Grassland is the vegetation type with the widest coverage on the Qinghai-Tibet Plateau. Under the influence of multiple factors, such as global climate change and human activities, grassland is undergoing temporal and spatially different disturbances and changes, and they have a significant impact on the grassland ecosystem of the Qinghai-Tibet Plateau. Therefore, timely and dynamic monitoring of grassland disturbances and distinguishing the reasons for the changes are essential for ecological understanding and management. The purpose of this research is to propose a knowledge-based strategy to realize grassland dynamic distribution mapping and analysis of grassland disturbance changes in the region that are suitable for the Qinghai-Tibet Plateau. The purpose of this study is to propose an analysis algorithm that uses first annual mapping and then establishes temporal disturbance rules, which is applicable to the integrated exploration of disturbance changes in highland-type grasslands. The characteristic indexes of greenness and disturbance indices in the growing period were constructed and integrated with deep neural network learning to dynamically map the grassland for many years. The overall accuracy of grassland mapping was 94.11% and that of Kappa was 0.845. The results show that the area of grassland increased by 11.18% from 2001 to 2017. Then, the grassland disturbance change analysis method is proposed in monitoring the grassland distribution range, and it is found that the area of grassland with significant disturbance change accounts for 10.86% of the total area of the Qinghai-Tibet Plateau, and the disturbance changes are specifically divided into seven types. Among them, the type of degradation after disturbance mainly occurs in Tibet, whereas the main types of vegetation greenness increase in Qinghai and Gansu. At the same time, the study finds that climate change, altitude, and human grazing activities are the main factors affecting grassland disturbance changes in the Qinghai-Tibet Plateau, and there are spatial differences.
In 2020 and 2021 the Southeast Coast Network (SECN) collected shoreline data at Fort Matanzas National Monument as a part of the NPS Vital Signs Monitoring Program. Monitoring was conducted following methods developed by the National Park Service Northeast Barrier Coast Network and consisted of mapping the high tide swash line using a global positioning system (GPS) unit in the spring of each year (Psuty et al. 2010). Shoreline change was calculated using the Digital Shoreline Analysis System (DSAS) developed by USGS (Theiler et al. 2008). Key findings from this effort: A mean of 2,255.23 meters (7,399 feet [ft]) of shoreline were mapped from 2020 to 2021 with a mean horizontal precision of 10.73 centimeters (4.2 inches [in]) at Fort Matanzas National Monument from 2020 to 2021. In the annual shoreline change analysis, the mean shoreline distance change from spring 2020 to spring 2021 was -7.40 meters (-24.3 ft) with a standard deviation of 20.24 meters (66.40 ft). The shoreline change distance ranged from -124.73 to 35.59 meters (-409.1 to 116.7 ft). Two erosion areas and one accretion area were identified in the study area beyond the uncertainty of the data (± 10 meters [32.8 ft]). The annual shoreline change from 2020 to 2021 showed erosion on the east and west sides of A1A where the Matanzas Inlet is located. Overall, the most dynamic area of shoreline change within Fort Matanzas National Monument appeared to be on the east and west side of A1A, along the Matanzas River inlet.
Abstract. 4D topographic point cloud data contain information on surface change processes and their spatial and temporal characteristics, such as the duration, location, and extent of mass movements, e.g., rockfalls or debris flows. To automatically extract and analyse change and activity patterns from this data, methods considering the spatial and temporal properties are required. The commonly used M3C2 point cloud distance reduces uncertainty through spatial averaging for bitemporal analysis. To extend this concept into the full 4D domain, we use a Kalman filter for point cloud change analysis. The filter incorporates M3C2 distances together with uncertainties obtained through error propagation as Bayesian priors in a dynamic model. The Kalman filter yields a smoothed estimate of the change time series for each spatial location, again associated with an uncertainty. Through the temporal smoothing, the Kalman filter uncertainty is, in general, lower than the individual bitemporal uncertainties, which therefore allows detection of more change as significant. In our example time series of bi-hourly terrestrial laser scanning point clouds of around 6 days (71 epochs) showcasing a rockfall-affected high-mountain slope in Tyrol, Austria, we are able to almost double the number of points where change is deemed significant (from 14.9 % to 28.6 % of the area of interest). Since the Kalman filter allows interpolation and, under certain constraints, also extrapolation of the time series, the estimated change values can be temporally resampled. This can be critical for subsequent analyses that are unable to deal with missing data, as may be caused by, e.g., foggy or rainy weather conditions. We demonstrate two different clustering approaches, transforming the 4D data into 2D map visualisations that can be easily interpreted by analysts. By comparison to two state-of-the-art 4D point cloud change methods, we highlight the main advantage of our method to be the extraction of a smoothed best estimate time series for change at each location. A main disadvantage of not being able to detect spatially overlapping change objects in a single pass remains. In conclusion, the consideration of combined temporal and spatial data enables a notable reduction in the associated uncertainty of the quantified change value for each point in space and time, in turn allowing the extraction of more information from the 4D point cloud dataset.
In this study, a time change analysis of fine particulate (PM2.5) emission in multi-resolution emission inventory in China (MEIC) from 2013 to 2016 was conducted. It was found that PM2.5 emissions showed a decreasing trend year by year, and that the annual total emission of PM2.5 decreased by 28.5% in 2016 compared with that of 2013. When comparing the observation data of PM2.5 and ozone (O3), it was found that both PM2.5 and O3 show obvious seasonal changes. The emission of PM2.5 in autumn and winter is higher than that in summer, while that of O3 is not. Our study showed that in the 2015–2020 period, annual mean concentrations of PM2.5 and O3 in Beijing varied from 80.87 to 38.31 μg m-3 and 110.75 to 106.18 μg m-3, respectively. Since 2015, the observed value of PM2.5 has shown an obvious downward trend. Compared with 2015, the average annual PM2.5 concentrations in Beijing, Shanghai, Xuzhou, Zhengzhou, and Hefei in 2020 had decreased by 52.62%, 40.35%, 22.2%, 46.84%, and 45.11%, respectively, while O3 showed an upward trend. Compared with the annual averages of 2015 and 2020, Beijing and Shanghai saw a decrease of 4.13% and 8.46%, respectively, while Xuzhou, Zhengzhou, and Hefei saw an increase of 7.08%, 19.46%, and 41.57%, respectively. The comparison shows that PM2.5 is becoming less threatening in China and that ozone is becoming more difficult to control. Air pollution is a modifiable risk factor. Appropriate sustainable control policies are recommended to protect public health.
Twitter recently celebrated its 15th anniversary. During this period, the platform has gone through several phases, culminating in a record number of subscribers and profits in 2021. Twitter is a household name all over the world and people know what it can or cannot provide, independent of the future growth that it may experience with new investments and updates. This article aims to verify two interrelated hypotheses, namely: the Spanish press already knows how to optimise the social network Twitter, as three decades have elapsed since its launch; and, secondly, the algorithm modification implemented by Twitter in 2018 has triggered a change in the positioning of the headers studied in this social network. In order to demonstrate both, the object of analysis will be conducted by a mixed approach through quantitative statistical processes (which will study the number of impacts and retweets and likes obtained), and inductive qualitative methods such as semi-structured interviews. This multidisciplinary approach will provide a more complete and in-depth analysis of the phenomenon. The research focuses on the period between 2018 and 2020, and addresses the participation on Twitter of the four main traditional newspapers (El País, La Vanguardia, ABC and El Mundo) as well as four native digital newspapers (20 Minutos, El Español, elDiario.es and El Huffpost). The analysis comprises more than 1.5 million tweets among the eight chosen newspapers.
Evaluating the long-term spatiotemporal variability in soil moisture (SM) over Africa is crucial for understanding how crop production is affected by drought or flooding. However, the lack of continuous and stable long-term series and high-resolution soil moisture records impedes such research. To overcome the inconsistency of different microwave sensors (Advanced Microwave Scanning Radiometer-EOS, AMSR-E; Soil Moisture and Ocean Salinity, SMOS; and Advanced Microwave Scanning Radiometer 2, AMSR2) in measuring soil moisture over time and depth, we built a time series reconstruction model to correct SM, and then used a Spatially Weighted Downscaling Model to downscale the SM data from three different sensors to a 1 km spatial resolution. The verification of the reconstructed data shows that the product has high accuracy, and can be used for application and analysis. The spatiotemporal trends of SM in Africa were examined for 2003–2017. The analysis indicated that soil moisture is declining in Africa as a whole, and it is notably higher in central Africa than in other subregions. The most significant decrease in SM was observed in the savanna zone (slope < −0.08 m3 m−3 and P < 0.001), followed by South Africa and Namibia (slope < −0.07 m3 m−3 and P < 0.01). Seasonally, the most significant downward trends in SM were observed during the spring, mainly over eastern and central Africa (slope < −0.07 m3 m−3, R < −0.58 and P < 0.001). The analysis of spatiotemporal changes in soil moisture can help improve the understanding of hydrological cycles, and provide benchmark information for drought management in Africa.