Sand and dust storms (SDS) are common phenomena in arid and semi-arid areas. In recent years, SDS frequencies and intensities have increased significantly in Iran. A research on SDS sources is important for understanding the mechanisms of dust generation and assessing its socio-economic and environmental impacts. In this paper, we developed a new approach to identify SDS source areas in Iran using a combination of nine related datasets, namely drought events, temperature, precipitation, location of sandy soils, SDS frequency, human-induced soil degradation (HISD), human influence index (HII), rain use efficiency (RUE) and net primary productivity (NPP) loss. To identify SDS source areas, we firstly normalized these datasets under uniform criteria including layer reprojection using Lambert conformal conic projection, data conversion from shapefile to raster, Min-Max Normalization with data range from 0 to 1, and data interpolation by Kriging and images resampling (resolution of 1 km). After that, a score map for the possibility of SDS sources was generated through overlaying multiple datasets under average weight allocation criterion, in which each item obtained weight equally. In the score map, the higher the score, the more possible a specific area could be regarded as SDS source area. Exceptions mostly came from large cities, like Tehran and Isfahan. As a result, final SDS source areas were mapped out, and Al-Howizeh/Al-Azim marshes and Sistan Basin were identified as main SDS source areas in Iran. The SDS source area in Al-Howizeh/Al-Azim marshes still keeps expanding. In addition, Al-Howizeh/Al-Azim marshes are now suffering rapid land degradation due to natural and human-induced factors and might totally vanish in the near future. Sistan Basin also demonstrates the impacts of soil degradation and wind erosion. With appropriate intensity, duration, wind speed and altitude of the dust storms, sand particles uplifting from this area might have developed into extreme dust storms, especially during the summer.

Google Earth (GE), a large Earth-observation data-based geographical information computer application, is an intuitive three-dimensional virtual globe. It enables archaeologists around the world to communicate and share their multisource data and research findings. Different from traditional geographical information systems (GIS), GE is free and easy to use in data collection, exploration, and visualization. In the past decade, many peer-reviewed articles on the use of GE in the archaeological cultural heritage (ACH) research field have been published. Most of these concern specific ACH investigations with a wide spatial coverage. GE can often be used to survey and document ACH so that both skilled archaeologists and the public can more easily and intuitively understand the results. Based on geographical tools and multi-temporal very high-resolution (VHR) satellite imagery, GE has been shown to provide spatio-temporal change information that has a bearing on the physical, environmental, and geographical character of ACH. In this review, in order to discuss the huge potential of GE, a comprehensive review of GE and its applications to ACH in the published scientific literature is first presented; case studies in five main research fields demonstrating how GE can be deployed as a key tool for studying ACH are then described. The selected case studies illustrate how GE can be used effectively to investigate ACH at multiple scales, discover new archaeological sites in remote regions, monitor historical sites, and assess damage in areas of conflict, and promote virtual tourism. These examples form the basis for highlighting current trends in remote sensing archaeology based on the GE platform, which could provide access to a low-cost and easy-to-use tool for communicating and sharing ACH geospatial data more effectively to the general public in the era of Digital Earth. Finally, a discussion of the merits and limitations of GE is presented along with conclusions and remaining challenges.

Since the Chinese government carried out the reform and opening up policy, Xishuangbanna Dai Autonomous Prefecture has experienced rapid urbanization and dramatic land use change. This research aims at analyzing urban expansion in Xishuangbanna and its impact on the land use pattern using combined methods, including radar graph, the gradient-direction method and landscape metrics. Seven land use maps from 1976 to 2015 were generated and analyzed, respectively. The results showed that urban and rubber expanded rapidly, while forest decreased during the last 40 years. The city proper, the county town of Menghai and the county town of Mengla showed the most significant and fastest urban expansion rates. In response to rapid urban expansion, land use types outside urban areas changed dramatically. In Jinghong and Mengla, urban areas were usually surrounded by paddy, shrub, rubber and forest in 1976, while most areas were dominated by rubber by 2015. With the development of Xishuangbanna, landscape diversity increased along urban-rural gradients, but decreased in some key urban areas. Urban expansion slightly reduced the connectivity of forest and increased agglomeration of rubber at the same time. Based on the analyses above, we moved forward to discuss the consequences of urban expansion, rubber plantations and land fragmentation.

The Greater Mekong Subregion (GMS) has experienced rapid economic growth and urbanization. However, few studies have paid attention to urban land use dynamics, especially spatiotemporal patterns of urban expansion and land use change, in this region. This research aimed to conduct a comprehensive study of urban land use change in Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City, from 1990 to 2015. The analysis was based on land use maps derived from Landsat satellite products and employed urban expansion intensity, sector analysis, gradient-direction analysis, and landscape metrics. The results show Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City all experienced dramatic urban expansion and land use change since 1990, with urban expansion intensities of 15.01, 5.26, 9.15, 1.56, 11.88 and 11.91, respectively. The landscape metrics analysis indicated that urban areas were always aggregated and self-connected, while other land use types showed trends of disaggregation and fragmentation. In the process of urban expansion, paddy and natural land use types were commonly transformed to built up area. The results further reveal several common issues in urban land use, e.g., land fragmentation and loss of natural land use types. Finally, the discussion on the relationship between government policy and land use change for these cities shows land reform and attitude toward foreign direct investments played important roles in urban land use change in GMS.

Sand and dust storms (SDS) are global phenomena that significantly impact the socio-economy, human health, and the environment. The characterization of SDS intensity is a fundamental aspect of SDS issues and studies. In this study, a sand and dust storms index (SDSI) is developed to characterize SDS intensity by addressing the potential impacts of sand and dust storms on sensitive elements. Compared with other indices, SDSI includes four SDS-related components: SDS frequency, SDS visibility, SDS duration, and SDS wind speed. Using SDSI, this study characterizes the SDS intensity in the Three-North Forest Shelterbelt Program (TNFSP) region of China. The SDSI results show that high values of SDSI are mostly concentrated in southern Xinjiang, western and central Inner Mongolia, western and central Gansu, and northern Ningxia. By analyzing the SDSI components, over half of the stations experienced sand and dust storms no more than once per year on average. Most of the SDS events reduced horizontal visibility to less than 500 m, one-third of SDS events last more than two hours, and the wind speed of over half of the SDS events varied between 10–17 m/s. In comparison with SDS frequency, SDSI performs better in reflecting the spatial and temporal variation of SDS events. Therefore, instead of SDS frequency, SDSI can be applied to studies relevant to SDS intensity. Finally, five major SDS transportation routes were identified based on the surface prevailing wind direction, SDSI, and the existing literature. The SDS routes, combined with SDSI, could help governments and policy-makers cooperate on a regional level to combat SDS events more effectively.