Image data sharpening is a widely used method to increase a spatial resolution of images with a higher spectral and lower spatial resolution. In our study we focused on sharpening ASTER image data using a high spatial-resolution panchromatic band of WorldView-2 data. Both datasets were acquired within the framework of a geological mapping project in the southwest Mongolia. Primary remote sensing task was to produce mineral maps for the studied area. ASTER data providing several bands in the short wave infrared (SWIR) spectral region has a great potential for geological/mineral mapping. On the other hand, a spatial resolution is rather coarse for the geological mapping at a 1: 50, 000 scale. ENVI and Erdas Imagine software (SW) were used to test the available Principle Component Analysis sharpening algorithm; however satisfactory spectral mapping results have not been achieved. In the commercial SW, the first component (PC) is used for the sharpening process by default, but the 1st PC usually does not contain the main spectral variability considering mineralogy/geology. Therefore, a new approach using the other principal components for image sharpening was tested and compared with the approach available in the commercial SW. New processing was programmed in ENVI/IDL.

Physical modelling is usually a complicated way to estimate soil moisture content, while machine learning algorithms have the potential to retrieve information from remote sensing data. In this study, the neural network, one of the most common machine learning algorithms, was used to map soil moisture from active microwave and optical data in combination. The study area was set in the middle stream of Heihe River Basin in China, from where Landsat and Envisat ASAR data were acquired in July 2008. The neural networks were trained with ground truth data and input parameters extracted from remote sensing data including bands information, Normalized Difference Vegetation Index (NDVI), Brightness Index (BI), the dual polarizations (HH and VV) and the ratio (HH/VV). Compared to an existing output of an empirical model with purely Envisat ASAR data in the same area (with R²=0.71), this study showed a slightly better correlation between the measured and estimated soil moisture (R²=0.75). It also revealed that the model with multi-source data had a better performance than the one with only a single source data, and that the selection of input parameters and the number of hidden layers and nodes can also affect the model's accuracy. Finally, the verified model was applied to the whole study area, and it was showed that this method has operational potential for estimating soil moisture under vegetated area in the middle stream of Heihe River Basin.

Urbanization is indispensable for the development of humanity. The changes from the urbanization process have a significant impact on other components of natural systems in Ho Chi Minh City. The problem of filling low-lying swamps, ponds, lakes as well as canal encroachment has made ​​significant changes to the shape of the surface topography of the city and particularly affected the current flood situation around the city. The objective of the study was to map changes in surface topography in relation to urbanization process in central part of Ho Chi Minh city during the period 1989- 2011. Band ratio method and Maximum Likelihood classification were implemented to separate the objects of urban and low-lying swamp from 3 satellite images in 1989, 2003 and 2011. The change detection has been done by post-classification method combined with GIS and field data to detect changes in the disappearance of low-lying swamps as well as the existence of urban areas on it. Classification process has resulted in an overall accuracy greater than 89% with urban area increased to a half of the entire area within 22 years. Meanwhile the area of ​​low-lying swamps reduced almost 5 times compared to the existing area in ​​the early stage. Research has built spatial maps of the current status and changing as well as carried out the analysis and evaluation to affect flooding in the city. This is a proof of the lack of scientific methods in the urban management and the positive transformation to reduce flooding today is needed.

In the context of the project OpSSERVE - Optical Satellite Services for EMSA (European Maritime Safety Agency) – the European Space Imaging (EUSI) and the German Aerospace Center (DLR) established for the first time a fully operational and near real-time service to detect vessels and maritime activities with optical satellite imagery . The service was implemented in 2013 and contribute to maritime situation awareness, e.g. in order to reduce the risk of maritime accidents, marine pollution and the loss of human life at sea.

Since its activation in 2007, EMSA’s CleanSeaNet (CSN) system provides pollution detection services in support of maritime surveillance and decision making for all participating European member states based on SAR satellite data. The main advantage of SAR data is their independence of weather conditions (cloud coverage), but the identification of small vessels is rather difficult. The use of very high resolution (VHR) optical data provides valuable information to identify small vessels.

From the VHR optical imagery, actionable information products are created in an automatic processing chain including image pre-processing, data transcription, automatic vessel detection, GUI based interactive vessel and activity detection and finally the delivery of standardized products to EMSA. The service provides access to globally collected satellite imagery and receives data and derived products in near real-time (1 or 3 hours). In OpSSERVE, five satellite missions are available: WorldView-1, WorldView-2, GeoEye-1, IKONOS and EROS-B. The poster will present the experiences of the operational service and give an overview on future aspects and possible applications.

With the advancement of Urban Intelligence and Smart Cities, the importance of remote monitoring and data collection increased. Concerning monitoring, IP-Proxy is an important equipment to perform the interconnection between the sensors and the Internet. In the recent literature, several gateway architectures for network sensors have been proposed to integrate Wireless Sensor Networks (WSN) and Internet. Most  implementations of WSN gateways retrieve sensors data on the WSN and display the results to customers through the web. The disadvantage of these solutions is that they use specific protocols to connect the sensors, thereby prohibiting the direct interaction between customers and sensor nodes. As an option, the adoption of the SNMP protocol for sensor management has the potential to reduce the gateway complexity of most gateway.  For this reason, this article presents the design and implementation of a low-cost open platform IP-Proxy with the usage and modification of a commercial out-of-the-shelf wireless router, with serial connection to communicate with the sensors, that are connected to an expanded microcontroller Arduino Nano board. The results of the experiments showed that the IP-Proxy can successfully interconnect sensor networks and the Internet, where data can be worldwide broadcasted via Ethernet or WLAN. His features include ease of implementation, integration and robust operation.