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Near-real time water segmentation with medium resolution satellite imagery plays a critical role in water management. Automated water segmentation of satellite imagery has traditionally been achiev...

Electron microscopy (EM) continues to provide near-atomic resolution structures for well-behaved proteins and protein complexes. Unfortunately, structures of some complexes are limited to low- to medium-resolution due to biochemical or conformational heterogeneity. Thus, the application of unbiased systematic methods for fitting individual structures into EM maps is important. A method that employs co-evolutionary information obtained solely from sequence data could prove invaluable for quick, confident localization of subunits within these structures. Here, we incorporate the co-evolution of intermolecular amino acids as a new type of distance restraint in the Integrative Modeling Platform (IMP) in order to build three-dimensional models of atomic structures into EM maps ranging from 10-14 a in resolution. We validate this method using four complexes of known structure, where we highlight the conservation of intermolecular couplings despite dynamic conformational changes using the BAM complex. Finally, we use this method to assemble the subunits of the bacterial holo-translocon into a model that agrees with previous biochemical data. The use of evolutionary couplings in integrative modeling improves systematic, unbiased fitting of atomic models into medium- to low-resolution EM maps, providing additional information to integrative models lacking in spatial data. This article is protected by copyright. All rights reserved.

Land-cover classification especially global mapping has become a new trend in recent years. Traditional convolutional neural network (CNN) methods for land-cover classification are usually patch ba...

Water vapor plays a key role in weather, climate and environmental research on local and global scales. Knowledge about atmospheric water vapor and its spatiotemporal variability is essential for climate and weather research. Because of the advantage of a unique temporal and spatial resolution, satellite observations provide global or regional water vapor distributions. The advanced Medium Resolution Spectral Imager (MERSI) instrument—that is, MERSI-II—onboard the Fengyun-3D (FY-3D) meteorological satellite, has been one of the major satellite sensors routinely providing precipitable water vapor (PWV) products to the community using near-infrared (NIR) measurements since June 2018. In this paper, the major updates related to the production of the NIR PWV products of MERSI-II are discussed for the first time. In addition, the water vapor retrieval algorithm based on the MERSI-II NIR channels is introduced and derivations are made over clear land areas, clouds, and sun-glint areas over the ocean. Finally, the status and samples of the MERSI-II PWV products are presented. The accuracy of MERSI-II PWV products is validated using ground-based GPS measurements. The results show that the accuracies of the water vapor products based on the updated MERSI-II instrument are significantly improved compared with those of MERSI, because MERSI-II provides a better channel setting and new calibration method. The root-mean-square error and relative bias of MERSI-II PWV products are typically 1.8–5.5 mm and −3.0% to −14.3%, respectively, and thus comparable with those of other global remote sensing products of the same type.

This article elaborates a processing chain devised to recognize the ships existing on medium resolution multispectral imageries (MSI). The chain consists of the following three steps. Firstly, an adaptive local saliency mapping technique is instigated on open ocean regions to obtain all floating objects. Secondly, to extract the ship candidates, two-step verification is applied based on specific spectral and geometric information of the ships. Lastly, a calculation to determine the properties of the ships, including their length, breadth, and heading, is then carried out. Furthermore, we propose a novel method for correcting miscalculated ship heading; by combining wake segmentation and Radon Transform ( RT ) approaches to locate the position and estimate the length of the wake generated by the ships. With the detected wake length, ship velocity can also be assessed. The developed chain is then tested using imageries acquired by LAPAN-A3 microsatellite, and the results are compared to those reported by the Automatic Identification System (AIS). Experimental results indicate that the proposed chain achieves higher detection performance and can produce better heading information compared to the existing methods.

A considerable portion of the natural inland lakes has been gradually transformed into aquaculture ponds to meet the enormous demand for aquaculture products. The changes in ponds area can be used to measure the impact of human activities on inland lakes. However, aquaculture ponds and inland lakes are often intermingled with each other especially in the areas close to the lake shore, posing great difficulties for the extraction of aquaculture ponds from medium resolution (15–30 m) multispectral imagery, such as Landsat TM, OLI, and Geofen-1 WFV images. This study proposes a contour-based regularity measurement for water segments, which evaluates the zero-curvature portions of the boundaries, to distinguish aquaculture ponds from natural water. Water surfaces are firstly extracted from satellite images, and then boundary trace of each water segment is carried out to evaluate the geometrical feature of its contour, including perimeter, curvature and the proposed contour-based regularity. Eventually, SVM classification based on these geometrical features separates the aquaculture ponds from inland lakes. Experiments on Landsat TM, OLI, and Geofen-1 WFV images showed that the combination of perimeter, area and proposed contour-based regularity outperforms other feature combinations and produced the most accurate classification. Therefore, the proposed method can be used to extract all aquaculture ponds from all historic Landsat images to monitor the changes in inland aquaculture.

This study presents a fuzzy approach, for detection of transitioned building footprints in urban area using medium resolution datasets. Multi-temporal remote sensing data sets from Landsat-8 Operational Land Imager and Sentinel-2A were used for generation of temporal indices database. The database was generated using class-based sensor independent-normalized difference vegetation index approach, with an aim to reduce spectral dimensionality of each image and maintain temporal dimensionality. The temporal indices database was subsequently used as input in Modified Possibilistic c-means classifier for transitioned building footprints extraction. The identified transitioned building locations were validated using ground samples as well as from Google images at four different test sites. For accuracy assessment, F-measure was calculated and its value was 0.75 or higher for all training and testing sites. Thus, using proposed fuzzy approach, transitioned building footprints were accurately identified compared to traditional techniques.

Electron microscopy (EM) continues to provide near-atomic resolution structures for well-behaved proteins and protein complexes. Unfortunately, structures of some complexes are limited to low- to medium-resolution due to biochemical or conformational heterogeneity. Thus, the application of unbiased systematic methods for fitting individual structures into EM maps is important. A method that employs co-evolutionary information obtained solely from sequence data could prove invaluable for quick, confident localization of subunits within these structures. Here, we incorporate the co-evolution of intermolecular amino acids as a new type of distance restraint in the Integrative Modeling Platform (IMP) in order to build three-dimensional models of atomic structures into EM maps ranging from 10-14 Å in resolution. We validate this method using four complexes of known structure, where we highlight the conservation of intermolecular couplings despite dynamic conformational changes using the BAM complex. Finally, we use this method to assemble the subunits of the bacterial holo-translocon into a model that agrees with previous biochemical data. The use of evolutionary couplings in integrative modeling improves systematic, unbiased fitting of atomic models into medium- to low-resolution EM maps, providing additional information to integrative models lacking in spatial data.

Malawi, a developing country in Sub-Saharan Africa declared 15 of its 28 districts flood prone areas. Based on the review done by the Department of Disaster Management in Malawi, the country faces the challenge of inaccurate or unreliable flood monitoring and warning services. Lack of high-resolution data contributes to this challenge. Therefore, this research has analysed the extent of applying low to medium resolution data (DEM, soil, land use) in Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) by the Hydrologic Engineering Center of the Army Corps of Engineers in the United States of America. The HEC-HMS hydrological model was simulated to provide the following outputs: the peak discharge (m3/s); total flow (m3/s); and volume (m3) per given drainage area (km2) for all the subbasins, reaches, and junctions. The statistical analysis of the model outputs presented the Standard Deviation Ratio (RSR) of 0.458, Percent Volume Bias (PEV) of 0.10%, and, Percentage Error in Peak Flow (PEPF) of 2.89%. The overall Nash-Sutcliffe Efficiency (NSE) coefficient, that measures the accuracy of the model prediction with respect to mean of the observed values, was 0.79. The coefficient of 0.79 was more than 0.75 which means it was a very good model prediction. According to the standard general performance ratings for recommended statistics, the model was rated very good.

We present the design and lab performance of a prototype lenslet-slicer hybrid integral field spectrograph (IFS), validating the concept for use in future instruments like SCALES/PSI-Red. By imaging extrasolar planets with IFS, it is possible to measure their chemical compositions, temperatures and masses. Many exoplanet-focused instruments use a lenslet IFS to make datacubes with spatial and spectral information used to extract spectral information of imaged exoplanets. Lenslet IFS architecture results in very short spectra and thus low spectral resolution. Slicer IFSs can obtain higher spectral resolution but at the cost of increased optical aberrations that propagate through the down-stream spectrograph and degrade the spatial information we can extract. We have designed a lenslet/slicer hybrid that combines the minimal aberrations of the lenslet IFS with the high spectral resolution of the slicer IFS. The slicer output f/\# matches the lenslet f/\# requiring only additional gratings., Comment: 8 pages, 9 figures, SPIE Optical Engineering + Applications 2021

On 2002, a novel initiative was undertaken by the local water administration of Catalonia (the Agencia Catalana de l'Aigua) and the Universitat Autonoma de Barcelona, leading to a ten-year project where a high number of medium resolution satellite images (MODIS and Landsat) were integrated to the daily water management to improve decision making effectiveness. This paper describes the methodology followed in the successful application of remote sensing, as well as the main problems that had to be overcome during its execution. It also presents the products that have been calculated. These are integrated into the Agency's corporate GIS and immediately available via the intranet for the staff, and a selection is available on the Internet.

SOXS (Son Of X-Shooter) will be the new medium resolution (R~4500 for 1'' slit), high-efficiency, wide band spectrograph for the ESO NTT at La Silla, optimized for classification and follow-up of transient events. SOXS will simultaneously cover UV optical and NIR bands (0.35-2.00 micron) using two different arms and a pre-slit Common Path feeding system. The instrument will be also equipped by a Calibration Unit and an Acquisition Camera (AC) System. In this paper we present the final opto-mechanical design for the AC System and we describe its development status. The project is currently in manufacturing and integration phases., 8 pages, 8 figures, SPIE conference. arXiv admin note: text overlap with arXiv:1809.01526

"The astronomical community finds great utility in broadband medium resolution spectroscopy. Since its com- missioning, X-shooter has been one of the most heavily over-subscribed instruments on the VLT. To increase the availability of such facilities, the Son Of X-shooter instrument is being built for the NTT. Here we present the BRoadband Optical NIR COncept (BRONCO). This design is inspired by the novel SOXS visual arm, which splits the optical band into four sub-band and images them onto a single detector. BRONCO is a design for an 8.2m class telescope, and improves on the original design in several key ways: improved division of the bands, implementing it for a NIR arm and covering 0.32-1.8 um at resolution ~4500 for a 0.811 slit. Moreover we show an improved catadioptric camera design which uses the corrector in double pass, greatly simplifying the detector mounting and mechanics. Due to its optimized design BRONCO is expected to have throughput > 60%, excluding telescope and detector efficiencies-roughly 50% - 100% higher than X-shooter."

We present our progress on the UV-VIS arm of Son Of X-Shooter (SOXS), a new spectrograph for the NTT. Our design splits the spectral band into four sub-bands that are imaged onto a single detector. Each band uses an optimized high efficiency grating that operates in 1st order (m=1). In our previous paper we presented the concept and preliminary design. SOXS passed a Final Design Review in July 2018 and is well into the construction phase. Here we present the final design, performances of key manufactured elements, and the progress in the assembly. Based on the as-built elements, the expected throughput of the visual arm will be > 55%. This paper is accompanied by a series of contributions describing the progress made on the SOXS instrument.

Observation satellites orbiting the Earth provide important surveillance information that helps in identifying various types of land cover. As such, the resolution of satellite images is critical to ensure high accuracy in classifying land cover types. Clearly, high-resolution images are desirable, but such images are prohibitively expensive. Hence, the use of medium-resolution satellite images seems more economical and practical. Several techniques have been developed to monitor the conditions of land covers across the world, such as aerial photography, ground survey, and remote sensing. Among the three techniques, remote sensing is the best, given its ability to monitor vast geographical areas more accurately and much faster compared to those of the other two techniques. In recent decades, many countries have been relying on remote sensing to monitor the conditions of coastal areas by extracting shorelines from satellite images. To date, several pixel-based methods have been proposed for the extraction of shorelines, but most of these methods are fraught with problems. Thus, the object-based approach is proposed using a combination of segmentation algorithms, namely Felzenswalb, Quickshift, and SLIC, together with 15 machine learning classifiers, to classify segmented images of Langkawi Island. The performance of the segmentation algorithms and machine learning classifiers were assessed in terms of segmentation time and overall accuracy in four experimental settings comprising of three different parameters. The research findings showed that the proposed hybridization of SLIC segmentation and Extra Tree classifier was the most efficient and accurate technique compared to other combinations of techniques in extracting the shoreline of the study area. Specifically, SLIC was faster than Felzenswalb and Quickshift by as much as 37 times and 500 times, respectively. Together with Extra Tree classifier, SLIC managed to achieve 100% overall accuracy in the object-based classification in three out of four settings compared to the rest of the techniques tested in the study.

In today's world, satellite images are being utilized for the identification of built-up area, urban planning, disaster management, insurance & tax assessment in an area, and many other social-economic activities. The extraction of the accurate building footprints in densely populated urban areas from medium resolution satellite images is still a challenging task which requires the development of the new methods to solve such problem. In this paper, a novel Dilated-ResUnet deep learning architecture for building extraction from Sentinel-2 satellite images has been proposed. The proposed model has been tested on three novel building datasets that are prepared for three densely populated cities of India (viz. Delhi, Hyderabad and Bengaluru) using Sentinel-2 satellite images and Planet OSM. First FCC (false colour composite) dataset prepared by merging NIR, Red, Green bands, second FCC dataset prepared by merging NIR, Red, Green and Blue bands and third is TCC (true colour composite) dataset by merging red, green and blue bands. The proposed architecture is applied to both the FCC datasets and TCC dataset separately; it has been identified that the proposed model has obtained better building extraction results using FCC (NIR, Red, Green) dataset. The input satellite image enhancement and extensive experimentations to identify the optimal deep learning hyper-parameters using FCC spatial dataset have also been carried out to further improve the performance of the proposed model. The results of the experimentations reveal that the proposed model has out-performed the state of the art models available in literature by achieving the F1-score of 0.4718 and Mean IoU of 0.582 for building extraction from Sentinel-2 satellite images. The outcome of the research work can be utilized for urban planning and management, generate more ground truths for Sentinel-2 satellite images which further can be useful for other societal applications.

On-board radiometric calibration is the most efficient method to improve the measurement accuracy of satellite-borne sensors. Chinese Medium Resolution Spectral Imager (MERSI), loaded on the Fengyu...

During the last decades, Greater Cairo, Egypt, is increasing in population and in built-up extension. Some of the new buildings are informal, constructed in absence of government planning processes, and threaten the Heritage Cultural Site of the Giza Pyramids. In addition, the fertile land of the Nile floodplain is being urbanized despite the government's building prohibition since the 1990s. Therefore, constant monitoring of construction activity is crucial in the rapidly changing environment of this area. Here, we present a data fusion approach that overcomes the limitations of single medium resolution sensor approaches, and also identifies areas in transition from desert to urban. We use multi-temporal multi-sensor supervised land use classification and include a new land use class for detecting undefined disturbances. Synthetic aperture radar (SAR) data is combined with multi-spectral data for creating the land use land cover (LULC) maps using artificial neural networks (ANN). Specifically, ERS SAR data is combined with Landsat 5TM for 1998 and Envisat ASAR IMS with Landsat 7 ETM+ for 2004 and 2010. With this data fusion approach, it is measured an increase of 73% of Greater Cairo built-up extent from 1998 to 2010. Finally, we show the relationship between the aforementioned disturbances and the new built-up areas, detecting 26% of the total new built-up areas constructed from 1998 to 2010 where undefined disturbances were identified in previous land use maps.

The authors developed a land cover and land use mapping framework specifically designed for agricultural systems of the Sudan-Sahel region. The mapping approach extracts information from inter and intra annual vegetation dynamics from dense stacks of Landsat 8 images. They applied this framework to create a 30-m spatial resolution land use map with a focus on agricultural landscapes of northern Nigeria for 2015. This map provides up-to-date information with a higher level of spatial and thematic detail resulting in a more precise characterization of agriculture in the region. The map reveals that agriculture is the main land use in the region. Arable land represents on average 52.5 percent of the area, higher than the reported national average for Nigeria (38.4 percent). Irrigated agriculture covers nearly 2.2 percent of the total area, reaching nearly 20 percent of the cultivated land when traditional floodplain agriculture systems are included, above the reported national average (0.63 percent). There is significant variability in land use within the region. Cultivated land in the northern section can reach values higher than 75 percent, most land suitable for agriculture is already under cultivation and there is limited land for future agricultural expansion. Marginal lands, not suitable for permanent agriculture, can reach 30 of the land at lower altitudes in the Northeast and Northwest. In contrast, the southern section presents lower land use intensity that results in a complex landscape that intertwines areas farms and larger patches of natural vegetation. This map improves the spatial detail of existing sources of LCLU information for the region and provides updated information of the current status of its agricultural landscapes. This study demonstrates the feasibility of multi temporal medium resolution remote sensing data to provide detailed and up-to-date information about agricultural systems in arid and sub arid landscapes of the Sahel region.

Sargassum washing ashore on the beaches of the Caribbean Islands since 2011 has caused problems for the local environments, tourism, and economies. Although preliminary results of Sargassum distributions in the nearby oceans have been obtained using measurements from the Medium Resolution Imaging Spectrometer (MERIS), MERIS stopped functioning in 2012, and detecting and quantifying Sargassum distributions still face technical challenges due to ambiguous pixels from clouds, cloud shadows, cloud adjacency effect, and large-scale image gradient. In this paper, a novel approach is developed to detect Sargassum presence and to quantify Sargassum coverage using the Moderate Resolution Imaging Spectroradiometer (MODIS) alternative floating algae index (AFAI), which examines the red-edge reflectance of floating vegetation. This approach includes three basic steps: 1) classification of Sargassum-containing pixels through correction of large-scale gradient, masking clouds and cloud shadows, and removal of ambiguous pixels; 2) linear unmixing of Sargassum-containing pixels; and, 3) statistics of Sargassum area coverage in pre-defined grids at monthly, seasonal, and annual intervals. In the absence of direct field measurements to validate the results, limited observations from the Hyperspectral Imager for the Coastal Ocean (HICO) measurements and numerous local reports support the conclusion that the elevated AFAI signals are due to the presence of Sargassum instead of other floating materials, and various sensitivity analyses are used to quantify the uncertainties in the derived Sargassum area coverage. The approach was applied to MODIS observations between 2000 and 2015 over the Central West Atlantic (CWA) region (0–22°N, 63–38°W) to derive the spatial and temporal distribution patterns as well as the total area coverage of Sargassum. Results indicate that the first widespread Sargassum distribution event occurred in 2011, consistent with previous MERIS findings. Since 2011, only 2013 showed a minimal Sargassum coverage similar to the period of 2000 to 2010; all other years showed significantly more coverage. More alarmingly, the summer months of 2015 showed mean coverage of > 2000 km2, or about 4 times of the summer 2011 coverage and 20 times of the summer 2000 to 2010 coverage. Analysis of several environmental variables provided some hints on the reasons causing the inter-annual changes after 2010, yet further multi-disciplinary research (including in situ measurements) is required to understand such changes and long-term trends in Sargassum coverage.

Agroforestry is a land use where trees are deliberately grown with agricultural crops either within the field or on the bunds/ boundary. There are innumerable examples of this traditional land use practices in many parts of the world and has long tradition in India too. In the state of Punjab, farmers prefer to grow tree species like Eucalyptus and Populus on their fields with agricultural crops. Reason for adopting these species by the farmers is their fast growth and use of wood in paper and plywood industries. These agroforestry systems are not only remunerative to the farmers but also improve soil fertility of agricultural fields. Area under agroforestry in Bathinda and Patiala districts has been assessed by applying pixel and sub-pixel classifiers on medium resolution LISS III data. In case of pixel based classification, area under agroforestry was estimated to be 7.09 and 4.95 % in the two districts, respectively. Whereas, area under agroforestry come out to be 14.76 and 13.25 %, respectively in case of sub-pixel based classification. Improved results were obtained in case of sub-pixel classifier with more than 85 % accuracy. Hence, sub-pixel classifier may be used with medium resolutions data for accurate assessment of area under agroforestry.

The fundamental data required by urban planners and policy makers is accurate information on current landuse practices in a city or town and how it changes over the past for carrying out various urban planning and management activities. The free satellite imagery provided in global landcover facility (GLCF) which can be used to prepare the landuse maps as attempted in many studies has certain limitations. The images are of lower or medium resolution type and in many cases it may not be possible to obtain the latest image. To overcome this, one has to buy latest high resolution satellite image which is more expensive to purchase and sometimes it may not be possible to get the data due to security reasons. An alternative solution is to utilize Google earth imagery which is open source and provides clear view of buildings, roads, etc. and hence can be best utilized for urban related applications. The present study is an attempt in this direction, in which 340 individual tiles of Google earth images covering Vellore in Tamilnadu were extracted using Elshayal Smart open source software. They were then mosaicked and clipped to facilitate onscreen digitizing using GIS software. The area of various landuse classes was found using the prepared landuse map and zone-wise/ward-wise analysis was also performed. It was found that the area occupied by open land is 56.07 sq.km, which is the highest when compared to other landuse classes. Next to open land, built-up area occupies an area of 28.83 sq.km. The percentage split of all the four landuse classes were 60.69, 31.21, 7.83 and 0.26 for open land, built-up, agricultural and water bodies respectively. Use of Google earth imagery in urban change detection analysis was also explored by utilizing the images of 2007 and 2014. If budget is a constraint in purchasing high resolution satellite imagery, then one could consider utilizing free Google earth images as proposed in the present study for urban related applications.

A new set of gratings with medium resolution (R ∼ 7500) has been mounted on the LAMOST spectrographs, and the wavelength windows range in 490 ∼ 540nm and 640 ∼ 690 nm respectively for blue and red spectrograph arm. Commissioning observation has been conducted to test the survey based on 16 spectrographs and 4000 fibers. Meanwhile, a spectral analysis pipeline has been developing to get more precise stellar parameters, radial velocities and abundance of chemical elements. Instrument profiles are calculated for each fiber at each exposure according to emission lines both from arc lamp. A template grid spectra with R ∼ 7500 for fundamental parameter (Teff, logg, and [Fe/H] ) are selected from Elodie. During the commissioning observation, each star have been visited for several times, and a fraction targets include APOGEE, Kepler and PASTEL objects which have high precisely measured parameters. With the commissioning spectra, we can understand instrument performance, intrinsic precision of repeat observations, and the accuracy of the pipeline.

The flat-cone diffractometer E2 at the research reactor BER II is a thermal neutron single-crystal diffractometer for 3D reciprocal space mapping by using four delay-line area detectors (300 × 300 mm2). Alternatively it is suitable for powder measurements with medium resolution and broad 2-theta scattering range.

The distributions of stars, gas, and dark matter in disk galaxies provide important constraints on galaxy formation models, particularly on small spatial scales (, Accepted to appear in AJ (28 pages, 22 Figures)

The work is part of "HELI-DEM" (HELvetia-Italy Digital Elevation Model) project, funded by the European Regional Development Fund within the Italy-Switzerland cooperation program. The aim of the project is the creation of a unique DTM for the alpine and subalpine area between Italy (Piedmont, Lombardy) and Switzerland (Ticino and Grisons Cantons); at present, different DTMs, that are in different reference frames and have been obtained with different technologies, accuracies, and resolutions, have been acquired. The final DTM should be correctly georeferenced and produced validating and integrating the data that are available for the project. DTMs are fundamental in hydrogeological studies, especially in alpine areas where hydrogeological risks may exist. Moreover, when an event, like for example a landslide, happens at the border between countries, a unique and integrated DTM which covers the interest area is useful to analyze the scenario. In this sense, HELI-DEM project is helpful. To perform analyses along the borders between countries, transnational geographic information is needed: a transnational DTM can be obtained by merging regional low resolution DTMs. Moreover high resolution local DTMs should be used where they are available. To be merged, low and high resolution DTMs should be in the same three dimensional reference frame, should not present biases and should be consistent in the overlapping areas. Cross-validation between the different DTMs is therefore needed. Two different problems should be solved: the merging of regional, partly overlapping low and medium resolution DTMs into a unique low/medium resolution DTM and the merging with other local high resolution/high accuracy height data. This paper discusses the preliminary processing of the data for the fusion of low and high resolution DTMs in a study-case area within the Lombardy region: Valtellina valley. In this region the Lombardy regional low resolution DTM is available, with a horizontal resolution of 20 meters; in addition a LiDAR DTM with a horizontal resolution of 1 meter, which covers only the main hydrographic basins, is also available. The two DTMs have been transformed into the same reference frame. The cross-validation of the two datasets has been performed comparing the low resolution DTM with the local high resolution DTM. Then, where significant differences are present, GPS survey have been used as external validation. The results are presented. Moreover, a possible strategy for the future fusion of the data, is shortly summarized at the end of the paper.

Son Of X-Shooter (SOXS) is the new instrument for the ESO 3.5 m New Technology Telescope (NTT) in La Silla site (Chile) devised for the spectroscopic follow-up of transient sources. SOXS is composed by two medium resolution spectrographs able to cover the 350-2000 nm interval. An Acquisition Camera will provide a light imaging capability in the visible band. We present the procedure foreseen for the Assembly, Integration and Test activities (AIT) of SOXS that will be carried out at sub-systems level at various consortium partner premises and at system level both in Europe and Chile.

Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced cloud computing facilities and the open Landsat data archives available, NDUI has the potential for global studies at the 30 m scale.

The ability to register image data to a common coordinate system is a critical feature of virtually all imaging studies that require multiple subject analysis, combining single subject data from multiple modalities, or both. However, in spite of the abundance of literature on the subject and the existence of several variants of registration algorithms, their practical utility remains problematic, as commonly acknowledged even by developers of these methods because the complexity of the problem has resisted a general, flexible, and robust theoretical and computational framework. To address this issue, we present a new registration method that is similar in spirit to the current state-of-the-art technique of diffeomorphic mapping, but is more general and flexible. The method utilizes a Hamiltonian formalism and constructs registration as a sequence of symplectomorphic maps in conjunction with a novel phase space regularization based on the powerful entropy spectrum pathways (ESP) framework. The method is demonstrated on the three different magnetic resonance imaging (MRI) modalities routinely used for human neuroimaging applications by mapping between high resolution anatomical (HRA) volumes, medium resolution diffusion weighted MRI (DW-MRI) and HRA volumes, and low resolution functional MRI (fMRI) and HRA volumes. The typical processing time for high quality mapping ranges from less than a minute to several minutes on a modern multi core CPU for typical high resolution anatomical (~256x256x256 voxels) MRI volumes., Comment: 26 pages, 7 figures

In the city of Bueno Brandão, South of Minas Gerais State, Brazil, the Watershed of Rio das Antas is located prior to the public water supply and is susceptible to hydro-degradation due to the intensive agricultural activities developed in the area. The potato plantation is the most significant cropping in the city. Because of the possibility of interfering in the preservation areas, mainly the ones surrounding water courses and springs, it is very important to do the assessment of the plantation sites, in order to avoid the risk of water contamination. The procedures adopted by the agro activity farmers generally present the following features: intensive use of agro-chemicals, cropping in places with slopes which are higher than 20%, close to or in permanent preservation areas. The scope of this study was to develop the proper methodology for the assessment of the plantation areas, regarding the short time of procedure, as the period between the plantation and the harvest occurs in six months the furthest. These areas vary year in year out, as the plantation sites often change due to the land degradation. Because of that, geotechnologies are recommended to detect the plantation areas by the use of satellite images and accurate data processing. Considering the availability of LANDSAT medium resolution images, methods for their appropriate classification were approached to provide effective target detection.

In this paper, a novel minutiae based fingerprint matching system is proposed. The system is suitable for medium resolution fingerprint images obtained by low cost commercial sensors. The paper presents a new thinning algorithm, a new features extraction and representation, and a novel feature distance matching algorithm. The proposed system is rotation and translation invariant and is suitable for complete or partial fingerprint matching. The proposed algorithms are optimized to be executed on low resource environments both in CPU power and memory space. The system was evaluated using a standard fingerprint dataset and good performance and accuracy were achieved under certain image quality requirements. In addition, the proposed system was compared favorably to that of the state of the art systems.

Soil is a natural resource that has been affected by human pressures beyond its renewal capacity. For this reason, large agricultural areas that were productive have been abandoned due to soil degradation, mainly caused by the erosion process. The objective of this study was to apply the Universal Soil Loss Equation to generate more recent estimates of soil loss rates for the state of São Paulo using a database with information from medium resolution (30 m). The results showed that many areas of the state have high (critical) levels of soil degradation due to the predominance of consolidated human activities, especially in growing sugarcane and pasture use. The average estimated rate of soil loss is 30 Mg ha-1 yr-1 and 59 % of the area of the state (except for water bodies and urban areas) had estimated rates above 12 Mg ha-1 yr-1, considered as the average tolerance limit in the literature. The average rates of soil loss in areas with annual agricultural crops, semi-perennial agricultural crops (sugarcane), and permanent agricultural crops were 118, 78, and 38 Mg ha-1 yr-1 respectively. The state of São Paulo requires attention to conservation of soil resources, since most soils led to estimates beyond the tolerance limit.

We present the latest results of the sensitivity estimate for spectrometers of the SPICA Mid-Infrared Instrument (SMI). SMI has three spectroscopic channels; low resolution spectrometer (LRS), medium resolution spectrometer (MRS) and high resolution spectrometer (HRS). Taking account of the results of optical design of each spectrometer and the latest information of the expected performance of detector arrays, the continuum sensitivity for a point source, the continuum sensitivity for an extended source, the line sensitivity for a point source, the line sensitivity for an extended source, and the saturation limit are calculated for LRS, MRS and HRS and are provided in this paper.

The European Space Agency (ESA) has initiated a free and open service providing weekly surface reflectance composites for Europe and Africa. The project is named Culture-MERIS and targets land activities for agriculture and food security user communities. The Culture-MERIS demonstration products are based on data from Envisat's Medium Resolution Imaging Spectrometer (MERIS) in full resolution (FR) (300 m), with improved geolocation and atmospheric correction, and are updated every Wednesday, providing information for the full previous week.

Remote sensing has been used in forestry for a long time. Aerial photography has mainly been used to support national and operational inventories and medium resolution satellite data for large area inventories. Today through the availability of new technologies there are greatly extended possibilities for remote sensing in the forestry sector. The major change is the better availability of 3-D information, which allows a much better modelling of forests and forest attributes. The paper presents an overview of the newest remote sensing technologies, such as the systems carried by satellite which can be used to record changes or degradation in the area covered by forest for the global forestry resources assessment of the UN World Food and Agriculture Organisation or are examined in connection with the United Nations REDD program. Besides these, other small scale applications are presented based on optical or laser systems carried by aircraft. The article finishes with an outlook on expected developments in the near future. It is expected that the future of forestry remote sensing will be characterized above all by the combination of information obtained from diverse sources, such as data based on terrestrial and on remotely sensed sources.

We used satellite imagery from the European Space Agency (ESA) Medium Resolution Imaging Spectrometer (MERIS) optical sensor to make the first mapping of the full distribution and movement of the population of Sargassum in the Gulf of Mexico and the western Atlantic. For most of the years for which we have data (2002 to 2008), the results show a seasonal pattern in which Sargassum originates in the northwest Gulf of Mexico in the spring of each year, and is then advected into the Atlantic. The Sargassum appears east of Cape Hatteras as a 'Sargassum jet' in July and ends northeast of the Bahamas in February of the following year. This pattern is consistent with historical surveys from ships. MERIS provides a spectral band that greatly improves the discrimination of floating vegetation from confusing signals such as cloud and sunglint.

Satellite image classification is an imperative system utilised as a part of remote sensing. Primary data of extraordinary significance to different difficulties can be acquired straightforwardly from Land-cover observation. Different information partitions inferred by various clustering methods can be gathered into a new solution by cluster ensembles. Supervised iterative expectation-maximisation (EM) method can be initialised by cluster ensemble based strategy which will be examined in the paper. The performance of clustering of the proposed method is compared with individual clustering of the ensemble for medium resolution and a very high spatial resolution images. The accuracy measurements have done with different test points. The state of art techniques are giving less accuracy and are not well defined. This paper will explore the possibility of all accuracy parameters with supervised classification results. The accuracy parameters are well tested and compared in this paper with various start of art techniques.

The present work presents a SWAN model (Simulating Waves Nearshore) implementation focused on the nearshore of the Black Sea Western coast. The global implementation of the SWAN model, previously calibrated for the entire Black Sea basin, is used as driver. A first area of medium resolution, that covers all the West coast of the sea, is nested into the global model. The wave observations made at the Gloria drilling unit, located in the central part of the area was considered as a check point, both for the wind input and for the wave model output. Subsequently two higher resolution areas were nested, as a third level, to cover the Romanian and Bulgarian coastal areas, respectively. Accurate estimation of wind wave characteristics in coastal environment is of considerable importance because it can help in case of environmental alerts for assessing the oil spills propagation and also to estimate the wave impact on the costal and marine structures.

Iznik tiles dated from the 16th century, copies of tiles and pottery of Theodore Deck from the 19th century and also tiles without any information on their origin were analyzed with both laboratory and portable Raman instruments. As the original tiles are generally fixed on the walls of historical buildings, the portable Raman spectrometer is more convenient for the analysis but the information obtained from the spectra is not very useful because of the medium resolution and complex baseline of the instrument in spite of its speed and ease of use. The Raman signature of the glazes is the most pertinent and easily accessible fingerprint of the artifacts. The differentiation between Iznik ceramics and other samples could be made with Raman spectrometers, according to the specific signature of SiO stretching and bending bands of Iznik glazes. Copyright © 2009 John Wiley & Sons, Ltd.

From early 2004, Lake Maracaibo (northwest Venezuela) experienced an unprecedented invasion of duckweed Lemna obscura. Recurrent blooms of the plant in the past 2 years illustrate the need for an automatic monitoring method to follow the plant cover with time and to plan contingency measures. We present an approach that allows the cover of the duckweed to be quantified through the classification of MODIS 250 m RGB composite images available from the internet. The method improves the accuracy of the results of the Support Vector Machine (SVM) algorithm for classification by including a bootstrap step during the training phase. Using only 200 pixels for training (

A new small-angle scattering instrument SANS-1 will be installed on beamline NL 4a at the Heinz Maier-Leibnitz Forschungsneutronenquelle (FRM II). It is a joint venture between the Technische Universitat Munchen and the Geesthacht Neutron Facility (GENF). SANS-1 has been optimized to be one of the most intense and versatile small-angle scattering instruments within the boundaries of available space and interaction with neighbouring instruments. Using the program McStas, the dimensions and the features of the different optical components were investigated and compared for the final selection. A vertical S-shaped neutron guide, a tower with two possible selectors, one for medium resolution at high intensity and one for high resolution, and two optimized transmission polarizers are the main advantages of SANS-1 compared with traditional instruments at other facilities.

The current work presents updated measurements of narrow-band transmission for the 2.0, 2.7 and 4.3 μ m bands of CO 2 at temperatures of up to 1550 K. In addition, measurements for the 15 μ m band of CO 2 are also presented for the first time. Data were collected with an improved drop tube design (as compared to the earlier measurements) and an FTIR-spectrometer. The measured data were compared with the CDSD and HITEMP databases, as well as with previous data obtained from the old drop tube apparatus. The new data have less uncertainties at extreme temperatures than the old data and eliminate some of the problems associated with subtraction of the emission signal with the old apparatus. The data show minor discrepancies with the high-resolution databases, particularly with HITEMP at higher temperatures, but in general agreement is good.

International audience; This paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detec- tion algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (Automatic Identification System) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels' length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations.

The open data policy, the availability of high resolution imagery and the capability to cover fast-growing economies are among the main advantages of CBERS. Unfortunately, data produced by this satellite suffer of geographic misplacement, forcing to apply pre-processing techniques to stabilize the imagery. This paper introduces a feature-based technique developed to pre-process CBERS imagery over an area of interest. In particular, the algorithm is able to fix the shift among HRC high resolution (2.5 meters) and CCD medium resolution (20 meters). The final goal is to combine the advantages of high resolution and radiometric properties for built-up area extraction purposes.