A computationally efficient forest succession model: Design and initial tests

Due to the limitations of hardware technology, satellite sensors cannot obtain images with high temporal, spatial, and spectral resolutions at the same time. Current spatiotemporal fusion methods try to solve the contradiction between temporal resolution and spatial resolution, which cannot achieve good reconstruction accuracy partly because the data sources are from heterogeneous platforms with long chains difficult to be modeled. Different from the crossing-platform fusion, this work proposes to improve the spatial and temporal resolutions on a single platform. For the 2-m panchromatic images, 8-m multispectral images, and 16-m wide-field-view images captured by the Gaofen-1 satellite, our goal is to produce 2-m multispectral images with high temporal resolutions. Two convolutional neural networks are built to solve this spatiotemporal-spectral fusion issue with pansharpening and spatiotemporal fusion in serial. In the validation stage, the 2-m multispectral images are built and evaluated with the panchromatic images and 8-m multispectral images. The digital and visual evaluations show that our method can produce visually acceptable fusion quality, which may enhance the feasibility of the Gaofen-1 data.

Introduction: Neural crest (NC) cells are involved in cardiac development, and NC defects account for over 10% of all congenital heart disease. The migratory and morphogenic patterns of these NC cells are not well understood because conventional optical microscopes struggle to acquire images of live embryos with sufficient temporal and spatial resolution without causing major photo-damage. To address these issues, the lab has designed and built a light-sheet fluorescence microscope (LSFM) that is capable of acquiring dual-channel signals with high spatial and temporal resolution. Hypothesis: We hypothesize that the LSFM system will capture heart tube development at a cellular level and show the time point and location at which the NC cells integrate with the heart tube. Methods and Results: The transgenic line that is manipulated for this study is the Tg(neural crest:nfsB-mCherry, cardiac: eGFP) in which the heart tube fluoresces green while NC-derived cells fluoresce red. 10 zebrafish were imaged using the 3-D time-lapse LSFM setup from 15 hours post fertilization (hpf) to 36 hpf. The imaging data was qualitative and consistent across fish. Fig. 1 (a) shows mCherry NC-derived cells (red) integrating with eGFP cardiomyocytes (green) and areas of colocalization (yellow). Fig 1 (b) and (c) show the time course of the NC cell migration into the heart tube. By 26-27 hpf, the NC cells contacted the dorsal surface of the heart tube. By the 29 hpf, the NC-derived cells integrated into the heart tube and expressed nuclear eGFP, which is visualized by the yellow fluorescing portions of the image. Conclusions: We conclude that the LSFM was successful in acquiring distinct signals from the heart tube cells and the NC-derived cells. The temporal and spatial resolution was sufficient to observe NC migration at the single cell level and map out exactly where along the heart tubule the NC-cells entered.

Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to “zoom out” by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.

Introduction Dynamic MRI may be used to image motion that is needed for accurate radiotherapy treatment planning. The tradeoff between temporal and spatial resolution has been a major issue affecting the use of dynamic MRI. Here we propose to improve temporal resolution by reconstructing high spatial resolution components with deformably registered motion data. Dynamic MRI with increased temporal resolution without scarifying spatial resolution for several sites are presenting. Methods and Materials High and low spatial resolution components of the image typically move similarly, i.e. share most of the motion vector information. The motion vectors can be obtained by deformable registration of the central k‐space, and applied to the high spatial resolution data. This allows one to acquire the peripheral k‐space at a lower temporal resolution than the central k‐space therefore increase the overall sampling rate. We tested the possible gain by this method for 2‐dimensional dynamic MRIs (SIEMENS 3T Verio) of swallowing, breathing and peristalsis. Results The mean error in the displacement vectors obtained using only the central 20% of k‐space vs. using the full k‐space was less than 0.5mm, which reduced to 0.2mm if 50% of the radius is used. Peristalsis motion with sudden changes shared least information between central and peripheral k‐space. The reconstructed images with as low as central 20% of the k‐space radius are very similar to images reconstructed with full k‐space. The resulting probability distance histograms and regions of interest show minimal variation. Conclusion Temporal resolution can be improved considerably without compromising the spatial resolution if the low‐resolution vector information is used to construct high spatial resolution data as demonstrated in this work. The deformable registration accuracy is highly critical for the overall performance of the method. Conflict of Interest: This work is supported partially by Siemens Healthcare.

For more than four decades, geostationary weather satellites have been used successfully by NOAA's National Weather Service for daily weather forecasting and for tracking storm events. At the same time, polar orbit systems, such as NOAA/AVHRR, SeaWIFS and Landsat TM, have provided valuable information about slowly varying features and processes in the open ocean and on land. However, coastal and estuarine applications of remote sensing require significantly finer temporal, spatial, and spectral resolution than open ocean studies. Current polar orbit satellites lack the temporal resolution, while present geostationary orbit satellites have limited spatial and spectral resolution. Geostationary satellites can provide images of clouds, ocean and land surfaces very frequently, since they are positioned above a selected point on the equator and continuously view the same one third of the Earth. However, in order to have the same angular velocity as the Earth, they must be located in an orbit 23,000 miles above the equator, which limits their spatial resolution. NOAA, with support from NASA and the Navy, is developing GOES-R, a geostationary satellite. GOES-R will include a Hyperspectral Environmental Suite for Coastal Waters (HES-CW) and atmospheric sounding sensors which should provide the resolution levels required for coastal and estuarine observations. To achieve this, full use will be made of the fact that a geostationary platform offers a very high sampling frequency and long integration times, allowing for high signal-to-noise (S/N) ratios. Thus, useful trade-offs can be made between S/N, number of spectral bands, and sampling frequency, even though the spatial resolution is constrained to 150-300 m at the equator by system design considerations, such as mirror size, stability and pointing of the spacecraft. The objective of this paper is to illustrate coastal and estuarine resolution requirements for typical coastal features and processes such as: 1) chlorophyll concentration and coastal productivity, 2) harmful algal blooms and eutrophication, 3) current measurement by feature tracking, 4) oil slick drift and capture by estuarine fronts, 5) dynamic behavior of turbidity plumes and ocean-dumped waste, 6) propagation of internal waves on the continental shelf, 7) ice cover for coastal shipping, 8) coastal flooding and other catastrophic events, and 9) coastal ecosystem and wetland habitat changes. These resolution requirements are then compared to current GOES-R specifications.

In order to improve the accuracy of forest inventory works, tables of stem volume by height classes of willow trees in the northern taiga region of the European part of the Russian Federation were created. Forest inventory standards have not previously been developed for this species in the northern taiga region. The analytical technique proposed by I.I. Gusev in 1971 for spruce forests of the European North was used in compiling the height class volume tables. It was later adopted for the description of other tree species as well. Diameters and heights of trees were measured at 41 sample plots laid out in the Verkhnyaya Toyma and Arkhangelsk forestries of the Arkhangelsk region; 105 model trees were sampled. In most cases, sample plots were laid out in mixed plantations with the predominance of willow. The data obtained on the sample plots served to determine the parameters of the height class scale. The information source for stem volume of willow trees used to be the stem volume tables for aspen trees. A comparative analysis of the tables compiled for willow trees stems with the tables for aspen taken from the Forest Valuation Handbook for the Northeast of the European part of the Russian Federation was carried out. The analysis shows significant discrepancy in the scales for willow and aspen. The use of aspen height class volume tables for willow leads to a systematic overestimation of wood volumes. Tables of stem volumes by height classes of willow trees are used to determine stand stock with the enumeration data by 4-centimeter diameter classes. The developed tables meet the forest practice requirements. The volume tables by height classes are used to calculate timber reserves when laying out sample plots; to determine the damage level in case of stand damage; and for other practical purposes. For citation: Paramonov A.A., Tretyakov S.V., Koptev S.V., Bogdanov A.P., Tsvetkov I.V. Stem Volume Tables by Height Classes of Willow Trees in the Northern Taiga Region of the European Part of the Russian Federation. Lesnoy Zhurnal [Russian Forestry Journal], 2021, no. 6, pp. 69–78. DOI: 10.37482/0536-1036-2021-6-69-78

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

Global surveillance based on remote sensing always has a trade-off between spectral, temporal, and spatial resolutions of the sensor data. Since unique land cover information could be estimated at a good spatial scale than the temporal resolution, the Sentinel sensor, for example, has a revisit of 5 days and gathers images at a 10-20m spatial resolution. The Landsat sensor can only be revisited every 16 days with a spatial scale of 30 m. Fine temporal and spatial resolution data are essential for seasonable observation of dynamic agricultural, environmental, or ecological phenomena. This trade-off problem can be solved by using fusion methods based on the linear differences between images with fine (Sentinel) and coarse (Landsat) spatial resolution. As a result, an image with high spatial resolution can be predicted than acquired from a single Landsat-like sensor. The proposed work will combine Landsat and Sentinel data to bridge the gap between the Landsat image (spatial resolution,30m) and the Sentinel image (temporal resolution,10m or 20m). As a result, detailed investigations of the Earth’s surface and dynamics will be possible. Evaluation metrics – RMSE, SRE, and SSIM are used to compare the predicted image with the Sentinel image of the predicted date. The metrics analysis found that the predicted image is like the reference image with optimal values for all three-evaluation metrics.

Functional magnetic resonance imaging (fMRI) is widely used to explore the brain because of its high temporal and spatial resolution. Resting-state fMRI data were studied by many researchers who found the existence of dynamic functional connectivity (dFC). However, it is unclear whether estimation of functional connectivity (FC) states is dependent on temporal and spatial resolution of sampling in fMRI data. In this paper, we addressed this concern by comparing the FC states with varying spatiotemporal resolution of data where different number of regions of interest (ROIs) were randomly chosen to extract the timecourses. These timecourses were then down-sampling to different temporal resolution. Finally, a sliding-window approach was used to estimate the potential FC states in resting-state dFC. The results show that the detection of brain connectivity is insensitive to the spatial and temporal resolution of sampled data in fMRI data, which provides a dimension reduction perspective for research based on fMRI data.

Abstract. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) – Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.

The aim of this study was to evaluate free-breathing single-shot real-time cine imaging for functional cardiac imaging at 3 T with increased spatial resolution. Special emphasis of this study was placed on the influence of parallel imaging techniques. Gradient echo phantom images were acquired with GRAPPA and modified SENSE reconstruction using both integrated and separate reference scans as well as TGRAPPA and TSENSE. In vivo measurements were performed for GRAPPA reconstruction with an integrated and a separate reference scan, as well as TGRAPPA using balanced steady-state free precession protocols. Three clinical protocols, rtLRInt (Tres = 51.3 milliseconds; voxel, 2.5 × 5.0 × 10 mm³), rtMRSep (Tres = 48.8 milliseconds; voxel, 1.9 × 3.1 × 10 mm³), and rtHRSep (Tres = 48.3 milliseconds; voxel, 1.6 × 2.6 × 10 mm), were investigated on 20 volunteers using GRAPPA reconstruction with internal as well as separate reference scans. End-diastolic volume, end-systolic volume, ejection fraction, peak ejection rate, peak filling rate, and myocardial mass were evaluated for the left ventricle and compared with an electrocardiogram-triggered segmented readout cine protocol used as standard of reference. All studies were performed at 3 T. Phantom and in vivo data demonstrate that the combination of GRAPPA reconstruction with a separate reference scan provides an optimal compromise of image quality as well as spatial and temporal resolution. Functional values (P values) for the standard of reference, rtLRInt, rtMRSep, and rtHRSep end-diastolic volume were 141 ± 24 mL, 138 ± 21 mL, 138 ± 19 mL, and 128 ± 33 mL, respectively (P = 0.7, 0.7, 0.4); end-systolic volume, 55 ± 15 mL, 61 ± 14 mL, 58 ± 12 mL, and 55 ± 20 mL, respectively (P = 0.23, 0.43, 0.62); ejection fraction, 61% ± 5%, 57% ± 5%, 58% ± 4%, and 56% ± 8%, respectively (P = 0.01, 0.11, 0.06); peak ejection rate, 481 ± 73 mL/s, 425 ± 62 mL/s, 434 ± 67 mL/s, and 381 ± 86 mL/s, respectively (P = 0.03, 0.04, 0.01); peak filling rate, 555 ± 80 mL/s, 480 ± 70 mL/s, 500 ± 70 mL/s, and 438 ± 108 mL/s, respectively (P= 0.007, 0.05, 0.004); and myocardial mass, 137 ± 26 g, 141 ± 25 g, 141 ± 23 g, and 130 ± 31 g, respectively (P = 0.62, 0.54, 0.99). Using a separate reference scan and high acceleration factors up to R = 6, single-shot real-time cardiac imaging offers adequate temporal and spatial resolution for accurate assessment of global left ventricular function in free breathing with short examination times.

A method in near-surface estimation of air temperature (NEAT) in times following the satellite passing time using MODIS images

Impact of image spatial, temporal, and velocity resolutions on cardiovascular indices derived from color-Doppler echocardiography

The temporal and spatial resolution requirements for a soil moisture mission are addressed through a synthetic identical twin data assimilation study. Simulations were made for observations with various temporal resolutions (1, 2, 3, 5, 10, 15, 20 and 30 days), and spatial resolutions (0.5, 6, 12 18, 30, 60 and 120 minutes of arc). It was found that daily observations of surface soil moisture achieved the best predictions of soil moisture and evapotranspiration, with the greatest impact of temporal resolution being for 1 to 5 days. It was also found that observations with a spatial resolution less than the model resolution produced the best results, with spatial resolutions greater than the model resolution yielding only a slight degradation.