[Spatio-temporal variations of vegetation cover in semi-arid regions and its response to climate change: A case study of Xilin Gol, Inner Mongolia, China]. / åŠå¹²æ—±åŒºæ¤è¢«è¦†ç›–æ—¶ç©ºå˜åŒ–ç‰¹å¾åŠå ¶å¯¹æ°”å€™å˜åŒ–çš„å“åº”: 以锡林郭勒盟为例.

The quantitative analysis of spatio-temporal variations of vegetation cover and its correlation with climate are of great significance for understanding of ecological environment, ecological civilization construction, and sustainable development in semi-arid areas. We investigated the spatio-temporal variations of normalized difference vegetation index (NDVI) and its response to climate change during 2000-2020 in Xilin Gol, Inner Mongolia, by using trend analysis, regression analysis and partial correlation analysis based on the data of MODIS-NDVI, tempe-rature, precipitation, digital elevation model. The results showed that vegetation cover in Xilin Gol had been increased from 2000 to 2020, which generally included three phases, i.e., stable fluctuation, rapid growth, and steady growth. The mean NDVI showed a zonal increasing distribution from southwest to northeast, and had a strong correlation with elevation and population density in Xilin Gol region. The high values of NDVI were mainly in the east, with a significant increasing trend, and the low values were in the southwest, with a local degradation. The sensitivity of vegetation cover to climate change showed spatial and temporal variations. The spatial variation of vegetation was more sensitive to temperature and the interannual variation was sensitive to annual precipitation. In summary, vegetation cover improved overall in Xilin Gol, but there was degradation in some areas. We should formulate differentiated and precise vegetation restoration and ecological environmental protection policies.

Abnormal resting-state hyperconnectivity in schizophrenia: A whole-head near-infrared spectroscopy study.

Near-infrared spectroscopy (NIRS) is a noninvasive functional neuroimaging modality that can detect changes in blood oxygenation levels by tracking cortical neural activity. We recorded the resting-state brain activity of 24 individuals with schizophrenia and 90 healthy controls for 8 min using a whole-head NIRS arrangement and then used partial correlation analysis to estimate the resting-state functional connectivity (RSFC) between 17 cortical regions. We found that the RSFC between the bilateral orbitofrontal cortices (OFCs) and between the right temporal and parietal lobes was significantly higher in patients with schizophrenia than in healthy controls. The RSFC between the bilateral OFCs was positively correlated with negative symptom severity, whereas the RSFC between the right temporal and parietal lobes was positively correlated with the chlorpromazine equivalent for antipsychotics prescribed to patients with schizophrenia. This finding was consistent with that for the RSFC calculated using the anterior 52-channel signals. Our results suggest that NIRS-based RSFC measurements have potential clinical applications.

Spatiotemporal evolution characteristics and driving forces of vegetation cover variations in the Chengdu-Chongqing region of China under the background of rapid urbanization.

The research on the spatiotemporal changes and driving factors of ecosystems in rapidly urbanizing regions has always been a topic of widespread concern. As the fourth pole of China's economic development, the research on the Chengdu-Chongqing region has reference significance for the urbanization process of developing countries such as India, Brazil, and South Africa.The normalized difference vegetation index (NDVI) has been widely applied in studies of plant and ecosystem changes. Based on MODIS NDVI data from 2001 to 2020 and meteorological data of the same period, this study reveals the evolution of NDVI in the Chengdu-Chongqing region from three aspects: the spatiotemporal variation characteristics of NDVI, the prediction of future trends in vegetation coverage, and the response of vegetation to climate change and human activities. During the period of plant growth, the mean NDVI achieved a value of 0.78, and the vegetation coverage rate is increasing year by year. According to the Hurst index, the future NDVI in Chengdu-Chongqing region will tend to decrease, and its trend is opposite to that of the past period of time. The Chengdu-Chongqing region vegetation positively affected by human activities is greater than those negatively affected, and in terms of vegetation degradation, the impact of human activities is greater than climate change.

Mapping and analyzing the spatiotemporal dynamics of forest aboveground biomass in the ChangZhuTan urban agglomeration using a time series of Landsat images and meteorological data from 2010 to 2020.

In the context of global warming, there is a substantial demand for accurate and cost-effective assessment and comprehensive understanding of forest above-ground biomass (AGB) dynamics. The timeliness and low cost of optical remote sensing data enable the mapping of large-scale forest AGB dynamics. However, mapping forest AGB with optical remote sensing data presents challenges primarily due to data uncertainty and the complex nature of the forest environment. Previous studies have demonstrated the potential of meteorological data in enhancing forest AGB mapping. To accurately capture the dynamics of forest AGB, we initially acquired Landsat datasets, digital elevation model (DEM), and meteorological datasets (temperature, humidity, and precipitation) from 2010 to 2020 in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) located in Hunan Province, China. Spectral variables (SVs), including spectral bands and vegetation indices, were extracted from Landsat images, while meteorological variables (MVs) were derived from the monthly meteorological data using the Savitzky-Golay (S-G) filtering algorithm. Additionally, terrain variables (TVs) were also extracted from the DEM data. Three modelling models, multiple linear regression (MLR), K nearest neighbor (KNN) and random forest (RF), were developed for mapping the dynamics of forest AGB in CZT. The result revealed that MVs have the potential to improve forest AGB mapping. Integration of MVs into the models resulted in a significant reduction in root mean square error (RMSE) ranging from 32.85 % to 19.25 % compared to utilizing only SVs. However, minimal improvement was observed with the inclusion of TVs due to negligible topographic relief within the study area. An upward trend of forest AGB in CZT was observed during this period, which can be attributed to the effective implementation of government environmental protection policies. It is confirmed that the meteorological data has significant contribution to forest AGB mapping, thereby endorsing advancements in forest resource monitoring and management programs.

Coupled analysis of arable land input intensity and output intensity based on sliding windows.

Sustainable intensification (SI) of agriculture can produce more food to meet the demand of a growing population while considering ecosystem health. The current SI estimation framework ignores the complex coupling between input and output intensity of arable land. A method for coupled analysis of arable land input intensity and output intensity based on sliding windows is proposed. By calculating the correlation coefficient and partial correlation coefficient between input intensity and output intensity in different value ranges as the order parameter, the phase transition and the influence process of input intensity on output intensity can be explained. Meanwhile, a python-based framework is developed. An application of the method was made to reveal the interaction process between annual provincial input intensity and output intensity in mainland China. Researchers in many fields may benefit from the method by obtaining a fast way to analysis the coupling relationship between driving and dependent variables in complex systems.•New method for SI estimation is presented.•The order parameter of the coupling relationship between input and output intensity is calculated based on sliding windows.•Analysis of coupling relationships between driving and dependent variables in complex systems.