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Collichthys lucidus is a small fish found in offshore waters that is economically important for China. It is imperative to understand its distribution characteristics and driving factors. Based on survey data of trawl fishery resources offshore of Zhejiang province, China, in spring (April) and autumn (November) from 2018 to 2022, the spatial and temporal distributions of C. lucidus in this area were analyzed. The random forest (RF) model was used to determine the important marine factors affecting the distribution of C. lucidus. The relationship between the distributions of the important variables was analyzed. The results showed that C. lucidus was mainly distributed in coastal waters. The tail density of the species exhibited obvious seasonal variation and was significantly greater in autumn than in spring. The most important factor affecting the distribution of this species in spring and autumn was water depth. The bottom temperature, bottom salinity and dissolved oxygen concentration were also important influencing factors. The importance of these factors differed among the different seasons, while the chlorophyll a concentration and pH had no significant effect on the species distribution. This study revealed the distribution pattern of C. lucidus in offshore waters of Zhejiang Province and the influence of important marine factors on its distribution. This study can enrich the survey data on C. lucidus and provide basic data for its scientific management and protection. [ABSTRACT FROM AUTHOR]

Moso bamboo (Phyllostachys edulis) is a crucial species among the 500 varieties of bamboo found in China and plays an important role in providing ecosystem services. However, remote sensing studies on the invasion of Moso bamboo, especially its impact on forest biodiversity, are limited. Therefore, we explored the feasibility of using Sentinel-2 multispectral data and digital elevation data from the Shuttle Radar Topography Mission and random forest (RF) algorithms to monitor changes in forest diversity due to the spread of Moso bamboo. From October to November 2019, researchers conducted field surveys on 100 subtropical forest plots in Zhejiang Province, China. Four biodiversity indices (Margalef, Shannon, Simpson, and Pielou) were calculated from the survey data. Subsequently, after completing 100 epochs of training and testing, we developed the RF prediction model and assessed its performance using three key metrics: coefficient of determination, root mean squared error, and mean absolute error. Our results showed that the RF model has a strong predictive ability for all indices except for the Pilou index, which has an average predictive ability. These results demonstrate the feasibility of using remote sensing to monitor forest diversity changes caused by the spreading of Moso bamboo. [ABSTRACT FROM AUTHOR]

China has implemented a series of forestry law, policies, regulations, and afforestation projects since the 1970s. However, their impacts on the spatial and temporal patterns of forests have not been fully assessed yet. The lack of an accurate, high-resolution, and long-term forest disturbance and recovery dataset has impeded this assessment. Here we improved the forest loss and gain detections by integrating the LandTrendr change detection algorithm with the Random Forest (RF) machine-learning method and applied it to assess forest loss and gain patterns in the Zhejiang, Jiangxi, and Guangxi Provinces of the subtropical vegetation in China. The accuracy evaluation indicated that our approach can adequately detect the spatial and temporal distribution patterns in forest gain and loss, with an overall accuracy of 93% and the Kappa coefficient of 0.89. The forest loss area was 8.30 × 104 km2 in the Zhejiang, Jiangxi, and Guangxi Provinces during 1986–2019, accounting for 43.52% of total forest area in 1986, while the forest gain area was 20.25 × 104 km2, accounting for 106.19% of total forest area in 1986. Although the interannual variation patterns were similar among three provinces, the forest loss and gain area and the magnitude of change trends were significantly different. Guangxi has the largest forest loss and gain area and increasing trends, followed by Jiangxi, and the least in Zhejiang. The variations in annual forest loss and gain area can be mostly explained by the timelines of major forestry policies and regulations. Our study would provide an applicable method and data for assessing the impacts of forest disturbance events and forestry policies and regulations on the spatial and temporal patterns of forest loss and gain in China, and further contributing to regional and national forest carbon and greenhouse gases budget estimations. [ABSTRACT FROM AUTHOR]

As an important economic tree species in China, tea trees play a critical role in promoting economic development. However, the expanding area of tea plantations in China over the recent years has reduced the biodiversity of the ecosystem and accelerated ecological problems. Identifying the spatial distribution of the tea plantation accurately is important for the monitoring of the tea plantation as well as the development of the tea industry. In this study, Wangzhai Town, located in the central of Wuyi County in Zhejiang Province, was chosen as the study area, and Gaofen-1 (GF-1) data on December 28, 2017, and all available Sentinel-2 data in 2017 were downloaded to study the tea plantations extraction method. The spectral and texture features of tea plantations are similar with crops on the high spatial resolution remote sensing image which results in the mixture of the tea plantations and crops. The Normalized Difference Vegetation Index (NDVI) time series data can reflect the phenological changes of the tea trees and crops, which can be used to improve the separability of tea plantations and crops. In this study, a phenology-based approach was applied to the NDVI time series to distinguish tea plantations from crops by comparing the similarity of the seasonal changes of NDVI time series. Due to the influence of cloud and fog on the satellite image, the complete Sentinel-2 time series data is difficult to be obtained. Therefore, the high temporal resolution Moderate-resolution Imaging Spectroradiometer (MODIS) reflection product (MCD43A4) in 2017 was used to reconstruct the complete Sentinel-2 NDVI time series data (5 m spatial resolution, 5 d temporal resolution) based on Bayesian Maximum Entropy (BME) spatial-temporal fusion algorithm. Then, the Dynamic Time Wraping (DTW) distance was calculated based on the DTW algorithm to characterize the similarity of NDVI time series between the tea plantation samples and other pixels to be classified. Combining the advantages of GF-1 in spatial details and the high temporal resolution of reconstructed Sentinel-2 NDVI time series in capturing the difference of growth process of tea trees, the tea plantation was identified based on random forest algorithm. Two identification schemes: 1) spectral and texture features based on GF-1; 2) spectral, texture features based on GF-1 and DTW distance were set up to investigate the impact of NDVI time series on the tea plantation identification. The results showed that: 1) the reconstructed NDVI time series data derived from MODIS and Sentinel-2 based on spatio-temporal fusion algorithm is able to capture the seasonal dynamics of different objects, and the DTW distance can capture the difference of NDVI time series data between crops and tea plantations and can be used for tea plantations extraction; 2) the accuracy、 error rate、precision、recall and F1score were 96.91%、3.09%、89.00%、83.09% and 0.86 respectively combining texture and spectral information of GF-1 and Sentinel-2 NDVI time series information, which were 94.72%、5.28%、73.09%、84.61% and 0.78 respectively without Sentinel-2 NDVI time series information. Overall, the results of combining GF-1 and Sentinel-2 time series data performed better than that only based on the spectral and texture features of GF-1. These results confirmed that combining high spatial resolution with high temporal resolution time series remote sensing data is an effective method to improve the identification accuracy of tea plantations. [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 large-scale proliferation of China's new type of agricultural entities has given rise to a higher demand for funds. Farmers have insufficient effective collateral, which makes it difficult for them to obtain sufficient loans. Chinese financial institutions have developed a biological asset mortgage loan business to cope with this situation. China has not considered biological mortgages but has been using real estate and asset mortgage models with strong realizability. This innovative financial business has achieved positive results since it was attempted, but it also faces many risks. It is very important to comprehensively and accurately consider the risk factors of biological asset mortgage loans. Based on 1249 production and operation data samples of new agricultural entities in Zhejiang, Henan, and Shandong provinces, this study constructs an XGBoost model for empirical analysis and compares it with logical regression, support vector machine, and random forest algorithms to obtain the optimal model and feature importance value. According to the characteristic importance value, a biological asset mortgage loan risk assessment system with 4 primary indicators and 20 secondary indicators is established, which can effectively identify the biological asset mortgage loan risk of new agricultural entities. [ABSTRACT FROM AUTHOR]

Due to inherent errors in the chemical transport models, inaccuracies in the input data, and simplified chemical mechanisms, ozone (O 3) predictions are often biased from observations. Accurate O 3 predictions can better help assess its impacts on public health and facilitate the development of effective prevention and control measures. In this study, we used a random forest (RF) model to construct a bias-correction model to correct the bias in the predictions of hourly O 3 (O 3 -1h), daily maximum 8-h O 3 (O 3 -Max8h), and daily maximum 1-h O 3 (O 3 -Max1h) concentrations from the Community Multi-Scale Air Quality (CMAQ) model in the Yangtze River Delta region. The results show that the RF model successfully captures the nonlinear response relationship between O 3 and its influence factors, and has an outstanding performance in correcting the bias of O 3 predictions. The normalized mean biases (NMBs) of O 3 -1h, O 3 -Max8h, and O 3 -Max1h decrease from 15.8%, 20.0%, and 17.0.% to 0.5%, −0.8%, and 0.1%, respectively; correlation coefficients increase from 0.78, 0.90, and 0.89 to 0.94, 0.95, and 0.94, respectively. For O 3 -1h and O 3 -Max8h, the original CMAQ model shows an obvious bias in the central and southern Zhejiang region, while the RF model decreases the NMB values from 54% to −1% and 34% to −4%, respectively. The O 3 -1h bias is mainly caused by the bias of nitrogen dioxide (NO 2). Relative humidity and temperature are also important factors that lead to the bias of O 3. For high O 3 concentrations, the temperature bias and O 3 observations are the major reasons for the discrepancy between the model and the observations. [Display omitted] • The RF model greatly improves accuracy of hourly O 3 predictions from CMAQ. • The RF model significantly reduced O 3 bias in the central and southern Zhejiang. • O3-1h bias is mainly caused by the bias of nitrogen dioxide. • Relative humidity and temperature have important impacts on the O 3 bias. [ABSTRACT FROM AUTHOR]

[Display omitted] • Untargeted metabolomics was used to reveal the chemical differences of Hangbaiju. • Influence of soil mineral elements on the content of differential metabolites was investigated. • Relationship between metabolites and mineral elements was explored by chemometric analysis. • Differential metabolites were significantly correlated with mineral elements in planting soils. • This work provides new insights for better understanding geographical differences of Hangbaiju. Chrysanthemum morifolium Ramat cv. "Hangbaiju" (HBJ), known as one of the "eight flavors of Zhejiang", is commonly used as a classical tea material for both food and medicine over three thousand years in China. The quality of HBJ is closely related to its geographical origins. However, the mechanism underlying the geographical differences of HBJ remains to be elucidated. In this study, an untargeted metabolomic strategy based on UHPLC-QTOF-MS was established to discover the differential metabolites in HBJ samples from four different origins and explore the possible relationship with mineral elements in planting soils by chemometric analysis. Eight compounds were screened and identified as the key differential metabolites in HBJ samples from different origins. Among them, four important pharmacodynamic compounds including L-arginine, rutin, chlorogenic acid and apigenin-7-O-glucoside are the most abundant in HBJ samples from Tongxiang region, which suggests that HBJ planted in Tongxiang has higher medicinal values. Pearson correlation analysis revealed that the contents of soil mineral elements are positively correlated with those of chlorogenic acid, rutin, apigenin-7-O-glucoside in HBJ samples. Furthermore, an interrelationship model based on random forest algorithm was established to successfully predict the contents of differential metabolites in HBJ samples by soil mineral elements. All these results indicated that the contents of differential metabolites in HBJ samples seemed to be affected by soil mineral elements and therefore resulted in the geographical differences of HBJ. [ABSTRACT FROM AUTHOR]