Concurrent and lagged effects of drought on grassland net primary productivity: a case study in Xinjiang, China

Xinjiang grasslands play a crucial role in regulating the regional carbon cycle and maintaining ecosystem stability, and grassland net primary productivity (NPP) is highly vulnerable to drought. Drought events are frequent in Xinjiang due to the impact of global warming. However, there is a lack of more systematic research results on how Xinjiang grassland NPP responds to drought and how its heterogeneity is characterized. In this study, the CASA (Carnegie Ames Stanford Application) model was used to simulate the 1982–2020 grassland NPP in Xinjiang, and the standardized Precipitation Evapotranspiration Index (SPEI) was calculated using meteorological station data to characterize drought. The spatial and temporal variability of NPP and drought in Xinjiang grasslands from 1982 to 2020 were analyzed by the Sen trend method and the Mann-Kendall test, and the response characteristics of NPP to drought in Xinjiang grasslands were investigated by the correlation analysis method. The results showed that (1) the overall trend of NPP in Xinjiang grassland was increasing, and its value was growing season > summer > spring > autumn. Mild drought occurred most frequently in the growing season and autumn, and moderate drought occurred most frequently in spring. (2) A total of 64.63% of grassland NPP had a mainly concurrent effect on drought, and these grasslands were primarily located in the northern region of Xinjiang. The concurrent effect of drought on NPP was strongest in plain grassland and weakest in alpine subalpine grassland. (3) The lagged effect is mainly in the southern grasslands, the NPP of alpine subalpine meadows, meadows, and alpine subalpine grasslands showed mainly a 1-month time lag effect to drought, and desert grassland NPP showed mainly a 3-month time lag effect to drought. This research can contribute to a reliable theoretical basis for regional sustainable development

Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region

Genetic structure and insecticide resistance characteristics of fall armyworm populations invading China

The rapid wide‐scale spread of fall armyworm (Spodoptera frugiperda ) has caused serious crop losses globally. However, differences in the genetic background of subpopulations and the mechanisms of rapid adaptation behind the invasion are still not well understood. Here we report the assembly of a 390.38Mb chromosome‐level genome of fall armyworm derived from south‐central Africa using Pacific Bioscience (PacBio) and Hi‐C sequencing technologies, with scaffold N50 of 12.9 Mb and containing 22260 annotated protein‐coding genes. Genome‐wide resequencing of 103 samples and strain identification were conducted to reveal the genetic background of fall armyworm populations in China. Analysis of genes related to pesticide‐ and Bt‐resistance showed that the risk of fall armyworm developing resistance to conventional pesticides is very high. Laboratory bioassay results showed that insects invading China carry resistance to organophosphate and pyrethroid pesticides, but are sensitive to genetically modified maize expressing the Bacillus thuringiensis (Bt) toxin Cry1Ab in field experiments. Additionally, two mitochondrial fragments were found to be inserted into the nuclear genome, with the insertion event occurring after the differentiation of the two strains. This study represents a valuable advance toward improving management strategies for fall armyworm

Deep Learning Technique Based Surveillance Video Analysis for the Store

AI technology has developed so fast, and it has been applied to the commercial area. In order to predict the customer preference and adjust the placement of product or advertisement, etc., the intelligent surveillance video analysis technique has been proposed to gather the sufficient customer information and realize crowd counting and density map drawing. In this paper, a series of deep learning techniques are adopted to realize surveillance video analysis. This work covers different subproblems such as object detection, tracking and human identification. A skeleton recognition algorithm is adopted instead of object detection algorithm to overcome the severe occlusion problem. A multiple human tracking algorithm combing the human re-identification technology is adopted to realize the human tracking and counting. Finally, the density map and statistics information are obtained which can be used to evaluate and adjust the current business plan. A real store surveillance video is analyzed by the algorithm, and the results show the advantage of the algorithm

An Accurate Digital Subsidence Model for Deformation Detection of Coal Mining Areas Using a UAV-Based LiDAR

Coal mine surface subsidence detection determines the damage degree of coal mining, which is of great importance for the mitigation of hazards and property loss. Therefore, it is very important to detect deformation during coal mining. Currently, there are many methods used to detect deformations in coal mining areas. However, with most of them, the accuracy is difficult to guarantee in mountainous areas, especially for shallow seam mining, which has the characteristics of active, rapid, and high-intensity surface subsidence. In response to these problems, we made a digital subsidence model (DSuM) for deformation detection in coal mining areas based on airborne light detection and ranging (LiDAR). First, the entire point cloud of the study area was obtained by coarse to fine registration. Second, noise points were removed by multi-scale morphological filtering, and the progressive triangulation filtering classification (PTFC) algorithm was used to obtain the ground point cloud. Third, the DEM was generated from the clean ground point cloud, and an accurate DSuM was obtained through multiple periods of DEM difference calculations. Then, data mining was conducted based on the DSuM to obtain parameters such as the maximum surface subsidence value, a subsidence contour map, the subsidence area, and the subsidence boundary angle. Finally, the accuracy of the DSuM was analyzed through a comparison with ground checkpoints (GCPs). The results show that the proposed method can achieve centimeter-level accuracy, which makes the data a good reference for mining safety considerations and subsequent restoration of the ecological environment

A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation

Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection

LiDAR SLAM algorithm considering dynamic extraction of feature points in underground coal mine

Aiming at the problem that there is no GNSS signal in the coal mine, the state-of-the-art LiDAR SLAM algorithm is prone to degenerate due to insufficient feature constraints, a tight coupling SLAM algorithm of LiDAR and IMU for the coal mine environment is proposed. First, we design a dynamic feature point extraction method, by detecting whether there is degradation in the underground environment of coal mine to dynamically adjust the number of feature points extracted, build a rich and good feature information constraint matrix, improve the accuracy of pose estimation; then, the factor diagram optimization is used to realize the robust and accurate SLAM in the coal mine. Finally, a wide range of experimental analysis is carried out through the measured data in the coal mine. The results show that the proposed laser SLAM algorithm performs well, the pose estimation error is reduced by 50.93% in the horizontal direction and 42.13% in the vertical direction compared with LIO_SAM, it can provide technical reference for intelligent perception and safety inspection of coal mine robots

Combined Effects of Meteorological Factors, Terrain, and Greenhouse Gases on Vegetation Phenology in Arid Areas of Central Asia from 1982 to 2021

Spatiotemporal variations in Central Asian vegetation phenology provide insights into arid ecosystem behavior and its response to environmental cues. Nevertheless, comprehensive research on the integrated impact of meteorological factors (temperature, precipitation, soil moisture, saturation vapor pressure deficit), topography (slope, aspect, elevation), and greenhouse gases (carbon dioxide, methane, nitrous oxide) on the phenology of Central Asian vegetation remains insufficient. Utilizing methods such as partial correlation and structural equation modeling, this study delves into the direct and indirect influences of climate, topography, and greenhouse gases on the phenology of vegetation. The results reveal that the start of the season decreased by 0.239 days annually, the length of the season increased by 0.044 days annually, and the end of the season decreased by 0.125 days annually from 1982 to 2021 in the arid regions of Central Asia. Compared with topography and greenhouse gases, meteorological factors are the dominant environmental factors affecting interannual phenological changes. Temperature and vapor pressure deficits (VPD) have become the principal meteorological elements influencing interannual dynamic changes in vegetation phenology. Elevation and slope primarily regulate phenological variation by influencing the VPD and soil moisture, whereas aspect mainly affects the spatiotemporal patterns of vegetation phenology by influencing precipitation and temperature. The findings of this study contribute to a deeper understanding of how various environmental factors collectively influence the phenology of vegetation, thereby fostering a more profound exploration of the intricate response relationships of terrestrial ecosystems to environmental changes