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.

La Salle and the Ebro Observatory have been involved in a joint project about remote sensing in Antarctica during the last 11 years (approximately a solar cycle). The Ebro Observatory has been monitoring the geomagnetic and the ionospheric activity in the Juan Carlos I Antarctic Spanish Station (ASJI), for more than fifteen years. La Salle has two main goals in the project; on one hand, the data transmission and reception to obtain a historical series of channel sounding of a 12760 km ionospheric HF radio link, and on the other hand, the establishment of a stable data communication system between the ASJI and Cambrils (Spain) to transmit the data from the remote sensors located in the island. Currently, only a part of the processed geomagnetic data can be sent to Ebro Observatory when the ASJI is unattended through satellite communications. However, during the austral summer, when the ASJI is operative, the RAW data from the geomagnetic network sensors can be fully transmitted through ionospheric communications. In 2008, we developed the first channel characterization approach, using multipath delay spread, Doppler spread, SNR and availability of the narrowband and wideband soundings conducted at certain frequencies at some hours of the day. In 2012, the narrowband sounding was improved using several windowing of the received data. In this paper, both narrowband and wideband soundings have been taken into account to determine channel availability performed using a frequency range from 2 to 30 MHz with 0.5 MHz step during the 24 hours of the day, which is a wider sweep of channel measurements than previously. This paper presents the results for the austral summer in 2014. These measurements were performed using a monopole antenna at the transmitter and an inverted V on the receiver side.

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.

Tree biomass estimate is essential for carbon accounting, bioenergy feasibility studies, and forest sustainable management. This fact, added to the availability of airborne laser scanning (ALS) information, provided by the Spanish National Plan for Aerial Orthophotography (PNOA), and the existence of little research focusing on the use of ALS technology in Mediterranean Aleppo pine (Pinus halepensis Mill.) forest, determined the main objective of this research. Thus, this study aims to test the suitability of the low point density (0.5 points/m²), discrete, multiple-return, PNOA-ALS data, to estimate and map the total biomass (TB) and its carbon content in Pinus halepensis Mill. forest stands, located in Aragón (north-eastern Spain). TB was calculated in 45 field plots, using allometric equations, and related through a multivariate linear regression analysis with a collection of independent variables extracted from the ALS data. The predictive model was validated using a leave-one-out cross-validation (LOOCV) technique. Then, a regular grid with cell size 25 x 25 m corresponding to the sample plot size was generated by means of GIS, in order to compute TB at stand level and convert biomass to carbon by using the 0.5 conversion factor. The maximum height, kurtosis and the percentage of returns above 1 meter, were the ALS metrics included in the fitted model, which presented a R² value of 0.89. The implementation of the model in a GIS showed an average of 68633 kg/ha of TB and 34247.95 kg/ha of carbon fixed. The results indicate that despite the low point density of the ALS data, the final model is accurate enough to be used in forestry applications.

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.

This paper introduces a novel geospatial identification approach to distinguish areas of mixed use from predominantly residential areas within urban agglomerations. Carried out within the framework of the World Bank’s Country Disaster Risk Profiles (CDRP) project initiative - currently being implemented in Central America - global applicability and easy transferability is considered crucial. Therefore global spatial datasets are used throughout in the setup of the disaggregated property stock exposure model, one of the key elements for subsequent disaster risk and loss estimation. After initial urban-rural classification at a 1km grid level, predominantly residential areas need to be identified as opposed to areas of mixed use in order to spatially link accordingly compiled property stock information (e.g. from global tabular databases such as PAGER-STR). Impervious Surface Area (ISA) data based on remotely sensed nighttime lights from the DMSP-OLS sensor are used as proxy to identify areas of peak human activity. Intense lighting in that context is associated with a high likelihood of commercial and/or industrial presence, commonly clustered in certain parts of a city (such as central business districts and or peripheral commercial zones). Areas of low light intensity, in turn, can be considered more likely residential. Several light intensity threshold are tested for Cuenca City, Ecuador, in order to best match the situation on the ground, where local-level cadastral land use data show a 75-25 distribution ratio of residential vs. mixed use. Results will be presented first-hand in this paper and future work will be addressed highlighting the relevance of remote sensing data for top-down modeling approaches at wide spatial scale.