Increasing human activity around the world has greatly changed the natural ecosystem and the services it provides. In the past few decades, a series of significant changes have taken place in land ...use/land cover (LULC) in China due to the rapid growth in population, particularly in the cities of the Zhujiang Deita. However, there have been few attempts to study the co-evolution of land use/land cover change and ecosystem service value (ESV) in the main urban area of Guangzhou. Therefore, based on Landsat TM/OLI images from 1987, 1993, 1999, 2005, 2011 and 2017, the weight vector AdaBoost (WV AdaBoost) multi-classification algorithm was utilized to extract LULC data sets, and the spatiotemporal patterns of LULC over these periods were studied. The ESV was estimated and the driving force was analysed. The effect of LULC dynamics on the ESV was evaluated. The results showed that great changes have taken place in LULC in the main urban area of Guangzhou from 1987 to 2017, of which the most significant was the large-scale expansion of the built-up area that occurred through degradation of the forest and cultivated land. The proportion of forest and cultivated land decreased from 43.12% and 34.23% to 25.88% and 12.59%, respectively. The results between periods revealed a decrease in total ESVs from 5.63 × 10
yuan in 1987 to 5.27, 4.16, 4.62, 3.76 and 4.47 × 10
yuan in 1993, 1999, 2005, 2011 and 2017, respectively. In total, ESVs decreased by 1.16 billion yuan (20.61%) from 1987 to 2017. Water supply, food production, nutrient cycling and gas regulation were the four principal ecosystem service functions that affected the total ESVs. Forest, water body and cultivated land areas played a key role in ecosystem services. Therefore, we advocate that when protecting natural ecosystems in the future land use management in Guangzhou should be prioritized.
Aerosol optical depth (AOD) is one of essential atmosphere parameters for climate change assessment as well as for total ecological situation study. This study presents long-term data (2000-2017) on ...time-space distribution and trends in AOD over various ecological regions of China, received from Moderate Resolution Imaging Spectroradiometer (MODIS) (combined Dark Target and Deep Blue) and Multi-angle Imaging Spectroradiometer (MISR), based on satellite Terra. Ground-based stations Aerosol Robotic Network (AERONET) were used to validate the data obtained. AOD data, obtained from two spectroradiometers, demonstrate the significant positive correlation relationships (r = 0.747), indicating that 55% of all data illustrate relationship among the parameters under study. Comparison of results, obtained with MODIS/MISR Terra and AERONET, demonstrate high relation (r = 0.869 - 0.905), while over 60% of the entire sampling fall within the range of the expected tolerance, established by MODIS and MISR over earth (±0.05 ± 0.15 × AOD
and 0.05 ± 0.2 × AOD
) with root-mean-square error (RMSE) of 0.097-0.302 and 0.067-0.149, as well as low mean absolute error (MAE) of 0.068-0.18 and 0.067-0.149, respectively. The MODIS search results were overestimated for AERONET stations with an average overestimation ranging from 14 to 17%, while there was an underestimate of the search results using MISR from 8 to 22%.
This study provides characteristics of aerosol columnar properties, measured over ten countries in Eastern Europe from 2002 to 2019. Aerosol optical depth (AOD) and Ångström exponent (AE) were ...obtained with the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 merged Dark Target and Deep Blue aerosol product. The product is validated using ground-based Aerosol Robotic Network (AERONET) situated at Minsk, Belsk, Moldova and Kyiv. The results showed that 76.15% of retrieved AOD data are within the expected error. It was established that 64.2% of AOD points are between 0 and 0.2 and 79.3% of all AE points are over 1. Mean AOD values in the region vary from 0.130 ± 0.04 (Moldova) to 0.193 ± 0.03 (Czech Republic) with mean value in the region 0.162 ± 0.05. Seasonal mean AOD (AE) values were at the maximum during the summer from 0.231 ± 0.05 (1.482 ± 0.09 in winter) to minimum 0.087 ± 0.04 during the winter (1.363 ± 0.17 in summer). Gradual AOD reduction is observed in all countries with annual trend from −0.0050 (Belarus) to −0.0029 (Russia). Finally, the relationship between AOD and AE was studied to classify various aerosol types and showed seasonal non-uniformity of their contribution depending on variation in sources. The entire region is under significant impact of various aerosol types, including clean continental (СС), mixed (MX) and anthropogenic/burning (AB) aerosols types that are at 59.77%, 24.72%, and 12.97% respectively. These results form an important basis for further regional studies of air quality and distribution of sources of pollution.
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•Long-term (2002–2019) study on aerosol optical properties covering entire Eastern Europe.•The study region is strongly influenced by clean continental and mixed type aerosols.•Highest frequency of low AOD and high AE observed.•Retrieval verification of AOD daily means, obtained from MOD04_L2, and AERONET sun-photometer measurements.
•The proportion of landslide binary pixels was used as label data input in network models.•MSCNN model was carried out for LSM the first time.•A unified network in parallel with GRU and MSCNN was ...proposed for LSM.
Landslide susceptibility mapping (LSM) is very important for hazard risk identification and prevention. Most of existing neural network models extract a pixel neighborhood feature or a pixel sequence feature of landslide factors on one side, which leads to the generalization ability of the network models difficultly, and had a low prediction accuracy in complex scenes. In this paper, a new unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood is proposed for LSM. Different from the traditional prediction model framework, the landslide conditioning factors are merged into a unified network model in parallel with the pixel sequence features and pixel neighbourhood features. In the experiment, we take the proportion of landslide binary pixels as label data, which represents the landslide possibility in the neighbourhood. We propose a pixel sequence feature extraction algorithm based on a gated recurrent unit (GRU) network and a pixel neighbourhood feature extraction algorithm based on a multi-scale convolution neural network (MSCNN). In this study, the landslide conditioning factors were analysed by multicollinearity analysis and the frequency ratio (FR) method. The performance of the modes was evaluated by statistical indexes and the correlation analysis. The LSM results were verified by google earth images and field investigation. Our research shows that the proposed model can greatly improve the accuracy of LSM compared with the individual GRU and MSCNN, especially, the proposed model had 6.1% more improvement than the GRU model in terms of the area under curve (AUC). Therefore, we suggest that the proposed model is a suitable technology for use in early identification and landslide prediction.
This paper presents an algorithm for point cluster generalization. Four types of information, i.e. statistical, thematic, topological, and metric information are considered, and measures are selected ...to describe corresponding types of information quantitatively in the algorithm, i.e. the number of points for statistical information, the importance value for thematic information, the Voronoi neighbors for topological information, and the distribution range and relative local density for metric information. Based on these measures, an algorithm for point cluster generalization is developed. Firstly, point clusters are triangulated and a border polygon of the point clusters is obtained. By the border polygon, some pseudo points are added to the original point clusters to form a new point set and a range polygon that encloses all original points is constructed. Secondly, the Voronoi polygons of the new point set are computed in order to obtain the so-called relative local density of each point. Further, the selection probability of each point is computed using its relative local density and importance value, and then mark those will-be-deleted points as ‘deleted’ according to their selection probabilities and Voronoi neighboring relations. Thirdly, if the number of retained points does not satisfy that computed by the Radical Law, physically delete the points marked as ‘deleted’ forming a new point set, and the second step is repeated; else physically deleted pseudo points and the points marked as ‘deleted’, and the generalized point clusters are achieved. Owing to the use of the Voronoi diagram the algorithm is parameter free and fully automatic. As our experiments show, it can be used in the generalization of point features arranged in clusters such as thematic dot maps and control points on cartographic maps.
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•Contemporary analysis of three severe sand and dust storm events.•Incorporates the use of both ground and satellite data.•Determines both the source regions of the sand and dust and ...downwind areas affected.•Suggests changes in human activity that could reduce the severity of dust and sand storm events.
Every spring, a large part of China is confronted with sand and dust storms (SDS) – mainly originating in the Gobi (including Chinese and Mongolian Gobi) and Taklamakan deserts. In March-April 2023, most of northern, northwestern and northeastern China was struck by three sandstorms that affected an area with more than 500 million people. In this study, aerosol optical, microphysical and radiative properties were studied during these SDS events using an integrated approach that combines satellite, terrestrial and re-analysis data. The results showed that dusty conditions were observed in most areas north of the Yangtze River (Chang Jiang) with daily average PM10 concentrations exceeding 1000 µg/m3 in many cities. VIIRS aerosol optical depth (AOD) at 550 nm during three SDS events exceeded a value of 1 throughout nearly the entire northern part of the country. The AERONET data obtained from the AOE_Baotou site showed a significant increase in total AOD and a corresponding decrease in AE during the SDS. The single scattering albedo (SSA), asymmetry parameter (ASY), real refractive index (RRI) and imaginary refractive index (IRI) values indicate an abundance of scattering coarse-mode particles. Aerosol radiative forcing (ARF) at top of the atmosphere and at the earth's surface was nearly always negative during the period and ranged from −48.5 to +2.7 Wm−2 and from −180.8 to −66.6 Wm−2, resulting in high positive ARF values at ATM (from +63.8 to +132.3 Wm−2). Each of these affects the heating of the atmosphere and cooling on the earth's surface. The atmospheric heating rates ranged from 1.8 to 3.7 K day−1. The formation of these SDS mainly resulted from the passage of cold fronts associated with low pressure systems in the Gobi and Taklamakan deserts, creating conditions for dust to rise into the atmosphere and move further downwind.
The Qilian Mountains (QLMs), an important ecological protective barrier and major water resource connotation area in the Hexi Corridor region, have an important impact on ecological security in ...western China due to their ecological changes. However, most existing studies have investigated vegetation changes and their main driving forces in the QLMs on the basis of a single scale. Thus, the interactions among multiple environmental factors in the QLMs are still unclear. This study was based on normalised difference vegetation index (NDVI) data from 2000 to 2019. We systematically analysed the spatial and temporal characteristics of the QLMs at multiple time scales using trend analysis, ensemble empirical mode decomposition, Geodetector, and correlation analysis methods. At different time scales under single-factor and multi-factor interactions, we examined the mechanisms of the vegetation changes and their drivers. Our results showed that the vegetation in the QLMs showed a trend of overall improvement in 2000–2019, at a rate of 0.88 × 10−3, mainly in the central western regions. The NDVI in the QLMs showed a short change cycle of 3 and 5 years and a long-term trend. Sunshine time and wind speed were the main drivers of the vegetation variation in the QLMs, followed by temperature. Precipitation affected the vegetation spatial variation within a certain altitude range. However, temperature and precipitation had stronger explanatory powers for the vegetation variation in the western QLMs than in the eastern part. Their interaction was the dominant factor in the regional differences in vegetation. The responses of the NDVI to temperature and precipitation were stronger in the long time series. The main drivers of vegetation variation were land surface temperature and precipitation in the east and temperature and evapotranspiration in the west. Precipitation was the main driver of vegetation growth in the northern and southwestern QLMs on both the short- and long-term scales. Vegetation changes were more significantly influenced by short-term temperature changes in the east but by a combination of temperature and precipitation in most parts of the QLMs on a 5-year time scale.
The network Voronoi diagram has been extensively applied in many fields, such as influence area construction, location selection and urban planning, owing to its high accuracy and validity in space ...division. Taking advantage of parallel processing and auto-wave division of the pulse coupled neural network (PCNN), an algorithm for generating a weighted network Voronoi diagram is proposed in this paper. First, in order to better accommodate the scenes of urban facility points and road networks, the PCNN is improved. Second, the speed of the auto-wave in the improved PCNN is calculated by the weights of the facility points and the attributes of the related road network. Third, the nodes in the road network are considered as neurons, the facility points are projected onto the nearest road segments and the projected points are treated as initial neurons. The initial neurons generate auto-waves simultaneously, and the auto-waves transmit along the shortest path from neurons to other neurons with the calculated speed until all the neurons are fired. During this procedure, the road network and the corresponding space are assigned to the initial neurons and the weighted network Voronoi diagram is constructed. The experiments on the specific region with the real POIs present the feasibility, applicability and efficiency of the algorithm.
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