scholarly journals A New Design of Large-format Streak Tube with Single-lens Focusing System

2021 ◽  
Vol 21 (6) ◽  
pp. 191-196
Author(s):  
Liping Tian ◽  
Lingbin Shen ◽  
Lin Chen ◽  
Lili Li ◽  
Jinshou Tian ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Detection System ◽  
Monte Carlo Sampling ◽  
Dynamic Mode ◽  
Large Format ◽  
Single Lens ◽  
Screen Model ◽  
Streak Tube

Abstract Streak tubes with large-format and high spatial resolution are central to mm-spatial-resolved STIL detection system and hyperspectral resolved ICF experiment. In this paper, we established a large-format streak tube with a three-coaxial-cylindrical single-lens focusing system, a spherically curved photocathode and phosphor screen model in CST Particle Studio. The temporal and spatial resolution were calculated and mimicked based on the Monte-Carlo sampling method in static and dynamic mode. The simulated results show that the static spatial resolution reaches 50 lp/mm over the whole 50 mm effective photocathode length, and the physical temporal resolution is better than 45 ps. Furthermore, in dynamic working mode, the streak tube can achieve spatial resolution of 10 lp/mm and temporal resolution of 60 ps. The simulation results will be used to guide the design and production for large-format with high spatial resolution streak tube development.

A four-channel ICCD framing camera with nanosecond temporal resolution and high spatial resolution

2021 ◽  
pp. 1-9
Author(s):  
Yuman Fang ◽  
Minrui Zhang ◽  
Junfeng Wang ◽  
Lehui Guo ◽  
Xueling Liu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Framing Camera

scholarly journals Evaluating the Impacts of Continuous and Rotational Grazing on Tallgrass Prairie Landscape Using High-Spatial-Resolution Imagery

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 238 ◽  
Author(s):  
Shengfang Ma ◽  
Yuting Zhou ◽  
Prasanna H. Gowda ◽  
Liangfu Chen ◽  
Patrick J. Starks ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Tallgrass Prairie ◽  
Landscape Metrics ◽  
High Spatial Resolution ◽  
Shrub Encroachment ◽  
Rotational Grazing ◽  
Grass Growth

This study evaluated the impacts of different grazing treatments (continuous (C) and rotational (R) grazing) on tallgrass prairie landscape, using high-spatial-resolution aerial imagery (1-m at RGB and near-infrared bands) of experimental C and R pastures within two replicates (Rep A and Rep B) in the southern Great Plains (SGP) of the United States. The imagery was acquired by the National Agriculture Imagery Program (NAIP) during the agricultural growing season of selected years (2010, 2013, 2015, and 2017) in the continental United States. Land cover maps were generated by combining visual interpolation, a support vector machine, and a decision tree classifier. Landscape metrics (class area, patch number, percentage of landscape, and fragmentation indices) were calculated from the FRAGSTATS (a computer software program designed to compute a wide variety of landscape metrics for categorical map patterns) based on land cover results. Both the metrics and land cover results were used to analyze landscape dynamics in the experiment pastures. Results showed that both grass and shrubs of different pastures differed largely in the same year and had significant annual dynamics controlled by climate. High stocking intensity delayed grass growth. A large proportion of bare soil occurred in sub-paddocks of rotational grazing that were just grazed or under grazing. Rep A experienced rapid shrub encroachment, with a large proportion of shrub at the beginning of the experiment. Shrub may occupy 41% of C and 15% of R in Rep A by 2030, as revealed by the linear regression analysis of shrub encroachment. In contrast, shrub encroachment was not significant in Rep B, which only had a small number of shrub patches at the beginning of the experiment. This result indicates that the shrub encroachment is mainly controlled by the initial status of the pastures instead of grazing management. However, the low temporal resolution of the NAIP imagery (one snapshot in two or three years) limits our comparison of the continuous and rotational grazing at the annual scale. Future studies need to combine NAIP imagery with other higher temporal resolution imagery (e.g., WorldView), in order to better evaluate the interannual variabilities of grass productivity and shrub encroachment.

scholarly journals Estimating 2009–2017 Impervious Surface Change in Gwadar, Pakistan Using the HJ-1A/B Constellation, GF-1/2 Data, and the Random Forest Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 443 ◽  
Author(s):  
Bian ◽  
Li ◽  
Zuo ◽  
Lei ◽  
Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Satellite Images ◽  
Reference Data ◽  
High Spatial Resolution ◽  
Impervious Surface ◽  
Urbanization Process ◽  
Rapid Urbanization

The China–Pakistan Economic Corridor (CPEC) is the flagship project of the Belt and Road Initiative. At the end of the CPEC, the Gwadar port on the Arabian Sea is being built quickly, providing an important economical route for the flow of Central Asia’s natural resources to the world. Gwadar city is in a rapid urbanization process and will be developed as a modern, world-class port city in the near future. Therefore, monitoring the urbanization process of Gwadar at both high spatial and temporal resolution is vital for its urban planning, city ecosystem management, and the sustainable development of CPEC. The impervious surface percentage (ISP) is an essential quantitative indicator for the assessment of urban development. Through the integration of remote sensing images and ISP estimation models, ISP can be routinely and periodically estimated. However, due to clouds’ influence and spatial–temporal resolution trade-offs in sensor design, it is difficult to estimate the ISP with both high spatial resolution and dense temporal frequency from only one satellite sensor. In recent years, China has launched a series of Earth resource satellites, such as the HJ (Huangjing, which means environment in Chinese)-1A/B constellation, showing great application potential for rapid Earth surface mapping. This study employs the Random Forest (RF) method for a long-term and fine-scale ISP estimation and analysis of the city of Gwadar, based on the density in temporal and multi-source Chinese satellite images. In the method, high spatial resolution ISP reference data partially covering Gwadar city was first extracted from the 1–2 meter (m) GF (GaoFen, which means high spatial resolution in Chinese)-1/2 fused images. An RF retrieval model was then built based on the training samples extracted from ISP reference data and multi-temporal 30-m HJ-1A/B satellite images. Lastly, the model was used to generate the 30-m time series ISP from 2009 to 2017 for the whole city area based on the HJ-1A/B images. Results showed that the mean absolute error of the estimated ISP was 6.1–8.1% and that the root mean square error (RMSE) of the estimation results was 12.82–15.03%, indicating the consistently high performance of the model. This study highlights the feasibility and potential of using multi-source Chinese satellite images and an RF model to generate long-term ISP estimations for monitoring the urbanization process of the key node city in the CPEC.

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

scholarly journals A New High Spatial Resolution Electronic Streak Tube

1982 ◽  
Vol 29 (1) ◽  
pp. 904-906
Author(s):  
C. K. Hinrichs ◽  
R. M. Estrella
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Streak Tube

An automated airplane detection system for large panchromatic image with high spatial resolution

Optik ◽  
2014 ◽  
Vol 125 (12) ◽  
pp. 2768-2775 ◽  
Author(s):  
Zhenyu An ◽  
Zhenwei Shi ◽  
Xichao Teng ◽  
Xinran Yu ◽  
Wei Tang
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Detection System ◽  
Panchromatic Image

scholarly journals Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data

2021 ◽  
Vol 13 (4) ◽  
pp. 732
Author(s):  
Ryota Nomura ◽  
Kazuo Oki
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
High Spatial Resolution ◽  
Target Area ◽  
Modis Ndvi ◽  
Acceptable Accuracy ◽  
Sar Data ◽  
Sentinel 2

The normalized difference vegetation index (NDVI) is a simple but powerful indicator, that can be used to observe green live vegetation efficiently. Since its introduction in the 1970s, NDVI has been used widely for land management, food security, and physical models. For these applications, acquiring NDVI in both high spatial resolution and high temporal resolution is preferable. However, there is generally a trade-off between temporal and spatial resolution when using satellite images. To relieve this problem, a convolutional neural network (CNN) based downscaling model was proposed in this research. This model is capable of estimating 10-m high resolution NDVI from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m resolution NDVI by using Sentinel-1 10-m resolution synthetic aperture radar (SAR) data. First, this downscaling model was trained to estimate Sentinel-2 10-m resolution NDVI from a combination of upscaled 250-m resolution Sentinel-2 NDVI and 10-m resolution Sentinel-1 SAR data, by using data acquired in 2019 in the target area. Then, the generality of this model was validated by applying it to test data acquired in 2020, with the result that the model predicted the NDVI with reasonable accuracy (MAE = 0.090, ρ = 0.734 on average). Next, 250-m NDVI from MODIS data was used as input to confirm this model under conditions replicating an actual application case. Although there were mismatch in the original MODIS and Sentinel-2 NDVI data, the model predicted NDVI with acceptable accuracy (MAE = 0.108, ρ = 0.650 on average). Finally, this model was applied to predict high spatial resolution NDVI using MODIS and Sentinel-1 data acquired in target area from 1 January 2020~31 December 2020. In this experiment, double cropping of cabbage, which was not observable at the original MODIS resolution, was observed by enhanced temporal resolution of high spatial resolution NDVI images (approximately ×2.5). The proposed method enables the production of 10-m resolution NDVI data with acceptable accuracy when cloudless MODIS NDVI and Sentinel-1 SAR data is available, and can enhance the temporal resolution of high resolution 10-m NDVI data.

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