Search Results (5)

As a critical aquatic–terrestrial ecological transition zone, the lake littoral zone exhibits steep biogeochemical gradients and plays a vital role in regulating submerged microbial communities and their functions. This study aims to reveal how multi-scale environmental gradients influence microbial succession processes along desert lake littoral zones, as well as the distribution patterns of functional genes involved in carbon (C), nitrogen (N), and sulfur (S) cycling. The results demonstrated that microbial alpha-diversity in the vadose zone exhibited significant individual variability horizontally, while showing pronounced inter-group differences vertically. Horizontally, a distinct functional succession was observed from the shore to the water’s edge, with microbial potential shifting progressively from aerobic oxidative types toward anaerobic reductive types. Vertically, the root-intensive layer fostered more complex co-occurrence networks through enhanced interspecific interactions, suggesting higher functional resilience compared to other layers. Further analysis identified soil moisture as the primary environmental filter driving microbial composition, explaining 27.7% of the variation. Structural equation modeling (SEM) further elucidated that pH and Total Organic Carbon (TOC) were the key regulators of carbon fixation and sulfur oxidation genes, while Total Nitrogen (TN) dominated the distribution patterns of nitrogen cycling genes. These findings deepen the mechanistic understanding of microbial-mediated element cycling in desert lakeshore zones and provide a theoretical basis and data support for maintaining the functions of these fragile ecosystems. Full article

The WuShen counties in the hinterland of the Maowusu Sandland are located in the “ecological stress zone” of the forest–steppe desert, with low vegetation cover, a strong ecosystem sensitivity, and poor stability under the influence of human activities. Therefore, it is important to study and analyze the changes in vegetation growth in this region for the purpose of objectively evaluating the effectiveness of desertification control in China’s agricultural and pastoral intertwined zones, and formulating corresponding measures in a timely manner. In this paper, the spatial and temporal variations in the vegetation NDVI in the WuShen counties of the Maowusu Sandland and their response relationships with driving factors were investigated by using a trend test, center of gravity transfer model, partial correlation calculation, and residual analysis, and by using the MOD13A3 vegetation NDVI time series data from 2000 to 2020, as well as the precipitation, temperature, and potential evapotranspiration data from the same period. The results showed the following: ① The regional vegetation NDVI did not fluctuate significantly with latitude and longitude, and the NDVI varied between 0.227 and 0.375 over the 21-year period, with a mean increase of 0.13 for the region as a whole and an increase of 0.61 for the region of greatest change. Of the area, 86.83% experienced a highly significant increase, and the trend in increase around rivers and towns was higher than that in the northwestern inland flow area, with the overall performance of “low in the west and high in the east”. ② Only 2.07% of the vegetation NDVI center of gravity did not shift, and the response with climate factors was mainly characterized by having consistent or opposite center of gravity changes with precipitation and potential evapotranspiration. ③ Human activities have been the dominant factor in the vegetation NDVI change, with 75.89 percent of the area positively impacted by human activities, and human activities in the southwest inhibiting the improvement of vegetation in the area. The impact of human activities on the unchanged land type area is increasing, most obviously in the farmland area, and the impact of human activities on the changed land type area is gradually decreasing in the area where the farmland becomes impervious. The vegetation in the area above 1300 m above sea level is degraded by the environment and human activities. The research results can provide scientific support for the implementation of ecological fine management and the formulation of corresponding ecological restoration and desertification control measures in the Maowusu Sandland. At the same time, it is expected to serve as a baseline for other studies on the evolution of vegetation in agro-pastoral zones. Full article

To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study’s findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions. Full article

It is essential to monitor the dynamics of vegetation at different scales in space and time to promote the sustainable development of terrestrial ecosystems. We used the Google Earth Engine (GEE) cloud platform to perform a comprehensive analysis of the changes in normalized difference vegetation index (NDVI) Mann-Kendall (MK) + Sen trend in the hinterland region of the Maowusu sandland in China over the last two decades. We performed bias-correlation studies using soil and climate data. Furthermore, we performed a partial Mantel test to analyze the spatial and temporal fluctuations of vegetation health-related indices. Additionally, we developed a logistic dual model of the phenology index using the Lenvenberg–Marquardt technique. The objective was to uncover the factors contributing to the regional shifts in vegetation dynamics. We provide a comprehensive analytic method designed to monitor vegetation over some time and forecast its future recovery. The findings indicate that over the past 20 years, more than 90% of the regional NDVI in the study area has exhibited a consistent and significant upward trend. This trend is primarily influenced by the adverse impact of temperature and the beneficial impact of precipitation. Additionally, long-term phenological indicators in the study area reveal that the vegetation’s growth cycle commences on the 125th day of the year and concludes on the 267th day of the year. This suggests that the shorter duration of the vegetation’s growth season may be attributed to the local climate and unfavorable groundwater depth conditions. levated temperatures throughout the next spring and autumn seasons would significantly affect the wellbeing of plants, with soil moisture being a crucial determinant of plant development in the examined region. This study presents a wide range of analytical tools for monitoring vegetation over a long period and predicting its future recovery. It considers factors such as vegetation health, phenology, and climatic influences. The study establishes a solid scientific foundation for understanding the reasons behind regional vegetation changes in the future. Full article

Terrestrial ecosystems depend heavily on their vegetation; it is possible to forecast future growth trends of regional vegetation by keeping an eye on changes in vegetation dynamics. To circumvent the potential reduction in prediction accuracy caused by the non-stationarity of meteorological changes, we analyzed the characteristics of NDVI (Normalized Difference Vegetation Index) spatial and temporal changes and the influencing factors over the past 20 years in the Maowusu Sandland of China via attribution analysis. We also constructed a comprehensive analysis system for vegetation pre-restoration. Moreover, we combined meteorological data from 2000 to 2018 and presented a deep-learning NDVI-Informer prediction model with a self-attentive mechanism. We also used distillation operation and fusion convolutional neural network for NDVI prediction. Incorporating a probsparse self-attention method successfully overcomes Transformer weaknesses by lowering the memory use and complexity of large time series. It significantly accelerates the inference speed of long time series prediction and works well with non-smooth data. The primary findings were: (1) the Maowusu Sandland’s 20-year average showed a consistent increasing trend in the NDVI at 0.0034 a⁻¹, which was mostly caused by climate change, with a relative contribution rate of 55.47%; (2) The Informer-based model accurately forecasted the NDVI in the research region based on meteorological elements and conducted a thorough analysis of the MAPE (mean absolute percentage error) (2.24%). This suggests that it can effectively lower the data’s volatility and increase prediction accuracy. The anticipated outcomes indicate that the trend will stabilize during the following ten years. To attain more sustainable and efficient agricultural production, the results of this study may be used to accurately estimate future crop yields and NDVI using previous data. Full article