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.

Urbanisation is accelerating at a rapid rate, which has introduced many challenges, especially in the field of urban planning. Under the backdrop of global urbanisation, some cities are particularly vulnerable to climate change and natural disasters that are influenced by unplanned urban expansion. Rational planning of urban functional areas needs to be strengthened to improve the scientific approach of urban planning and urbanisation. In this study, the classification of urban functional areas based on dual-modal data (i.e., remote sensing image and user behaviour data) was implemented using machine learning (ML) algorithms. After the set test, the classification accuracy of urban functional areas reached 82.45%. Through analysis, it could be concluded that the use of data of two modalities achieved a higher classification accuracy than that achieved by using data of a single modality. The data of the two modalities complement each other, and the use of ML algorithms to train such data can yield good results.

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.

Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem for traditional CD methods, as they can only detect areas with highly changed radiometric and geometric information. Optical flow-based methods are able to detect the pixel-based motion tracking at fast speed; however, they are difficult to determine an optimal threshold for separating the changed from the unchanged part for CD problems. To overcome the above problems, this paper proposed a novel automatic change detection framework: OFATS (optical flow-based adaptive thresholding segmentation). Combining the characteristics of optical flow data, a new objective function based on the ratio of maximum between-class variance and minimum within-class variance has been constructed and two key steps are motion detection based on optical flow estimation using deep learning (DL) method and changed area segmentation based on an adaptive threshold selection. Experiments are carried out using two groups of video sequences, which demonstrated that the proposed method is able to achieve high accuracy with F1 value of 0.98 and 0.94, respectively.

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.

Near-nadir interferometric imaging SAR (Synthetic Aperture Radar) techniques are promising in measuring global water extent and surface height at fine spatial and temporal resolutions. The concept of near-nadir interferometric measurements was implemented in the experimental Interferometric Imaging Radar Altimeters (InIRA) mounted on Chinese Tian Gong 2 (TG-2) space laboratory. This study is focused on mapping the extent of high mountain lakes in the remote Qinghai–Tibet Plateau (QTP) areas using the InIRA observations. Theoretical simulations were first conducted to understand the scattering mechanisms under near-nadir observation geometry. It was found that water and surrounding land pixels are generally distinguishable depending on the degree of their difference in dielectric properties and surface roughness. The observed radar backscatter is also greatly influenced by incidence angles. A dynamic threshold method was then developed to detect water pixels based on the theoretical analysis and ancillary data. As assessed by the LandSat results, the overall classification accuracy is higher than 90%, though the classifications are affected by low backscatter possibly from very smooth water surface. The algorithms developed from this study can be extended to all InIRA land measurements and provide support for the similar space missions in the future.