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Quickly and accurately obtaining information on rice planting areas is crucial for maintaining food security and social stability. Based on the time series of Sentinel-1A images, this study proposes an automatic rice mapping method called the Dynamic Threshold Time-Weighted Dynamic Time Warping method (DT-TWDTW). This method consists of two parts. First, rice samples are extracted automatically by a dynamic threshold method based on the unique flooding signal before and after rice transplanting. Then, the Time-Weighted Dynamic Time Warping (TWDTW) method is used for rice mapping based on the cumulative cost matrix between unknown pixels and rice samples. The results show that the DT-TWDTW method can achieve satisfactory mapping accuracy with an overall accuracy of 94.6%, higher than random forest (89.9%), and support vector machine (91.7%). This study provides a new approach and perspective for remote sensing monitoring of rice, especially for some regions without prior phenological knowledge and collection of the rice samples. [ABSTRACT FROM AUTHOR]

The quantification of grassland above-ground biomass (AGB) and grazing intensity (GI) and their distribution in space is of great significance to grassland management and eco-conservation. Remote-sensing technology is widely applied, but it is difficult to measure accurately when monitoring GI. In this study, the neural network, random forest and statistical function models of the relationship between Landsat NDVI and AGB were constructed by field survey and literature data collection in Inner Mongolia grassland, China. By comparing the accuracy among the three models, we constructed a remote-sensing retrieving model of grass AGB. We also estimated the grassland AGB during the peak growing season (August) for Inner Mongolia. Frequency histograms were then made to identify AGB thresholds under four GI levels (light or ungrazed, moderate grazing, overgrazing and extreme grazing) for each of three grassland types (meadow steppe, typical steppe and desert steppe). This study shows that the random forest model simulates grass AGB more accurately than other models. The spatial distribution of AGB in Inner Mongolia grasslands showed a tendency of decreasing from southeast to northwest, with an increasing trend in the last 10 years. The four GI levels in 2021 accounted for 18%, 25%, 36% and 21% of the grasslands in Inner Mongolia, respectively, and over the last 10 years the GI first improved and then deteriorated. This study provides a guideline to remote monitoring for grassland AGB and GI, and supplies scientific support for sustainable management and grassland restoration of large-scale grasslands. [ABSTRACT FROM AUTHOR]

This study uses five machine learning algorithms (Stochastic gradient descent (SGD), Decision tree, Random forest, Gradient boosting decision tree (GBDT), and Convolutional neural networks (CNN)) to explore their prediction effects on China's stock market. It constructs a monthly rolling model for stock return prediction. Selecting stocks of the CSI 300 index from January to June 2021 as specific monthly samples and classifying three factors – fundamentals, volatility(risk) and technical indicators, the results demonstrate that (1) machine learning brings favorable investment returns in simulated quantitative trading of China's A-share market (2) the technical indicator factor is the most valuable, with the momentum technical factor having greatest influence, followed by the volatility (risk) factor and fundamental factors. Therefore, this study has a critical reference value and is significant in guiding yield forecasting in intricate stock markets. [ABSTRACT FROM AUTHOR]

To effectively explore the impacts of several key factors on taxi drivers' traffic violations and provide traffic management departments with scientific decisions to reduce traffic fatalities and injuries. 43,458 electronic enforcement data about taxi drivers' traffic violations in Nanchang City, Jiangxi Province, China, from July 1, 2020, to June 30, 2021, were utilized to explore the characteristics of traffic violations. A random forest algorithm was used to predict the severity of taxi drivers' traffic violations and 11 factors affecting traffic violations, including time, road conditions, environment, and taxi companies were analyzed using the Shapley Additionality Explanation (SHAP) framework. Firstly, the ensemble method Balanced Bagging Classifier (BBC) was applied to balance the dataset. The results showed that the imbalance ratio (IR) of the original imbalanced dataset reduced from 6.61% to 2.60%. Moreover, a prediction model for the severity of taxi drivers' traffic violations was established by using the Random Forest, and the results showed that accuracy, m_F1, m_G-mean, m_AUC, and m_AP obtained 0.877, 0.849, 0.599, 0.976, and 0.957, respectively. Compared with the algorithms of Decision Tree, XG Boost, Ada Boost, and Neural Network, the performance measures of the prediction model based on Random Forest were the best. Finally, the SHAP framework was used to improve the interpretability of the model and identify important factors affecting taxi drivers' traffic violations. The results showed that functional districts, location of the violation, and road grade were found to have a high impact on the probability of traffic violations; their mean SHAP values were 0.39, 0.36, and 0.26, respectively. Findings of this paper may help to discover the relationship between the influencing factors and the severity of traffic violations, and provide a theoretical basis for reducing the traffic violations of taxi drivers and improving the road safety management. [ABSTRACT FROM AUTHOR]

Drought disasters significantly threaten the stability of terrestrial ecosystems in arid and semi-arid regions. Although the impact of drought on vegetation has been extensively researched, there is still a lack of understanding regarding the ecological impacts of drought on the heterogeneous vegetation in northern China's arid and semi-arid regions. To investigate the spatial patterns of vegetation and differences in response to drought climate, the Normalized Difference Vegetation Index (NDVI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to determine vegetation characteristics and the seasonal sensitivity of vegetation types to drought response, as well as to identify the factors driving the sensitivity differences. In the study area, 83.1% of the vegetation exhibited a significant positive correlation with the drought index. The maximum correlation coefficient between vegetation and drought was 0.62, demonstrating that the majority of vegetation was influenced by drought. It was observed that different vegetation types responded differently to drought at varying spatial and temporal scales. The main drivers of vegetation response to drought in arid and semi-arid zones were quantified by using the random forest algorithm. The results indicated that climate and soil factors are the primary limiting factors. These findings enhance our comprehension of arid and semi-arid areas, the effect of drought on different plant species, and the factors that affect vegetation's response to drought. [ABSTRACT FROM AUTHOR]

Accurate fractional crop-planting area (FCPA) mapping is a challenging task as it requires leveraging the advantages of geographic data in detailed spatial expression and agricultural statistics in the description of crop types and quantitative characteristics. We present a robust method to disaggregate the agricultural statistics within each county unit to 1-km scale grid by combining particle swarm optimization (PSO)-based feature selection with Random Forest (RF) regression, and an iterative area-gapallocation(IAGA) method is implemented to reconcile the discrepancies between the disaggregating results and statistics. The agriculture in Gansu Province, China, is characterized by complex heterogeneous smallholder farming landscapes. We tested this methodology in Gansu and explored the synergistic estimation of FCPA for six types of crops (i.e. wheat, maize, oil-bearing, vegetable,orchards, and other crops) in 2010. The results showed that the derived FCPA maps matched well with the statistics in terms of quantity, while also providing spatial details. The quantitative evaluation results indicated that the derived FCPA had good accuracy,with a higher R2 above 0.97, a lower RMSE below 1%, and an absolute error between 1.53-5.24%. The proposed methodology provides valuable insights for practical large-scale FCPA mapping at a high spatial resolution in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Remote sensing and land resource surveys have been used in recent decades for land use/land cover (LULC) mapping; however, keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging. This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data. This study employed Landsat imagery time series, change detection algorithms, sample migration, and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022. The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products. Additionally, a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps, increasing the correlation coefficient (r) with China's second and third national land survey statistics from 0.41–0.89 to 0.86–0.99. Thus, the framework and products developed in this study provide valuable tools for sustainable land use and policy planning. [ABSTRACT FROM AUTHOR]

Correct purchase decisions are required for the successful implementation of government procurement of public services (GPPS). Based on the limitations of decision variables in a single dimension, we consider multiple environments and propose purchasing decision variables from three dimensions: service characteristics, government capability, and environmental factors. By collecting data on decision variables from cases of government purchasing and cases of government providing services, a purchase decision model is constructed based on the random forest algorithm to help governments decide between purchasing public services and providing services. The results indicate that the decision model that considers the three dimensions has better prediction accuracy than the one that only considers service characteristics. Moreover, the results of the importance of the decision variables indicate that the financial capacity and administrative level of the government, and economic environment have a significant impact on the purchase decision of the government. [ABSTRACT FROM AUTHOR]

Urban landscape forms can be effective in reducing increasing PM2.5 concentrations due to urbanization in China, making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM2.5 variations. Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020, using a spatiotemporal geographically weighted regression model, random forest models and partial dependence plots. The results show that there are spatiotemporal differences in the impacts of landscape indices on PM2.5. the proportion of urban green infrastructure (PLAND-UGI) and the fractal dimension of urban green infrastructure (FRACT-UGI) exacerbate PM2.5 concentrations in the northwest, the proportion of impervious surfaces (PLAND-Impervious) mitigates air pollution in northwest and southwest China, and shannon's diversity index (SHDI) has seasonal differences in the northwest. PLAND-UGI is the landscape index with the largest contribution (30%) and interpretable range. The relationship between FRACT and PM2.5 was more complex than for other landscape indices. The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM2.5, contributing to clean urban development and sustainable development. [ABSTRACT FROM AUTHOR]

The selection of discretization criteria and interval numbers of landslide-related environmental factors generally fails to quantitatively determine or filter, resulting in uncertainties and limitations in the performance of machine learning (ML) methods for landslide susceptibility mapping (LSM). The aim of this study is to propose a robust discretization criterion (RDC) to quantify and explore the uncertainty and subjectivity of different discretization methods. The RDC consists of two steps: raw classification dataset generation and optimal dataset extraction. To evaluate the robustness of the proposed RDC method, Lushan County of Sichuan Province in China was chosen as the study area to generate the LSM based on three datasets (optimal dataset, original dataset with continuous values, and statistical dataset) using three popular ML methods, namely, convolution neural network, random forest, and logistic regression. The results show that the areas under the receiver operating characteristic curve (AUCs) of the optimal dataset for the abovementioned ML models are 0.963, 0.961, and 0.930 which are higher than those of the original dataset (0.938, 0.947, and 0.900) and statistical dataset (0.948, 0.954, and 0.897). In conclusion, the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods. [ABSTRACT FROM AUTHOR]

Fire was a serious natural disturbance to forest ecosystems. In this study, a national-scale forest fire risk model (RF_NTL) was proposed in mainland China by incorporating a new human activity factor (NTL) from nighttime light data. For objective verification of model's performance, the logistic regression model (LR) and random forest model (RF) were also built in this paper. Results showed that the RF_NTL model fitted with an AUC value up to 0.95, which was superior to the conventional LR model and RF model. The RF_NTL model also had good generalization ability and stability on annual and monthly scales, helping to accurately forecast forest fire risk. Moreover, the spatial distribution of forest fire risk based on RF_NTL model was detailed and reasonable on a national scale. Forest fire high-risk areas in China were mainly concentrated in the northeast and southwest regions. This study could provide references for future relevant research. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the potential of Machine Vision (MV) approaches for the classification and identification of 12 varieties of guava. There are leaf images of the 12 local varieties of guava (Psidium guajava) that include Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. A digital camera captured these images of guava varieties in a natural environment. Multi-features were extracted from these images. It was a composite of histograms, binary features, textures, rotational, spectral, and translational features (RST). Total 47 multi-features were collected for each non-overlapping guava leaf image, i.e., 256 × 256 and 512 × 512 more, the supervised correlation-based feature selection (CFS) method with the best search algorithm was used to optimize 18 features instead of 47 multi-features. Several ML classifiers, including Instant base Identifier (IBI), Random Forest (RF), and Meta Bagging (MB), using 10-fold cross-validation, were applied to the optimized multi-features. IBI results performed better than other classifiers with an average overall accuracy of 93.01% on AOIs, 512 × 512 . In addition, IBI detected 90.5%, 89.5%, 94%, 97%, 95.5%, 97%, 99%, 96.5%, 99%, 80.5%, 88%, and 81.5% accuracy values for the 12 varieties of guava leaves, namely Bangkok Red, China Surahi, Moti Surahi, Choti Surahi, Golden Gola, China Gola, Multani Sada Gola, Sadda Bahar Gola, Larkana Surahi, Black Guava, Hyderabadi Safeeda, Strawberry Pink Gola. The proposed study could play a significant role for the early and accurate identification of Guava varieties, and it would also be helpful for export quality measures for the national economy of the country. [ABSTRACT FROM AUTHOR]

Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period. Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recruited in the China Health and Retirement Longitudinal Study (CHARLS) were included in the current analysis. The latent class growth modeling (LCGM) and growth mixture modeling (GMM) were used to classify different trajectory classes. Based on the identified trajectory patterns, three ML classification algorithms (i.e. gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUC). Four trajectories were identified for the depressive symptoms: no symptoms (63.9%), depressive symptoms onset {incident increasing symptoms [new-onset increasing (16.8%)], chronic symptoms [slowly decreasing (12.5%), persistent high (6.8%)]}. Among the analyzed baseline variables, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) score, cognition, sleep time, self-reported memory were the top five important predictors across all trajectories. The mean AUCs of the three predictive models had a range from 0.661 to 0.892. ML techniques can be robust in predicting depressive symptom onset and trajectories over a 7-year period with easily accessible sociodemographic and health information. Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2031868 [ABSTRACT FROM AUTHOR]

Flash floods are one of the most serious mountain disasters in Linzhi, China, pose a grave threat to the lives and property of the residents. However, the current regional vulnerability research cannot meet the needs of refined disaster prevention and mitigation in mountainous areas. Therefore, to address this knowledge gap, this study presented a vulnerability assessment framework base on spatialization technique along with the triangular fuzzy number-based analytic hierarchy process (TFN-AHP) and random forest (RF) model. To achieve this goal, an assessment system containing 12 indicators was constructed from the three perspectives of exposure, susceptibility and coping capacity. Firstly, factor spatialization is introduced in data preprocessing. Then TFN-AHP and the RF model were used to calculate the exposure, susceptibility, and coping capacity. Finally, the spatial distribution of the vulnerability throughout Linzhi was obtained. According to the results, the exposure, susceptibility and coping capacity in most areas are extremely low and low, accounting for 74.1%, 99% and 99%, respectively. The vulnerability is similar to the exposure that most of the regions have extremely low vulnerability and low vulnerability, accounting for 83.5%. The moderate vulnerability regions account for 8.5%, and the high vulnerability regions account for only 1%. The assessment results show that highexposure areas mainly exhibit a linear distribution, and mainly include river valleys, cultivated land, and residential areas. Obviously, these areas are mainly distributed in the population and economic agglomeration areas and on low-altitude terrain. The distribution of high and comparatively high vulnerability zones, where economic and human activities are considerably high, are closely to the topography of the catchment. Thus, the results of this study provide scientific and technological evidence for resettlement, population distribution adjustment, and prevention and mitigation of flash floods in Linzhi. [ABSTRACT FROM AUTHOR]

The past landslides from landslide inventory are essential for machine learning (ML)-based landslide susceptibility assessment (LSA). They determine not only the positive samples (characterized by the past landslides) but also the negative samples (randomly generated based on past landslides) for training and validating the ML models. However, the number of past landslides is often limited because of the constraints of time, budget, and resources available, etc. In other words, the available data for establishing landslide susceptibility ML models are limited, which indicates that the accuracy and reliability of the corresponding models are insufficient. This article, therefore, proposes using a new landslide susceptibility index-based sampling strategy to enhance the positive and negative samples for model training and validation to reach an improved ML-based LSA. To realize this idea, landslide susceptibility analysis based on initial datasets compiled from landslide inventory by using three ML models, i.e., random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM), are first conducted to obtain the initial landslide susceptibility indices at different space locations. Then, the landslide susceptibility indices are analyzed with the proposed sampling strategy which considers directly the grid units with very high and very low landslide susceptibility indices as potential positive and negative samples, respectively, to enrich the initial dataset; and the number of these positive/negative samples is determined by a Bayesian optimization algorithm. Thereafter, the ML models are updated with the enriched datasets. Finally, to verify the effectiveness of the proposed strategy, the improved models are applied to assess the landslide susceptibility of Taojiang County, China, and the results are compared with those from initial corresponding models without updating. The results show that compared with the initial RF, GBDT and SVM models, the corresponding improved models have a better performance in accuracy, precision, recall rate, and specificity. In particular, the AUC values of the three models are increased by 11.94%, 10.72% and 0.57%, respectively. [ABSTRACT FROM AUTHOR]

This study compares the capability of Landsat-8, Sentinel-2, and ASTER multispectral data in mapping lithological units in the Bukadaban Peak, China, by evaluating the performance of the Random Forest (RF) classifier. Moreover, the study assesses the importance of remote sensing original bands and the bands derived from enhancement techniques such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) to the classification process. Results revealed that RF selected PC8, PC9, PC12, and MNF1 as the most important features in the Sentinel-2 dataset. Several MNF bands of ASTER were more important than the original and PC bands. The original bands were the strongest predictors in Landsat-8 dataset, while PC2, PC5, and MNF5 were relatively significant. Sentinal-2 and ASTER datasets achieved very similar classification accuracy but outperformed Landsat-8 dataset. ASTER dataset yielded the highest overall accuracy 81.8%, which is 0.18% higher than Sentinel-2 and 3.86% higher than Landsat-8. [ABSTRACT FROM AUTHOR]

Quantifying the uncertainties in the prediction of landslide displacement is important for making reliable predictions and for managing landslide risk. This study develops a novel approach for the interval prediction (i.e. uncertainty) of landslide with step-like displacement pattern in the Three Gorges Reservoir (TGR) area using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTEENN) based Random Forest (RF) and bootstrap-Multilayer Perceptron (MLPs). DBSCAN was employed to carry out clustering analysis for different deformation states of the landslide with step-like displacement pattern. The SMOTEENN based RF classifier was trained to deal with imbalanced classification problems. A dynamic switching prediction scheme to construct high-quality Prediction Intervals (PIs) using bootstrap-MLPs was established. The concepts of Pareto front and Knee point were adopted to select the PIs that could provide the best compromise between reliability and accuracy. The proposed DBSCAN-RF-bootstrap-MLP method is illustrated and verified with one typical landslide with step-like displacement pattern, the Bazimen landslide from the TGR area in China. The method showed to perform well and provides the uncertainties associated with landslide displacement prediction for decision making. [ABSTRACT FROM AUTHOR]

Flash flooding is a type of global devastating hydrometeorological disaster that seriously threatens people's property and physical safety, as well as the normal operation of water conservancy facilities, such as reservoirs, so an accurate assessment of reservoir risk for certain areas is necessary. Therefore, the purpose of this study was to propose a novel methodological approach for reservoir risk modelling based on the feature selection method (FSM) and tree-based ensemble methods (Bagging and Random Forest [RF]). The results showed that: (1) the J48-GA based ensemble models achieved higher learning and predictive capabilities compared to conventional ensemble models without the FSM. (2) For the classification accuracy, the J48-GA-RF (96.4%) outperformed RF (96.0%), J48-GA-Bagging (93.9%) and Bagging (93.5%). And the J48-GA-RF achieved the highest prediction AUC value (0.995), an almost perfect Kappa indexes value (0.926) and the best practicality value (30.88%). (3) In particular, the results indicated that all of the models showed high performance, both in training and in the validation of a dataset. Additionally, this study could provide a reference for disaster managers, hydraulic engineers and policy makers to implement location-specific flash flood risk reduction strategies. [ABSTRACT FROM AUTHOR]

Fine-grained inner-annual population data are instrumental in climate change response, resource allocation, and epidemic control. However, such data are currently scarce due to the lack of human-related indicators with both high temporal resolution and long-term coverage that can be used in the process of population spatialization. Here, we estimate monthly 1-km gridded population distribution across China in 2015 using time-series mobile phone positioning data. We construct a hybrid downscaling model to map the gridded population by incorporating random forest and area-to-point kriging. The estimated monthly population products appear to capture inner-annual population variations, especially during special periods, such as the festival, holiday, and short-term labor flow period, which are characterized by large-scale population movements. Additionally, compared with census data, the hybrid model-based results obtained exhibit higher consistency than popular global population products across all spatial extents. Our monthly 1-km data products for the population distribution across China in 2015 provide a credible dataset that can be employed in studies aimed at accurate population-dependent decisions. [ABSTRACT FROM AUTHOR]

The increasing and widespread usage of social media enables the investigation of public preference using the web as a device. Public sentiment as expressed in 44,319 massive open online course (MOOCs) related microblogs from January to December 2017 was examined on Sina Weibo (the Chinese equivalent of Twitter) to obtain broad insight into how MOOCs are viewed by the public in the Chinese educational landscape. Despite the unstable upward trend of public interest in MOOCs over the past 12 months, the public opinion on MOOCs was largely positive. Content and sentiment analyses were conducted to facilitate a better understanding of what is communicated on social media. A general model of public opinions of MOOCs in China has been developed based on the findings. Individuals were classified into a threefold typology based on the sources and purposes of how this recent form of distance education was perceived. Based on the seven themes, the public views towards MOOCs were differentiated among 'promoters', 'commenters' and 'experiencers'.Implications of the findings were also discussed. [ABSTRACT FROM AUTHOR]

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

The extensive use of greenhouses has brought soared economic benefits for farming practitioners in China and an overview of the spatio-temporal distribution of greenhouses is of great interest to agricultural practitioners and decision-makers. In this study, Landsat image based greenhouse maps in Guanzhong Plain, Shaanxi, China were made using random forest classification algorithm through visual interpretation on the Google Earth Engine. The 7-year's changes in greenhouse areas were investigated (i.e. 2000, 2003, 2006, 2010, 2013, 2015 and 2019) with yearly overall accuracy more than 90%. The results showed that the total area of greenhouses in Guanzhong Plain demonstrated an increasing trend, from 5.92 km2 in 2000 to 194.42 km2 in 2019 with a considerable growth between 2010 and 2015. The dominant drivers for the increase are largely attributed to the government policy as well as economic profitability. The distribution of greenhouse shifts to central and eastern regions of Guanzhong Plain. Greenhouses preferentially expand to the area near to rural roads, main rivers, and high elevation, with more than 45% greenhouses distributed within 1 km of the county rural road. The principal component analysis based suitability evaluation showed that a total of 38.44% of the area was suitable for greenhouse. [ABSTRACT FROM AUTHOR]

Spartina alterniflora has become one of the top exotic invasive plants in coastal China. Accurate and timely mapping of coastal wetlands is critical to understand the spatiotemporal dynamics of S. alterniflora invasion and native species degradation. Due to the humid weather and frequent cloud cover in coastal wetlands, pixel-based classifications based on time-series satellite observations have been increasingly utilized to mitigate this problem. However, the spatial and temporal variations in valid observations might affect pixel-based classification accuracies, while this issue has been paid little attention in previous research. In this paper, we conducted annual mapping of the Yellow River Delta (YRD) wetland from 2008 to 2019 based on time-series Landsat 5/7/8 images using harmonic regression analysis and random forest classification on Google Earth Engine. We further analysed how valid observations and temporal distribution of observations affected the classification accuracy. Our results showed that the overall classification accuracies ranged from 87.25% in 2012 to 92.36% in 2018. The mapping results showed that S. alterniflora expanded from 39.91 ha to 4672.38 ha from 2008 to 2019, encroaching seagrass beds with 902.32 ha and encroaching S.salsa and bare flat with 3730.15 ha. This is the first time that maps of coastal wetlands of YRD are produced at an annual time step. Our analysis showed that the number of valid observations affected classification accuracies both temporally and spatially. Years with higher density of observations witnessed higher overall accuracies compared to the years with fewer observations. Pixels with more observations had a higher chance to be correctly classified compared to the pixels with fewer observations. Harmonic regression features helped improve classification accuracies (increase in overall accuracies from 0.15% to 3.32%), especially for vegetation types (increase in F-score from 0.19% to 3.86%). Greater number of valid observations enhances the importance of harmonic regression features in classification. The critical months identified were March, July, and October, and the combinations of these months achieved better accuracies (91.24%) than using all other observations (89.68%) in 2018, suggesting that temporal distributions of observations could be more important than the number of observations for harmonic-based classification of coastal wetlands such as YRD. [ABSTRACT FROM AUTHOR]

Soybean is one of the most important crops in the world. There were dramatic changes in soybean plantation in China in recent decades. Heilongjiang Province has the greatest acreage of soybean plantation in China. Therefore, it is taken as a representative soybean region in this study for monitoring purposes. A 30-m dataset that mapped the areas of soybean planting in Heilongjiang Province was produced, for the period of 1984 ~ 2020, based on Landsat-5/7/8 images and the Google Earth Engine (GEE) platform. Maps were made with a random forest classifier and had overall accuracies better than 84%. The areas planted with soybean were calculated at both the provincial and city level and these were compared with the areas obtained from official statistics. At the provincial level, for more than 85% of the time periods, the areas given in the official statistics fell within the confidence intervals of the estimates. At the city level, soybean areas were also in good agreement with the statistical data in most years giving average R2 values as 0.75. These results demonstrate the effectiveness of using phenological features extracted by a double-logistic model and a linear harmonic model with the RF classifier to obtain a long time-series of soybean maps without using samples obtained by field measurements every year. [ABSTRACT FROM AUTHOR]

The increasing area of Plastic-Mulched Farmland (PMF) is aggravating the conflict between agricultural development and environmental protection. The spatial distribution of PMF requires an effective and economic technique. However, most works are currently carried out on pixel-level algorithms, which leads inevitably to mixed spectral errors. In this connection, PMF has been mapped with Pléiades and Radarsat-2 data combining object-based image analysis (OBIA) and Random Forest (RF). At first, through visual interpretation, the outcomes of various segmenting scenarios were used to select the optimum segmentation parameters. The spectral characteristics, textural and geometric features were then extracted and tailored to the best PMF mapping function subset. Finally, we map the PMF using the optimized object-level feature subset based on RF. The results show that the ability of Pléiades data to map PMF in Northern China is higher than that of Radarsat-2. The overall mapping accuracy achieved is 90.27%. In general, the precision and reliability of the mapping are the product of extensive structural data and object-level features that can reduce the reliance on spectral data. [ABSTRACT FROM AUTHOR]

Through large-scale afforestation and reforestation, China has increased its forest area and improved forest quality, making considerable contributions to carbon stocks of the world's forests. As one of the important indicators of forest quality and carbon potential, vegetation carbon density (VCD) is affected by different factors at different scales, but little is known about the effect of topographic factors on the VCD of subtropical forests in the southeast of China, especially at a large regional scale. Based on the national forest continuous inventory protocols, more than 2000 permanent samples (2061 samples in 2009, 2115 samples in 2014, and 2132 samples in 2016) of major forest types (Bamboo forests (BB), broad-leaved forests (BF), mixed forests (MF), coniferous forests (CF)) were grouped by topographic factors to compare the distribution of VCD in Zhejiang Province of China. The four topographic factors (altitude, slope gradient, aspect, and slope position) explained 16.2%~28.4% of the variance in different forest types in the random forest model and the importance order of the four terrain variables is basically corresponding to the significance in the radar charts. The VCD of each forest type varies significantly at different altitudes (p <.05, except for the BB samples in 2009). In general, VCD tends to be higher at high altitudes. The VCD in BB and BF varies significantly with slope gradient (p <.05, the largest on the steep slope and the smallest on the gentle slope); however, the VCD of CF and MF showed almost no difference in different slope gradients. We found significant differences in VCD between the eight aspects in the mixed forest (p <.05, the largest in the S and SW aspects), but not in the other three forest types. As for different slope positions, the VCD of ridges is relatively large, and the VCD of the upslope is generally larger than that of the downslope (more pronounced in BB and BF, all p <.05). This study made a detailed analysis of the greening distribution in different topographic factors, and the research conclusion could provide a scientific basis for the selection of afforestation location and forest scientific management, and contribute to the improvement of the capability of coping with climate change in subtropical forests. [ABSTRACT FROM AUTHOR]

The aim of this study was to illustrate the applicability of chemometric intraregional discrimination of three famous Chinese liquors in Sichuan province of China based on multi-element analytical results by atomic spectrometry. Twenty-one minor/trace and six macro elements were measured by inductively coupled plasma mass spectroscopy (ICP-MS) and inductively coupled plasma optical emission spectroscopy (ICP-OES), respectively. A two-step chemometric procedure was proposed, firstly finding out important element variables according to the random forests score of importance, and secondly predicting by random forests (RF) and support vector machine (SVM). It turned out that the liquor brand can be traced by constructing prediction models with 4 typical elements, Sr, Mg, Ca and Se. Meanwhile, an attempt at vintage prediction of two liquors from the same manufacturer was performed. These investigations showed that the brands of liquors in a small geographical area could be clearly identified by chemometric techniques combined with element analysis with only a few numbers of elements, and this will provide a convenient methodology for liquor differentiation and identification. [ABSTRACT FROM AUTHOR]

Diacetyl imparts a characteristic buttery aroma to foods and beverages. However, when present at high levels in Maotai-flavor liquor, it generates an undesirable buttery off-odor. The aim of this study was to identify the diacetyl-producing microorganisms in Maotai-flavor liquor with an ultimate aim to develop methods that could reduce diacetyl production. To explore the microbial origin of diacetyl in Maotai-flavor liquor, the off-odor intensity and diacetyl content of stacking fermented grains, which are used in the production of Maotai-flavor liquor, were evaluated. Then, 16S rRNA V3-V4 high-throughput sequencing and the random forest algorithm were combined to analyze the potential diacetyl producers. The featured selection results indicated that Lactobacillus was the major diacetyl contributor in the fermented grains, followed by Staphylococcus, Weissella, Pediococcus, and Klebsiella. To verify this, 123 lactic acid bacteria (LAB) isolates were selected from the high diacetyl off-odor group samples, and the genus Lactobacillus accounted for more than 90% of the identified isolates. The results of our study showed that L. plantarum, L. pentosus, and L. fermentum were the major diacetyl-producing microorganisms (>90 mg/L). Moreover, the fermentation characteristics of the high-yield strain, L. plantarum MTL-09, showed that temperature and pH had strong effects on diacetyl production, which may offer a strategy for inhibiting the off-odor by controlling the pH or temperature during fermentation. The methods may also be useful for identification of key microorganisms for fermented foods and alcoholic beverages. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the contributing factors to serious casualty crashes in China. Crashes with deaths greater than 10 people are defined as serious casualty crashes in China. The serious casualty crash data were collected from 2009 to 2014. The random forest analysis was first conducted to select the candidate variables that affect the risks of serious casualty crashes. The Bayesian random parameters accelerated failure time (AFT) model was then developed to link the probability of the serious casualty crash with road geometric conditions, pavement conditions, environmental characteristics, collision characteristics, vehicle conditions, and driver characteristics. The AFT model estimation results indicate that overload driving, country road, northwest china region, turnover crash, private car, snowy or icy road surface and sight distance conditions have significant fixed effects on the likelihood of serious casualty crashes. In addition to these fixed-parameter variables, freeway, clear weather conditions, coach drivers, and upgrade horizontal curve affect the likelihood of serious casualty crashes with varying magnitude across observations. One of the important findings is that the serious casualty crash likelihood does not always decrease with an increase in the driving experience (number of years driven). Before the inflection point of 7 years, the serious casualty crash likelihood increases as the driving experience grows. The results of this study can help to develop effective countermeasures and policy initiatives for the prevention of serious casualty crashes. [ABSTRACT FROM AUTHOR]

Forest city (FC) usually refers to an urban area with high forest coverage. It is a green model of urban development that has been strongly advocated for by governments of many nations. Forest fire is a prominent threat in FC development, but the causes of fires in FCs are usually different and more complex than in pure forested areas since more socio-economic factors and human activity are involved in the ignition and spread of fire. The large and increasing number of lives being exposed to wildfire hazard highlights the need to understand the characteristics of these fires so that forest fire prediction and prevention can be efficient. In this study, Ripley's K(d) function and Random Forests (RF) were applied to analyze the drivers, spatial distribution and risk patterns of fires in Yichun, a typical FC in China. The results revealed a clustered distribution of forest fire ignitions in Yichun, as well as identified the driving factors and their dynamic influence on fire occurrence. Fire risk zones were identified based on RF modelling. Improved preventive measures can be implemented in the fire prone areas to reduce the risk of fire in Yichun by considering the factors identified in this study. [ABSTRACT FROM AUTHOR]