAV images, and can quickly and simply distinguish understory, without vegetation cover. Combined with fractional vegetation coverage (FVC) and vegetation dispersion, understory, with no vegetation area, can be successfully recognized, and the Pr, Re and F1 are all above 85%. The proportion of bare soil under forest in our study area is 20.40%, 19.98% and even 41.69%. The study area is located in Changting County, Fujian Province, which is a typical red soil area in China where serious soil erosion is taking place in the forest. The method provides a promising, quick and economic way of estimating understory vegetation coverage with high spatial accuracy. [ABSTRACT FROM AUTHOR]

The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we mainly discuss three non-parametric models—the artificial neural network (ANN), random forests (RFs), and the quantile regression neural network (QRNN) based on 146 sample plots and Sentinel-2 images in Shangri-La City, China. Moreover, we selected the corresponding optical quartile models with the lowest mean error at each AGB segment to combine as the best QRNN (QRNNb). The results showed that: (1) for the whole biomass segment, the QRNNb has the best fitting performance compared with the ANN and RFs, the ANN has the lowest R2 (0.602) and the highest RMSE (48.180 Mg/ha), and the difference between the QRNNb and RFs is not apparent. (2) For the different biomass segments, the QRNNb has a better performance. Especially when AGB is lower than 40 Mg/ha, the QRNNb has the highest R2 of 0.961 and the lowest RMSE of 1.733 (Mg/ha). Meanwhile, when AGB is larger than 160 Mg/ha, the QRNNb has the highest R2 of 0.867 and the lowest RMSE of 18.203 Mg/ha. This indicates that the QRNNb is more robust and can improve the over-estimation and under-estimation in AGB estimation. This means that the QRNNb combined with the optimal quantile model of each biomass segment provides a method with more potential for reducing the uncertainties in AGB estimation using optical remote sensing images. [ABSTRACT FROM AUTHOR]

The impacts of land use/cover changes (LUCCs) on groundwater resources are a global issue. The Shiyang River Basin of China is a typical, ecologically fragile area. Focusing on the Wuwei sub-basin of the central plain, this study analyzed typical remote sensing image data for 17 specific dates since 1970. Before the Comprehensive Treatment Program in 2007, the area of natural oases decreased at a rate of 16.25 km2/year, while the area of farmland expanded at a rate of 13.85 km2/year. The farmland expansion preferentially occurred in low-vegetation-coverage oases, where the groundwater depth increased from 4 to 20 m. The consumption of groundwater increased from 7319.5 × 104 m3/year to 12,943.2 × 104 m3/year. During the period 2008–2018, the areas of both the natural oases and farmland decreased at rates of 2.57 km2/year and 8.99 km2/year, respectively. The groundwater level rose significantly in the south and west, as well as near the main river channel. Groundwater consumption has been restored to 7270.4 × 104 m3/year. Only 0.12 km2 of every 1.17 km2 of the original natural oases were restored through the natural farmland–natural oases conversion process. Groundwater depth increased significantly with the continuous expansion of farmland. Since the farmland area was effectively controlled, the trend of groundwater-level decline was significantly improved. These findings provide scientific support for the ecological restoration and reconstruction of oases, as well as an efficient and balanced development of river basin water resources. [ABSTRACT FROM AUTHOR]

Top coal caving is a process for the rational extraction of large amounts of coal resources. However, this process readily causes release of excessive amounts of gangue during the coal release process. The conventional technique, which involves visual inspection, is not only labor-intensive but also can introduce inaccuracies. Coal and gangue identification methods used during the top coal caving process are reviewed herein. The methods developed and studied over the last 20 years, such as gamma identification, acoustic identification, radar detection, vibration technology, image identification, and infrared positioning, are described separately, and their principles for use are explained. The advantages and disadvantages of these methods are analyzed, and an evaluation index for coal gangue identification methods is established by using hierarchical analysis (AHP). The results indicate that the γ-ray method is optimal to use under the current system. A future gangue identification method is conceived and proposed, which has reference value for the research and applications of gangue identification technology. [ABSTRACT FROM AUTHOR]

It is difficult to accurately identify the winter wheat acreage in the Jianghuai region of China, and the fusion of high-resolution images and medium-resolution image data can improve the image quality and facilitate the identification and acreage extraction of winter wheat. Therefore, the objective of this study is to improve the accuracy of China's medium-spatial resolution image data (environment and disaster monitoring and forecasting satellite data, HJ-1/CCD) in extracting the large area of winter wheat planted. The fusion and object-oriented classification of the 30 m × 30 m HJ-1/CCD multispectral image and 2 m × 2 m GF-1 panchromatic image (GF-1/PMS) of winter wheat at the jointing stage in the study area were studied. The GF-1/PMS panchromatic images were resampled at 8 m, 16 m and 24 m to produce panchromatic images with four spatial resolutions, including 2 m. They were fused with HJ-1/CCD multispectral images by Gram Schmidt (GS). The quality of the fused images was evaluated to pick adequate scale images for the field pattern of winter wheat cultivation in the study area. The HJ-1/CCD multispectral image was resampled to obtain an image with the same scale as the suitable scale fused image. In the two images, the training samples SFI (samples of fused image) and SRI (samples of resampled image) containing spectral and texture information were selected. The fused image (FI) and resampled image (RI) were used for winter wheat acreage extraction using an object-oriented classification method. The results indicated that the fusion effect of 16 m × 16 m fused image was better than 2 m × 2 m, 8 m × 8 m and 24 m × 24 m fused images, with mean, standard deviation, average gradient and correlation coefficient values of 161.15, 83.01, 4.55 and 0.97, respectively. After object-oriented classification, the overall accuracy of SFI for the classification of resampled image RI16m was 92.22%, and the Kappa coefficient was 0.90. The overall accuracy of SFI for the classification of fused image FI16m was 94.44%, and the Kappa coefficient was 0.93. The overall accuracy of SRI for the classification of resampled image RI16m was 84.44%, and the Kappa coefficient was 0.80. The classification effect of SFI for the fused image FI16m was the best, indicating that the object-oriented classification method combined with the fused image and the extraction samples of the fused image (SFI) could extract the winter wheat planting area with precision. In addition, the object-oriented classification method combining resampled images and the extraction samples of fused images (SFI) could extract the winter wheat planting area more effectively. These results indicated that the combination of medium spatial resolution HJ-1/CCD images and high spatial resolution GF-1 satellite images could effectively extract the planting area information of winter wheat in large regions. [ABSTRACT FROM AUTHOR]

Esophageal cancer is one of the malignant tumors with poor prognosis in China. Currently, the treatment of esophageal cancer is still based on surgery, especially in early and mid-stage patients, to achieve the goal of radical cure. However, esophageal cancer is a kind of tumor with a high risk of recurrence and metastasis, and locoregional recurrence and distant metastasis are the leading causes of death after surgery. Although multimodal comprehensive treatment has advanced in recent years, the prediction, prevention and treatment of postoperative recurrence and metastasis of esophageal cancer are still unsatisfactory. How to reduce recurrence and metastasis in patients after surgery remains an urgent problem to be solved. Given the clinical demand for early detection of postoperative recurrence of esophageal cancer, clinical and basic research aiming to meet this demand has been a hot topic, and progress has been observed in recent years. Therefore, this article reviews the research progress on the factors that influence and predict postoperative recurrence of esophageal cancer, hoping to provide new research directions and treatment strategies for clinical practice. [ABSTRACT FROM AUTHOR]

Currently, the monitoring of rocky desertification(RD) is concentrated in the karst area, whereas the study in red-bed areas is rare. In this paper, we present a multi-scale classification framework for RD monitoring based on spectral-spatial features. At the pixel scale, we explored several spectral indices based on spectral statistics and separability analysis of land cover samples. The homogeneous land covers were classified by the decision rules from the selected spectral indices (such as NDIOI, NRRI and NDGI); At the patch scale, RD classes were further distinguished by spatial decision rules based on multiple neighborhood features including proximity, linear density, and buffer distance. The method was applied on an OLI image over the red-bed area of northwestern Jiangxi, south central China, and validated using ground-based observations. The experimental results of verification and comparison are satisfactory. This work demonstrates a methodological supplement to the monitoring of red bed RD. [ABSTRACT FROM AUTHOR]

With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors. [ABSTRACT FROM AUTHOR]

A winter storm triggered a significant sea smoke with the northwesterly wind to the Yellow Sea, China, on 7 January 2021. The ocean responses to this event lasted about 3 days. Satellite observations show that the sea surface temperature dropped from 5.7° to 4.7°C on the following day and then recovered to the previous level; the chlorophyll-a, a bio-growth indicator, increased from 3.6 to 3.9 mg m−3 due to cooling-induced coastal upwelling between 7 and 9 January. Two buoys measurements showed that the air temperature dropped to −13.3°C and high relative humidity with a maximum value of 89.0% above the sea surface, creating favorable conditions for sea smoke generation. A Regional Ocean Modeling System (ROMS) and Weather Research and Forecasting (WRF) Model coupled model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and analyze the air–sea interaction. The 20°C temperature difference between extreme cold air (−13.3°C) and the relatively warm stable sea surface (4.7°–5.7°C) enhanced the seawater evaporation. In addition, we suppose the concentration of sea salt, a kind of condensation nucleus, with a particle diameter of 0.5–1.5 μm above the sea surface increased quickly on 7 January. The boiling-water-like sea surface was imaged on a synthetic aperture image. We developed an image analysis method to describe the cell-shaped texture characteristics imaged by Synthetic Aperture Radar (SAR). We also found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability. Significance Statement: On 7 January 2021, a significant sea smoke event happened in the Yellow Sea. The ocean response to the event lasted 3 days. First, on a synoptic scale, this study presents the comprehensive satellite observations of the sea surface temperature drop and chlorophyll-a increase associated with the sea smoke. Second, a coupled air–sea interaction model with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) chemical module was implemented to reproduce this sea smoke phenomenon and identify which condensation nucleus induced such heavy sea smoke. Third, we developed an image analysis method to analyze high-resolution synthetic aperture radar images and found that the sea surface imprints of sea smoke are governed by the thermal, not the dynamical instability. [ABSTRACT FROM AUTHOR]

The expansion of plantation poses new challenges for mapping forest, especially in mountainous regions. Using multi-source data, this study explored the capability of the random forest (RF) algorithm for the extraction and mapping of five forest types located in Yanqing, north China. The Google Earth imagery, forest inventory data, GaoFen-1 wide-field-of-view (GF-1 WFV) images and DEM were applied for obtaining 125 features in total. The recursive feature elimination (RFE) method selected 32 features for mapping five forest types. The results attained overall accuracy of 87.06%, with a Kappa coefficient of 0.833. The mean decrease accuracy (MDA) reveals that the DEM, LAI and EVI in winter and three texture features (entropy, variance and mean) make great contributions to forest classification. The texture features from the NIR band are important, while the other texture features have little contribution. This study has demonstrated the potential of applying multi-source data based on RF algorithm for extracting and mapping plantation forest in north China. [ABSTRACT FROM AUTHOR]

Urban road network information is an important part of modern spatial information infrastructure and is crucial for high-precision navigation map production and unmanned driving. Synthetic aperture radar (SAR) is a widely used remote-sensing data source, but the complex structure of road networks and the noises in images make it very difficult to extract road information through SAR images. We developed a new method of extracting road network information from SAR images by considering angular (A) and texture (T) features in the sliding windows and points of interest (POIs, or P), and we named this method ATP-ROAD. ATP-ROAD is a sliding window-based semi-automatic approach that uses the grayscale mean, grayscale variance, and binary segmentation information of SAR images as texture features in each sliding window. Since POIs have much-duplicated information, this study also eliminates duplicated POIs considering distance and then selects a combination of POI linkages by discerning the direction of these POIs to initially determine the road direction. The ATP-ROAD method was applied to three experimental areas in Shanghai to extract the road network using China's Gaofen-3 imagery. The experimental results show that the extracted road network information is relatively complete and matches the actual road conditions, and the result accuracy is high in the three different regions, i.e., 89.57% for Area-I, 96.88% for Area-II, and 92.65% for Area-III. Our method together with our extraction software can be applied to extract information about road networks from SAR images, providing an alternative for enriching the variety of road information. [ABSTRACT FROM AUTHOR]

The estimation of forest aboveground biomass (AGB) using Landsat 8 operational land imagery (OLI) images has been extensively studied, but forest aboveground biomass (AGB) is often difficult to estimate accurately, in part due to the multi-level structure of forests, the heterogeneity of stands, and the diversity of tree species. In this study, a habitat dataset describing the distribution environment of forests, Landsat 8 OLI image data of spectral reflectance information, as well as a combination of the two datasets were employed to estimate the AGB of the three common pine forests (Pinus yunnanensis forests, Pinus densata forests, and Pinus kesiya forests) in Yunnan Province using a parametric model, stepwise linear regression model (SLR), and a non-parametric model, such as random forest (RF) and support vector machine (SVM). Based on the results, the following conclusions can be drawn. (1) As compared with the parametric model (SLR), the non-parametric models (RF and SVM) have a better fitting performance for estimating the AGB of the three pine forests, especially in the AGB segment of 40 to 200 Mg/ha. The non-parametric model is more sensitive to the number of data samples. In the case of the Pinus densata forest with a sample size greater than 100, RF fitting provides better fitting performance than SVM fitting, and the SVM fitting model is better suited to the AGB estimation of the Pinus yunnanensis forest with a sample size of less than 100. (2) Landsat 8 OLI images exhibit superior accuracy in estimating the AGB of the three pine forests using a single dataset. Variables, such as texture and vegetation index variables, which can reflect the comprehensive reflection information of ground objects, play a significant role in estimating AGBs, especially the texture variables. (3) By incorporating the combined dataset with characteristics of tree species distribution and ground object reflectance spectrum, the accuracy and stability of AGB estimation of the three pine forests can be improved. Moreover, the employment of a combined dataset is also effective in reducing the number of estimation errors in cases with AGB less than 100 Mg/ha or exceeding 150 Mg/ha. [ABSTRACT FROM AUTHOR]

Thematic sculpture creation is an important part of sculpture creation. In particular, the current thematic sculpture creation in China has broken the shackles of serving politics and has shown a variety of aspects such as theme selection, modelling method, material language, and creative purpose. Since the new century, Chinese thematic sculptors have made in-depth analysis and research on the diversity of creative objects, the infinite possibilities of creative expression, and the diversity of creative purposes, especially through the phenomenon of different thematic sculptures to explore their existing predicaments, and give them reflection. If the time is too long, it will reduce the production efficiency of the sculpture production line and increase the operating cost of the enterprise. The Gabor wavelet transform is used to obtain the texture features of the sculpture surface, and the generated high-dimensional features are subjected to random projection dimension reduction. Finally, the binarization algorithm can be used to detect the defect location quickly and accurately. The feature map is extracted by selecting a deep network. The neural network algorithm structure for the sculpture dataset is presented, and the activation function and regularization method are improved on this basis. [ABSTRACT FROM AUTHOR]

Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R2 = 0.8485, RMSE = 12.25 Mg/hm2, and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 107 Mg, and the estimation result with the sampling population was 1.24 × 107 Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm2 and 67.51 Mg/hm2, with a relative precision of 98.39%. The estimation results of the two sample groups were consistent. [ABSTRACT FROM AUTHOR]

With the successful launch of China's high spatial resolution satellite Gaofen-2 (GF-2), the use of high spatial resolution satellite images for land change detection has high research potential. Based on the images from GF-2, this study combines principal component analysis and the spectral feature change method to identify different land changes in the form of different coloured patches. Then, three decision tree classification models are constructed to automatically detect the change, which includes information on the increase in the number of airports and buildings and increased or decreased vegetation. Further, through Quick Bird images for identical regions in the same periods, a sample of 2624 pixels is selected using a stratified random sampling method to verify the accuracy of the results indicating a change. The results show that the overall accuracy of the extracted information on land change was 98.21%, and the Kappa coefficient was 0.9604. Therefore, the method for land change detection and extraction of land change information used in this study is proven to be effective. [ABSTRACT FROM AUTHOR]