The geophysical observatory in the Antarctic Spanish Station Juan Carlos I (BAE), on Livingston Island (62.6S, 60.4W), has been monitoring the magnetic field in the Antarctic region for more than fifteen years. In 2004, a vertical incidence ionospheric sounder was incorporated to the observatory, which brings a significant added value in a region with low density of geophysical data. A High Frequency (HF) communications system was installed in 2004 in order to transmit the geomagnetic station recordings throughout the year, due to the fact that the BAE is only accessible during the austral summer. As the power supply is very limited when the station is not accessible, we had to design a low-power HF transceiver with a very simple antenna, due to environmental restrictions. Moreover, the flow of information is unidirectional, so the modulation has to be extremely robust since there is no retransmission in case of error. This led us to study the main parameters of the ionospheric channel (Signal to Noise Ratio -SNR-, delay spread, Doppler spread and availability) with narrowband and wideband soundings, and the design of modulations specially adapted to very low SNR scenarios with high levels of interference. In this poster, a review of the design of our remote geophysical observatory and associated transmission system from Antarctica to Spain (12760 km) during the last decade is presented.

The project PHAROS (Project on a Multi-Hazard Open Platform for Satellite Based Downstream Services) designs and implements a multi-hazard open service platform which integrates space-based earth observation, satellite communications and navigation (Galileo/GNSS) assets to provide sustainable (pre-operational) services for a wide variety of users in multi-application domains, such as prediction/early detection of emergencies, population alerting, environmental monitoring and crisis management. While the service platform is designed to be multi-hazard, the specific developments for the pre-operational system and pilot demonstration will be focused on the forest fire scenario. The platform will integrate data from EO satellites and in-situ sensors process it and provide the results to a series of key services for disaster management in its different phases. One of the main concerns is to provide fire hot spots as an input for the PHAROS Simulation Service.

These fire hot spots (thermal anomalies) are derived automatically and in near real time (NRT) from MODIS data. The MODIS data are available in a high (1d) temporal and in a medium (250m – 1000m) spatial resolution. For the detection of high temperature events (HTE) the MOD14 algorithm is used. The algorithm is based on the shift of the radiances/reflectance to shorter wavelengths (middle infrared) with an increasing surface temperature. MOD14 is well documented and tested in operational services and guarantees comparability and reproducibility as well as a standardized international acknowledged product. The thermal information is collected at 1000 m spatial resolution twice daily by each sensor (Terra and Aqua) providing up to four thermal observations daily. The MODIS images used for fire detection are acquired from two direct broadcast receiving stations from DLR located in Oberpfaffenhofen and Neustrelitz (Germany).

This Poster will give an overview of the processing chain from the reception, the processing and derivation of the fire hot spots to the dissemination in the Pharos system.

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.

Nowadays, global warming has become more interested for scientist, because the global surface temperature has been increased since last century. More than fifty percent of people are living in cities, in this regard, urbanization has become a key factor for global warming. The urban heat island (UHI) refers to the event of higher atmospheric and surface temperatures occurring in cities than in the surrounding rural areas due to urbanization. The annual average air temperature of urban area with almost one million people can be one to three degree warmer than its surroundings. This phenomena can affect societies by increasing summertime, air pollution, air conditioning costs, heat related illness, greenhouse gas emissions and water quality.

Tehran, a capital city of Iran is case study of this research. Additionally, Tehran is one of megacities of the world. A megacity is usually defined as a metropolitan area with a total population in excess of ten million people. Due to rapid urbanization progress that has resulted in significant UHI effect in this area. Furthermore, Tehran houses to almost twenty percent of Iranian people. In this study, new launched Landsat series (Landsat 8) was used for monitoring UHI and retrieving the brightness temperatures and land use/cover types. The Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has former Landsat bands, with three new bands: a deep blue band for coastal/aerosol studies (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment band. The Thermal Infrared Sensor (TIRS) sensor provides two high resolution (near to 30 meters) thermal bands (band 10, 11). These sensors both use corrected signal-to-noise (SNR) radiometric quantized over a 12-bit. Corrected SNR performance cause better determination of land cover type. Moreover, Landsat 8 images incorporate two valuable thermal bands in 10.9 µm and 12.0 µm. These two thermal bands improve estimation of UHI by incorporating split-window methods.

Recently, quantitative models for urban thermal environment and related factors have been studied, for example, the relation between UHI and land cover structure and established corresponding regression equation. Similar works have been done and models of the relation between the surface temperature and various vegetation Indices have been established. In order to monitor the relationship between UHI and land cover indices, this paper tried to employ a quantitative approach for exploring the relationship land surface temperature and common land cover indices and select suitable indices by incorporating supervised Feature Selection (FS) procedures, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI) in two definition, Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BI), Urban Index (UI), Index-based Built-Up Index (IBI) and Enhanced Built-Up and Bareness Index (EBBI). In this regards, the objectives of this research are to develop a non-linear analysis model for urban thermal environment by employing Support Vector Regression (SVR) method and Multivariate Regression (MR) algorithms. In addition, providing the hazard map for Tehran city is also one of the byproducts of proposed methods for managing and mitigating UHI effects. 

In recent years, due to the expansion of the vehicles transportation system and concerns about the lack of accurate calculation of vehicle weight, a system that is able to calculate the vehicle's weight at any moment, it seems necessary. Given that the transportation electronic management is related to the location and movement data of vehicle, the information about movement, speed and time, traveled path, the weight sensors and fuel for the quick and timely decisions are required. Therefore the design and implementation of modern systems for monitoring and control of these devices to make quick decisions and plan codified is essential. In this paper firstly, the different ways of measuring the vehicle weight and the problems of each them has been described then the weight sensor device which is equipped with a AVL system, and its application in urban management (Waste collection) has been described, and finally, the advantages of this devise has been proposed.