388 research outputs found

    Land and cryosphere products from Suomi NPP VIIRS: overview and status

    Get PDF
    [1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS

    Remote Sensing

    Get PDF
    This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

    A Review of Classification Problems and Algorithms in Renewable Energy Applications

    Get PDF
    Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field

    Characterizing Tropical Forest Cover Loss Using Dense Sentinel-1 Data and Active Fire Alerts

    Get PDF
    Fire use for land management is widespread in natural tropical and plantation forests, causing major environmental and economic damage. Recent studies combining active fire alerts with annual forest-cover loss information identified fire-related forest-cover loss areas well, but do not provide detailed understanding on how fires and forest-cover loss are temporally related. Here, we combine Sentinel-1-based, near real-time forest cover information with Visible Infrared Imaging Radiometer Suite (VIIRS) active fire alerts, and for the first time, characterize the temporal relationship between fires and tropical forest-cover loss at high temporal detail and medium spatial scale. We quantify fire-related forest-cover loss and separate fires that predate, coincide with, and postdate forest-cover loss. For the Province of Riau, Indonesia, dense Sentinel-1 C-band Synthetic Aperture Radar data with guaranteed observations of at least every 12 days allowed for confident and timely forest-cover-loss detection in natural and plantation forest with user’s and producer’s accuracy above 95%. Forest-cover loss was detected and confirmed within 22 days in natural forest and within 15 days in plantation forest. This difference can primarily be related to different change processes and dynamics in natural and plantation forest. For the period between 1 January 2016 and 30 June 2017, fire-related forest-cover loss accounted for about one third of the natural forest-cover loss, while in plantation forest, less than ten percent of the forest-cover loss was fire-related. We found clear spatial patterns of fires predating, coinciding with, or postdating forest-cover loss. Only the minority of fires in natural and plantation forest temporally coincided with forest-cover loss (13% and 16%) and can thus be confidently attributed as direct cause of forest-cover loss. The majority of the fires predated (64% and 58%) or postdated forest-cover loss (23% and 26%), and should be attributed to other key land management practices. Detailed and timely information on how fires and forest cover loss are temporally related can support tropical forest management, policy development, and law enforcement to reduce unsustainable and illegal fire use in the tropics

    Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

    Get PDF
    Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

    Non-local tensor completion for multitemporal remotely sensed images inpainting

    Get PDF
    Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method
    • …
    corecore