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Mapping wall-to-wall forest aboveground biomass (AGB) at large scales is critical for understanding global climate change and the carbon cycle. In previous studies, a regression-based method was commonly used to map the spatially continuous distribution of forest AGB with the aid of optical images, which may suffer from the saturation effect. The Global Ecosystem Dynamics Investigation (GEDI) can collect forest vertical structure information with high precision on a global scale. In this study, we proposed a collaborative kriging (co-kriging) interpolation-based method for mapping spatially continuous forest AGB by integrating GEDI and Sentinel-2 data. First, by fusing spectral features from Sentinel-2 images with vertical structure features from GEDI, the optimal estimation model for footprint-level AGB was determined by comparing different machine-learning algorithms. Second, footprint-level predicted AGB was used as the main variable, with rh95 and B12 as covariates, to build a co-kriging guided interpolation model. Finally, the interpolation model was employed to map wall-to-wall forest AGB. The results showed the following: (1) For footprint-level AGB, CatBoost achieved the highest accuracy by fusing features from GEDI and Sentinel-2 data ( R 2 = 0.87, RMSE = 49.56 Mg/ha, rRMSE = 27.06%). (2) The mapping results based on the interpolation method exhibited relatively high accuracy and mitigated the saturation effect in areas with higher forest AGB ( R 2 = 0.69, RMSE = 81.56 Mg/ha, rRMSE = 40.98%, bias = −3.236 Mg/ha). The mapping result demonstrates that the proposed method based on interpolation combined with multi-source data can be a promising solution for monitoring spatially continuous forest AGB. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48). [ABSTRACT FROM AUTHOR]

Introduction: Internal migrant workers have a great chance to experience defeat due to their low social status and economic situation. It has been reported that defeat might play a prospective role in predicting depression and anxiety; however, defeat is rarely explored among internal migrant workers due to the lack of appropriate measurement scales. The defeat scale (DS) can measure the feeling of defeat, social hierarchy reduction, and loss in social struggle. But its reliability and validity among internal migrant workers have not been reported. This study aimed to verify the content validity and structural validity of the DS among internal migrant workers in China and to explore its correlations with anxiety and depression. Methods: 1805 internal migrant workers (IMWs) were recruited by stratified multistage sampling from 16 factories in Shenzhen, China. The content validity index (CVI) was used to assess content validity. Cronbach's coefficient alpha of each factor and the total scale were calculated to assess the reliability of DS. The scree test was used to determine the number of factors. Convergent validity and discriminant validity were estimated by calculating the average variance extracted and composite reliability. Logistic regression was performed to explore the effects of DS scores on anxiety and depression. Results: Mean score of DS among IMWs was 18.42 ± 9.40. There were 606 (33.6%) IMWs who were considered to have depression symptoms, and 524 (29.0%) IMWs were considered to have anxiety symptoms. A two-factor model was obtained and fitted well (CFI = 0.956, GFI = 0.932, IFI = 0.956, RMSEA = 0.068, SRMR = 0.052). Cronbach's alpha reliability coefficient for the DS was 0.92. Logistic regression showed that DS scores were positively associated with anxiety and depression among IMWs. Conclusions: DS performed well among IMWs on content validity and structural validity, and it was suitable as a measurement instrument to assess defeat among this population. Defeat was positively associated with anxiety and depression and might play an important role in the mental health of IMWs. [ABSTRACT FROM AUTHOR]

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts' diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members' diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis. [ABSTRACT FROM AUTHOR]