scholarly journals Real-time high spatial resolution dose verification in stereotactic motion adaptive arc radiotherapy

2018 ◽  
Vol 19 (4) ◽  
pp. 173-184 ◽  
Author(s):  
Mitchell Duncan ◽  
Matthew K. Newall ◽  
Vincent Caillet ◽  
Jeremy T. Booth ◽  
Paul J. Keall ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Dose Verification

Design and Characterization of a Real Time, Large Area, High Spatial Resolution Particle Tracker Based on Scintillating Fibers

2013 ◽  
pp. 159-174 ◽  
Author(s):  
D. Lo Presti ◽  
D. L. Bonanno ◽  
F. Longhitano ◽  
C. Pugliatti ◽  
S. Aiello ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Large Area ◽  
Scintillating Fibers

scholarly journals FEASIBILITY STUDY OF LANDSAT-8 IMAGERY FOR RETRIEVING SEA SURFACE TEMPERATURE (CASE STUDY PERSIAN GULF)

2016 ◽  
Vol XLI-B8 ◽  
pp. 1107-1110
Author(s):  
F. Bayat ◽  
M. Hasanlou
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Real Time ◽  
Spatial Resolution ◽  
Persian Gulf ◽  
High Spatial Resolution ◽  
Sea Surface ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Sst Retrieval

Sea surface temperature (SST) is one of the critical parameters in marine meteorology and oceanography. The SST datasets are incorporated as conditions for ocean and atmosphere models. The SST needs to be investigated for various scientific phenomenon such as salinity, potential fishing zone, sea level rise, upwelling, eddies, cyclone predictions. On the other hands, high spatial resolution SST maps can illustrate eddies and sea surface currents. Also, near real time producing of SST map is suitable for weather forecasting and fishery applications. Therefore satellite remote sensing with wide coverage of data acquisition capability can use as real time tools for producing SST dataset. Satellite sensor such as AVHRR, MODIS and SeaWIFS are capable of extracting brightness values at different thermal spectral bands. These brightness temperatures are the sole input for the SST retrieval algorithms. Recently, Landsat-8 successfully launched and accessible with two instruments on-board: (1) the Operational Land Imager (OLI) with nine spectral bands in the visual, near infrared, and the shortwave infrared spectral regions; and (2) the Thermal Infrared Sensor (TIRS) with two spectral bands in the long wavelength infrared. The two TIRS bands were selected to enable the atmospheric correction of the thermal data using a split window algorithm (SWA). The TIRS instrument is one of the major payloads aboard this satellite which can observe the sea surface by using the split-window thermal infrared channels (CH10: 10.6 μm to 11.2 μm; CH11: 11.5 μm to 12.5 μm) at a resolution of 30 m. The TIRS sensors have three main advantages comparing with other previous sensors. First, the TIRS has two thermal bands in the atmospheric window that provide a new SST retrieval opportunity using the widely used split-window (SW) algorithm rather than the single channel method. Second, the spectral filters of TIRS two bands present narrower bandwidth than that of the thermal band on board on previous Landsat sensors. Third, TIRS is one of the best space born and high spatial resolution with 30&thinsp;m. in this regards, Landsat-8 can use the Split-Window (SW) algorithm for retrieving SST dataset. Although several SWs have been developed to use with other sensors, some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this paper is to develop a SW, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment. In this research, that has been done for modelling SST using thermal Landsat 8-imagery of the Persian Gulf. Therefore, by incorporating contemporary in situ data and SST map estimated from other sensors like MODIS, we examine our proposed method with coefficient of determination (R2) and root mean square error (RMSE) on check point to model SST retrieval for Landsat-8 imagery. Extracted results for implementing different SW's clearly shows superiority of utilized method by R<sup>2</sup>&thinsp;=&thinsp;0.95 and RMSE&thinsp;=&thinsp;0.24.

Carbon Monitor Gridded Dataset (CMGD), a near-real-time high resolution gridded CO2 emission estimates dataset

2021 ◽  
Author(s):  
Xinyu Dou ◽  
Zhu Liu
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Energy Use ◽  
High Spatial Resolution ◽  
Spatial Scales ◽  
Energy Transition ◽  
High Temporal Resolution ◽  
Fine Grained ◽  
Gridded Dataset ◽  
Emission Changes

&lt;p&gt;The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) emissions. This research first presented near-real-time high-spatial-resolution(0.1&amp;#176;*0.1&amp;#176;) and high-temporal-resolution(daily) gridded estimates of CO&lt;sub&gt;2&lt;/sub&gt; emissions for different sectors named Carbon Monitor Gridded Dataset(CMGD). This dataset responds to the growing and urgent need for high-quality, fine-grained CO&lt;sub&gt;2&lt;/sub&gt; emission estimates to support global emissions monitoring on the refined spatial scale. CMGD is derived from our Carbon Monitor, a near-real-time daily dataset of global CO&lt;sub&gt;2&lt;/sub&gt; emission from fossil fuel and cement production, including detailed information in 6 sectors and main countries. Based on EDGAR v5.0 gridded CO&lt;sub&gt;2&lt;/sub&gt; emissions map and other geospatial proxies, we finally constructed CMGD with a high spatial resolution of 0.1 degree. Here, we provided the total emissions of specific countries and analyzed the countries with larger emissions (including the EU). Furthermore, we analyzed the daily emission changes of several typical cities around the world and provided insights on the contributions of various sectors. Through CMGD, we can get a much faster and more fine-grained overview, including timelines that show where and how emissions decreases have corresponded to lockdown measures at the finer spatial scales. The fine-grain and timeliness of CMGD emissions estimates will facilitate more local and adaptive management of CO&lt;sub&gt;2&lt;/sub&gt; emissions during both the pandemic recovery and the ongoing energy transition.&lt;/p&gt;

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