Technical Note: Inter‐annual analysis of deforestation hotspots in Madagascar from high temporal resolution satellite observations

2005 ◽  
Vol 26 (7) ◽  
pp. 1447-1461 ◽  
Author(s):  
J. C. Ingram ◽  
T. P. Dawson
Keyword(s):  
Temporal Resolution ◽  
Technical Note ◽  
Satellite Observations ◽  
High Temporal Resolution

scholarly journals A Software Architecture to Mimic a Ventricular Tachycardia in Intact Murine Hearts by Means of an All-Optical Platform

2019 ◽  
Vol 2 (1) ◽  
pp. 7 ◽  
Author(s):  
Francesco Giardini ◽  
Valentina Biasci ◽  
Marina Scardigli ◽  
Francesco S. Pavone ◽  
Gil Bub ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Electrical Activity ◽  
Real Time ◽  
Temporal Resolution ◽  
Technical Note ◽  
High Temporal Resolution ◽  
Excitable Cells ◽  
New Approach ◽  
Cardiac Electrical Activity ◽  
All Optical

Optogenetics is an emerging method that uses light to manipulate electrical activity in excitable cells exploiting the interaction between light and light-sensitive depolarizing ion channels, such as channelrhodopsin-2 (ChR2). Initially used in the neuroscience, it has been adopted in cardiac research where the expression of ChR2 in cardiac preparations allows optical pacing, resynchronization and defibrillation. Recently, optogenetics has been leveraged to manipulate cardiac electrical activity in the intact heart in real-time. This new approach was applied to simulate a re-entrant circuit across the ventricle. In this technical note, we describe the development and the implementation of a new software package for real-time optogenetic intervention. The package consists of a single LabVIEW program that simultaneously captures images at very high frame rates and delivers precisely timed optogenetic stimuli based on the content of the images. The software implementation guarantees closed-loop optical manipulation at high temporal resolution by processing the raw data in workstation memory. We demonstrate that this strategy allows the simulation of a ventricular tachycardia with high stability and with a negligible loss of data with a temporal resolution of up to 1 ms.

scholarly journals High Temporal Resolution Satellite Observations of Fire Radiative Power Reveal Link Between Fire Behavior and Aerosol and Gas Emissions

2020 ◽  
Vol 47 (23) ◽  
Author(s):  
Elizabeth B. Wiggins ◽  
Amber J. Soja ◽  
Emily Gargulinski ◽  
Hannah S. Halliday ◽  
R. Bradley Pierce ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Fire Behavior ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Gas Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

scholarly journals Technical Note: High temporal resolution characterization of gating response time

2016 ◽  
Vol 43 (6Part1) ◽  
pp. 2802-2806 ◽  
Author(s):  
Rodney D. Wiersma ◽  
Bradley P. McCabe ◽  
Andrew H. Belcher ◽  
Patrick J. Jensen ◽  
Brett Smith ◽  
...  
Keyword(s):  
Response Time ◽  
Temporal Resolution ◽  
Technical Note ◽  
High Temporal Resolution

scholarly journals Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

2021 ◽  
Vol 25 (6) ◽  
pp. 3207-3225
Author(s):  
Sebastian Scher ◽  
Stefanie Peßenteiner
Keyword(s):  
Probability Distribution ◽  
Temporal Resolution ◽  
Radar Data ◽  
Technical Note ◽  
Geographical Information ◽  
Generative Adversarial Networks ◽  
High Temporal Resolution ◽  
Adversarial Networks ◽  
Rainfall Patterns ◽  
Lower Temporal Resolution

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

scholarly journals Technical Note: Testing an improved index for analysing storm nutrient hysteresis

2015 ◽  
Vol 12 (8) ◽  
pp. 7875-7892 ◽  
Author(s):  
C. E. M. Lloyd ◽  
J. E. Freer ◽  
P. J. Johnes ◽  
A. L. Collins
Keyword(s):  
Hysteresis Loop ◽  
Temporal Resolution ◽  
Nutrient Transport ◽  
Technical Note ◽  
High Temporal Resolution ◽  
The Impact ◽  
Process Controls ◽  
Extreme Flow

Abstract. Analysis of hydrochemical behaviour in extreme flow events can provide new insights into the process controls on nutrient transport in catchments. The examination of storm behaviours using hysteresis analysis has increased in recent years, partly due to the increased availability of high temporal resolution datasets for discharge and nutrient parameters. A number of these analyses involve the use of an index to describe the characteristics of a hysteresis loop in order to compare different storm behaviours both within and between catchments. This technical note reviews the methods for calculation of the hysteresis index (HI) and explores a new more effective methodology. Each method is systematically tested and the impact of the chosen calculation on the results is examined. Recommendations are made regarding the most effective method of calculating a HI which can be used for comparing data between storms and between different parameters and catchments.

scholarly journals High Temporal Resolution Satellite Observations of Fire Radiative Power Reveal Link Between Fire Behavior and Aerosol and Gas Emissions

2020 ◽  
Author(s):  
Elizabeth Brooke Wiggins ◽  
Amber Jeanine Soja ◽  
Emily M. Gargulinski ◽  
Hannah Selene Halliday ◽  
Brad Pierce ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Fire Behavior ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Gas Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

scholarly journals Technical Note: Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

2020 ◽  
Author(s):  
Sebastian Scher ◽  
Stefanie Peßenteiner
Keyword(s):  
Probability Distribution ◽  
Temporal Resolution ◽  
Radar Data ◽  
Technical Note ◽  
Geographical Information ◽  
Generative Adversarial Networks ◽  
High Temporal Resolution ◽  
Adversarial Networks ◽  
Rainfall Patterns ◽  
Lower Temporal Resolution

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly under-determined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of Generative Adversarial Networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly lead to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

scholarly journals Modifying an Image Fusion Approach for High Spatiotemporal LST Retrieval in Surface Dryness and Evapotranspiration Estimations

2020 ◽  
Vol 12 (3) ◽  
pp. 498 ◽  
Author(s):  
Tri Wandi Januar ◽  
Tang-Huang Lin ◽  
Chih-Yuan Huang ◽  
Kuo-En Chang
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
Thermal Infrared ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Spatiotemporal Resolution

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.

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